DOI: 10.3897/jucs.169476

Authors: Jefferson Martins, Rodrigo Andrade, Luis F. Alves Pereira

Abstract: Skilled software developers play a crucial role in creating energy-efficient code, contributing to the reduction of carbon emissions and promoting sustainable computing practices. In parallel, Large Language Models (LLMs) have recently emerged as powerful programming assistants, providing support for developers in complex coding tasks. In this context, this study investigates the ability of LLMs to generate computationally efficient implementations of classic algorithms - such as Heap Sort and Binary Search - across different programming languages, with a primary focus on optimizing energy consumption, memory usage, and execution time. Our results show that, in most cases, different LLMs are able to generate these algorithm implementations correctly. Furthermore, in the majority of the evaluated scenarios, LLM-generated code demonstrates superior performance over human-written algorithm code with regard to energy consumption, memory efficiency, and execution time.

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DOI: 10.3897/jucs.166567

Authors: Sava Stanisic, Borislav Djordjevic, Branislav Belotic, Olga Ristic, Ivan Tot, Kristina Zivanovic, Dimitrije Kolasinac

Abstract: he orchestration of microservices in distributed cloud environments poses significant challenges due to the heterogeneous nature of cluster nodes and dynamic workload patterns. Traditional scheduling strategies in Kubernetes often fail to optimize latency-sensitive applications effectively. This paper proposes a latency-aware orchestration framework that integrates reinforcement learning techniques to dynamically schedule and migrate microservices across heterogeneous Kubernetes clusters. The proposed approach leverages a deep Q-network (DQN) agent trained to minimize end-to-end response times while balancing resource utilization and avoiding service-level objective (SLO) violations. Experiments conducted on a hybrid testbed comprising virtual and physical nodes demonstrate that the reinforcement learning-based scheduler reduces latency by up to 25% compared to default Kubernetes scheduling policies. The results highlight the potential of intelligent orchestration methods to enhance performance in complex cloud-native deployments.

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DOI: 10.3897/jucs.152399

Authors: Abdelhady Naguib, Abdulaziz Shehab

Abstract: The importance of localization algorithms is due to their uses in various wireless sensor network applications. A single anchor movement can be used to aid in localization to reduce the cost of using multiple anchors or equipping sensor nodes with GPS units, but the main challenge here is choosing the best path of movement while avoiding potential obstacles. This paper proposes a path planning algorithm called Square Spiral with Obstacle Avoidance (SQSPOA) which allows a mobile anchor node to track an optimal path while broadcasting its current coordinates to the unknown sensor nodes. During its movement, the mobile anchor node faces many obstacles that may hinder its mobility; but as a result of the superiority of the proposed algorithm the mobile anchor can avoid these obstacles while still broadcasting its coordinates to sensor nodes. The performance of the proposed algorithm was evaluated at the presence of variable-sized obstacles and was compared with recent path planning algorithms. Simulation results proved the superiority of the proposed algorithm with respect to localization error, percent of localized sensor node and trajectory length.

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DOI: 10.3897/jucs.130529

Authors: Moulay Youssef Ichahane, Noureddine Assad, Hassan Ouahmane

Abstract: The diagnosis of rheumatoid arthritis (RA) diagnosis demands precise detection methods due to its complex symptomatology. This study presents a novel hybrid diagnostic framework that is the first to integrates Case-Based Reasoning (CBR) with deep learning and introduce three key innovations: (i) a dual-pathway architecture that combine electronic health records with imaging analysis, (ii) an Enhanced Clustering-Based K-nearest neighbors (ECB KNN) model for optimal feature selection, and (iii) a dynamic K-means clustering approach for handling class imbalance. We evaluated our framework using two comprehensive datasets: MIMIC-IV-Hosp, containing clinical data and MIMIC-CXR containing 377,110 chest X-ray images. The model employs a VGG16-based CNN for radiological feature extraction, with a particular focus on pulmonary manifestations, which is combined with our ECB KNN algorithm for patient-specific clinical data analysis. Using five-fold cross-validation, our framework is shown to achieve superior performance metrics (precision: 0.90-0.95, recall: 0.89-0.93, F1-score: 0.91) compared to baseline methods (traditional CNN: precision 0.82, recall 0.79; standard CBR: precision 0.85, recall 0.83). This significant improvement in diagnostic accuracy demonstrates the potential of our framework in terms of enhancing early RA detection and clinical decision support. The architecture of the model architecture is designed to allow for extensibility to other rheumatic conditions, thereby offering a comprehensive solution for multi-disease diagnosis in rheumatology.

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DOI: 10.3897/jucs.157024

Authors: Mashael M. Alsulami, Kholoud Althobaiti, Haneen Algethami

Abstract: Job recommendation systems play a critical role in matching individuals with relevant career opportunities based on their skills and experiences. However, many existing systems struggle to balance precision and contextual relevance, leading to mismatches in job recommendations. In this paper we introduce JobMatcher, a multilayered recommendation system that integrates a well established technique, cosine similarity and KNN clustering with ChatGPT based evaluation. Initial recommendations are generated through content-based filtering and refined via clustering similar job descriptions aligned with user profiles by seniority and trajectory. To enhance contextual accuracy, GPT 3.5 turbo was prompted to act as an expert evaluator, scoring top recommendations based on skill relevance and career fit using structured and unbiased prompts. In a user study with seven domain experts and ten user profiles, system-selected jobs scored significantly higher (mean = 3.43 compared to 2.99 for KNN clustering, p = 0.0035), with moderate inter-rater agreement (Kendall’s W = 0.417). JobMatcher bridges algorithmic filtering with human like evaluation, offering a scalable, intelligent solution for improved job matching.

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DOI: 10.3897/jucs.150681

Authors: Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

Abstract: Ensuring secure and reliable person authentication is a critical challenge in modern security systems. Traditional biometric systems relying on physiological traits like fingerprints, iris, and facial recognition often suffer from spoofing vulnerabilities. In contrast, electroencephalogram (EEG) signals, characterized by unique temporal and cognitive patterns, provide a robust authentication mechanism. This paper introduces DeepV-Net, a multimodal fully convolutional neural network that leverages both EEG signals and dynamic handwritten signature data acquired from Wacom devices. The proposed model integrates spatial and temporal features of EEG signals with distinctive movement-based signature patterns through an end-to-end multimodal fusion strategy. Experimental evaluations on benchmark datasets demonstrate that DeepV-Net outperforms unimodal approaches and state-of-the-art authentication methods, achieving a training accuracy of 99.1% and a validation accuracy of 93.3%. These findings highlight the complementary nature of EEG and signature modalities, paving the way for more secure and efficient biometric authentication systems.

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DOI: 10.3897/jucs.137544

Authors: Igor Tomičić, Petra Grd, Andrija Bernik

Abstract: This paper presents a comprehensive analysis of the integration of artificial intelligence (AI) into threat intelligence (TI) systems, focusing on its potential to enhance cybersecurity operations. The paper presents an extensive literature review, covering the current state of AI applications in TI, including machine learning, deep learning, and natural language processing for automating threat detection, classification, and analysis. It outlines the core functions of AI in TI, such as threat detection, correlation, prediction, and automated response, and introduces a detailed taxonomy categorizing AI techniques based on their roles in enhancing TI processes. Additionally, the paper proposes a conceptual workflow for AI-powered TI, illustrating how AI can streamline data collection, threat analysis, and incident response. A roadmap for future research is further provided, highlighting key areas for development, including explainable AI, federated learning, and edge computing. This work contributes to the field by offering a structured framework for integrating AI into TI and identifying critical challenges and future directions in AI-driven cybersecurity.

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DOI: 10.3897/jucs.132502

Authors: Sorav Kumar Singh, Alak Roy, Rajneesh Raushan

Abstract: In the field of underwater exploration and research, Underwater Wireless Sensor Networks (UWSNs) play a vital role in understanding the marine environment, oceanography, and marine biology. A key strategy used in UWSNs to aggregate sensor nodes and improve network performance while extending battery life through lower energy usage is clustering. However, available clustering algorithms do not specifically address all the underwater problems, viz., communication is constrained by the limited bandwidth and high latency of acoustic signals, while energy consumption is critical due to the difficulty of recharging or replacing underwater batteries. The harsh underwater environment, with varying pressure, salinity, and movement, affects sensor performance and durability. Accurate localization is difficult without GPS and relies on less precise acoustic methods. So, this paper proposes a Residual Energy-Aware Fuzzy-Based Clustering Algorithm (REAFCA) for UWSNs which presents a novel framework intended to improve network performance and address issues with energy usage. For effective data routing, the REAFCA dynamically arranges clusters based on important factors such as node rank, radius, threshold, angular velocity, and residual energy. To maximize leadership inside the clusters, the adaptive threshold method makes sure that only superior cluster heads are chosen. The algorithm also incorporates dynamic range changes for communication to adapt to changing network circumstances. This algorithm mainly focuses on clustering in an underwater environment while improving the energy efficiency and network life of the nodes. Simulation results demonstrate the superiority of the proposed algorithm over K-means, K-meansA, LEACH, PEGASIS, HEED, DB-SCAN and HEER algorithms in terms of energy efficiency and throughput while achieving comparable average delay.

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DOI: 10.3897/jucs.164737

Authors: Leo Buron, Andreas Erbslöh, Gregor Schiele

Abstract: For embedded devices, both memory and computational efficiency are essential due to their constrained resources. However, neural network training remains both computation and memory intensive. Although many existing studies apply quantization schemes to mitigate memory overhead, they often employ stochastic rounding for both inference and gradient computation. Notably, no prior work has explored its advantages exclusively in parameter updates. Here, we in-troduce Quantized Parameter Updates (QPU), which uses stochastic rounding (SQPU) to achieve improved and more stable training outcomes. Our fixed-point quantization scheme quantizes parameters (weights and biases) upon model initialization, conducts high-precision gradient com-putations during training, and applies stochastically quantized updates thereafter. This approach substantially lowers memory usage and enables mostly quantized inference, thereby accelerating calculations. Furthermore, storing quantized inputs for gradient computation reduces memory demands even more. When tested on the FASHION-MNIST dataset, our method matches the Straight-Through Estimator (STE) in performance, delivering 0.92% validation accuracy while consuming just 57% of the memory during training. Accepting a slight 1.5% drop in accuracy yields a 50% memory reduction. Additional techniques include stochastic rounding in inference, the use of higher precision for parameters than for layer outputs to limit overflow, L2 regularization via weight decay, and adaptive learning-rate scheduling for improved optimization across a range of batch sizes.

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DOI: 10.3897/jucs.164671

Authors: Claudio Gutiérrez-Soto, Marco A. Palomino, Patricio Galdames

Abstract: In recent years, we have witnessed the proliferation of applications that generate thousands of terabytes of data per day, due to the explosive increase in storage capacity across various devices. As a consequence, a new concept called Data Deluge has emerged. Data deluge refers to the situation where the quantity of data generated exceeds the processing power available, and spatio-temporal data is no exception to this phenomenon. In this context, the efficient processing of spatio-temporal queries becomes crucial to address this challenge, as slow query processing can result in obsolete answers, which may lead to errors. Considering this dynamic context of storage and processing, we explore a new online workload algorithm in a distributed parallel environment using hybrid spatio-temporal indexes. This algorithm is able to update the indexes with the most appropriate data, aiming to achieve more efficient query processing. To measure the efficiency of this algorithm, we present its time complexity along with an empirical evaluation of its performance, considering processing time, number of accessed nodes, and communication costs. The empirical results show a significant reduction in processing time, communication costs, and number of accessed nodes.

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DOI: 10.3897/jucs.165358

Authors: Mohini Sharma, Raghav Sandhane, Jaydeep Rajeshkumar Katariya

Abstract: The metaverse seamlessly integrates physical and digital spaces, enabling AI-driven innovations in virtual interactions, autonomous avatars, and real-time experiences. However, increased reliance on AI brings sweeping cybersecurity challenges, such as adversarial attacks, deep fake impersonation, and AI-driven phishing campaigns. The security of the metaverse is vital for the sustainability of user trust and system integrity. As AI assumes a larger role in virtual environments, proactive cybersecurity measures must be taken to counter emerging threats. This paper introduces DAI-TIRS, a holistic security framework designed to proactively secure the metaverse. DAI-TIRS is the integration of machine learning-based anomaly detection, dynamic honeypots, and predictive threat modeling that detect, classify, and mitigate AI-driven threats in real-time. By utilizing MITRE ATT&CK and the PyTM framework, it constantly learns new emerging threats through advanced behavioral analytics and keeps pace with the adversarial AI model’s evolution. The experimental results from a simulated metaverse environment demonstrate that DAI-TIRS achieves 93% accuracy in threat detection, 90% precision in classifying the severity, and a 36.9% faster threat mitigation response time than the average performance of baseline models, as detailed in the paper. These findings underscore the critical need for adaptive AI-based cybersecurity solutions that will enhance the resilience, trust, and integrity of metaverse ecosystems. This research establishes DAI-TIRS as an advanced cybersecurity framework that has demonstrated its adaptability and effectiveness in countering AI-driven threats across multiple sectors. The source code for DAI-TIRS is available on GitHub: [https://github.com/sharmamohini762/DAI-TIRS-Code]

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DOI: 10.3897/jucs.164654

Authors: Wolfram Luther, A. J. Han Vinck

Abstract: This volume presents a conference paper selection from the 4th Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2024): Data Science and Reliable Machine Learning, held in Yerevan, Armenia, October 3-6, 2024, https://codassca2024.aua.am/. The special issues guest editors invited five groups of authors from Armenia, Chile, Germany, the UK, and the USA to submit enlarged versions of their CODASSCA 2024 papers There was also a J.UCS open call so that any author could submit papers on the highlighted subjects. The invitation to review the 16 contributions received was accepted by 16 experts, and, after three rounds, seven articles were finally accepted for publication in the special issue.

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DOI: 10.3897/jucs.135666

Authors: Davut Çulha

Abstract: Widespread adoption of blockchain technologies requires scalability. To achieve scalability, various methods are applied, including new consensus algorithms, directed acyclic graph solutions, sharding solutions, and off-chain solutions. Sharding solutions are particularly promising as they distribute workload across different parts of the blockchain network. Similarly, directed acyclic graphs use graph data structures to distribute workload effectively. In this work, a binary tree data structure is used to enhance blockchain scalability. Binary trees offer several advantages, such as the ability to address nodes with binary numbers, providing a straightforward and efficient method for identifying and locating nodes. Each node in the tree contains a block of transactions, which allows for transactions to be directed to specific paths within the tree. This directionality not only increases scalability by enabling parallel processing of transactions but also ensures that the blockchain can handle a higher volume of transactions without becoming congested. Moreover, transactions are decomposed into transaction elements, improving the immutability of the binary tree blockchain. This novel decomposition process helps to minimize the computational overhead required for calculating account balances, making the system more efficient. By breaking down transactions into their fundamental components, the system can process and verify transactions more rapidly and accurately. This approach effectively realizes implicit sharding using a binary tree structure, distributing the processing load more evenly and reducing bottlenecks. The proposed method is simulated to assess its performance. Experimental results demonstrate the method's scalability, showing that it can handle a significantly higher transaction throughput compared to traditional blockchain structures.

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DOI: 10.3897/jucs.123539

Authors: Kiran K A, Jaison Jacob

Abstract: Network-on-chip (NoC) architectures have emerged as a potential solution for facilitating communication between processing elements (PEs) in modern multi-core systems. The design and optimization of NoC architectures are critical for achieving efficient communication, reduced energy consumption, and improved overall system performance. In this study, we investigate and compare the performance of two prominent optimization algorithms, like Genetic Algorithm (GA) and CastNet Algorithm, for 2D and 3D mesh NoC architectures. The study’s objective is to estimate these algorithms’ effectiveness in optimizing communication cost, communication energy, and CPU run time in both 2D and 3D mesh NoC architectures. Performance metrics such as communication cost, communication energy consumption, and CPU run time are measured and compared between the two algorithms and carried out on real and custom benchmark applications like MWD, VOPD and MPEG4.

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DOI: 10.3897/jucs.120840

Authors: Soraya Setti Ahmed, Yahya Slimani, Riadh Frefita

Abstract: Fault tolerance approaches in distributed systems are essentially based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper has two objectives: first, it proposes a fault tolerance approach based on the nodes status of a distributed system. For this purpose, it defines 3 nodes status: safety, faulty and potentially faulty. With respect of classical node status (safety, faulty), it introduces a new status that we call potentially faulty. This last node allows to enhance the availability of a distributed system. Second, it discusses the efficiency of the proposed model on two types of architectures: virtual multi-node cluster and a physical multi-node cluster with WIFI connection. Experiments have showed that proposed approach increases the system performance throughput and its fault tolerance level.

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DOI: 10.3897/jucs.119739

Authors: Maroua Chemlal, Amina Zedadra, Ouarda Zedadra, Antonio Guerrieri, Med Nadjib Kouahla

Abstract: Recommendation systems have been developed to address the immense volume of information accessible on the Internet. These systems employ filtering methodologies and customize their recommendations by drawing insights from user profiles, ultimately enhancing the relevance and utility of their suggestions. In this paper, we present our system called Smart Food and Restaurant Advisor based on nutritional needs and user profiling (SFRA); a multi-criteria food and restaurant recommendation system designed to prioritize personalized and health-conscious selections. This system uses user profiling to offer customized recommendations that cater to individual preferences, requirements, and geographic locations. Our primary objective is to enhance users’ decision-making processes by promoting healthier and more refined lifestyle choices. The results of the proposed system demonstrate that it is well-suited for promoting a healthier lifestyle while offering comprehensive coverage of users’ practices and preferences.

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DOI: 10.3897/jucs.116026

Authors: Abdullilah A. Alotaibi, Salman A. AlQahtani

Abstract: Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

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DOI: 10.3897/jucs.134762

Authors: Lasse Nitz, Avikarsha Mandal

Abstract: The use of machine learning has shown to benefit a wide range of applications, especially for classification tasks. As such, the detection of algorithmically generated domains to identify corrupted machines has proven itself to be a mature use case with good classification performance. The use of privacy and security sensitive data, however, raises concerns in scenarios that require interaction with external parties. As one of such scenarios, we consider the training of domain generation algorithm detection classifiers in a Machine-Learning-as-a-Service (MLaaS) scenario. We evaluate the use of a Bloom encoding approach from the area of privacy-preserving record linkage to prevent the MLaaS provider from getting to know the exact classification task as well as the data samples transmitted for training and classification. We investigate the threat associated with pattern mining attacks by performing a privacy analysis for two versions of these encodings (basic and randomized). We further identify sets of parameter values which we find to provide an adequate level of protection against these attacks. We see the potential for this approach in machine learning use cases dealing with sensitive data or tasks, especially for MLaaS scenarios dealing with short data samples that lack a clear structure.

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DOI: 10.3897/jucs.108811

Authors: Carina Heßeling, Sebastian Litzinger, Jörg Keller

Abstract: With the advent of the Internet-of-Things, sensor data streams are widespread and thus a promising target for network steganography including covert channels. Therefore, we present an approach where the covert sender and receiver first build an archive of values that occur in the stream over a time interval, and then encode bits of the secret message via sensor stream values belonging to the class of seen values or not. If a sensor value’s category does not match the required bit value, it is adapted to switch its category. The chosen value minimizes the distance to the original sensor value to improve stealthiness. Furthermore, we present an application scenario and an analysis of the channel’s detectability, in particular how detectability and steganographic bandwidth can be traded against each other. We have tested the approach and the proposed detec-tion metrics via simulation experiments.

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DOI: 10.3897/jucs.105702

Authors: Yasir Yakup Demircan, Serhat Ozekes

Abstract: Steganography is a process to hide data inside a cover file mostly used in media files like image, video, and audio files. Least significant bit (LSB) steganography is a technique where the least significant bits of pixels are used for information hiding. The purpose of using only those bits is to minimize the visual impact of the hidden data on the image file. LSB technique of steganography is one of the most popular forms of steganography available today. As a result, various steganalysis techniques are developed for this steganography technique. One of them is the visual analysis of pixels through pixel modifications to expose hidden data in a visual manner. The proposed method achieves resistance to this attack using an image segmentation model and extracting the most texture-complex areas of an image and hiding information in these specific areas as pseudo-randomized least significant bit replacements. As the outcome of the study, an alternative approach to LSB steganography that results competitively with existing methods is provided.

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DOI: 10.3897/jucs.108446

Authors: Shanshan Jia, Gaukhar A. Kamalova, Dmytro Mykhalevskiy

Abstract: The importance of the topic under investigation stems from the burgeoning prominence of the Internet of Things (IoT) paradigm and the consequent imperative to devise and institute an efficacious methodology for neighbour detection that facilitates supplementary connections to mobile nodes. This research primarily aims to elucidate a mobile handover technique aligning with the neighbour discovery paradigm inherent in 6LoWPAN. The methodological framework employed herein amalgamates a systematic analysis of the foundational tenets governing 6LoWPAN communication standards over the IPv6 protocol in the context of IEEE 802.15.4aa personal wireless networks, supplemented with empirical evaluation of message propagation dynamics among discrete nodes of a designated personal wireless network. The findings of this investigation underscore the pre-eminence of the 6LoWPAN networking standard as a prime vehicle for bolstering the dependable communication of IoT's remote entities, contingent upon the utilization of the IPv6 communication protocol. This iteration of the Internet protocol is superior, particularly in surveillance contexts, given its expansive address capacity (128 bits) juxtaposed against its antecedent, the IPv4 protocol (32 bits). The inherent adaptability of this network connection augments the quality of mobile handover, especially when one considers the real-world application of 6LoWPAN's neighbour discovery auxiliary communication standard. The tangible implications of these findings beckon their potential utility in satiating the demands of IoT system users and actualizing the network collaboration ethos intrinsic to the 6LoWPAN communication standard.

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DOI: 10.3897/jucs.99223

Authors: Ana Cristina Alves de Oliveira, Marco Aurélio Spohn, Christof Fetzer, Le Quoc Do, André Martin

Abstract: Data-as-a-Service (DaaS) is a branch of cloud computing that supports “querying the Web”. Due to its ultrahigh scale, it is essential to establish rules when defining resources’ costs and guidelines for infrastructure investments. Those decisions should prioritize minimizing the incidence of agreement breaches that compromise the performance of cloud services and optimize resources’ usage and services’ cost. This article aims to address the cost problem of DaaS by developing a model that optimizes the costs of querying distributed data sources over virtual machines spread across multisite data centers. We have designed and analyzed a cost model for DaaS, besides implementing a scheduling system to perform a cost-based VM assignment. To validate our model, we have studied and characterized a real-world DaaS system’s network and processing workloads. On average, our cost-based scheduling performs at least twice as well as the traditional round-robin approach. Our model also supports load balancing and infrastructure scalability when combined with an adaptive cost scheme that prioritizes VM allocation within the underutilized data centers and avoids sending VMs to data centers in the eminence of becoming over-utilized.

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DOI: 10.3897/jucs.87563

Authors: Jan Bergstra, John V. Tucker

Abstract: An outline is provided of a new perspective on elementary arithmetic, based on addition, multiplication, subtraction and division, which is informal and unique and may be considered naive when contrasted with a plurality of algebraic and logical, axiomatic formalisations of elementary arithmetic.

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DOI: 10.3897/jucs.94462

Authors: Longhao Li, Taieb Znati, Rami Melhem

Abstract: At extreme scale, the frequency of silent errors – a class of errors that remain undetected by low-level error detection mechanisms – increases significantly with the computational complexity of the application and the scale of the computing infrastructure. As hardware and software advances are made to usher in the next scientific era of computing, developing new approaches to mitigate the impact of silent errors remains a challenging problem. In this work, we propose an energy-aware fault-tolerance model, referred to diffReplication to overcome silent errors. In the proposed model, the main process is associated with one replica that executes at the same rate as the main process, and one diffReplica that is executed at a fraction of the main process' execution rate. If the main and its replica reach consensus at the end of a computation phase, the state of the diffReplica is updated and computation is resumed. If the synchronization attempt results in a disagreement, however, the diffReplica increases its execution speed to complete the computation and quickly reach the synchronization barrier. Assuming a single error over any given synchronization interval, a majority voting is used to reach consensus and tolerate silent errors. To further enhance its performance, diffReplication is augmented with speculative execution, whereby the main or its fast replica is selected to continue execution without waiting for the diffReplica. The selection process is based on the previous behaviour of the main and its replica. A performance analysis study is carried out to assess the performance of diffReplication, in terms of the energy saving and time-to-completion reduction achieved by the diffReplication scheme. The experiment shows that speculative execution reduces the time to completion with additional energy, and dynamic decision-making balances the energy consumption and time to completion.

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DOI: 10.3897/jucs.93533

Authors: Kashif Mehboob Khan, Warda Haider, Najeed Ahmed Khan, Darakhshan Saleem

Abstract: The amount of data is increasing rapidly as more and more devices are being linked to the Internet. Big data has a variety of uses and benefits, but it also has numerous challenges associated with it that are required to be resolved to raise the caliber of available services, including data integrity and security, analytics, acumen, and organization of Big data. While actively seeking the best way to manage, systemize, integrate, and affix Big data, we concluded that blockchain methodology contributes significantly. Its presented approaches for decentralized data management, digital property reconciliation, and internet of things data interchange have a massive impact on how Big data will advance. Unauthorized access to the data is very challenging due to the ciphered and decentralized data preservation in the blockchain network. This paper proposes insights related to specific Big data applications that can be analyzed by machine learning algorithms, driven by data provenance, and coupled with blockchain technology to increase data trustworthiness by giving interference-resistant information associated with the lineage and chronology of data records. The scenario of record tampering and big data provenance has been illustrated here using a diabetes prediction. The study carries out an empirical analysis on hundreds of patient records to perform the evaluation and to observe the impact of tampered records on big data analysis i.e diabetes model prediction. Through our experimentation, we may infer that under our blockchain-based system the unchangeable and tamper-proof metadata connected to the source and evolution of records produced verifiability to acquired data and thus high accuracy to our diabetes prediction model.

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DOI: 10.3897/jucs.90346

Authors: Seyedeh Mahsa Mirhoseini-Moghaddam, Mohammad Reza Yamaghani, Adel Bakhshipour

Abstract: In this study, a combined system of electronic nose (e-nose) and computer vision was developed for the detection of adulteration in extra virgin olive oil (EVOO). The canola oil was blended with the pure EVOO to provide adulterations at four levels of 5, 10, 15, and 20%. Data collection was carried out using an e-nose system containing 13 metal oxide gas sensors, and a computer vision system. Applying principal component analysis (PCA) on the e-nose-extracted features showed that 93% and 92% of total data variance was covered by the three first PCs generated from Maximum Sensor Response (MSR), Area Under Curve (AUC) features, respectively. Cluster analysis verified that the pure and impure EVOO samples can be categorized by e-nose properties. PCA-Quadratic Discriminant Analysis (PCA-QDA) classified the EVOOs with an accuracy of 100%. Multiple Linear Regression (MLR) was able to estimate the adulteration percentage with the R2 of 0.8565 and RMSE of 2.7125 on the validation dataset. Moreover, factor analysis using Partial Least Square (PLS) introduced the MQ3 and TGS2620 sensors as the most important e-nose sensors for EVOO adulteration monitoring. Application of Response Surface Methodology (RSM) on RGB, HSV, L*,a*, and b* as color parameters of the EVOO images revealed that the color parameters are at their optimal state in the case up to 0.1% of canola impurity, where the obtained desirability index was 97%. Results of this study demonstrated the high capability of e-nose and computer vision systems for accurate, fast and non-destructive detection of adulteration in EVOO and detection of food adulteration may be more reliable using these artificial senses.

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DOI: 10.3897/jucs.85703

Authors: Marko Zekan, Igor Tomičić, Markus Schatten

Abstract: Numerous advanced methods have been applied throughout the years for the use in Network Intrusion Detection Systems (NIDS). Among these are various Deep Learning models, which have shown great success for attack classification. Nevertheless, false positive rate and detection rate of these systems remains a concern. This is mostly because of the low-sample, imbalanced nature of realistic datasets, which make models challenging to train.Considering this, we applied a novel semi-supervised EC-GAN method for network flow classifi- cation of CIC-IDS-2017 dataset. EC-GAN uses synthetic data to aid the training of a supervised classifier on low-sample data. To achieve this, we modified the original EC-GAN to work with tabular data. In our approach, WCGAN-GP is used for synthetic tabular data generation, while a simple deep neural network is used for classification. The conditional nature of WCGAN-GP diminishes the class imbalance problem, while GAN itself solves the low-sample problem. This approach was successful in generating believable synthetic data, which was consequently used for training and testing the EC-GAN.To obtain our results, we trained a classifier on progressively smaller versions of the CIC-DIS-2017 dataset, first via a novel EC-GAN method and then in the conventional way, without the help of synthetic data. We then compared these two sets of results with another author’s results using accuracy, false positive rate, detection rate and macro F1 score as metrics. Our results showed that supervised classifier trained with EC-GAN can achieve significant results even when trained on as little as 25% of the original imbalanced dataset.

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DOI: 10.3897/jucs.80625

Authors: Derya Yiltas-Kaplan

Abstract: Software-defined networking (SDN) has emerged as a solution to the cumbersome structures of classical computer networks. It separates control and data planes to give independence to devices with respect to either traffic routing or network management. The two isolated planes communicate with each other via the help of software modules, which are located in an SDN controller, such as Floodlight, NOX, or Ryu. In this study, Floodlight is used and an SDN topology with 20 switches is constructed with Python code in Mininet. All algorithms have been coded with Java. The default routing algorithm in Floodlight is Dijkstra’s algorithm. Four different network optimization algorithms, namely Bellman-Ford, Ford-Fulkerson, Auction, and Dual Ascent algorithms, are utilized in ordinary network routing instead of Dijkstra’s algorithm. None of these four algorithms were used in SDN before and network implementations using Ford-Fulkerson, Auction, or Dual Ascent algorithms were scarce in the literature. The results are analyzed with multiple types of normalization on a new user interface communicating with Floodlight part via HTTP requests. There has not been a user interface that performs the same operations in Floodlight. In the future, this study may possibly be improved with considering normalization processes based on various proportions among the metric values and accounting the computational time of the algorithms.

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DOI: 10.3897/jucs.69069

Authors: Mahmut Ünver, Atilla Ergüzen, Erdal Erdal

Abstract: Information technologies have invaded every aspect of our lives. Distance education was also affected by this phase and became an accepted model of education. The evolution of education into a digital platform has also brought unexpected problems, such as the increase in internet usage, the need for new software and devices that can connect to the Internet. Perhaps the most important of these problems is the management of the large amounts of data generated when all training activities are conducted remotely. Over the past decade, studies have provided important information about the quality of training and the benefits of distance learning. However, Big Data in distance education has been studied only to a limited extent, and to date no clear single solution has been found. In this study, a Distributed File Systems (DFS) is proposed and implemented to manage big data in distance education. The implemented ecosystem mainly contains the elements Dynamic Link Library (DLL), Windows Service Routines and distributed data nodes. DLL codes are required to connect Learning Management System (LMS) with the developed system. 67.72% of the files in the distance education system have small file size (<=16 MB) and 53.10% of the files are smaller than 1 MB. Therefore, a dedicated Big Data management platform was needed to manage and archive small file sizes. The proposed system was designed with a dynamic block structure to address this shortcoming. A serverless architecture has been chosen and implemented to make the platform more robust. Moreover, the developed platform also has compression and encryption features. According to system statistics, each written file was read 8.47 times, and for video archive files, this value was 20.95. In this way, a framework was developed in the Write Once Read Many architecture. A comprehensive performance analysis study was conducted using the operating system, NoSQL, RDBMS and Hadoop. Thus, for file sizes 1 MB and 50 MB, the developed system achieves a response time of 0.95 ms and 22.35 ms, respectively, while Hadoop, a popular DFS, has 4.01 ms and 47.88 ms, respectively.

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DOI: 10.3897/jucs.80688

Authors: Daniel Gomez González, Luis Llana Díaz, Cristóbal Pareja

Abstract: The Brandes algorithm has the lowest computational complexity for computing the betweenness centrality measures of all nodes or edges in a given graph. Its numerous applications make it one of the most used algorithms in social network analysis. In this work, we provide a parallel version of the algorithm implemented in Spark. The experimental results show that the parallel algorithm scales as the number of cores increases. Finally, we provide a version of the well-known community detection Girvan-Newman algorithm, based on the Spark version of Brandes algorithm.

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DOI: 10.3897/jucs.71645

Authors: Roberto Cavicchioli, Riccardo Martoglia, Micaela Verucchi

Abstract: Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. In the context of the H2020 CLASS project, we describe an innovative framework aiming to facilitate the design of advanced big-data analytics workflows. The proposal covers the whole compute continuum, from edge to cloud, and relies on a well-organized distributed infrastructure exploiting: a) edge solutions with advanced computer vision technologies enabling the real-time generation of “rich” data from a vast array of sensor types; b) cloud data management techniques offering efficient storage, real-time querying and updating of the high-frequency incoming data at different granularity levels. We specifically focus on obstacle detection and tracking for edge processing, and consider a traffic density monitoring application, with hierarchical data aggregation features for cloud processing; the discussed techniques will constitute the groundwork enabling many further services. The tests are performed on the real use-case of the Modena Automotive Smart Area (MASA).

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DOI: 10.3897/jucs.77046

Authors: Mohamed Talha, Anas Abou El Kalam

Abstract: Big Data often refers to a set of technologies dedicated to deal with large volumes of data. Data Quality and Data Security are two essential aspects for any Big Data project. While Data Quality Management Systems are about putting in place a set of processes to assess and improve certain characteristics of data such as Accuracy, Consistency, Completeness, Timeliness, etc., Security Systems are designed to protect the Confidentiality, Integrity and Availability of data. In a Big Data environment, data quality processes can be blocked by data security mechanisms. Indeed, data is often collected from external sources that could impose their own security policies. In many research works, it has been recognized that merging and integrating access control policies are real challenges for Big Data projects. To address this issue, we suggest in this paper a framework to secure data collection in collaborative platforms. Our framework extends and combines two existing frameworks namely: PolyOrBAC and SLA- Framework. PolyOrBAC is a framework intended for the protection of collaborative environments. SLA-Framework, for its part, is an implementation of the WS-Agreement Specification, the standard for managing bilaterally negotiable SLAs (Service Level Agreements) in distributed systems; its integration into PolyOrBAC will automate the implementation and application of security rules. The resulting framework will then be incorporated into a data quality assessment system to create a secure and dynamic collaborative activity in the Big Data context.

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DOI: 10.3897/jucs.77247

Authors: Christopher Cichiwskyj, Gregor Schiele

Abstract: When the complexity of a problem rises, its solution requires more hardware resources. A usual way to solve this is to use larger processors and add more memory. When using Field Programmable Gate-Arrays (FPGAs), which can instantiate arbitrary circuit designs, a larger, more costly and power hungry chip is used. In this paper we propose a different approach, namely to split the problem into a graph of interdependent smaller tasks and to reconfigure a small FPGA during runtime to execute each of these tasks efficiently sequentially. This can result in cheaper and more energy efficient systems that can execute very complex problems locally. We present a basic analytical model, evaluate its accuracy and discuss initial insight from it.

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DOI: 10.3897/jucs.69750

Authors: Abdelouafi Ikidid, Abdelaziz El Fazziki, Mohammed Sadgal

Abstract: Artificial technologies are rapidly becoming one of the most powerful and popular technologies for solving complicated problems involving distributed systems. Nevertheless, their potential for application to advanced artificial transportation systems has not been sufficiently explored. This paper presents a traffic optimization system based on agent technology and fuzzy logic that aims to manage road traffic, prioritize emergency vehicles, and promote collective modes of transport in smart cities. This approach aims to optimize traffic light control at a signalized intersection by acting on the length and order of traffic light phases in order to favor priority flows and fluidize traffic at an isolated intersection and for the whole multi-intersection network, through both inter- and intra-intersection collaboration and coordination. Regulation and prioritization decisions are made on real-time monitoring through cooperation, communication, and coordination between decentralized agents. The performance of the proposed system is investigated by implementing it in the AnyLogic simulator, using a section of the road network that contains priority links. The results indicate that our system can significantly increase the efficiency of the traffic regulation system.

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DOI: 10.3897/jucs.71831

Authors: Nermin Kajtazovic, Peter Hödl, Leo Happ Botler

Abstract: Coexistence of software components and functions of different criticality in a single computing platform has challenged the safety community for the past two decades. Despite efforts that have been made so far, dealing with mixed-criticality has still left some room for improvements. One particular concern here is that partitioning of hardware and software resources with regard to criticality (safety related, non-safety related) has direct implications on how safety measures need to be realised. For example, a self-test that must meet certain diagnostic coverage for the microcontroller core by inspecting its instructions, needs to cover only those instructions which are able to affect a safety function. Available software mechanisms and tools are to a certain extent still unable to deal with such a fine-grained selection of resources. In this work, we introduce a compiler extension and language support which enable accurate selection of data based on their criticality. The compiler extension serves to establish detailed traceability between the software code and its representation in runtime memory. With the language support, the individual data elements can be classified based on the desired safety integrity level. As a result, safety measures that operate on data (e.g. Abraham test for SRAM can achieve better coverage. The method has been evaluated and applied to industrial safety controllers. We provide here relevant performance figures and discuss possible applications of the method in other fields.

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DOI: 10.3897/jucs.70326

Authors: Mathieu Briland, Fabrice Bouquet

Abstract: False data injection is an attack in which an attacker injects fabricated data into a system with the objective to change the behaviour and the decision-making of the system. Many industrial data-based devices are vulnerable to such attacks, this work presents an approach for testing False Data Injection Attack. This approach uses a Domain-Specific Language to generate altered data with two objectives, to provide sophisticated attacks scenarios to increase the resilience of vulnerable systems against False Data Injection Attack and to train detection tools.

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DOI: 10.3897/jucs.70325

Authors: Manel Mena, Javier Criado, Luis Iribarne, Antonio Corral

Abstract: Cyber-Physical Systems (CPS) and Internet of Things (IoT) devices are handled by numerous different protocols. The management and connection to those devices tend to create usability and integrability issues. This brings about the need for a solution capable of facilitating the communication between different platforms and devices. The Web of Things (WoT) describes interfaces and interaction patterns among things, thereby abstracting itself from the underlying protocols used to manage those things and their implementation strategies. This paper describes the concept of Digital Dice, an abstraction of IoT devices and CPS capable of leveraging the advantages of microservices architectures and inspired by the concept of Digital Twins. A Digital Dice is a servient system of the WoT domain that represents a device by the features of the device, hence different WoT description models result in different microservices related to the particular thing. The paper explores the definition of Digital Dices and the conversion between WoT Thing Description Models and Digital Dices and the architecture that sustains the system.

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DOI: 10.3897/jucs.65102

Authors: Liliana Machuca-Villegas, Gloria Piedad Gasca-Hurtado, Solbey Morillo Puente, Luz Marcela Restrepo Tamayo

Abstract: In terms of productivity in software development, there is specific interest in identifying its influencing factors. For this purpose, several classification approaches have been previously used, which have already recognized technical factors, organizational factors, product factors, project factors, and personal factors. However, these approaches often focus on technical factors over social and human factors (SHFs). Nevertheless, in addition to the obvious technical aspects, the software development process involves problem-solving skills and cognitive aspects and social interaction. In this sense, determining SHFs can lead to software organizations designing strategies for improving team productivity. In this study, we first conducted a preliminary classification of the SHFs identified in the literature. Because this study seeks to assess the factors from the standpoint of software development professionals, we developed and validated an instrument to measure the perception of software development team members about SHFs that may be affecting their productivity. For this purpose, the first four stages of survey-based research were followed: objective definition, survey design, instrument construction, instrument validity, and reliability assessment. The instrument included 79 items assessing 13 different SHFs. After assessing both their validity and reliability, the results demonstrated that the instrument is a valid and reliable tool for measuring SHFs perception among software development team members.

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DOI: 10.3897/jucs.2020.079

Authors: Ján Molnár, Simona Kirešová, Tibor Vince, Dobroslav Kováč, Patrik Jacko, Matej Bereš, Peter Hrabovský

Abstract: IoT technology is gaining more and more popularity in practice, as it collects, processes, evaluates and stores important measured data. The IoT is used every day in the work, in the home or smart houses or in public areas. It realizes the connectivity between real world and digital world which means, that it converts physical quantities of the real world in the form of analog signals into digital numbers stored in clauds. It is essential that students gain practical experience in the design and implementation of the IoT systems during their studies. The article first describes IoT issues and communication protocols used in IoT generally are closer described. Then the design and implementation of an educational model of IoT system - Weather station with the ThingSpeak cloud support is described. The created IoT model interconnects microcontroller programming, sensors and measuring, cloud API interfaces, MATLAB scripts which are useful to analyses the stored data, Windows and Android application developing.

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DOI: 10.3897/jucs.2020.077

Authors: Lelio Campanile, Mauro Iacono, Fiammetta Marulli, Michele Mastroianni, Nicola Mazzocca

Abstract: Technological development and market expansion offer an increased availability of resources and computing power on IoT nodes at affordable cost. The edge computing paradigm allows keeping locally on the edge of the network a part of computing, while keeping all advantages of the cloud and adding support for privacy, real-time and network resilience. This can be further improved in IoT applications by exibly harvesting resources on IoT nodes, by moving part of the computing tasks related to data from the edge server to the nodes, raising the abstraction level of the data aspects of the architecture and potentially enabling larger IoT networks to be efficiently deployed and managed, in a stand-alone logic or as a component of edge architecture. Anyway, an e_cient energy management mechanism is needed for battery powered IoT networks, the most exible implementations, that dynamically balances task allocation and execution in order to In this paper we present a fuzzy logic based power management strategy for IoT subsystem that aims at maximizing the duration of the network by locally migrating part of the computing tasks between nodes. As our goal is to enable the deployment of semi-autonomic large IoT networks, our proposal does not rely on external resources for migration control and operates on a local basis to ensure scalability: at the best of our knowledge, this differentiates our proposal with respect to similar solutions available in literature.

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DOI: 10.3897/jucs.2020.076

Authors: Łukasz Apiecionek, Matusz Biedziak

Abstract: One of huge problem for recent IT systems are attacks on their resources called Distributed Denial of Service attacks. Many servers which are accessible from public network were a victim of such attacks or could be in the future. Unfortunately, there is still no effective method for protecting network servers against source of the attack, while such attack could block network resources for many hours. Existing solutions for protecting networks and IoT systems are using mainly firewalls and IDS/IPS mechanisms, which is not sufficient. This article presents the method minimizing the DDoS attacks. Proposed method provides possibilities for the network administrators to protect their servers and IoT network resources during the attack. The proposed fuzzy adaptive algorithm is using Ordered Fuzzy Numbers for predicting amount of packets which could be passed over the network boarder gateway. Proposed solution will give the opportunity for ordinary users to finish their work when the attack occurs.

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DOI: 10.3897/jucs.2020.074

Authors: Tibor Vince, Matej Bereš, Irena Kováčová, Ján Molnár, Branislav Fecko, Jozef Dziak, Iveta Tomčiková, Milan Guzan

Abstract: The paper presents analyses of the implementation of IoT in the patent technology of the remote measuring laboratory (VMLab). The technology allows to create a new electrical connection between different devices, where the connection diagram was not defined before. This may be done remotely around the world. To begin with, an application of this technology as a remote measuring laboratory is presented and described. Analyses of the possible application of IoT technology in the Remote Measurement Laboratory (VMLAB) with a final design are also presented. The research focuses on an efficient way to retrieve measured values synchronized over the Internet from multiple measuring devices and controllable devices, without an Ethernet or Wi-Fi interface from the manufacturer. The analyses may also be useful when implementing an additional IoT approach to existing systems.

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DOI: 10.3897/jucs.2020.072

Authors: Sathishkumar Natesan, Rajakumar Krishnan

Abstract: The Routing Protocol for Low Power and Lossy Networks (RPL) is operated by gadgets comprised of many devices of embedded type with limited energy, memory as well as resources that do their process. The improvements in the life of the network and energy conservation are the key challenging features in Low Power and Lossy Networks (LLN). Obviously, the LLN has a key strategic part in routing. The Internet of Things (IoT) device is expected to make the apt choice. In LLN, the poor routing choice leads to traffic congestion, reduction in power as well as packet loss ratio. The task in the proposal analyzes Delay (D), Load (L) and Battery Discharge Index (BDI) pivoted Energy Efficient Composite Metric Routing (EECMR) protocol for LLN. The performance of the work in the proposal is evaluated by the COOJA simulator. It outperforms with respect to Network Lifetime (NL), Delay as well as Packet Delivery Ratio (PDR) contrasted to the routing metrics like Traffic Load (TL), Link Quality (LQ), Residual Energy (RE), RE-Battery Discharge Index (RE-BDI) and Hop Count (HC).

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DOI: 10.3897/jucs.2020.028

Authors: Sławomir Bujnowski, Tomasz Marciniak, Beata Marciniak, Zbigniew Lutowski

Abstract: The subject of this article is to present the results of the analysis of the possibilities to create optimal third-degree Reference Graphs which may be used to model ICT networks. The first part discusses the causes for the interest in the subject of the thesis. Basic definitions and terms related to the analysed type of graphs describing ICT networks are given. The second part consists of the analysis of the possibilities to construct regular third-degree structures bases on the usage of one ring of the parameters typical for Reference Graphs. A method of conduct is presented and then used for checking the potential possibility to construct optimal graphs. In the subsequent chapter the results of analysis of the possibilities to create optimal third-degree graphs with a double ring is discussed. The paper is concluded by observations and conclusions resulting from the research.

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DOI: 10.3897/jucs.2020.027

Authors: Tomasz Andrysiak, Łukasz Saganowski

Abstract: One of the basic elements of every Smart City is currently a system of managing urban infrastructure, in particular, smart systems controlling street lighting. Ensuring proper level of security, continuity and failure-free operation of such systems, in practice, seems not yet a solved problem. In this article we present proposals of a system allowing to detect different types of anomalies in network traffic for Smart Lighting critical infrastructure realized with the use of Power Line Communication technology. Furthermore, there is proposed and described the structure of the examined Smart Lighting Communications Network along with its particular elements. We discuss key security aspects which affect proper operation of advance communication infrastructure, i.e. possibility of occurrence of abuse connected both to activity of external factors which could disturb transmission of steering signals, as well as active forms of attack aiming at influencing the informative content of the transmitted data. In the article, there is also presented an effective and quick anomaly detection method in the tested network traffic represented by suitable time series. At the initial stage of the method, the process of detection and elimination of potential outlying observations was realized by one-dimensional quartile criterion. Data prepared in this manner was used for learning recurrent neural networks, i.e. Long and Short-Term Memory types, in order to predict values of the analyzed time series. Further, tests were performed on relations between the forecasted network traffic and its real variability in order to detect abnormal behavior which could mean an attempt of an attack or abuse. Due to a possibility of occurrence of significant fluctuations in real network traffic of the tested Smart Lighting infrastructure, we propose a procedure of recurrent learning with the use of neural networks to obtain more accurate forecasting. The results achieved by means of the performed experiments confirmed effectiveness of the presented method and proper choice of the Long Short-Term Memory neural network for forecasting the analyzed time series.

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DOI: 10.3217/jucs-025-11-1396

Authors: Steffen Wendzel, Florian Link, Daniela Eller, Wojciech Mazurczyk

Abstract: Network covert channels enable stealthy communications for malware and data exfiltration. For this reason, developing effective countermeasures for these threats is important for the protection of individuals and organizations. However, due to the large number of available covert channel techniques, it is considered impractical to develop countermeasures for all existing covert channels. In recent years, researchers started to develop countermeasures that (instead of only countering one particular hiding technique) can be applied to a whole family of similar hiding techniques. These families are referred to as hiding patterns. Considering above, the main contribution of this paper is to introduce the concept of countermeasure variation. Countermeasure variation is a slight modification of a given countermeasure that was designed to detect covert channels of one specific hiding pattern so that the countermeasure can also detect covert channels that are representing other hiding patterns. We exemplify countermeasure variation using the compressibility score, the ε-similarity and the regularity metric originally presented by Cabuk et al. All three methods are used to detect covert channels that utilize the Inter-packet Times pattern and we show that countermeasure variation allows the application of these countermeasures to detect covert channels of the Size Modulation pattern, too.

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DOI: 10.3217/jucs-025-09-1131

Authors: Hui Tian, Meilun Huang, Chin-Chen Chang, Yongfeng Huang, Jing Lu, Yongqian Du

Abstract: Steganography is a promising technique for covert communications. However, illegal United States of Americage of this technique would facilitate cybercrime activities and thereby pose a great threat to information security. Therefore, it is crucial to study its countermeasure, namely, steganalysis. In this paper, we aim to present an efficient steganalysis method for detecting adaptive-codebook based steganography in adaptive multi-rate (AMR) speech streams. To achieve this goal, we first design a new low-dimensional feature set for steganalysis, including an improved calibrated Markov transition probability matrix for the second-order difference of pitch delay values (IC-MSDPD) and the probability distribution of the odevity for pitch delay values (PDOEPD). The dimension of the proposed feature set is 14, far smaller than the feature set in the state-of-the-art steganalysis method. Employing the new feature set, we further present a steganalysis scheme for AMR speech based on support vector machines. The presented scheme is evaluated with a large number of AMR-encoded speech samples, and compared with the state-of-the-art one. The experimental results show that the proposed method is effective, and outperforms the state-of-the-art one in both detection accuracy and computational overhead.

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DOI: 10.3217/jucs-025-09-1109

Authors: Jędrzej Bieniasz, Monika Stępkowska, Artur Janicki, Krzysztof Szczypiorski

Abstract: This article addresses the problem of network attacks using steganographic techniques based on the manipulation of time relationships between IP packets. In the study, an efficient method to detect such attacks is presented. The proposed algorithm is based on the Change Observation Theory, and employs two types of agents: base and flying ones. The agents observe the time parameters of the network traffic, using proposed meta-histograms and trained machine learning algorithms, in the node where they were installed. The results of experiments using various machine learning algorithm are presented and discussed. The study showed that the Random Forest and MLP classifiers achieved the best detection results, yielding an area under the ROC curve (AUC) above 0.85 for the evaluation data. We showed a proof-of-concept for an attack detection method that combined the classification algorithm, the proposed anomaly metrics and the mobile agents. We claim that due to a unique feature of self-regulation, realized by destroying unnecessary agents, the proposed method can establish a new type of multi-agent intrusion detection system that can be applied to a wider group of IT systems.

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DOI: 10.3217/jucs-025-09-1066

Authors: Yosra Lakhdhar, Slim Rekhis, Noureddine Boudriga

Abstract: Presently, attackers succeed to damage different cyber systems no matter whether cyber security solutions are implemented or not. This fact can be explained by the information insufficiency regarding the attack environment and the deployed solutions, in addition to the predominant use of pre-built cyber attack databases, making the supervised system incapable of defending itself against zero-day attacks. We present in this paper an enhanced cyber defense model to assess the effectiveness of the deployed security solutions to defend against potential generated attack scenarios under various contexts (the configuration of distributed security solutions, named observer agents, the type and location of reaction systems, and the type of data visible by the deployed solutions). Furthermore, we propose a model ensuring the generation of known and unknown attack scenarios starting from the formal description of system variables and their interactions. In addition, we develop the concept of observable executable scenario that ensures the step by step observation of attack scenarios execution, the assessment of observer agents' reactions, and the detection of attack occurrence in a distributed system. The results of the conducted simulations using real case studies are presented to exemplify the proposal.

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DOI: 10.3217/jucs-025-08-0925

Authors: Ashot Harutyunyan, Arnak Poghosyan, Naira Grigoryan, Narek Hovhannisyan, Nicholas Kushmerick

Abstract: We address several problems in intelligent log management of distributed cloud computing applications and their machine learning solutions. Those problems concern various tasks on characterizing data center states from logs, as well as from related or other quantitative metrics (time series), such as anomaly and change detection, identification of baseline models, impact quantification of abnormalities, and classification of incidents. These are highly required jobs to be performed by today's enterprise-grade cloud management solutions. We describe several approaches and algorithms that are validated to be effective in an automated log analytics combined with analytics from time series perspectives. The paper introduces novel concepts, approaches, and algorithms for feasible log-plus-metric-based management of data center applications in the context of integration of relevant technology products in the market.

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DOI: 10.3217/jucs-025-07-0788

Authors: Robin Mueller-Bady, Martin Kappes, Inmaculada Medina-Bulo, Francisco Palomo-Lozano

Abstract: In this paper, the applicability of heuristic methods for an automated and reactive optimization of network infrastructures in highly-dynamic communication networks is studied. With an increasing amount of (mobile) participants and at the same time significantly growing quality requirements in communication networks over the past years, optimization of communication infrastructures will become an inevitable challenge in providing a reliable and high-quality communication service. Mostly, changes in highly-dynamic networks, which may be planned or unplanned, happen swiftly, such that it is not possible to apply manual optimization. Thus, an automated and reactive optimization becomes necessary to address this problem. Two major issues arise from the optimization of highly-dynamic communication networks. First, the complexity of problems, which is either implied by the complex optimization problem or the number of different possibly concurrent goals subject to optimization. Second, the highly-dynamic optimization search space, where network topologies may change rapidly introducing severe challenges for the optimization process. Here, different evolutionary and greedy optimization heuristics for the optimal selection of monitors in communication networks are studied and compared. Monitor selection is a well-known, important, and complex (NP-hard) optimization problem, serving as a current and actual use case for the general concept of highly-dynamic communication network optimization. As the results show, two of three methods reliably provide solutions of sufficiently high quality in reasonable time, enabling the applicability of heuristic methods of optimization in highly-dynamic communication networks. Results of the experiments are obtained using state-of-the-art statistical methods for evaluation of (evolutionary) search heuristics on a set of 39 real-world and synthetic benchmark problem instances.

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DOI: 10.3217/jucs-025-06-0591

Authors: Sławomir Bujnowski, Tomasz Marciniak, Beata Marciniak, Zbigniew Lutowski

Abstract: The paper shows the analysis of the influence of the introduction of network resources control on the transmission properties of the networks described with irregular graphs. The method developed and used by the authors in reference to regular graphs was applied. It consists in distributing the transmission resources according to specified unevenness coefficients of United States of Americage of individual edges. The way of determining these coefficients was described as well as the applied rule of distribution of the resources in case of equivalent and various density of the traffic generated by the network users. The improvement of the transmission properties of the analysed networks, understood to be the lowering of the probability of rejecting a service call resulting from the introduction of network resources control, was proved by the results of simulation research of simple and more complex network structures.

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DOI: 10.3217/jucs-025-06-0571

Authors: Łukasz Apiecionek, Marcel Großmann, Udo Krieger

Abstract: In recent years we have realized a rapid development regarding the Internet of Things (IoT). Its goal is to interconnect all possible devices to the Internet and to enhance these physical objects by new functionalities. In this way a user's life standard shall be improved. Regarding the application of Internet of Things concepts, there are some commonly known types of an IoT architecture which can provide different technical opportunities. However, comparative studies on Internet of Things architectures are rare. To relieve the difficulties of establishing a single universal IoT architecture, we describe some well-known architectures and compare these proposals with a special regard to important security aspects. A major focus is devoted to methods repulsing Denial-of-Service attacks. We compile a set of criteria that support network administrators in their decision-making processes with regard to a considered specific IoT scenario and its solution. The goal is to fit optimally to the requirements of these solutions. Finally, the proposed approach is illustrated by three already deployed IoT systems and a comparison of their related architectures and functionalities is presented.

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DOI: 10.3217/jucs-025-05-0465

Authors: Bingqing Shen, Jingzhi Guo

Abstract: Virtual world technologies provide new and immersive space for learning, training, and education. They are enabled by the content creation and content sharing function for allowing users to create and interoperate various learning objects. Unfortunately, virtual world content sharing based on persistent virtual world content storage, to the best of our knowledge, does not exist. In this paper, we address this problem by proposing a content sharing scheme based on Virtual Net, a virtual world persistency framework. For efficient content retrieval, three strategies have been proposed to reduce communication overhead and content load delay. By integrating these strategies, a virtual world content search and retrieval algorithm has been devised. The experiment results verify the effectiveness of the algorithm.

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DOI: 10.3217/jucs-025-03-0294

Authors: Bagus Santoso, Chunhua Su

Abstract: Recently, in order to guarantee security against quantum adversaries, several identification (ID) schemes based on computational problems which are supposed to be hard even for quantum computers have been proposed. However, their security are only proven against non-quantum adversaries. In this paper, we proposed a novel four-pass code-based identification scheme. By using quantum random oracle model, we provide a security proof for our scheme against quantum adversaries which aim to impersonate the prover under concurrent active attacks, based on the hardness assumption of syndrome decoding (SD) problem. Our security proof is interesting in its own right, since it only requires a non-programmable quantum random oracle, in contrast to existing security proofs of digital signatures generated from ID scheme via Fiat-Shamir transform which require programmable quantum random oracles.

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DOI: 10.3217/jucs-025-03-0245

Authors: Meijuan Huang, Bo Yang, Yi Zhao, Kaitai Liang, Liang Xue, Xiaoyi Yang

Abstract: Deterministic public-key encryption, encrypting a plaintext into a unique ciphertext without involving any randomness, was introduced by Bellare, Boldyreva, and O'Neill (CRYPTO 2007) as a realistic alternative to some inherent drawbacks in randomized public-key encryption. Bellare, Kiltz, Peikert and Waters (EUROCRYPT 2012) bring deterministic public-key encryption to the identity-based setting, and propose deterministic identity-based encryption scheme (DIBE). Although the construc- tions of chosen plaintext attack (CPA) secure DIBE scheme have been studied intensively, the construction of chosen ciphertext attack (CCA) secure DIBE scheme is still challenging problems. In this paper, we introduce the notion of identity-based all-but-one trapdoor functions (IB-ABO-TDF), which is an extension version of all-but-one lossy trapdoor function in the public-key setting. We give a instantiation of IB-ABO-TDF under decisional linear assumption. Based on an identity-based lossy trapdoor function and our IB-ABO-TDF, we present a generic construction of CCA-secure DIBE scheme.

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DOI: 10.3217/jucs-025-02-0074

Authors: Jędrzej Bieniasz, Krzysztof Szczypiorski

Abstract: This paper summarizes research on methods for information hiding in Open Social Networks. The first contribution is the idea of StegHash, which is based on the use of hashtags in various open social networks to connect multimedia files (such as images, movies, songs) with embedded hidden data. The proof of concept was implemented and tested using a few social media services. The experiments confirmed the initial idea. Next, SocialStegDisc was designed as an application of the StegHash method by combining it with the theory of filesystems. SocialStegDisc provides the basic set of operations for files, such as creation, reading or deletion, by implementing the mechanism of a linked list. It establishes a new kind of mass-storage characterized by unlimited data space, but limited address space where the limitation is the number of the hashtags' unique permutations. The operations of the original StegHash method were optimized by trade-offs between the memory requirements and computation time. Features and limitations were identified and discussed. The proposed system broadens research on a completely new area of threats in social networks.

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DOI: 10.3217/jucs-024-12-1776

Authors: J. Kokila, N. Ramasubramanian, Ravindra Thamma

Abstract: Recent processors are shrinking in size due to the advancement of technology. Reliability is an important design parameter along with power, cost, and performance. The processors need to be fault tolerant to counter reliability challenges. This work proposes a dynamic thermal and voltage management (DTVM) system which ensures a reasonable level of fault tolerance. The fault tolerance system (FTS) identifies and subsequently can forecast temporary failures at run-time. The temporary failures are dynamically estimated on SoC FPGAs for a class of heterogeneous applications. The dynamic priority scheduling based on absolute deadline is adopted to improve the nature of FTS. Experimental results indicate that the failure rate reduces by 7.2% with the variation of 2% and 12% in temperature and voltage respectively.

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DOI: 10.3217/jucs-024-10-1452

Authors: Rodrigo Souza, João Lopes, Cláudio Geyer, Anderson Cardozo, Adenauer Yamin, Jorge Luis Victória Barbosa

Abstract: The recent advances in the Internet of Things (IoT), which has provided increasing availability of networked sensors and actuators, have given context awareness research in the UbiComp area a new perspective. In this sense, the main contribution of this paper is the proposition of a distributed architecture for IoT, called CoIoT (Context awareness in the Internet of Things). This architecture is designed to provide proactive management of the interactions with the physical environment. To evaluate the functionalities of the proposed architecture we implemented a case study in the agricultural area, specifically in the monitoring of seed analysis laboratory.

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DOI: 10.3217/jucs-024-09-1282

Authors: Pir Shah, Peer Shah, Sadaf Yasmin, Zahoor Ur-Rehman, Akhlaque Ahmad, Yunyoung Nam, Seungmin Rho

Abstract: Content Centric Networking (CCN) is recently gaining attention for its potential to support emerging applications within Internet of Things (IoT). IoT generates huge volumes of data, with millions of nodes frequently requesting or generating different contents. CCN helps in breaking the coupling between a content and its location by addressing data with name rather than its location. This paper supports a CCN based approach in the IoT environment. To address scalability problems associated with CCN-assisted IoTs, we propose a new crumbling walls log quorum system based name resolution routing technique. The proposed system supports scalability, as the intersection property of a quorum system, inevitably minimizes the read/write operations overhead on distributed name resolution servers. Results show that the proposed technique increases the availability of the system along with low lookup overhead, latency, and load on distributed servers.

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DOI: 10.3217/jucs-024-09-1258

Authors: Nipon Theera-Umpon, Kun-Hee Han, Woo-Sik Bae, Sanghyuk Lee, Van Pham

Abstract: The evolving Internet of Things (IoT) technology has driven the advancement of communication technology for implantable devices and relevant services. Still, concerns are raised over implantable medical devices (IMDs), because the wireless transmission section between patients and devices is liable to intrusions on privacy attributable to hacking attacks and resultant leakage of patients' personal information. Also, manipulating and altering patients' medical information may lead to serious leakage of personal information and thus adverse medical incidents. To address the foregoing challenges, the present paper proposes a security protocol that copes with a range of vulnerabilities in communication between IMDs and other devices. In addition, the proposed protocol encrypts the communication process and data to eliminate the likelihood of personal information being leaked. The verification highlights the safety and security of the proposed protocol in wireless communication.

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DOI: 10.3217/jucs-024-07-0864

Authors: Waldemar Graniszewski, Jacek Krupski, Krzysztof Szczypiorski

Abstract: Due to high efficiency and relatively ease of use, application-layer covert channels, especially HyperText Transfer Protocol (HTTP), have been extensively studied in recent years. This paper extends a new steganographic method where the covert channel is created within the HTTP protocol header, i.e., trailer field. HTTP is the most popular protocol for browsing the Internet and gives the possibility of information sharing. The popularity of HTTP traffic is one of the requirements for undetectable message exchange. This paper presents SOMSteg - a framework for a covert channel, and its detection as a countermeasure, within HTTP. The server's and client's parts are implemented in the JavaScript language and based on the Node.js. Several machine learning techniques can be used for anomaly detection. We tested the detection possibility of such hidden communication by Self Organizing Maps (SOMs). SOMs were also used for tuning the parameters of the covert channel settings within the HTTP trailer. The results of the performed studies are also presented.

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DOI: 10.3217/jucs-024-05-0622

Authors: Barbara Bobowska, Michał Choraś, Michał Woźniak

Abstract: Cyber threats are nowadays a major danger to critical infrastructures and to homeland security. For several years now, the focus have been targeted at the physical protection of critical infrastructures. Currently, experts realize that the critical infrastructure can be also attacked via the application layer of computer networks. In order to efficiently protect such critical systems, the huge amount of data has to be efficiently analyzed and correlated. Therefore, this paper focuses on the overview of the advanced data stream processing methods to be applied in the domain of cybersecurity and critical infrastructure protection. The major contribution of this work is the analysis of such innovative aspects as concept drift analysis deployed as the pre-processing step dedicated for anomaly detection systems to counter cyber attacks. Moreover, we discuss the different challenges in data streams analysis including data imbalance and provide solid reasoning why applying a concept drift detector is crucial when designing a modern cybersecurity systems.

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DOI: 10.3217/jucs-024-05-0577

Authors: S. Prabavathy, K. Sundarakantham, S. Mercy Shalinie

Abstract: The rapid growth of Internet of Things(IoT) has reached all the facets of life including critical infrastructures. It has become the foundation for most of the critical infrastructures. The increased connectivity and the heterogeneity in IoT have widened the attack surface of critical infrastructures for attackers to exploit. Certain cyberattacks in critical infrastructures can lead to catastrophe and hence the attack has to be identified as early as possible to stop or reduce its impact by activating suitable responses. Therefore, the critical infrastructures require an intelligent security mechanism which can intelligently interpret the attacks from the IoT traffic and efficiently handle the attack scenario by activating appropriate response at faster rate. In this work, an autonomic security system with intelligent self-protect mechanism has been proposed for critical infrastructures. The autonomic security system can autonomously detect known attacks using Extreme Learning Machine, predict the unknown attacks using Gaussian process regression, and select suitable response for handling the attack using fuzzy logic. This intelligence of self-protect mechanism is incorporated in the distributed fog nodes to handle the attack scenario at faster rate and protect the critical infrastructures with minimal human intervention. The experimental analysis of the proposed autonomic security system proves to be efficient in detecting and defending the cyber-attacks with high accuracy and success rate. The results on network load and response time indicates the effectiveness of fog computing in proposed system.

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DOI: 10.3217/jucs-024-04-0515

Authors: Xiang Wang, Weike Wang, Bin Xu, Pei Du, Lin Li, Muyang Liu

Abstract: Embedded systems are subjected to various adversaries including software attacks, physical attacks, and side channel attacks. Most of these malicious attacks can lead to the invalid execution of programs, and launch of destructive actions or reveal critical information. However, most previous security mechanisms suffer from coarse checking granularity and unacceptable performance overhead, due to strict restriction on system resources. This paper presents a fine-grained hardware-based security approach to ensure runtime code integrity in the embedded systems by offline profiling of the program features and runtime integrity check. We design a hardware implemented instruction stream integrity checker (ISIC) to perform runtime checking of pre-extracted features. Any invalid execution of the program will trigger the corresponding exception signal. We implement the ISIC with OR1200 processor on XC5VLX50T field-programmable gate array (FPGA). The experimental results show that the proposed approach can detect all the attacks destructing integrity of the instruction stream, and the performance overhead induced by the security mechanism is less than 3.45% according to the selected benchmarks.

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DOI: 10.3217/jucs-024-04-0341

Authors: Alawi Al-Saggaf

Abstract: Cryptography and biometrics are widely used in providing information security. Cryptographic systems rely on keys for secure information. Unlike biometric systems, the keys in cryptographic systems must be exactly reproducible and not strongly linked to the user identity. Each of the two systems has some issue associated with it. Combining cryptography and biometrics in a secure way can mitigate these issues. This paper presents a secure cryptographic authentication method based on the discrete logarithm problem. Through the security analysis of the proposed scheme, we prove that the security characteristics of the proposed scheme relative to the properties of the discrete logarithm problem. Based on the proposed scheme, a biometric cryptosystem is constructed. The proposed system evaluated using CASIA iris database from 70 different eyes, with 7 samples of each eye. Experimental results demonstrate that the proposed system is more effective, promising in terms of cope up to 21.41% percent of error bits within an iris code, and can generate up to 98 bits of the cryptographic key from the iris codes.

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DOI: 10.3217/jucs-023-12-1113

Authors: Murat Topaloglu, Dilek Kırar

Abstract: The BYOD programme is a trend that aims to provide companies and workers with the next generation of security methods and flexible business models. These have been developed recently as a result of technological developments, especially in smart devices. Individuals from the "Y generation", who are also called the millennials, have a significant influence on shaping the present and future technology. Y generation employees want to use their own devices, including their own personal applications. Allowing employees to use their own devices does not mean that you will lose anything or have no control. For this reason, the BYOD policy, when implemented at a good level, significantly increases business performance and increases the productivity with the benefits provided by mobility. The BYOD tendency, which is difficult to avoid, increases the productivity of employees and the flexibility of the company in the eyes of the employees, by letting them use their own devices in the business environment. Moreover, it reflects positively on the employees' morale, with a subsequent increase in company loyalty. The aim of this study is to evaluate the validity and reliability analyses done during the development of the scale which aims to measure the effects of BYOD on workers and to assess its security components, benefits, applicability and sustainability. Our goal is to revise the previous research done and present objective values and findings obtained from the analyses. These values were based on the demographic information and the answers given by participants about to what extent BYOD is known and legal, its vulnerabilities in infrastructure and data security, the way it affects workers' perceptions individually and in general, and the benefits it provides. SPSS 20 program was used for descriptive statistics, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), item analyses and correlation coefficients.

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DOI: 10.3217/jucs-023-05-0454

Authors: Grzegorz Blinowski, Krzysztof Szczypiorski

Abstract: One of the areas in which wireless networks based on visible light communication (VLC) is considered superior to traditional radio-based communication is security. The common slogan summarizing VLC security features is WYSIWYS - "What You See Is What You Send". However, especially in the case of infrastructure downlink communication, security must be accounted for also in case of VLC. In this paper, we analyze the possibilities of implementing a steganographic channel in VLC systems on the physical (PHY) and medium access control (MAC) layers. We introduce new steganographic techniques for VLC: hiding data in dimming patterns and color visibility dimming (CVD) frames, and adapt methods previously proposed for radio networks, such as using unused header bits and pseudo-corrupted frames to hide data. We use the IEEE 802.15.7 standard as a reference model for protocol structure, but the steganographic techniques that we describe can also be used in networks based on different standards.

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DOI: 10.3217/jucs-023-04-0404

Authors: Kai Simon, Cornelius Moucha, Jörg Keller

Abstract: The increasing number of attacks on internet-based systems calls for security measures on behalf those systems' operators. Beside classical methods and tools for penetration testing, there exist additional approaches using publicly available search engines. We present an alternative approach using contactless vulnerability analysis with both classical and subject-specific search engines. Based on an extension and combination of their functionality, this approach provides a method for obtaining promising results for audits of IT systems, both quantitatively and qualitatively. We evaluate our approach and confirm its suitability for a timely determination of vulnerabilities in large-scale networks. In addition, the approach can also be used to perform vulnerability analyses of network areas or domains in unclear legal situations.

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DOI: 10.3217/jucs-022-09-1249

Authors: Friedrich Praus, Wolfgang Kastner, Peter Palensky

Abstract: With today's ongoing integration of heterogeneous building automation systems, increased comfort, energy efficiency, improved building management, sustainability as well as advanced applications such as active & assisted living scenarios become possible. These smart homes and buildings are implemented as decentralized systems, where embedded devices are connected via networks to exchange their data. Obviously, the demands - especially regarding security - increase: Secure communication becomes equally important as secure software being executed on the embedded devices. While the former has been addressed by standardization committees, manufacturers and researchers, until now the problem of secure control applications in this domain has not been addressed extensively. This leads to insecure and unprotected software being executed on the embedded devices. Thus, adversaries are capable of attacking building automation systems. This paper introduces an architecture for distributed control applications in smart homes and buildings, which tackles the problem on how to secure software running on different device classes. The following novelties are contributed: an application model capable of depicting control applications in a formal way, the concept of security attributes, being able to formally specify a security policy, and a framework, which allows the secure development and execution of control applications, and an enforcement of the defined security policies.

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DOI: 10.3217/jucs-022-09-1225

Authors: Lukas Krammer, Wolfgang Kastner, Thilo Sauter

Abstract: Building automation is a widespread topic that evolved over the past decades. Initially, building automation systems were used for heating, ventilation and air conditioning applications as well as for lighting and shading. Today, the term building automation covers many more application domains such as alarm systems, access control or life safety systems. In particular in the latter application domains, reliable, safe and secure communication systems are of utmost importance. This paper introduces a generic concept for bringing dependability - especially reliability, safety and security { into the area of building automation. The proposed approach is able to extend existing building automation systems with dependability features. For this purpose, the communication stack of a particular system is extended by adding an intermediate layer. This so-called Generic Dependability Layer is transparent to provide seamless integration. Thereby, reliability is addressed in terms of fault tolerance by offering redundant network topologies. A heartbeat mechanism and an acknowledgment procedure as well as a specific message format satisfy the safety requirements. Moreover, the dependability layer offers security mechanisms that establish a secured channel among communicating nodes providing integrity, confidentiality, data freshness and availability.

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DOI: 10.3217/jucs-022-09-1203

Authors: Jernej Tonejc, Sabrina Güttes, Alexandra Kobekova, Jaspreet Kaur

Abstract: In recent years, the volume and the complexity of data in Building Automation System networks have increased exponentially. As a result, a manual analysis of network traffic data has become nearly impossible. Even automated but supervised methods are problematic in practice since the large amount of data makes manual labeling, required to train the algorithms to differentiate between normal traffic and anomalies, impractical. This paper introduces a framework which allows the characterization of BACnet network traffic data by means of unsupervised machine learning techniques. Specifically, we use clustering, random forests, one-class support vector machines and support vector classifier, after a pre-processing step that includes principal components analysis for dimensionality reduction. We compare the effectiveness of the methods in detecting anomalies by performing experiments on BACnet network traffic data from various sources. We describe which of these unsupervised methods work best in specific scenarios since each method has its distinct advantages and disadvantages. In particular, we discuss which method is best suited to detect new types of anomalies (novelty detection), or which method most reliably and efficiently finds new attacks of a type that has been captured in the data previously.

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DOI: 10.3217/jucs-022-07-0896

Authors: Unai Aguilera, Diego López-De-Ipiña

Abstract: Service discovery is a fundamental aspect of many services oriented applications; however, it cannot be directly applied to mobile ad hoc networks (MANET) due to their dynamic nature. The lack of central manager nodes results in the need of specific discovery solutions for this kind of networks. In this paper, we present a solution for the discovery of services in MANETs that is based on the classification of service's parameters according to a shared domain ontology. Our proposed discovery protocol encompasses two main processes: dissemination and search. The search process defines a pruning mechanism that enables to detect if search messages must be propagated or not, reducing the number of messages communicated through the network. The dissemination and search processes are integrated within the route management mechanism that enables to reduce the number of propagated messages. We have implemented and tested our discovery solution using the ns-2 network simulator. Experiments have been performed using rigorously constructed scenarios for testing the characteristics of the proposed solution. The obtained results show how the inclusion of the ontology not only increments the expressiveness of the search and discovery process, but also enables to reduce the number of propagated messages due to the grouping and pruning processes.

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DOI: 10.3217/jucs-022-05-0671

Authors: Yolanda Baca-Gomez, Alicia Martinez, Paolo Rosso, Hugo Estrada, Delia Irazu Hernandez Farias

Abstract: The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.

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DOI: 10.3217/jucs-022-04-0589

Authors: Gunupudi Kumar, Nimmala Mangathayaru, Gugulothu Narsimha

Abstract: Software Security and Intrusion Detection need to be dealt at three levels Network, Host level and Application level. In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.

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DOI: 10.3217/jucs-022-04-0521

Authors: Muneer Yassein, Yaser Khamayseh, Mai Abujazoh

Abstract: The security issue is essential and more challenging in Mobile Ad-Hoc Network (MANET) due to its characteristics such as, node mobility, self-organizing capability and dynamic topology. MANET is vulnerable to different types of attacks. One of possible attacks is black hole attack. Black hole attack occurs when a malicious node joins the network with the aim of intercepting data packets which are exchanged across the network and dropping them which affects the performance of the network and its connectivity. This paper proposes a new dataset (BDD dataset) for black hole intrusion detection systems which contributes to detect the black hole nodes in MANET. The proposed dataset contains a set of essential features to build an efficient learning model where these features are selected carefully using one of the feature selection techniques which is information gain technique J48 decision tree, Naïve Bayes (NB) and Sequential Minimal Optimization (SMO) classifiers are learned using training data of BDD dataset and the performance of these classifiers is evaluated using a learning machine tool Weka 3.7.11. The obtained performance results indicate that using the proposed dataset features succeeded in build an efficient learning model to train the previous classifiers to detect the black hole attack.

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DOI: 10.3217/jucs-022-02-0247

Authors: Dragos Datcu, Stephan Lukosch, Heide Lukosch

Abstract: While augmented reality research has grown into a mature field over the last years, the aspects of situational awareness and presence of augmented reality (AR) are still quite open research topics. This paper introduces a collaborative game to explore the different perception of situational awareness and presence in a physical and an AR environment. The game is employed as an approximation of collaboratively solving complex problems. The goal of the game is to jointly build a tower with either physical blocks in a physical environment or virtual blocks in an augmented reality environment. A first study with 18 users shows the feasibility of the game and questionnaire design for studying the different perception of situational awareness and presence in a physical and an AR environment. The study further identifies necessary future research with regard to the perception of presence and awareness in AR.

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DOI: 10.3217/jucs-022-01-0124

Authors: Dilek Karahoca, Adem Karahoca, Ayça Kurnaz

Abstract: This study aims to investigate certain communication dimensions to assess the impact of Computer Mediated Communication (CMC) tendencies of students on ubiquitous environments. Communication dimensions were determined as message content, message interaction, semantic of message, emotional situation, and security. Message content was used as a determiner indicator to measure the effectiveness of communication methods in ubiquitous environments. The effects on the inclination to communicate in ubiquitous learning environments are explained in detail with Partial Least Squares Structural Equation Modeling (PLS-SEM). Message interaction, semantic of message, and security all have a great impact on the adoption of message content; therefore, they promote students' engagement through ubiquitous learning. According to the correlation values, we observed that the user is prone to participate in CMC if the message is meaningful, replied to immediately, sent from someone that looks familiar and sent to the right person.

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DOI: 10.3217/jucs-021-12-1594

Authors: Shantanu Pal

Abstract: With the tremendous growth of mobile devices, e.g, smartphones, tablets and PDAs in recent years, people are looking for more advanced platforms in order to use their computational applications (e.g., processing and storage) in a faster and more convenient way. In addition, mobile devices are capable of using cloud-based applications and the use of such technology is growing in popularity. However, one major concern is how to efficiently access these cloud-based applications when using a resource-constraint mobile device. Because it requires a continuous Internet connection which is difficult to obtain in challenged environments that lack an infrastructure for communication (e.g., in sparse or rural areas) or areas with infrastructure (e.g., urban or high density areas) with restricted/full of interference access networks and even areas with high costs of Internet roaming. In these situations the use of opportunistic networks may be extended to avail cloud-based applications to the user. In this paper we explore the emergence of extending mobile cloud platforms using opportunistic networks. The motivation of this paper is twofold. First, we classify the available mobile cloud architectures according to their intent, the way they deliver services, and survey the state of the art research and issues related to their performance (e.g., battery power, storage capacity, bandwidth utilisation), environments (e.g., heterogeneity of networks, user's collaborations), robustness (e.g., scalability and availability of resources) and efficiency (e.g., context aware mobile services, access costs, energy consumption). Second, we devise a new `mobile-opportunistic collaborative cloud' (MoCC) architecture which enhances mobile cloud platforms using opportunistic networks. Next, we outline the open issues and future research directions that need to be improved upon for delivering a more scalable, location-aware and context based service to the users.

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DOI: 10.3217/jucs-021-02-0177

Authors: Jose Gutiérrez-Rivas, Simon Holmbacka, Miguel Míndez-Macías, Wictor Lund, Sebastien Lafond, Johan Lilius, Javier Díaz-Alonso

Abstract: Safety-critical systems and certification standards are the bare essential elements for the development process of avionics, automotive and industrial embedded systems. The necessity of including non-safety capabilities to reduce the price of these systems has resulted in a new type of critical systems, the mixed-criticality ones. These systems should be able to execute safety-critical applications but, at the same time, to run non-safety-critical functionalities without affecting the integrity of the safety-critical tasks. This paper presents a new system architecture which includes safety-critical and non-safety-critical parts in order to form a mixed-criticality system. The system consists of a reliable platform with a dual-core processor (implemented using a FPGA) architecture designed as open-hardware, running two isolated real-time operating systems which are connected through a safe core-to-core communication channel that executes the safety-critical applications. Moreover, the safety-critical system is connected to an external processor, an ARM9, which is used as an external sensing system. The ARM9 runs the non-safety-critical applications and allows the system to insert modifications updating without affecting the safety capabilities of the safety-critical part. This platform is described providing evidences of the isolation between safety-critical (SC) and non-safety-critical (NSC) applications, as well as describing an updating methodology for non-safety-critical applications. This system is validated using a complete and reliable application for safe emergency stop applications for industrial machinery.

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DOI: 10.3217/jucs-021-09-1210

Authors: Libor Polčák, Barbora Franková

Abstract: Each clock has built-in deficiencies since the manufacturing process is not precise on atomic level. These inaccuracies cause each clock to drift in a unique way. Clock skew has been already studied and used to identify computers. Based on the previous research in clock-skew-based identification, this paper provides a summary of use cases and methods for clock-skew-based identification. Nevertheless, the main contribution of the paper is following: (1) A formal evaluation of the requirements for precise clock skew estimations. The formal approach is accompanied with an empirical study of 24,071 clock skew measurements. (2) A method that links IPv4 and IPv6 addresses of a single computer. (3) A scenario, during which a malicious attacker mimics clock skew of another computer and consequently, for example, penetrates through authentication mechanisms considered during previous research. (4) Even though the real network observations expose that current precision in clock skew estimation is not sufficient to uniquely identify devices in moderately-sized network, some IPv4 and IPv6 addresses can be linked based on unique clock skew shifts of a computer, for example caused by a running NTP daemon.

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DOI: 10.3217/jucs-021-03-0384

Authors: Xiaoding Wang, Li Xu, Shuming Zhou, Joseph Liu

Abstract: Abstract: Wireless Mesh Networks (WMNs) have emerged as a key technology for next-generation wireless networking. By adding some Long-ranged Links, a wireless mesh network turns into a complex network with the characteristic of small worlds. As a communication backbone, the high fault tolerance is a significant property in communication of WMNs. In this paper, we design a novel malfunctioned router detection algorithm, denoted by A-SRS, on searching resolving set based on private neighbor of dominating set. The A-SRS not only offers a highly efficient solution to position malfunctioned routers against intermitted communication that guarantees the availability of network services, but also pursues the minimum number of detecting routers due to limited resource of wireless mesh routers. We also explore the cardinality of resolving set and complexity of A-SRS based on the parameters: the minimum degree, the size of underlying graph G and the number of iterations. The algorithm enjoys better simulation results that it employs less detecting routers than the other strategies in the size of resolving set.

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DOI: 10.3217/jucs-021-02-0248

Authors: Daniel Perez-Gonzalez, Raimundo Díaz-Díaz

Abstract: Social, technological and economic changes, citizen demand of services modernization, new ICT developments related to the Internet of Things and an economic situation that urges more efficient public administrations, have allowed the adoption of ICT by municipalities in order to provide public services. All the foregoing constitutes a boost of the smart city concept, which is considered in the scientific literature mainly from a technical point of view, overlooking deeper analysis on the specific services being provided by means of smart technologies. The current research identifies services provided using smart technologies at 26 Spanish smart cities and the degree of smart development of those cities based on which services provide. The results highlight that the services most widely implemented are those that allow direct reductions in local administration expenditure. On the other hand, the remaining services enjoy greater perspectives of future development. Additionally, three groups of smart city development have been identified, which allows benchmarking analysis and enhances the exchange of information between the cities.

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DOI: 10.3217/jucs-020-14-1951

Authors: Michal Pańczyk, Halina Bielak

Abstract: Self-stabilizing algorithms model distributed systems, which automatically recover from transient failures in the state of the system. The center of a graph comprises a set of vertices with minimum eccentricity. Farley and Proskurowski showed the linear time algorithm for locating the center of an outerplanar graph in the classical computing paradigm. The present paper investigates the selfstabilizing algorithm for finding the center of maximal outerplanar graphs, using a new approach with dual trees.

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DOI: 10.3217/jucs-020-13-1855

Authors: Ming Yang, Bo Liu, Wei Wang, Junzhou Luo, Xiaojun Shen

Abstract: By exploiting multi-radio multi-channel technology, wireless mesh networks can effectively provide wireless broadband access to the Internet for mobile users. Due to the limited number of orthogonal channels, overlapping channel assignment is one of the main factors that greatly affect the network capacity. However, current results in this area are not so satisfying. In this paper, we first propose a model for measuring achieved network capacity in MR-WMNs. Then we prove that finding an optimal overlapping channel assignment in a given MR-WMN with odd number of channels, is equivalent to finding an optimal assignment by only using its orthogonal channels. This theory allows us to use fewer channels to solve complicated channel assignment problems. Third, we prove that in 802.11b/g MR-WMN the simplified optimization problem is a Max-3-Cut problem. Although this problem is NP-hard, it has an efficient approximation algorithm that achieves approximation ratio of 1.19616 probabilistically by using the algorithm for Max-Cut whose approximation ratio is 1.1383 probabilistically. Based on the algorithm for Max-Cut, this paper proposes Max-Cut based channel assignment (MCCA) which uses a heuristic method to adjust the result produced by the Max-Cut algorithm to achieve an even better result. Finally, we perform extensive simulations to compare the MCCA with a state-of-the-art Tabu-Search based algorithm. The results show that the Max-Cut based overlapping channel assignment algorithm effectively and efficiently improves on the network capacity compared with existing algorithms.

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DOI: 10.3217/jucs-020-13-1738

Authors: Davide Vega, Roc Meseguer, Felix Freitag, Sergio Ochoa

Abstract: Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.

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DOI: 10.3217/jucs-020-09-1327

Authors: João Lopes, Rodrigo Souza, Cláudio Geyer, Cristiano Costa, Jorge Luis Victória Barbosa, Ana Pernas, Adenauer Yamin

Abstract: The development of applications that adapt to the environment and remain running even when the user is moving or switching device, remains an open research challenge. In this article we present a view of the EXEHDA middleware and a new service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. To achieve this objective, EXEHDA employs various strategies in its services to allow the adaptation to the current context, such as on-demand adaptive service loading, and dynamic discovery and configuration. In that sense, EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the new service proposed for dynamic adaptation we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital. This work is sponsored by RNP, FINEP and CNPq - Brazilian Foundations.

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DOI: 10.3217/jucs-020-08-1130

Authors: Haithem Mezni, Walid Chainbi, Khaled Ghedira

Abstract: With the growing demand on Web Services, self-adaptation in the highly-dynamic environment is becoming a key capability of service-based systems. As a solution for Web services to provide added value and high QoS, combining self-* and policies allows reducing management complexity and effectively drives adaptation. Also, providers must participate in the self-adaptation process as they are aware of the capabilities of their offered services and exceptions that may occur. Despite the important role of service providers, existing approaches did not address this major issue. Thus, the description of self-adaptive Web services must not be limited to functional and QoS data. To address these issues, we extend the WS-Policy framework to represent capabilities and requirements of self-* Web services. We also extend UDDI in order to store and manage service policies, as the current UDDI model does not offer these capabilities. Finally, we propose an ECA-based planning mechanism to specify decision making in the self-adaptation process.

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DOI: 10.3217/jucs-020-08-1109

Authors: Dulce Domingos, Francisco Martins, Carlos Cândido, Ricardo Martinho

Abstract: Business processes can use Internet of Things (IoT) information to monitor context data in real-time and to respond to changes in their values in a timely fashion. For this matter, business process definition and execution languages should foresee an easy way for process modelers to define which values to monitor, and which automatic behaviors to adopt when these values change. In this paper, we propose the use of context variables to monitor sensor values, as well as a when-then language construct to detect and handle changes in these values within business processes. We define a Web Services Business Process Execution Language (WS-BPEL) extension to convey these constructs, and implement then using a "BPEL language transformation" approach. With these contributions, process modelers can define IoT-aware business processes avoiding the increase of process complexity and keeping their focus on modeling the processes' main logic. In addition, the language transformation approach assures the portability of processes using our constructs amongst WS-BPEL execution engines.

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DOI: 10.3217/jucs-020-04-0462

Authors: Sana Ajmal, Asim Rasheed, Amir Qayyum, Aamir Hasan

Abstract: Human safety considerations linked with rapidly growing auto mobile market has given special attention to the Intelligent Transportation System (ITS). ITS provides a set of standards for inter vehicular communication with emphasis on safety, traffic efficiency and infotainment related applications. In ITS, the vehicles acting as mobile nodes, form a specialized ad hoc network, known as Vehicular Ad-hoc Network (VANET). Although, VANET and ITS are under intense research since last decade, technology still lacks large scale deployment. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications are the main research goals of ITS. High relative node velocity and high active node density has presented peculiar challenges to connectivity within VANET. VANET connectivity and routing requirements range from the time critical safety applications, to the time and space hovering, delay tolerant and infotainment applications. This paper reviews connectivity issues in VANET with emphasis on routing, and offers comprehensive literature review on state of the art in VANET routing, with its detailed classifications. It also compares some standard architectures of VANET from MAC, routing and management perspective, i.e., WAVE by IEEE, CALM by ISO, C2CNet by C2C consortium / GeoNet.

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DOI: 10.3217/jucs-020-03-0366

Authors: Mariam Rady

Abstract: If we take a look at current cloud computing services, the only quality guarantee they provide are vague Service Level Agreements(SLA). In this paper we modelled some non-functional aspects in an ontology and used this ontology as a knowledge base to generate an excerpt from a service contract. We concentrate in this excerpt on availability as it is one of the most discussed attributes in current Service Level Agreements.

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DOI: 10.3217/jucs-019-17-2490

Authors: Juan Álvaro Muñoz Naranjo, Pablo Orduña, Aitor Gómez-Goiri, Diego López-De-Ipiña, Leocadio Casado

Abstract: This work introduces a novel access control solution for wireless network services in Internet of Things scenarios. We focus on a minimal use of computation, energy and storage resources at wireless sensors so as to address constrained devices: the proposed methods for key distribution and access control rely on extremely fast key derivation functions and, for the same reason, memory usage is reduced since keys are computed on the fly when needed. Our solution achieves privacy, authentication, semantic security, low energy, low computational demand and impacts mitigation of compromised devices on a simple manner. The access control provided is based on user identity and time intervals. We discuss these properties, compare our proposal to previous related work and provide experimental results that confirm its viability.

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DOI: 10.3217/jucs-019-16-2453

Authors: Ting Cui, Chenhui Jin, Guoshuang Zhang

Abstract: Impossible differential cryptanalysis is an important tool for evaluating the security level of a block cipher, and the key step of this cryptanalysis is to find the longest impossible differential. This paper focuses on retrieving impossible differentials for m-cell Skipjack-like structure with SP/SPS round function (named SkipjackSP and SkipjackSPS resp.). Up to now, known longest impossible differentials in m-cell Skipjack-like structures is m2 rounds. In this paper, we provide some new m2 rounds impossible differentials for these two structures. Further, we prove that if P layer is chosen from binary matrices, we can always retrieve m2 + 1 rounds impossible differentials for these two structures, and m2 + 2 rounds impossible differentials for SkipjackSP. Moreover, if P layer satisfies some satiable conditions, we may further obtain m2 + 2 rounds impossible differential for SkipjackSPS. Our results show that we should choose P layer carefully when employing these two structures.

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DOI: 10.3217/jucs-019-16-2437

Authors: Giuseppe Cattaneo, Giancarlo Maio, Umberto Petrillo

Abstract: The Global Systems for Mobile communications (GSM) is actually the most widespread mobile communication technology existing nowadays. Despite being a mature technology, its introduction dates back to the late eighties, it suffers from several security vulnerabilities, which have been targeted by many attacks aimed to break the underlying communication protocol. Most of these attacks focuses on the A5/1 algorithm used to protect over-the-air communication between the two parties of a phone call. This algorithm has been superseded by new and more secure algorithms. However, it is still in use in the GSM networks as a fallback option, thus still putting at risk the security of the GSM based conversations. The objective of this work is to review some of the most relevant results in this field and discuss their practical feasibility. To this end, we consider not only the contributions coming from the canonical scientific literature but also those that have been proposed in a more informal context, such as during hacker conferences.

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DOI: 10.3217/jucs-019-16-2385

Authors: Youngho Park, Chul Sur, Sanguk Shin, Kyung-Hyune Rhee, Changho Seo

Abstract: Vehicular Delay Tolerant Networks (VDTNs) are characterized model of Vehicular Ad Hoc Networks where vehicles disseminate messages through fixed relay nodes placed on roadside by utilizing a store-carry-forward method. In this paper, we propose a secure message delivery protocol for protecting receiver-location privacy in socialspot-based VDTN because location privacy is one of the most important security requirements. To design a simplified protocol, we eliminate the use of conventional pseudonym-based vehicle identification accompanied with a complex pseudonymous certificate management. Instead, we introduce an identity-hidden message indexing which enables a receiver vehicle to query a message whose destination is itself to the socialspot RSU without revealing its identity, and we make use of non-interactive key agreement scheme to establish a secure communication channel between message source and destination vehicles. Furthermore, we demonstrate experimental results to confirm the reduced cryptographic overhead and the effectiveness of privacy preservation for the proposed protocol.

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DOI: 10.3217/jucs-019-15-2292

Authors: Javier Fernández, Juan Augusto, Giuseppe Trombino, Ralf Seepold, Natividad Madrid

Abstract: Traders are required to work in the financial market with highly complex information and to perform efficiently under high levels of psychological pressure. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem not yet considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This paper takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision-making. The system has been designed considering both technical and physiological aspects to make information available in a suitable way. Biometric sensors are used to collect data associated with stress, a software platform then analyses this information and displays it to the trader. The resulting system is capable of making individual traders, as well as teams of traders, self-aware of their levels of stress. The system has been tested in real environments and the results provide evidence that self-aware traders benefit from the system by reducing risky decision-making.

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DOI: 10.3217/jucs-019-14-2112

Authors: Rafael Pastor, Agustín Caminero, Daniel Sánchez, Roberto Hernández, Salvador Ros, Antonio Robles-Gómez, Llanos Tobarra

Abstract: Society has evolved in such a way that individuals are required to embrace constant improvements in order to be able to perform their jobs properly. Distance education is a solution to this problem, as it allows students to obtain practical knowledge without the space and time constraints of classical face-to-face education, thus allowing them to fit their studies into possibly tight schedules. In order to obtain practical distance education on technical topics, the use of remote laboratories becomes more of a necessity rather than just being an option. To this end, the RELATED framework has been developed in order to permit the structural development of remote laboratories. It presents a structured methodology of remote/virtual lab development and also provides common facilities, such as user management, booking, or basic visualization. In the case that a high number of laboratories and students use RELATED, handling such information becomes a major issue for the proper functionality of RELATED. These issues can be efficiently tackled using cloud technologies. This paper proposes the use of cloud technologies to enhance RELATED and describes the cloud-based architecture that is under development at UNED, including details on its software components and the algorithms needed for resource provisioning.

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DOI: 10.3217/jucs-019-14-2037

Authors: Marc Jansen, Lars Bollen, Nelson Baloian, H. Hoppe

Abstract: The term "Cloud Computing" does not primarily specify new types of core technologies but rather addresses features to do with integration, inter-operability and accessibility. Although not new, virtualization and automation are core features that characterize Cloud Computing. In this paper, we intend to explore the possibility of integrating cloud services with educational scenarios without re-defining neither the technology nor the usage scenarios from scratch. Our suggestion is based on certain solutions that have already been implemented and tested for specific cases.

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DOI: 10.3217/jucs-019-09-1275

Authors: Maria-Aydee Sanchez-Santana, Jean-Baptiste Aupet, Marie-Laure Betbeder, Jean-Christophe Lapayre, Antonio Camarena-Ibarrola

Abstract: In this paper, we present a new telediagnosis environment for the detection of cardiovascular problems.This tool, called VACODIS (VAscular COllaborative teleDIagnosiS), allows practitioners to semi-automatically identify and quantify a patient's potential cardiovascular complications. The system generates first-time automatic detection of cardiovascular abnormalities using Doppler ultrasound images.The system then provides remote collaborative sharing of this information among different doctors to allow distance telediagnostics. With this new system, different actors in the field of medicine (nurses, practitioners, etc.) will be able to contribute to a more reliable diagnosis in the cardiovascular domain.

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DOI: 10.3217/jucs-019-09-1219

Authors: Antonio Jara, David Fernandez, Pablo Lopez, Miguel Zamora, Antonio Skarmeta, Leandro Marin

Abstract: Bluetooth Low Energy (BLE) is extending Bluetooth technology to devices with lower communication requirements and higher constraints in terms of memory capabilities and power autonomy. Thereby, BLE makes feasible the wireless transmission of information from smart objects such as wearable clinical devices, ambient sensors and actuators. These smart objects are starting to be internet-enabled devices, reaching the so denominated Internet of Things (IoT). Our research work is focused on analyze the capabilities of these technologies for continuous data transmission and integration of clinical sensors in home-care and Ambient Assisted Living environments. For this purpose, this work analyses exhaustively the capabilities from BLE and compare this with the capabilities from Bluetooth 2.1. In addition, it has been considered the communications requirements from different clinical devices such as an electrocardiogram (ECG) and a capnograph. It is concluded that the performance from BLE is lower than Bluetooth Classic (Bluetooth 2.1) for continuous communications, since BLE is based on attributes (datapoints) transfer instead of offering a communication session where can be transmitted as much packets as required, without any size constraint. Therefore, it is necessary to perform data compression/aggregation when the amount of data to send is too large to be stored in a BLE attribute. This work also presents how to apply pre-processing techniques that greatly reduces the transmission overload (performs signal compression) in order to allow the continuous transmission of the ECG and capnograph signal through BLE making so feasible the integration of continuous clinical devices via BLE.

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