Forthcoming and Online First Articles

International Journal of Ad Hoc and Ubiquitous Computing

International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC)

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International Journal of Ad Hoc and Ubiquitous Computing (31 papers in press)

Regular Issues

  • Multiscale Hierarchical Attention Fusion Network for Edge Detection   Order a copy of this article
    by Kun Meng, Xianyong Dong, Hongyuan Shan, Shuyin Xia 
    Abstract: Edge detection is one of the basic challenges in the field of computer vision.
    Keywords: Edge detection; Deep learning; Multiscale; Attention network.

  • Insider Threat Detection and Prevention using Semantic Score and Dynamic Multi Fuzzy Classifier   Order a copy of this article
    by Malvika Singh, S. Sangeetha, B.M. Mehtre 
    Abstract: Insider threat detection methods are usually based on machine and deep learning techniques. They consider insider threat as an anomaly detection problem. These methods are sophisticated in detection, but result in high false positives, poor threat detection rates and do not prevent malicious insiders. In this paper, an automatic insider threat detection and prevention system is proposed. It involves: data pre-processing for removal of noise; isometric feature mapping to minimize information loss while extracting features from high dimensional space; the emperor penguin algorithm due to its effective exploitation and exploration for optimum feature selection; semantic score computation using a combination of SentiWordNet and Deep-Q-Learning; and use of multi-fuzzy classifier to handle a variety of features in parallel for fast processing. After detecting malicious insiders, further access to organizational resources is denied by performing authentication. The proposed method is tested on CMU-CERT r4.2 dataset and the results outperform the existing methods.
    Keywords: Insider Threat Detection; User Behaviour Analysis; Anomaly Detection; Insider Threat Prevention.
    DOI: 10.1504/IJAHUC.2022.10046481
  • Target Node Selection for Data Offloading in Partially Connected Vehicular Adhoc Networks   Order a copy of this article
    by Shailendra Shukla 
    Abstract: Delay-sensitive applications such as video streaming, Intelligent Transportation Systems (ITS), and emergency services have given new challenges to the partially connected Vehicular Adhoc Networks (VANET). The significant challenges of VANET are massive data generation, high latency, and partial connection between the vehicles. The problem of large data generation can be addressed as the problem of data offloading. A naive approach would be to utilize the infrastructure, i.e., establishing the Road Side Unit (RSU), Femtocell, or wifi Access Point (AP) at equidistance in spatial locations in ITS. However, increasing the number of cellular towers, wifi, or femtocell is costlier, requires high maintenance, and has low RoI. If an accurate association is done between the Vehicle-to-Vehicle-to-Infrastructure (V2V2I), then the data offloading can be achieved with minimum network congestion and reduced user dissatisfaction. This paper proposes a data offloading approach for effective data delivery. The major contribution in this paper is threefold: a) hybrid approach for data offloading where both V2I and V2V are explored simultaneously to detect an optimized target-set selection (NP-hard in nature) and a clustering approach scrutiny the Target Set b) a selection encounter index methodology for the articulation point detection is proposed c) Proposed a novel policy-based relay selection and storage selection methodology. To validate our proposed scheme, we have to compare our proposed scheme with the community-based offloading approach and Delay Tolerant Network (DTN) based algorithms. The result shows that the proposed algorithm requires 10% to 30% less energy for target set selection, 50% times less load delay, and a 40% high delivery ratio compared to the community and epidemic approach.
    Keywords: VANET; Data offloading; Cut-Vertex; K-Means; Target Set Selection.

  • Knowledge-based flexible resource allocation optimization strategy for multi-tenant radio access network slicing in 5G and B5G   Order a copy of this article
    by NAVEEN KUMAR, Anwar Ahmad 
    Abstract: For next-generation wireless networks, formalization of the network slice as a resource allocation unit is considered a promising aspect since it enables scalable and flexible resource allocation among many tenants in 5G and beyond 5G (B5G) communication networks. The resources, which have been allocated independently in legacy networks, are now supposed to be allocated as per the precise service requirement expressed by the particular tenant. Typically, the tenants share a 2-tier 5G architecture, where central administration and transportation networks are in the upper-tier, while the lower-tier consists of edge and radio access networks with multiple access points. Due to the paucity of resources on the outskirts than central administration, the user traffic has to be passed through the central administration for processing, which leads to latency problems. To solve this problem, recent research works have suggested the fixed central to edge resource allocation ratios for three of the major 5G services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency (uRLLC), and massive Machine Type Communication (mMTC). However, this approach leads to over-provisioning of some resources. Motivated by this circumstance, this paper provides a flexible resource allocation approach for 5G slice networks operating in a heterogeneous environment with multiple tenants and tiers. In this paper, a radial basis-neural network is used to convert abstract specifications of simulation activities into precise resource needs in terms of their quantity and quality, then after formulating the problem as a maximum utility optimization problem, a genetic algorithm based flexible multi-resource allocation scheme is presented, where a versatile optimization framework is used where various fairness approached are taken into account. Further, the proposed scheme is compared with the existing static slicing resource allocation (SS-RA) scheme and optimal resource allocation (ORA) scheme, the results show that the proposed approach outperforms such existing schemes.
    Keywords: 5G; network slicing; radio access networks; multi-resource allocation; neural netwokrs; genetic algorithm.

  • Energy Consumption Models in VANET Simulation Tools for Electric Vehicles: a Literature Survey   Order a copy of this article
    by Insaf Sagaama, Amine Kchiche, Wassim Trojet, Farouk Kamoun 
    Abstract: Electric Vehicles (EVs) have been widely recognized as a key technology of the Intelligent Transportation Systems (ITS) to make both public and private transportation services more economic and ecologic. The energy-saving in the case of EVs is a viable solution to promote smart navigation and extending the driving range. Realistic traffic simulations contribute to the large-scale diffusion of EVs in the future market. In particular, Vehicular Ad-hoc NETworks (VANETs) simulation tools integrate often an energy model for calculating the vehicle energy consumption. Hence, the EVs raise a new challenge about integrating reliable and accurate energy models in the traffic simulators for this category of vehicles. In this paper, we present a thorough study about energy models elaborated in the automotive sector to provide valuable enhancements to VANET simulators. The main goal is to establish an accurate estimation of the EV consumption and recuperation in VANET simulation tools.
    Keywords: Electric vehicle; VANETs simulation tools; energy consumption models; energy recuperation; energy efficiency.

  • Theoretical Analysis of Biases in TLS Encryption Scheme Chacha 128   Order a copy of this article
    by Karthika SK, Kunwar Singh 
    Abstract: Chacha is a software-oriented Stream cipher designed by Daniel J Bernstein(2008). Chacha is a Salsa variant that is eSTREAM project's finalist candidate. Google added Chacha and a Message Authentication Code to their Transport Layer Security (TLS) and Datagram TLS (DTLS) protocols in 2016. Chacha has become an area of interest for cryptanalysis since its adoption by Google. Almost all the existing cryptanalysis are experimental. Experimental cryptanalysis identifies vulnerable areas of a cipher, whereas theoretical analysis, helps in the development of possible countermeasures. Differential cryptanalysis is a cryptanalytic technique that helps in discovering distinguishers on stream ciphers. Recently, Dey and Sarkar (2021) have theoretically explored the reason behind distinguishers in Salsa and Chacha 256 stream cipher. Motivated by this work, we have theoretically analyzed differential attacks on Chacha 128 (Chacha 256 variant) up to four rounds and we have the bias probabilities. Our theoretical analysis results match the experimental results.
    Keywords: Transport Layer Security; Stream cipher; Chacha; Theoretical analysis; Differential cryptanalysis.

  • 5G Network Traffic Control: A Temporal Analysis and Forecasting of Cumulative Network Activity using Machine Learning and Deep Learning technologies   Order a copy of this article
    by Ramraj Dangi, Praveen Lalwani, Manas Kumar Mishra 
    Abstract: Fifth Generation (5G), traffic forecasting is one of the target areas for research to offer better service to the users. In order to enhance the services, researchers have provided deep learning models to predict the normal traffic but, these suggested models are failing to predict the traffic load during the festivals time due to sudden changes in traffic conditions. In order to address this issue, a hybrid model is proposed which is the combination of Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called as ARIMA-CNN-LSTM, where we are forecasting the cumulative network traffic over specific intervals to scale up and correctly predict the availability of 5G network resources. In the comparative analysis, the ARIMA-CNNLSTM is evaluated with well known existing models, namely, ARIMA, CNN and LSTM. It is observed that the proposed model outperforms over the other tested deep learning models that predict the output in both usual and unusual traffic conditions.
    Keywords: 5G; IoT; Deep Learning; Traffic Prediction.
    DOI: 10.1504/IJAHUC.2023.10052396
  • FOESG: Anonymous session key agreement protocol for fog assisted smart grid communication   Order a copy of this article
    by Dheerendra Mishra, Saurabh Rana, Chirag Goyal, Gagandeep Singh 
    Abstract: To present secure and authorized communication among customers, smart meters, and utilities, smart grid (SG) communication should adopt a cryptographic mechanism to ensure involved entities' authentication, message integrity and confidentiality. To address efficient ways of authentication and secure session establishment, we construct an anonymous session key agreement protocol for the smart grid environment. This construction also uses fog as a specialized middle layer technique to make communication efficient. The formal proof of security is demonstrated under the widely adopted random oracle model (ROM) along with performance evaluation. Moreover, simulation for security validation is performed under broadly-accepted ``Automated Validation of Internet Security Protocols and Applications'', which indicates that the protocol is safe. Comparison with recent proposals demonstrates an advantage in efficiency. In nutshell, the proposed protocol achieves desirable security goals and performance attributes.
    Keywords: Smart grid; fog Computing; authentication; security.
    DOI: 10.1504/IJAHUC.2022.10051404
  • Intelligent Reflecting Surfaces for Cognitive Radio Networks   Order a copy of this article
    by Raed Alhamad 
    Abstract: In this paper, we derive the secondary throughput of cognitive radio networks with energy harvesting and adaptive transmit power. Intelligent Reflecting Surfaces (IRS) with N reflectors are deployed as a transmitter or a reflector so that all reflections are in phase at secondary destination. The analysis is performed in the absence or presence of interference from primary source. IRS with N = 128 reflectors offers 6,12,18,24 dB gain with respect to N = 64; 32; 16; 8. When the number of reflectors is doubled, we obtain 6 dB gain in throughput. IRS allow 25, 31, 38 and 44 dB gain with respect to the absence of IRS for a number of reflectors N = 8; 16; 32; 64. IRS deployed as a transmitter improves the throughput by 1 dB with respect to IRS deployed as a reflector. We also consider the use of multiple antennas at the secondary destination and evaluate packets waiting time and total delay.
    Keywords: IRS; 6G; Cognitive Radio Networks.

  • Joint Energy-Efficient Resource Allocation, Subcarrier Assignment, and SIC ordering for mmWave-enabled NOMA-UAV Networks   Order a copy of this article
    by Jiali Zhang, Ping Peng, Amir Ziaeddini 
    Abstract: Driven by massive connections and exponential growth of data of mobile devices, non-orthogonal multiple access has become a candidate access technology to meet the requirement of the next-generation mobile networks. In such networks the problem of energy efficiency is of great importance, because the uncontrolled installation of too many cells may increase operational costs and emit more carbon dioxide. Recently, unmanned aerial vehicles (UAV) offer an effective approach to guarantee the quality of service, increase network throughput, and meet the demanding performance requirements of applications in integrated wireless networks. We presented an efficient radio resource management algorithm for mmWave-enabled NOMA-UAV networks that applies UAV-cells to guaranty the essential coverage and capacity. This paper introduced a joint carrier allocation and power optimization approach to maximize the total downlink throughput. A matching algorithm is also introduced to achieve optimal carrier allocation, which first matches the user with the best carrier. Then the Dinkelbach-like algorithm is used to optimize resource allocation and meet users QoS requirements. We examined the system energy efficiency and the total system data rate, along with other key performance indicators. Simulation results show that the proposed algorithm can achieve higher energy efficiency compared to the existing algorithms. The numerical results also illustrate considerable power saving and throughput increase for the multi-layer optimization algorithm in comparison with the conventional RSRP-based schemes.
    Keywords: Energy Efficiency; Resource Management; Quality of Service; Carrier Allocation; Millimeter Wave Backhauling.
    DOI: 10.1504/IJAHUC.2022.10047611
  • Efficient multi-receiver signcryption scheme based on ring signature   Order a copy of this article
    by Pengfei Su, Muhammad Israr, Ruijiang Ma, Yong Xie 
    Abstract: With the development of society, the problem of the disclosure of the identity information of whistleblowers has become more and more prominent, resulting in few people willing to become whistleblowers. The decrease in the number of whistleblowers is not conducive to protecting the public from certain improper behaviours. Generally speaking, an anonymous signcryption scheme can be used to solve the problems faced by whistleblowers. Still, the existing schemes are difficult to meet the needs. An efficient multi-receiver signcryption scheme based on ring signature is proposed in this paper. The proposed scheme adopts the method of hiding the senders identity in the set of identities trusted by the receivers to realize the anonymity of the sender and the reliability of the sender. And the proposed scheme can send messages to multiple recipients at the same time in one operation. A strict security proof shows that the proposed scheme is secure. Complexity analysis and comparison show that the efficiency advantage of the proposed scheme is obvious.
    Keywords: Anonymity; multi-receiver signcryption; ring signature; security; efficient.

  • FETES: A Fast, Emergency Timeslot Allocation, and Three-Tier Energy Saving Based Task Execution Strategy for Wireless Body Area Network (WBAN)   Order a copy of this article
    by Mahfuzulhoq Chowdhury 
    Abstract: Wireless body area network (WBAN) is becoming increasingly popular among researchers and patients due to its real-time healthcare services. Mobile-edge computing (MEC) can play a major role to satisfy the latency requirements of WBANs healthcare applications. The development of a suitable computation offloading and energy savings scheme is critical for MEC-enabled WBANs due to different priorities of WBAN tasks, QoS, resource availability, and contention delay. To improve the end-to-end latency, this paper proposes a fast data transfer, emergency timeslot, processing node allocation, and energy-saving based task execution strategy(FETES) considering WBAN users task priority, multiple tasks, resources, availability, different delays, network connectivity, and processing devices for computation offloading. The FETES scheme supports energy-saving for sensors, hubs, and network access devices. To evaluate the delay, throughput, and energy dissipation performance of the proposed FETES scheme, a comprehensive analytical model is presented. The evaluation results show the efficacy of the proposed FETES scheme.
    Keywords: Wireless body area network (WBAN); Cloud computing services; Sensors;rnEmergency and non-emergency user’s timeslot; Fast data transfer; Energy saving; Utility.

  • Modeling and Optimization of High-speed KLEIN Architectures on FPGA and ASIC Platforms for IoT Applications   Order a copy of this article
    by Pulkit Singh, Rahul Kumar Chaurasiya, Bibhudendra Acharya 
    Abstract: Security and privacy are serious issues in the internet of things (IoT) emerging areas. The lightweight cryptographic algorithms are immensely important for secure communication in High-speed IoT applications. The objective of this work is to obtain optimized architectures from scalar and pipelined designs from modified KLEIN cipher implemented on field programmable gate arrays (FPGA) and application specific integrated circuits (ASIC) platforms. This analysis is carried out based on examined hardware metrics such as frequency, area, power, and energy consumption. A one-round scalar implementation shows 73.1% & 93.3% lesser power and 70.7% & 93.1% energy efficient compared to one-round pipelined implementation on both platforms. In addition, this paper demonstrates that modeled and optimized implementations of modified KLEIN cipher show good accuracy compared to state-of-the-art design models. Hence, this paper gives general guidelines for all lightweight block ciphers by noticing the behavior of modified KLEIN cipher.
    Keywords: Security; S-box; lightweight cryptography; block cipher; KLEIN.

  • Comparative Analysis of Image Classification with Retrieval System   Order a copy of this article
    by JATOTHU BRAHMAIAH NAIK, SivaNagiReddy Kalli, Ravi Boda 
    Abstract: Currently, the term Content-based Image Retrieval seems to be a highly attentive system for handling the broad image datasets since the data storage mechanisms & image acquisition are becoming the most empowered logic in image processing. The previous CBIR system has been proposed under nonlinear similarity matching measure in a logarithmic scale & informative pattern descriptor has quantified the range of similarity content. This article implements a novel CBIR system that emphasizes the classification concept using a Deep Belief Network (DBN) classifier. In this concept, apart from the image retrieval, the used classifier classifies the respective classes of retrieved images. Finally, the Proposed Local Vector Pattern (PLVP) with DBN classifier (PLVP-DBN) compares its performance over other conventional retrieval concepts: PLVP-With Log similarity, PLVP-Without Log Similarity, and also with Neural Network (NN) classifier.
    Keywords: Image retrieval; CBIR system; DBN; NN; PLVP-DBN.

  • Intrusion Detection System using Resampled Dataset - A Comparative Study   Order a copy of this article
    by N.D. Patel, B.M. MEHTRE, Rajeev Wankar 
    Abstract: Existing machine-learning research aims to improve the predictive capability of datasets using various feature selection and classification models. The intrusion detection data consists of normal data and a minimal number of attack data. This data imbalance causes prediction performance degradation due to factors such as prediction bias of small data presence of outliers. To address this issue, we oversampled the minority class of the existing intrusion detection datasets using four data oversampling methods and tested using three different classifiers. To further ensure the real-time applicability of these oversampling methods with these classifiers, we also generate a real-time testbed (RTT) resampled dataset. It is observed that CTGAN oversampling method, along with the LightGBM classifier, gives outperforming results on the existing CICIDS2018 and RTT resampled dataset. Test results also outperformed over the existing intrusion detection methods and datasets (Credit Card, Gambling Fraud, ISCX-Bot-2014, CICIDS2017) in terms of Accuracy, Precision etc.
    Keywords: Intrusion Detection System; Data Imbalance; SMOTE; BorderlineSMOTE; ADASYN; CTGAN; Oversampling; Classification Model; NSL-KDD;\r\nCIC-IDS2018; Attack Detection System.
    DOI: 10.1504/IJAHUC.2022.10050801
  • Game-based Congestion-Aware Routing Algorithm in Wireless Network on Chips   Order a copy of this article
    by Esmaeel Tahanian, Alireza Tajary, Mohsen Rezvani 
    Abstract: Wireless Network-on-chip (WiNoC) has been introduced to alleviate some challenges with conventional NoC such as high latency and powerrnconsumption. According to the limited resources, the performance of WiNoCrnis sensitive to routing algorithm. Routing the packet through the wireless linksrnfor arriving to far apart destination leads to a shorter path. Therefore, many ofrnnodes prefer to send their packets to nearest wireless router for routing on thernWiNoC. When the demand for a wireless node increases, the likelihood of therncongestion increases in that node and its neighboring nodes that consequentlyrndegrades the performance of the network. To address this problem, we propose a game-based yet simple routing algorithm to balance the traffic in thisrnpaper. The WiNoC is modeled with a mixed-strategy Bayesian-game in whichrnthe nodes are the players with two valid actions namely, routing the packets with and without the wireless links. By employing the Nash Equilibriumrnproperty, we determine the probability of choosing the valid actions by eachrnplayer. The simulation results show that using the proposed mixed-strategyrnfor routing the packets considerably improves the performance of the network,more precisely, the system performance is improved 10%-42% compared withrnthe previous related works.rn
    Keywords: Wireless Network on Chips (WiNoCs);Routing algorithm;Game theory.

  • A two dimensional Markov chain model for aggregation-enabled 802.11 networks   Order a copy of this article
    by Kaouther Mansour 
    Abstract: Frame aggregation technique opts for optimizing channel usage efficiency for 802.11-based networks by amortizing the transmission overhead over several aggregated packets. Despite its potential benefit, the gain achieved by this technique is still far from the expected levels. The underlying causes are attributed to certain deficiencies in the specification as well as the implementation of the conventional frame aggregation scheme. Throughout this paper, we focus on MAC Protocol Data Unit aggregation (A-MPDU) technique. We provide a simple, yet, highly accurate mathematical model for the conventional A-MPDU technique that reflects the effect of the Block Acknowledgement (Block Ack) window limit on the maximum aggregation size. The effectiveness of our model is validated by ns-3 simulator. An analytical-based study is further conducted to compare the performance of the greedy A-MPDU aggregation scheme and that of the conservative scheme supported by most of Wireless Fidelity (Wi-Fi) card drivers.
    Keywords: 802.11 WLANs; performance evaluation; analytical model; frame aggregation; A-MPDU.

  • Real-Time Face Mask Position Recognition System using YOLO models for Preventing COVID-19 Disease Spread in Public places   Order a copy of this article
    by Vishnu Kumar Kaliappan, Rajasekaran Thangaraj, Pandiyan P, Mohanasundaram K, Anandamurugan S, Dugki Min 
    Abstract: COVID-19's rapid spread has caused severe harm and infected tens of millions worldwide. Due to the lack of a specific cure, using facemasks has proven to be a more effective method of reducing COVID-19 transmission. However, their effectiveness has dwindled as a result of inappropriate facemask use. In this scenario, robust recognition technologies are expected to ensure that facemasks are worn in public locations. Many people do not wear their masks correctly owing to insufficient practices, bad behavior, or individual weaknesses. As a result, there is a greater demand for automatic detection of masks and their wear position. Using multiple variations of object detection models, specifically YOLOv4, Tiny YOLOv4, and YOLOv5, the proposed work classifies people into three categories: mask, without a mask, and mask with the wrong position. The experimental results revealed that the YOLOv5 model had the most excellent mAP value of 99.40 percent compared to other models.
    Keywords: YOLOrnMask positionrnobject detection modelrnCOVID-19rnSocial distancingrn.

  • Gene Expression Data Classification with Robust Sparse Logistic Regression using Fused Regularization   Order a copy of this article
    by KAMPA LAVANYA, Pemula Rambabu, Vijay Suresh, Rahul Bhandari 
    Abstract: Microarray technology has become popular and is extensively used in gene classification. It is essential to identify a set of gene expressions used to classify cancer data with better accuracy. However, microarray data is of kind high dimensional and penalized logistic regression (PLR) is good for variable selection and classification. The Lasso, Ridge and Elastic Net are limited to produce oracle property and sparsity. The regression with Weighted L1 and L2 penalty results the oracle property and sparsity. To extend robustness to the gene classification applied absolute deviation regression. In this work, a Fused Logistic Regression (FLR) has been introduced using Weighted L1 and L2 penalties for gene selection. The proposed work introduces smarter grouping effect by updating regression coefficients with Coordinate Descent Algorithm (CDA). The work tested over the simulated and real data sets and produced superior results than the existing methods.
    Keywords: Microarray Data; Regularization; Feature Selection; Sparse Logistic Regression; and Robust Lasso.

  • XACML-based Semantic Rules Language and ontological model for reconciling semantic differences of access control rules   Order a copy of this article
    by Manal Lamri, Sabri Lyazid 
    Abstract: Internet of Things interconnects increasing numbers of artefacts (e.g., robot), individuals, etc., allowing therefore set up Ambient Intelligence systems in multi-domains (e.g., homes, hospitals, airports, etc.). Thus, design methodologies and a suitablernarchitecture framework are required to ensure the efficiency and sustainability of the implementation of such systems. Consolidating public services about citizens safety and the authorization decisions when a resource is accessed in an open-dynamic environment (i.e., multi-domain) are the main challenges that can be highlighted. Thus, the semantic heterogeneity between the local policies of the different domains is a crucial lock for implementing this process. Ontology aims to reduce this ambiguity through semantic interoperability by providing a unified knowledge representation. The Semantic Web Languages appear unsuitable for managing dynamics knowledge. Our approach goes beyond the semantic web languages weakness by combing the XACML-based security policy model with a Semantic Rules Language developed during the European SembySem Project.rn
    Keywords: Ontology; Internet of Things; Distributed Systems; Authentication; Access Control; Multi-Domain; XACML.

  • WeECG: a low-cost real-time wireless ECG for new born monitoring with error concealment   Order a copy of this article
    by Djamel Sadok, Daniele Brooman, Andrea Maria Ribeiro, Jacinaldo Balbino De Medeiros Junior, Judith Kelner, José Henrique Moura 
    Abstract: Early neonatal death due to a lack of specialised care and adequate heart rate (HR) monitoring technology motivated this research. This work contributes towards neonatal delivery rooms modernisation by introducing a low cost wireless HR monitoring device, WeECG, that also stores the data for subsequent analysis.The WeECG presented a smaller measurement error when compared with the palpation method and a standard deviation of 0.4069 BPM when the Peak Interval algorithm was applied. We also examined packet loss and developed strategies to mitigate its effect, since signal sample loss may be very difficult to identify, but can significantly influence the heart rate measurement leading to wrong assessment and perhaps even a false diagnosis. Our loss mitigation methods, despite being simple, were efficient, making it possible to safely transmit ECG signals. We also analyse the solution in terms of cost, size and energy consumption.
    Keywords: ECG; wireless electrocardiogram; heart rate monitor; internet of things; medical devices; packet loss.

  • Identity-Based Ring Signature Scheme With Multi-Designated Verifiers   Order a copy of this article
    by Yunyun Qu, Jiwen Zeng 
    Abstract: As far as we know, there is only one ring signature scheme with multi-designated verifiers in the literature and the scheme is based on the bilinear pairings which need to consume a lot of computation and under the traditional public key infrastructure(PKI). In order to improve computational efficiency and solve the problem of certificate management in PKI, in this paper, we propose the first identity-based ring signature scheme with multi-designated verifiers without pairings. We prove that our novel schemes unforgeability, anonymity in the random oracle model and indistinguishability in the standard model under the intractability assumption of discrete logarithm problem and decisional Diffie-Hellman problem, and we prove that our novel schemes non-transferability in the standard model. We compare the new scheme with a previous scheme in terms of computation and communication at last. The results show that our new scheme is more efficient than the previous scheme and is more suitable for the multi-user setting.
    Keywords: Identity-based ring signature; Multi-designated verifiers; Discrete logarithm problem; Decision Diffie-Hellman problem.

  • Satisfaction-Driven Cooperative Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing   Order a copy of this article
    by Cuntao Liu, Yan Guo, Ning Li, Weibo Yu 
    Abstract: This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile edge computing scenario, where multiple UAVs provide computational services for user devices (UDs) with different service priorities. Taking into account UDs' different service priorities as well as diverse computation-offloading demands, a satisfaction-model is proposed, which gives consideration to UDs' satisfaction degree toward the offloading services as well as the fairness of their offloading amount. We aim to maximize UDs' sum satisfaction by jointly optimizing the offloading time allocation, computing time allocation, users' transmit power, as well as UAV trajectory. A non-convex optimization problem is formulated and an effective solution framework is proposed, where the problem is decomposed into three sub-problems that are solved alternately and iteratively by applying successive convex approximation (SCA) technique. Numerical results show that the proposed scheme achieves higher satisfaction with lower standard deviation as compared to benchmark schemes.
    Keywords: Unmanned Aerial Vehicle (UAV); Satisfaction; Cooperative trajectory optimization; Mobile Edge Computing (MEC).

  • Storage space reduction in Picture Archiving and Communication System using Generative Adversarial Network   Order a copy of this article
    by Bejoy Varghese, Krishnakumar S. 
    Abstract: This paper presents a new architecture of Picture Archiving and Communication System (PACS) based on Generative Adversarial Network (GAN) and Fractal Image Compression (FIC). The GAN architecture is modified to be a conditional GAN by conditioning the generator with the uncompressed image. Both the generator and discriminator networks utilize the Convolutional Neural Network (CNN) which enable the system to capture the similarity measures without using any handcrafted functions. Performance of the proposed design is evaluated by comparing it with the commonly used compression techniques in PACS and recently reported best performing machine learning compression techniques. The efficiency of the proposed architecture is tested by using a custom client program that sends the modality images to the PACS server. The simulation runs on computers in multiple networks to gather the data similar to real time healthcare institutions. The simulation shows that the storage space consumption of the proposed design is only 30% in comparison with PACS, which uses the latest Machine learning and conventional non fractal compression methods. It is also observed that the GAN based FIC can drastically reduce the compression time compared to the conventional fractal and non fractal compression methods. The empirical analysis shows that the proposed GAN architecture can be a promising method to reduce the space complexity of the system such as PACS.
    Keywords: Image Compression; Picture Archiving and Communication System ; Generative Adversarial Network ; Fractal Compression.

  • Efficacious Tuning in Energy Efficient Street Lighting   Order a copy of this article
    by Pragna Labani Sikdar, Abhinav Anurag, Parag Kumar Guha Thakurta 
    Abstract: An energy efficient street lighting system is proposed in this paper to obtain a balance between energy savings and utility in terms of illumination by the street lights. The finite coverage of every street light equipped with the sensor is divided into zones according to the distance of the nearest pedestrian or vehicle with respect to that street light. The illumination of the street light is adjusted accordingly with respect to zones. Every street is divided into sections depending on the average distance between street lights. The parameters namely the energy savings as well as the utility of the lights in a section are defined in terms of the values of length of a zone and the factor of brightness decrement per zone. The best value of this decrement factor is determined by tuning to obtain energy efficiency. Simulation results highlight that the proposed work outperforms the existing method. As an outcome, the proposed scheme obtains a superior % of energy savings over existing work without falling below minimum utility for a small inter-distance.
    Keywords: Street Lights; Energy; Power; Brightness; Tuning; Illumination.

  • Routing Techniques for Millimeter Wave Communications   Order a copy of this article
    by Faisal Alanazi 
    Abstract: In this paper, we propose three routing protocols for millimeter wave communications. One Hop Routing Protocol (OHRP) consists to choose the best relay in each hop. Optimal Routing Protocol (ORP) consists to activate the path with the highest end-to-end Signal to Interference plus Noise Ratio (SINR) among all available $N^{L-1}$ paths where $N$ is the the number of branches (number of relays in each hop) and $L$ is the number of hops. SubOptimal Routing Protocol (SORP) decomposes the network in $K$ subnetworks then the best path is activated in each subnetwork. Our analysis is valid for millimeter wave communications in the presence of $P$ interferers at each relay node.
    Keywords: Routing; Millimeter wave; outage probability.

  • Fitness approximation with RF algorithm dedicated to WSN node deployment for a soil monitoring application   Order a copy of this article
    by Soumaya Ferhat Taleb, Nour-El-Houda Benalia, Rabah Sadoun 
    Abstract: In order to solve the Wireless Sensor Network (WSN) node deployment optimization for an agricultural application, an hybridized Strength Pareto Evolutionary Algorithm II with the Random Forest Regressor (RF-SPEA II) was used. The SPEA II intended to optimize the deployment according to the classical constraints of coverage, over-coverage, connectivity and node number, in addition to the nodes separating distance constraint, which affects the predicted physical parameters models quality. Furthermore, the RF regressor was applied as a fitness approximation surrogate model, with the use of evolutionary control rate to avoid convergence to false optimums. Moreover, the application of RF features selection skill that helped to only keep important characteristics and gain more time. Consequently, this hybridization allowed finding results that exceeded the unaltered SPEA II in terms of solutions qualities and computational time. For example, for an agricultural plot of 400 m$^2$ of surface, the RF-SPEA II hybridized algorithm gave better constraint rates and was 5.82 times faster than the unaltered SPEA II.
    Keywords: Precise Agriculture; Wireless Sensor Network; Node Deployment; Fitness approximation; Random Forest; Strength Pareto Evolutionary Algorithm II.

  • A Survey of Intelligent Load Monitoring in IoT-Enabled Distributed Smart Grids   Order a copy of this article
    by Jixiang Gan, Lei Zeng, Qi Liu, Xiaodong Liu 
    Abstract: Power Load Monitoring has been a research hotspot since a few years ago.With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed IoT(Internet of Things) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including Data Acquisition,Load Disaggregation, Load Forecasting and so on. Finally, combined with various optimization methods, future research directions are prospected, including data and network security issues, challenges faced by cloud/edge architecture, adaptive fine-grained Load Disaggregation, Load Forecasting.
    Keywords: IoT; Smart Grids; Artificial Intelligence; Load Disaggregation; Load Forecasting.

  • A Novel Reformed Normalizer Free Network with U-Net Architecture for Semantic Segmentation   Order a copy of this article
    by Sai Prabanjan Kumar Kalvapalli, C. Mala, V. Punitha 
    Abstract: Recently developed semantic segmentation network architectures include BatchNorm layer and skip connections. They are outperforming with latest training techniques, but the BatchNorm has implicit limitations such as gradients calculation and memory overhead. Hence this paper proposes a novel architecture named as NF-Unet, that combines the simple, flexible and general framework of NF-Nets and the unique architecture of encoder decoder format of U-Net network that can train with huge batch sizes. The backbone of the contracting path consists of NF-net UNet for encoding the image, for identifying the objects in the image. The proposed architecture achieved 87.37 and 70.12 mean Intersection over Union (mIoU) on train and test dataset and outperforms the other approaches in the literature in terms of number of parameters.
    Keywords: BatchNorm; Nf-Nets; U-Net; mean Intersection over Union.

  • An Optimized Darknet Traffic Detection System using Modified Locally Connected CNN - BiLSTM Network   Order a copy of this article
    by Abdullah Abdul Sattar Shaikh, M.S. Bhargavi, Pavan Kumar C 
    Abstract: The contents of the darkweb have always been a major breach of security and privacy. Due to its anonymous nature, detection of traffic from Darknet becomes difficult. A robust classifier system that accurately predicts and classifies such traffic is a necessity. This research work aims to study the effects of the convolutional-long-short-term memory (CNN-LSTM) system of classification of Darknet through various deep layer modifications on the Nadam optimiser. Experimentations were carried out on different combinations of locally-connected CNNs (LcCNN) and bi-directional LSTM (BiLSTM) to improve accuracy. Data was subjected to various levels of synthetic minority oversampling techniques (SMOTE) to reduce overfitting, data imbalance and achieve better generalisation. A custom decaying call-back function implemented, cut down the learning rate by half and tended to improve accuracy. Results obtained outperformed the base CNN-LSTM system for traffic categorisation with an improved accuracy of 92.57% from 89% using the custom LcCNN-BiLSTM architecture.
    Keywords: Darknet; deep learning; convolutional neural network; CNN; bi-directional long short term memory; BiLSTM.
    DOI: 10.1504/IJAHUC.2022.10051751
  • A Trusted and Adaptive Security Mechanism for Wearable E-Healthcare Systems   Order a copy of this article
    by Geetanjali Rathee, Hemraj Saini, Shishir K. Shandilya, S. Rajasoundaran 
    Abstract: The wearable e-healthcare systems are a critical IoT mission having wearable sensors, wireless devices and intelligent monitoring of surroundings. The ultimate goal of e-healthcare systems is to identify or diagnose the patients by recognising their various features that are correlated among each other. The involvement of several malicious objects may try to hide the actual recognition of wearable objects for benefiting their own purposes. Though various researchers have proposed various security and efficient schemes, however, it may lead to several computations, management overhead. The aim of this paper is to propose a trusted and efficient e-healthcare communication mechanism while recognising the exact identification of wearable objects. In addition, the proposed mechanism is associated with blockchain mechanism to ensure the transparency and security inside the network while sharing the information. The proposed mechanism is further validated over several security threats against number of security parameters.
    Keywords: wearable devices; AHP; security mechanism; analysis process; secure e-healthcare systems.
    DOI: 10.1504/IJAHUC.2023.10052372