International Journal of Ad Hoc and Ubiquitous Computing (39 papers in press)
Lattice Based Lightweight Cryptosystem
by Shivani Jain, R. Padmavathy
Abstract: The lattice based cryptographic constructions are emerging as a major contender of post quantum cryptography. The power-of-2 cyclotomic rings are widely used in lattice based solutions but these rings have limitations in choice and are scarce. In our proposed work, prime cyclotomic rings are considered for investigation as they enjoy the same security and benefits as power-of-2 rings. In todays world of resource constraint devices, cryptographic constructions are expected to be simpler and lightweight, compared to conventional cryptography. The major concern in deployment of lattice based constructions is the size of the payloads. The byte modulus, which is an adaption of LAC scheme is used to overcome this problem. Our results show that the proposed scheme is more compact but reasonably secure and relatively efficient in comparison to other popular solutions like LAC. Further, to withstand with decryption failure, the popular BCH and Turbo Code are used as error correction with proposed scheme.The performance of BCH and Turbo code along with prime cyclotomic rings with or without byte modulus is studied and reported.
Keywords: Prime cyclotomic rings; Ring- Learning with errors; byte modulus; lattice based cryptography.
Hybrid Meta-heuristic-based Inventory Management using Block Chain Technology in Cloud Sector
by Chinnaraj Govindasamy, Arokiasamy Antonidoss
Abstract: Nowadays, the authenticity and accuracy of data regarding inventory is one of the key problems faced in supply chain industry. Throughout the years, many inventory management processes and tools have been adopted. Accurate record-keeping and hassle-free systems are maintained by streamlining the workflow with the inventory management system business. In such circumstances, cloud with block chain provides something that businesses have never asked for and bridges the chasm in this sector. This paper plans to develop an inventory management model in supply chain with the assistance of cloud and block chain. The supply chain is at the middle of a successful enterprise with the increase of competition in market economy. It is a predictable trend to optimize the inventory cost of supply chains in todays world. The inventory cost is controlled by delaying the complete system with traditional approaches by separating all aspects of the supply chain. This paper considers the inventory management of supply chain that involves multiple suppliers, a manufacturer and multiple distributors. Initially, the data to be supplied to the distributers are stored in the block chain in cloud. Here, the data management is done by the block chain technology, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The proposed model intends to reduce the multi-objective inventory cost involving transaction cost, inventory holding cost, shortage cost, transportation cost, and time cost. The hybridization of two meta-heuristic algorithms like Spider Monkey Optimization (SMO) and Sea Lion Optimization (SLnO) termed as spider Monkey Local Leader based Sea Lion Optimization Algorithm (SMLL-SLnO) is used to improve the inventory management model. Finally, the feasibility and effectiveness of the proposed optimization model are validated by comparing over the other traditional models.
Keywords: Inventory Management; Supply Chain Management; Block Chain Technology; Cloud Computing; Meta-heuristic Algorithm; Spider Monkey Local Leader based Sea Lion Optimization.
Intercept Analysis With Threshold-Based Diversity Reception for Cognitive Network
by Khyati Chopra
Abstract: In this study, we have investigated the threshold-based intercept performance of rn decode-and-forward (DF) underlay cognitive network.rn The secondary users have been restricted by primary users due to interference curtailment. It has been presupposed that the secondary relays are inadequate to do correct decoding always. Therefore, only when the predetermined threshold value is realized by the relays, they can propitiously decode the message. We have explored the worst case scenario where diversity reception schemes have been applied only at the eavesdropper or adversary. Asymptoticrn and diversity analysis are conferred in our study for both the balanced and the unbalanced cognitive system. It has been extensively manifested that how factors like eavesdropper link quality, predetermined threshold and interference power restrictions affect intercept performance of cognitive system. Also, it is graphically demonstrated that intercept performance is enhanced by utilizing the optimal relayrn selection scheme (OS) with surge in the number of relays.rn
Keywords: Cognitive system; DF relay; Diversity combining; Intercept probability; Threshold-based; Optimal relay selection.
A Location-based Multi-factor Authentication scheme for Mobile devices
by Bimal Meher, Ruhul Amin
Abstract: Mobile devices are becoming amazingly smart, portable, and ubiquitous in recent times. Several sectors like Banking, Insurance, Healthcare, Retail chain, Corporate houses provide numerous services for the benefit of their customers through mobile devices. Keeping in view the threat scenario, both the customer and service provider should mutually authenticate before facilitating a service or undertaking any transaction. In this paper, we propose a robust and user-friendly multi-factor authentication scheme without using a smart card. It reduces the operational overhead and hardware cost incurred due to the card reader. It also eliminates the issues when the smart card is lost/stolen. Secondly, we delegate a part of the computational load of the RAC to the Server to balance the load between RAC and Server. Thirdly, we introduce the location information of the users device as another factor without using the GPS service of the mobile device. It helps to authenticate a user with a single sign-on to receive several related services without entering his user-id, password and biometric data again and again. Our scheme uses elliptic curve-based cryptography (ECC) with three-factor authentication. It makes the system robust with less memory consumption and low-cost implementation. We have analysed our authentication scheme from several online security threats and verified it to be safe using the popular Scyther Tool. Therefore, our proposed authentication scheme provides a robust, practical and cost-effective mutual authentication solution to the user and the service provider with better security.authentication scheme from several online security threats and verified it to be safe using the popular Scyther Tool. Therefore, our proposed authentication scheme provides a robust, practical and cost-effective mutual authentication solution to the user and the service provider with better security.
Keywords: Authentication; Multifactor; Location; ECC.
Users Interests Profiling using Fuzzy Regression Tree
by Abd El Heq Silem, Hajer Taktak, Faouzi Moussa
Abstract: User modelling is an essential process for recommender systems. In user interests modelling, two kinds of approaches can be found: The first uses the text extracted from the users browsing history to predict his interest degree, while the second, besides the extracted text, adds the user actions as input. Both groups predict an incorrect interest degree due to the use of text-only or an incorrect assessment of the users actions. In this paper, we propose an architecture that employs a fuzzy regression tree algorithm to model the users interests. The architecture detects the users interests by evaluating his behaviour based on these factors: scrolling speed, time spent, location, number of visits, and clicked links. It also uses fuzzy logic to ensure the correct interpretation of each factor value according to the users habits. Finally, the results show that the proposed architecture has the minimum RMSE (? 0.06) compared to the existing solutions.
Keywords: User Modelling; Human-Computer Interaction; Ubiquitous Computing; User Profiling; User Modelling; Context-Awareness; Machine Learning; Regression Tree; Fuzzy Logic; User Behaviour.
Efficient Hardware Architectures of Lilliput Lightweight Algorithm for Image Encryption
by Pulkit Singh, K. Abhimanyu Kumar Patro, Bibhudendra Acharya, Rahul Kumar Chaurasiya
Abstract: With the advancement of communication networks, information security has become extremely crucial in storage and transmission. As images are used in most of the networks, hence image security is becoming a challenging task. This paper proposes two hardware architectures of Lilliput lightweight block cipher. These hardware architectures are implemented on FPGA and ASIC platforms compared with state-of-the-art designs. In these architectures, first, 8-bit and then 16-bit serial structures are designed to implement. The serialized designs of 8-bit and 16-bit imply low area by consuming less number of slices. Finally, these serialized architectures are utilized for image encryption with the help of a controller. The simulation results and security analysis for hardware generated encrypted images show the better performance of proposed architectures and stronger resistance against entropy attack, differential attack, and statistical attacks.
Keywords: Security; Lightweight cryptography; FPGA; ASIC; Image encryption.
Transmission Range Determination And Antenna Deployment to Recognize Fitness Actions Using Wi-Fi Signals
by Wei-Che Liang, Chih-Min Chao, Chih-Yu Lin, Chun-Chao Yeh
Abstract: To help users exercise effectively at home, many studies have used the radio channel state information (CSI) of Wi-Fi signals to recognize human motions. However, these studies do not address how to determine an appropriate distance between the Wi-Fi transceivers to achieve a target recognition accuracy. In this paper, we design WiFitness to recognize fitness actions and to find out the upper and lower bounds of the distance between the Wi-Fi transceivers for different fitness actions. Based on the fact that the strongest wireless signal occurs in the first Fresnel zone, WiFitness finds out the distance limit between two transceivers such that the first Fresnel zone covers the range of a particular fitness action and the required recognition accuracy of the fitness action is satisfied. Besides determining the distance limit, in WiFitness, the way to deploy antennas are also proposed to enlarge the sensing range.
Keywords: channel state information; fitness action recognition; wireless signal; device-free sensing.
ERMAP: ECC based Robust Mutual Authentication Protocol for Smart Grid Communication with AVISPA Simulations
by Sangeetha R., Satyanarayana Vollala, N. Ramasubramanian
Abstract: Veracious new architectures and authentication protocols have been proposed by researchers to provide better security solutions for smart grid application. This paper analyzes and discusses the limitations of the pairing-based authentication protocol proposed by Khalid Mahmood & others. It also proposes an ECC-based Robust Mutual Authentication Protocol for Smart Grid Communication with AVISPA Simulations
called ERMAP that overcomes the limitations of Khalid Mahmood & others authentication protocol. ERMAP does authentication between end-user(Ui) and the smart meter(SMe)
along with the authentication between service provider(SPr) and the SMe to add the security strength. That is not done by most of the authentication techniques used in Smart Grid systems. A formal model of ERMAP protocol has been implemented in the AVISPA tool using HLPSL (High-Level Protocol Specification Language). Also the results show better security strength. SMe theft issue, DOS attack, and other related
attacks are addressed by ERMAP that are essential now a day but not supported by many of the authentication techniques used in smart grid systems. ERMAP protocol doesnt need an extra supporting system to ensure SMe security. Computation cost and communication overhead of ERMAP is considerably less when compared with most of the other related authentication techniques.
Keywords: ECC; Smart grid; Pairing based authentication protocol; AVISPA; HLPSL.
On Indoor Performance Analysis of URLLC Service for Industrial IoT
by Yekta Turk
Abstract: The Ultra Reliable Low Latency Communication (URLLC) service consist of a great opportunity for Mobile Network Operators (MNOs) for the industrial internet-of-things (IIoT) use cases. The URLLC service deployment requires careful considerations, and 100% service availability needs to be the ultimate goal across the factory area. However, it is critical to model the service availability for capacity analysis and observe performance tests for the URLLC service. In this article, the possible deployment scenarios for the URLLC service that can be provided in a factory environment, are examined. Besides, a service availability model for URLLC is described. Afterwards, performance testing for realistic deployment scenarios that are based on Time Division Duplex (TDD), are carried out. The results reveal that the framing structure of TDD has a direct impact on the capacity of URLLC, so that framing needs to be selected as a result of a careful site planning process.
Keywords: Ultra Reliable Low Latency Communication; enhanced Mobile Broadband; indoor; industry; automation; Internet of Things.
Mobility Aware and Reliable Multipath Routing Protocol for MANETs
by Sajal Sarkar
Abstract: In this paper, a mobility aware and reliable multipath routing protocol (MR-AOMDV) is proposed for Mobile Ad-hoc Networks (MANETs). The proposed MR-AOMDV establishes multiple paths from source to destination considering mobility and reliability of nodes together as routing metrics for mobility aware and reliable data exchanges. In path discovery phase, the routing paths are constructed by comparing the values of mobility and reliability with the thresholds defined by a trust management module. In path maintenance phase, the constructed paths are adaptively modified by replacing, eliminating or including a node to make it more mobility aware and reliable paths. The constructed paths are stable and reliable, which are demonstrated by simulation results. MR-AOMDV improves performance and ensures security of data by exchanging it through the malicious-node free paths from source to destination. Simulation results of MR-AOMDV and existing protocols are presented for comparison and analyses which show that MR-AOMDV achieves promising performance gain compared to the existing protocols in terms of throughput, energy consumption, packet delivery ratio, routing
overhead, and end-to-end delay in different scenarios of a MANET.
Keywords: MANET; Mobility; Reliability; Multipath Routing Protocol; Trust; Security.
Insider Threat Detection and Prevention using Semantic Score and Dynamic Multi Fuzzy Classifier
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.
Target Node Selection for Data Offloading in Partially Connected Vehicular Adhoc Networks
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
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
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
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
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.
FOESG: Anonymous session key agreement protocol for fog assisted smart grid communication
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.
Intelligent Reflecting Surfaces for Cognitive Radio Networks
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
Keywords: IRS; 6G; Cognitive Radio Networks.
Joint Energy-Efficient Resource Allocation, Subcarrier Assignment, and SIC ordering for mmWave-enabled NOMA-UAV Networks
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.
Efficient multi-receiver signcryption scheme based on ring signature
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)
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
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
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
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.
Game-based Congestion-Aware Routing Algorithm in Wireless Network on Chips
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
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.
Gene Expression Data Classification with Robust Sparse Logistic Regression using Fused Regularization
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
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
A simulation study on the necessity of working breakdown in a state dependent bulk arrival queue with disaster and optional re-service
by C.K. Deena Merit, M. Haridass
Abstract: Sudden increase in service requests immediately after an occurrence of a disaster due to the scarcity of resources creates congestion in service. Effective queue management should ensure that the services are available for every request with an acceptable delay. Unexpected breakdowns causing huge delays were observed many times in real-life scenarios. The working breakdown is introduced in this paper to address this issue in an MX/G/1 disaster queueing system wherein an occurrence of a disaster at the main server is handled immediately by replacing a substitute server with a different or same service rate. State-dependent arrival and optional re-service are also included to make this model more robust. Various performance indices were established using the supplementary variable technique. Numerical illustration and simulation was done for validation and verification respectively. It has been proved that the working breakdown is effective when the arrival rate is tremendously larger immediately after a disaster.
Keywords: bulk arrival; bulk service; state dependent arrival; disaster; working breakdown; re-service; repair; ATM machine; ARENA; substitute server; simulation.
Identity-based secure data aggregation in big data wireless sensor networks
by Radhakrishnan Maivizhi, Palanichamy Yogesh
Abstract: Secure data aggregation (SDA) is an inherent paradigm in big data wireless sensor networks (WSNs) to reduce data transmissions, maximise the network lifetime and provide security. However, SDA protocols that use elliptic curve suffer from mapping and reverse mapping functions. Currently, there is no known mapping function which is both homomorphic and effective in reverse mapping. In addition, SDA protocols that use public key infrastructure incurs high computation and communication cost. To overcome these challenges, this paper proposes a novel identity-based secure data aggregation (ISDA) protocol for big data wireless sensor networks. This protocol is based on bilinear pairing and combines identity-based homomorphic encryption scheme with identity-based signature to achieve end-to-end security. In WSNs, this is the first SDA protocol that employs both identity-based encryption and identity-based signature scheme. ISDA shares the same public/private keys during encryption and signature generation, which significantly reduces the complexity of the protocol. Security analysis reveals that ISDA is secure against various internal and external attacks and proves the correctness of the proposed protocol. Performance evaluation shows that ISDA incurs less overhead than state-of-the-art bilinear pairing-based SDA schemes, thereby it minimises the energy consumption and increases the lifetime of wireless sensor networks.
Keywords: big data; secure data aggregation; SDA; wireless sensor networks; WSNs; confidentiality; integrity.
A concurrent prediction of criminal law charge and sentence using twin convolutional neural networks
by Tong-Ying Juang, Chih-Shun Hsu, Yuh-Shyan Chen, Wan-Chun Chen
Abstract: An intelligent law article prediction scheme, which solves the law articles imbalance problem and the missing value problem of the judgement, is proposed in this paper. This paper applies the law article description as the label attribute. Based on the property of the vector space, the missing value problem can be got over by learning a representative embedding vector through the vector similarity weighted mechanism. For the imbalance problem, we use a weight sharing classification layer which classifies the label according to the relevance between the fact vector and the law article vector of the vector space. We also use the transfer learning to train the model by the high-frequency law articles first, then share the weight as the prior knowledge to the low-frequency one to improve the classification performance. The proposed approach outperforms the performance on few-shot law article prediction.
Keywords: natural language processing; NLP; deep learning; few-shot learning; law article prediction; legal intelligent.
Hierarchical capacity management and load balancing for HetNets using multi-layer optimisation methods
by Khodadad Jalali Rad, A. Mirzaei
Abstract: This paper proposes a dynamic optimisation model which maximises the overall network capacity of IoT-based heterogeneous networks in addition to providing the essential coverage and capacity. In this paper, we propose a multi-layer optimisation approach based on the optimal transmission strategy to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The proposed algorithm was also employed to determine the best location for data replication in the cloud computing environment. Based on the fundamental trade-off between spectral efficiency and ergodic capacity, we study the joint spectral efficiency and resource optimisation problem in cellular IoT networks with small cells on/off control. Extensive simulation results prove that the proposed approach is able to achieve a better trade-off between spectral efficiency and total ergodic capacity with lower outage probability compared with the existing algorithms under various scenarios.
Keywords: hierarchical capacity management; power control; NB-IoT; multi-layer optimisation; load balancing; quality of service; QoS; outage probability; SWIPT.
A novel optimised apnea classification with AA-CNN method by utilising the EDR and ECG features
by A. Smruthy, M. Suchetha
Abstract: Convolution neural network (CNN) has shown promising growth in recent years. The main reason for the above growth is the highest classification accuracy achieved within a short span of time. However, the traditional CNN architecture limits the fixed window size of the convolution filter. Therefore the architecture fails to learn multiple features properly. In this scenario, we propose an adaptive attention convolution neural network (AA-CNN), which is able to learn multi-features. The proposed work is divided into two stages. In the first stage, electrocardiogram (ECG) and ECG-derived respiratory signal (EDR) were extracted using a novel two-level variational mode decomposition algorithm. In the next stage, the optimal convoluted features were derived using the AA-CNN architecture. To validate our proposed work, we have developed an apnea classification system by using the concept of AA-CNN and the support vector model. The overall accuracy of 98.18% is obtained for our proposed work.
Keywords: two-level variational mode decomposition; ECG derived respiratory signal; electrocardiogram; ECG; support vector machine; convolution neural network; CNN; multi-features.
A state-of-the-art review on person re-identification with deep learning
by Peng Gao, Xiao Yue, Wei Chen, Weidong Fang, Zijian Tian, Fan Zhang
Abstract: Person re-identification (ReID), as a sub-direction of computer vision, has attracted more and more attention. In recent years, we have witnessed significant progress of person ReID driven by deep neural network architectures. In this paper, we introduce the progress of person ReID based on deep learning in recent years, including representation learning methods, metric learning methods, part-based methods, GAN-based methods, and video-based methods. The class of methods are summarised and analysed, and then we introduce the image-based datasets and the video-based datasets. We further discuss some of the current challenges and introduce some potential solutions in person ReID. Finally, we present the possible future directions of person ReID, such as collecting more abundant pedestrian datasets, adopting semi-supervised or unsupervised methods in person ReID. The purpose of this paper is to provide insights for the research on the person ReID and to present different methods of person ReID based on deep learning.
Keywords: person re-identification; computer vision; deep learning; review.
Research on optimisation of energy efficient routing protocol based on LEACH
by Haibin Sun, Dijing Pan
Abstract: Although the low energy adaptive clustering hierarchy (LEACH) protocol can extend network lifetime, it still has some deficiencies. To further improve the energy efficiency of the network, a new low-power efficient routing protocol based on LEACH (LPE-LEACH) is proposed. Firstly, the optimal number of CHs is analysed by abstracting the network into a mathematical model. Secondly, the threshold for the election of CHs is redefined to guarantee that better nodes can be elected. Besides, spare CHs are introduced to decrease the probability of premature death of CHs. Next, the way ordinary nodes choose their CH is also improved. Last but not least, the method of data transmission is further enhanced. MATLAB simulation experiments show that the protocol extends network lifetime by 85%, 117.6%, 42.3% and 32.1% over LEACH, LEACH-C, LEACH-VA and IEE-LEACH, respectively.
Keywords: CH; clustering; data transmission; energy efficiency; lifetime; LEACH; low-power; network; routing protocol; sensor nodes.
Performance and communication energy constrained embedded benchmark for fault tolerant core mapping onto NoC architectures
by Aruru Sai Kumar, T.V.K. Hanumantha Rao, B. Naresh Kumar Reddy
Abstract: Due to the rapid growth of the components encapsulated on the on-chip architecture, the performance degradation and communication issues between the cores significantly impact NoC architecture. It also increases the possibility of core failures encountered in an application, leading to a faulty network. This research implemented fault-tolerant mapping algorithm (FTMAP) that focuses predominantly on replacing the faulty cores and assessing the communication and the execution time of the network by employing it on various multimedia benchmarks. The experimental outcomes reveal that it reduces the communication energy by 8%, 12%, 14% with respect to NFT, 1FT, 2FT compared to FTTG and 6%, 9%, 10% with respect to NFT, 1FT, 2FT when compared to K-FTTG. The reduction of the execution time has also outperformed by 18%, 24%, 26% with respect to NFT, 1FT, 2FT compared to FTTG and 13%, 19%, 21% with respect to NFT, 1FT, 2FT when compared to K-FTTG.
Keywords: system-on-chip; SoC; network-on-chip; NoC; core; core mapping; core failure; fault tolerance; FT; multimedia benchmarks; communication energy; system performance; execution time.
Colour image encryption using an improved version of stream cipher and chaos
by Subhrajyoti Deb, Bubu Bhuyan, Nirmalya Kar, K. Sudheer Reddy
Abstract: This paper proposes a novel image cryptosystem based on chaos theory and a modified version of Grain-128 cipher. Pixel and bit-level permutation of the plain image is performed by Hilbert curve and Hénon map to disrupt high correlation. The wellknown Grain-128 cipher is modified by replacing its Boolean function and reducing its state size. Output of modified Grain-128 cipher is bitwise XORed with randomised image data to produce cipher image. The modifications in the Grain-128 cipher is done in order to achieve higher encryption decryption efficiency. However, the proposed nonlinear function of the cipher shows superior performance compared to nonlinear functions used in several cryptographic algorithms in terms of nonlinearity value. Experimental result from statistical tests and efficiency analysis confirms the superiority of the proposed cryptosystem.
Keywords: Hilbert curve; Hénon map; stream cipher; security; image encryption; grain cipher; chaos; S-box; number of changing pixel rate; NPCR; unified averaged changed intensity; UACI.
Special Issue on: Recent Advances in Wearable Devices for Emerging Expert Systems
Real-Time Face Mask Position Recognition System using YOLO models for Preventing COVID-19 Disease Spread in Public places
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
Special Issue on: AI-Based Computing on IoT Applications
Multiscale Hierarchical Attention Fusion Network for Edge Detection
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.