Forthcoming and Online First Articles

International Journal of Communication Networks and Distributed Systems

International Journal of Communication Networks and Distributed Systems (IJCNDS)

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International Journal of Communication Networks and Distributed Systems (27 papers in press)

Regular Issues

  • CRLMDA: CRL Minimization and Distribution Algorithm in Cluster-based VANETs   Order a copy of this article
    by Dinesh Singh, Ashish Maurya, Ranvijay Ranvijay, R.S. Yadav 
    Abstract: This paper proposes an algorithm called CRLMDA (CRL Minimization and Distribution Algorithm) to minimize CRL in cluster-based VANETs. The algorithm monitors the use of certificates during communication. A cluster head (CH) vehicle informs regarding the certificate to the certificate authority to avoid lazy processing of safety applications. The CH vehicle that identifies a vehicle as malicious immediately initiates a local and global revocation process to avoid mishappening in network. The proposed algorithm compresses the CRL using Bloom filter and distributes it to the other vehicles via roadside units and the CH vehicles. Also, we give a message authentication algorithm of the received event reporting messages at the CH vehicle. The performance of our proposed CRLMDA is compared with two well-established algorithms, HHL and SCRLE, in varying transmission ranges and vehicle densities. The reported results show that the CRLMDA performs better in revocation overhead and CRL distribution delay than existing algorithms.
    Keywords: Vehicular Ad-hoc Network (VANET); Cluster Head (CH); Malicious Vehicle; Safety Applications; Certificate Authority.
    DOI: 10.1504/IJCNDS.2023.10047053
     
  • Cyber Defense Using Attack Graphs Prediction and Visualization   Order a copy of this article
    by Shailendra Mishra 
    Abstract: The use of the internet and other related technologies has increased dramatically in recent years. Since sensitive and critical data is readily available on these systems, this information can easily be accessed. Information leaks or attacks on networked devices are becoming more common every day. This research explores the visualisation of attack graphs in public cyberspace to predict exploit paths across networks. Vulnerability analysis reveals various aspects of the system that are exploited. By combining graph adjacency matrices cyber-attack graphs are created. With the attack graph, grey areas and research points can be easily identified. Cybersecurity and network administration can be achieved by analysing M-steps. Moreover, machine learning algorithms such as SVM, RF, KNN, LR, and multilayer perceptron (MLP) are used to detect the attack and analyse the performance of the proposed system. In terms of accuracy, recall, precession, and F-score, RF and MLP were the best classifiers.
    Keywords: IDS network security; attack graph; adjacency matrix; intrusion detection system; machine learning; cyber defence.
    DOI: 10.1504/IJCNDS.2023.10047420
     
  • IEEE 802.15.4 MAC protocol optimisation in body sensor networks: a survey, outlook and open issues   Order a copy of this article
    by Abdulwadood Mohamed Othman Alawadhi, Mohd. Hasbullah Omar, Noradila Nordin 
    Abstract: Body sensor networks (BSNs) have sparked a surge in interest and demand. BSNs is made up of miniature biosensors that gather and send data over a wireless network, allowing medical professionals to watch patients as they go about their regular lives and provide real-time opinions for medical diagnosis. This paper describes network architecture by BSN standard with many nodes that serve as biosensors in medical research to monitor patients’ health. Furthermore, IEEE 802.15.4 based on networks are often executed near other wireless networks operating in the same industrial, scientific and medical (ISM) band. In addition, this paper explained the media access control (MAC) protocol based on the IEEE 802.15.4 with operation modes distinguished by a variety of ruggedness which has led to its acceptance in many BSNs, and several restrictions recreate the crucial role in weakening its execution. This paper highlights, outlines, and critically evaluates two different MAC protocol optimisation approaches. A research plan will be demarcated for each approach for MAC protocol that has been rigorously studied the research challenges, previous solutions, and open research issues in this article.
    Keywords: duty cycle; media access control protocol; body sensor network; BSN; IEEE 802.15.4; backoff.
    DOI: 10.1504/IJCNDS.2022.10047499
     
  • Performance Analysis of the Hard-Decision Cooperative Multi-Stage Spectrum Sensing for Cognitive Radio.   Order a copy of this article
    by Mohammad Hallaq, Wissam Altabban, Omran Abbas 
    Abstract: The number of applications that depend on wireless communications is increasing consistently. Therefore, the spectrum, which is a scarce resource, is becoming more crowded. Recently, cognitive radio (CR) technology has stood out to be a solution for this issue. However, this technology still faces problem that drag its performance such as the hidden terminal problem which occurs while CR is performing the spectrum sensing process. This problem can be solved by using cooperative sensing, where we rely on multiple cognitive radios rather than using a single one. In this paper, the authors propose a novel cooperative spectrum sensing model with multi-stage local sensing. First, each CR applies two-stage local sensing, the first stage is energy detection, and the second is maximum-minimum eigenvalues detection. Afterwards, each CR sends its detection result to a fusion centre to make the final decision. The approach is analysed in light of different rules and parameters under IEEE 802.22 standard. Finally, the results demonstrate that the best fusion rule for this system is the majority rule.
    Keywords: cognitive radio; multi stage sensing; cooperative spectrum sensing; CSS; hard decision rules; hidden terminal problem; fusion centre; energy detection.
    DOI: 10.1504/IJCNDS.2023.10047753
     
  • Deep Reinforcement Learning Empowered Energy Efficient Task-Offloading in Cloud-Radio Access Networks   Order a copy of this article
    by Naveen Kumar, Anwar Ahmad 
    Abstract: Mobile applications often demand computationally heavy resources to attain high quality on the other hand running all programs on a single mobile device still consumes a lot of energy and generates a lot of delay. By breaking a single operation into distinct components, partial computational offloading may be intelligently investigated in mobile edge computing (MEC) to minimise energy consumption and service latency of mobile device. Some of the components run on the mobile device itself, while the rest are sent to a mobile edge server. Current task offloading systems, primarily focus on average-based performance indicators, failing to satisfy the deadline constraints. This paper offers a deep reinforcement learning (DRL) empowered energy efficient task offloading method, to optimise the reward under task deadline constraints. Simulation results show that the proposed technique can efficiently transfer traffic from cloud-radio access networks to next-generation node B, while also saving energy by turning off underutilised baseband processing units.
    Keywords: C-RAN; deep reinforcement learning; DRL; mobile edge computing; Q-learning; resource allocation; task offloading.
    DOI: 10.1504/IJCNDS.2023.10047962
     
  • Privacy and Security Improvement in UAV Network using Blockchain   Order a copy of this article
    by Hardik Sachdeva, Shivam Gupta, Anushka Misra, Khushbu Chauhan, Mayank Dave 
    Abstract: Unmanned aerial vehicles (UAVs), also known as drones, have exploded in every segment present in today’s business industry. They have scope in reinventing old businesses, and they are even developing new opportunities for various brands and franchisors. UAVs are used in the supply chain, maintaining surveillance and serving as mobile hotspots. Although UAVs have potential applications, they bring several societal concerns and challenges that need addressing in public safety, privacy, and cyber security. UAVs are prone to various cyber-attacks and vulnerabilities; they can also be hacked and misused by malicious entities resulting in cyber-crime. The adversaries can exploit these vulnerabilities, leading to data loss, property, and destruction of life. One can partially detect the attacks like false information dissemination, jamming, grey hole, blackhole, and GPS spoofing by monitoring the UAV behaviour, but it may not resolve privacy issues. This paper presents secure communication between UAVs using blockchain technology. Our approach involves building smart contracts and making a secure and reliable UAV ad hoc network. This network will be resilient to various network attacks and is secure against malicious intrusions.
    Keywords: unmanned aerial vehicles; UAVs; blockchain; data privacy; network security; smart contract; Ethereum.
    DOI: 10.1504/IJCNDS.2023.10048925
     
  • A Random Forest Algorithm under the Ensemble Approach for Feature Selection and Classification   Order a copy of this article
    by Ankit Kharwar, Devendra Thakor 
    Abstract: Over the many previous years, research analysts have proposed diverse intrusion detection systems (IDS) tactics to manage computer threats expanding number and complexity. IDS takes all the data over the network and analyses the data using machine learning for finding the attacks. It is tough to find attacks on the network because it contains fewer records than standard data. It is significantly challenging to design an IDS for high accuracy. It also foregrounds different feature selection methods to select the best feature subset. We use the random forest feature importance for finding the best features. Single classifiers can mislead the find result, so we use random forest as classification with the help of best features. The proposed model is assessed on standard datasets like KDD99, NSL-KDD, and UNSW-NB15. The experimental result shows that the proposed model outperforms the existing methods in terms of accuracy, detection rate, and false alarm rate.
    Keywords: intrusion detection; anomaly detection; machine learning; ensemble methods; random forest; feature selection; feature importance; classification; cybersecurity; network security.
    DOI: 10.1504/IJCNDS.2023.10049123
     
  • An ECC-based Enhanced and Secured Authentication Protocol for IoT and Cloud Server   Order a copy of this article
    by Bhanu Chander, Kumaravelan Gopalakrishnan 
    Abstract: Due to the tremendous development in various domains, both internet of things (IoTs) and cloud-based computing are recognised as emerged technologies of the 20th century. IoT collection of heterogeneous interconnected embedded devices, then cloud computing provides the possible infrastructure which could be used at any-time and from anywhere. In recent times, due to their intelligence, competence ability to provide remote services, IoT and cloud computing employed into various real-life domains. The integration of IoT with the cloud infrastructure may lead to security issues. Moreover, these IoT-based devices prerequisite to link thru high-resource hubs such as cloud server (CS) for communication and resource sharing. Such combination comprises security hazards and injects dishonest data that principal to system fiasco. Especially, IoT policies accommodate on a public web links which perturbs the secrecy of trustworthy operators. Hence, this article planned an authentication idea on ECC for IoT-based embedded devices with CS. We discover the fuzzy structure facility for channel non-reciprocal, then ECC with one-way hash functions for secure validation. In conclusion, we scrutinise the planned procedure thru the AVISPA and related the resource constraints costs with existing authentication procedures.
    Keywords: internet of things; IoTs; cloud server; one-way hash function; embedded device; fuzzy logic; ECC; security and privacy.
    DOI: 10.1504/IJCNDS.2023.10049289
     
  • Deep Learning based on Multimedia Encoding to Enhance of Video Quality   Order a copy of this article
    by Nagendra Panini Challa, Shanmuganathan C, Shobana M, Ch.Venkata Sasi Deepthi, Bharathiraja N 
    Abstract: Over the years, video compression has become a key method of sharing information and communicating in the world of media. The future image quality of the ISO standards groups will expect high quality images. Also, visual video codecs (VVC) have optimised encoding decisions that use the same average square error, or total square difference, for decades. At the same time, the sudden increase in deep learning (DL) methods raises the issue of whether DL could actually profoundly change the way the clip was programmed. We developed a new visual quality measurement (VQM) to find ways to make it better. The proposed approach could significantly reduce the encoding compute load while maintaining almost the same enhanced rate distortion optimisation (ERDO) efficiency as the previous encoder, based on experimental measurements. To further improve efficiency, the proposed methodology could be combined with fast motion search algorithms and filtration methods.
    Keywords: multimedia; deep learning; visual quality measurement; VQM; quality; encoding optimiser; visual video codecs; VVC; enhanced rate distortion optimisation; ERDO.
    DOI: 10.1504/IJCNDS.2023.10049772
     
  • Effect of Different Attack Strategies on Controllability Robustness of Directed Complex Networks   Order a copy of this article
    by Peng Geng, Annan Yang, Lai Wei, Rui Chen, Ziyu Pan 
    Abstract: This article summarises the controllability robustness of various directed complex networks under different attack strategies. Based on the theory of node-degree, edge-degree, node-betweenness and edge-betweenness, the controllability robustness evaluation criterion for complex networks is proposed. Considering directed complex networks, we describe the construction of seven network models (RGN, SFN, OLN, MCN, QSN, RTN and RRN). Based on six attack strategies (NABR, NABB, NABD, EABR, EABB and EABD), we conduct simulated attacks on the above seven network models and analyse the results. All the attacks are classified into node-based and edge-based. Through simulation experiments, we can see that under the same network environment, the damage caused by the betweenness-based attack to the network is greater than that of the degree-based attack. The controllability robustness of scale-free network and onion-like network is almost the same regardless of the attack. Compared with other networks, random rectangle network has the best controllability robustness. Therefore, the simulation results can also draw the conclusion that the multi-ring structure is helpful to improve the controllability robustness.
    Keywords: directed complex networks; controllability robustness; attack strategy.
    DOI: 10.1504/IJCNDS.2023.10049880
     
  • Internet of Things Producer Mobility Management in Named Data Networks: A Survey, Outlook, and Open Issues   Order a copy of this article
    by Ahmad Abrar, Suki Arif, Khuzairi Bin Mohd Zaini 
    Abstract: The sustainability of the current internet architecture is a challenging task under consideration of tremendous and dramatic growth of smart mobile devices and content demands. The mobility support plays an essential role in growing trend of smart mobile devices, making mobility more intriguing and widely discussed domain. Therefore, this study provides broad discussion on mobility support. Also, describes consumer and producer mobility in internet of things-based wireless body area networks (IoT-WBANs) using named data networks (NDN). NDN is a significant paradigm for data distribution in IoT networks, but it confronts considerable challenges in terms of producer mobility. We discuss producer mobility extensively and categories into different approaches based on their characteristics and functionalities. Moreover, we constructed a clear research roadmap regarding IoT in NDN and critically reviewed each approach to identify the various research concerns and long-standing challenges. Furthermore, we investigated various challenges in IoT-WBANs caused by producer mobility and suggested intuitive solutions.
    Keywords: named data networking; internet of things; IoT; wireless body area networks; WBANs; information centric networks; consumer mobility; producer mobility.
    DOI: 10.1504/IJCNDS.2023.10050099
     
  • ENERGY EFFICIENT CONGESTION CONTROL IN WIRELESS SENSOR NETWORKS USING META-HEURISTIC ALGORITHMS   Order a copy of this article
    by Abdul Ali, M. Vadivel 
    Abstract: Wireless sensor network (WSN) is an infrastructure-less wireless network that is used to monitor the physical or environmental conditions. In WSN, energy is the main constraint in congestion control that decreases the network’s lifetime. This papers’ main goal is to propose a novel clustering-based routing protocol to mitigate the congestion issue in the network and to maximise the lifespan of WSN. To control the congestion problem in WSN, an energy-efficient ultra scalable ensemble clustering is introduced in this paper. In addition, the flamingo search algorithm-based fuzzy inference system is applied for cluster head (CH) selection. Rat swarm optimisation is used to select the route between CH and BS. The experimental results demonstrated that the proposed methodology gives better results than the existing techniques and it enhances the energy efficiency of WSN. The residual energy of the proposed approach (5 J at 2,000 rounds) is greater than the existing methods.
    Keywords: wireless sensor network; WSN; ultra-scalable ensemble clustering; flamingo search algorithm; FSA; rat swarm optimisation; RSO; cluster head selection.
    DOI: 10.1504/IJCNDS.2023.10050125
     
  • In search of clusters Based routing Protocol for WSN using consensus algorithm.   Order a copy of this article
    by Vikram Dhiman, Manoj Kumar 
    Abstract: The wireless sensor network (WSN) is a resource-constrained network type with two important challenges: energy consumption and network management. The goal of our design is to research and develop the WSN routing protocol, which is based on clusters and Open Flow controllers, in order to improve overall network performance. The results reveal that the proposed protocol is extensible and substantially more streamlined than standard soft computing-based implementations. These developments enable sensor networks to have a higher level of flexibility and programmability when placing sensors to the target. Our simulation results demonstrate that SDN extends network life by 20% when compared to traditional clustered-based routing by sending fewer control messages and using less processing resources once the initial processing is complete. The controller performs route computations rather than usual routing sensor nodes, which saves energy and extends the network's total lifetime.
    Keywords: software-defined networking; SDN; wireless sensor network; WSN; soft-computing; clustering; OpenFlow controller.
    DOI: 10.1504/IJCNDS.2023.10050235
     
  • Leveraging capsule network to learn content text for collaborative filtering   Order a copy of this article
    by Ji Li, Suhua Wang 
    Abstract: At present, most of the building components, technologies and frameworks of deep learning are based on convolutional networks. However, some deep learning studies on image processing have shown that the capsule network can be more representational because it can capture various posture changes, including translation, rotation and scaling, and can remember the position relationship between parts. Despite the intriguing nature of capsule network and their potential to open up entirely new natural language processing architectures, little work has been done in this area. In this work, we use the capsule network to learn the content text of the item (such as the plot text of the movie or the description document of the product), so as to obtain a better representation of the item and help achieve a more accurate recommendation. We proposed leveraging capsule network to learn content text for collaborative filtering (CCCF). This model combines capsule network and neural matrix factorisation to effectively model text data and user-item ratings. Experiments conducted from different perspectives on two popular datasets show that CCCF achieves good performance in common recommendation tasks, which proves the effectiveness of capsule network in recommendation.
    Keywords: capsule network; content text; generalised matrix factorisation; collaborative filtering.
    DOI: 10.1504/IJCNDS.2023.10050805
     
  • Cluster-Based Combined Hybrid Relay Vehicle Selection Approach for Improving Performance and Reliability in Vehicular Ad-hoc Network   Order a copy of this article
    by Puja Padiya, Amarsinh V. Vidhate, Ramesh Vasappanavara 
    Abstract: One of the main objectives of smart transport systems is to improve road safety. Ad hoc vehicle network uses vehicle accident alert systems that broadcast crashes to vehicles. Reliable and timely receipts of messages are two of the main intents for the creation of VANET security alert protocols. Existing techniques are hard to meet both objectives at the same time. With proposed suggestion, the vehicle transmits by employing the combined cluster and broadcast technique all the accident warning messages. The multi-hop message dissemination in VANET involving the selection process of relay vehicles in our proposed combined approach helps to improve performance in terms of reduced collision and reliability in terms of improved reachability and thus helps faster dissemination of emergency messages which in turn reduces the further impact of emergency situations.
    Keywords: vehicular communications; reliability; relay vehicle; emergency messages; ad hoc networks; safety; broadcast; cluster; message dissemination; vehicular ad hoc network.
    DOI: 10.1504/IJCNDS.2023.10051054
     
  • Software-defined network planes - a survey on attacks and countermeasure   Order a copy of this article
    by Sendil Vadivu D, Narendran Rajagopalan 
    Abstract: A smartly managed framework that enables the network to be configured via software application is termed software-defined networks (SDN). SDN gracefully abstracts the network management regardless of the underlying technology. The fundamental drive of SDN is to move from distributed control architecture to centralised control architecture. Though SDN provides complex security policies that can be easily customised to safeguard the network, it suffers from a single point of failure and opens to many security challenges. In order to withstand this centralised architecture, it is crucial to study the security aspects of the SDN. This paper surveys the various security techniques of the SDN stack from the perspective of individual layers.
    Keywords: software-defined network; SDN; SDN planes; security; flow table; OpenFlow; attacks; countermeasure.
    DOI: 10.1504/IJCNDS.2023.10051589
     
  • A LEARNING-BASED APPROACH TO IMPROVING MULTICAST NETWORK PERFORMANCE   Order a copy of this article
    by Hazem A. Abdulmajeed, Hesham A. Hefny, Assem Alsawy 
    Abstract: A neural network approach is recommended by us in this research paper, to solve the problem of multicast routing subject to some quality of service (QoS) restrictions in communications and internet of things (IoT) networks, which is considered a complete nondeterministic polynomial (NP) problem. This approach was taken to identify a multicast tree that satisfies those restrictions, in particular cost, delay, and data loss rate. The exemplary (shortest) path is identified by the recommended routing algorithm considering the traffic conditions (the incoming traffic flow, routers occupancy, and link capacities). The experimental results showed a significant difference in obtaining the exemplary path that was executed by the recommended method using the Hopfield neural network (HNN) approach, besides the number of iterations. Furthermore, the execution time is less compared with the recommendations of heuristic algorithms, such as the ant colony optimisation algorithm (ACO) and genetic algorithms (GAs).
    Keywords: multicast routing; internet of things; IoT; neural network; quality of services; QoS; heuristic algorithms.
    DOI: 10.1504/IJCNDS.2023.10051590
     
  • Fuzzy Logic-Based Delay Efficient Data Collection Technique for IoT Environment   Order a copy of this article
    by Deepa Rani, Tanuj Wala, Rajeev Kumar, Naveen Chauhan 
    Abstract: The sensor nodes in WSNs are resource constraints and data collection is draining the sensor node’s energy. Therefore, collecting data in a single hop by the mobile device helps in preserving the sensor node energy. This paper is introducing a fuzzy logic-based one hop data collection path (FLO-DCP) algorithm to find stop points from the set of intersecting points of the overlapped clusters and to reduce the data collection time by shorting the path length of the mobile device and increasing the lifetime of the network by preserving the sensor node’s energy. The proposed method consists of three phases. First, fuzzy logic-based overlapped clusters are formed, thereafter the stop points and trajectory path for the mobile device is being computed, and last, the data collection process is done. Also, in comparison with NDCMC, CB, and ORLP-RP algorithms, simulation results show that the proposed algorithm has better performance.
    Keywords: internet of things; data collection; fuzzy C-mean; FCM; clustering; trajectory; one-hop transmission; mobile device.
    DOI: 10.1504/IJCNDS.2023.10051768
     
  • Atrial fibrillation medical image encryption algorithm based on deep learning and adaptive block   Order a copy of this article
    by Jiangjiang Li, Lijuan Feng, Xibin Guo 
    Abstract: In this paper, a deep learning and adaptive block-based chaotic encryption algorithm for atrial fibrillation medical image is proposed. Firstly, we use 2D Sine Logistic chaos system to generate two security sequences with good chaotic characteristics. Then the image is divided into fixed size image blocks, and the maximum pixel difference and variance of the image blocks are calculated. Finally, chaotic sequence 1 is used for ciphertext feedback encryption of smooth blocks, and chaotic sequence 2 is used for plaintext feedback encryption of complex blocks. So the encrypted image is obtained. The RBF network is used to predict the chaotic sequence, and the predictive key stream is obtained. Experiment results show that the proposed algorithm has high encryption efficiency, and the encryption speed is about 1 times higher than that of the existing methods. The new algorithm is suitable for real-time encryption of medical images with large amount of data.
    Keywords: atrial fibrillation medical image encryption; 2D sine logistic chaos system; deep learning; adaptive block; RBF neural network.
    DOI: 10.1504/IJCNDS.2023.10052828
     
  • Research on the optimization of whitelisting technology for network firewall in industrial control system using genetic algorithm
    by Xiuhong Zhou, Wenbing Shi 
    Abstract: Industrial control systems improve the efficiency of industrial production management but also bring network risks. This paper briefly introduced the industrial control system and the industrial firewall adopting whitelist policy and proposed to optimise the whitelist of industrial firewall with the genetic algorithm-support vector machine (GA-SVM) algorithm to make it learn the rules independently. Finally, simulation experiments were performed using industrial control data collected from light-emitting diode (LED) lamp production enterprises to compare the GA-SVM algorithm with K-means and traditional SVM algorithms. The results demonstrated that the GA-SVM algorithm had better detection accuracy and shorter detection time for abnormal industrial control data; the industrial firewall adopting the GA-SVM-optimised whitelist had lower false blocking rate.
    Keywords: industrial firewall; industrial control system; whitelist; genetic algorithm.

  • Cluster-based multiple malicious node detection using honeypot AODV (H-AODV) in MANETs
    by Sampada H. K. Kubsad, Shobha K. R 
    Abstract: Security and scalability are two major research areas in the field of mobile ad-hoc networks (MANETs). The existing solutions for security and scalability are majorly used for static networks e.g., sensor networks. The focus of the present work is to detect and remove the multiple malicious black holes (MBH) and multiple malicious grey hole (MGH) nodes from the dynamic networks e.g., MANETs. The proposed solution increases network security. An efficient weight-based clustering technique is used to enhance the stability and load balancing of the network. Cluster head (CH) is selected based on the maximum weight factor. The weight of the node is based on three factors constancy factor (Cx) trust value (Ty) and link factor (Lz). Weightage values for the parameters can be prioritised and tested for consistency using analytic hierarchy process (AHP) algorithm. Each CH executes honey pot-AODV (H-AODV) to find the MBH and MGH nodes in its network.
    Keywords: mobile ad hoc networks; MANETs; honeypot-AODV; H-AODV; modified-AODV; M-AODV; clustering; malicious blackhole/grayhole attack; MBH/MGH.
    DOI: 10.1504/IJCNDS.2024.10053453
     
  • Effect of User Mobility under Rician Fading on Power Allocation for Non-Orthogonal Multiple Access (PA-NOMA) Strategy   Order a copy of this article
    by Sandeep Singh Rana, Gaurav Verma, O.P. Sahu 
    Abstract: Non-orthogonal multiple access (NOMA) has been envisaged as a promising technique to meet the demand for massive machine-type communication (mMTC) devices. However, designing a NOMA system required a proper comprehensive study of user mobility, user switching, and imperfect successive interference cancellation (Im-SIC). In this paper, the effect of user mobility and Rician fading channel on power allocation for non-orthogonal multiple access (PA-NOMA) strategy is thoroughly investigated. This paper also discusses the challenges faced during the power allocation to multiple NOMA users. Mobility of the users violates the basic channel gain conditions of the NOMA users, therefore, to maintain the fairness index of the individual cell edge users dynamic power allocation strategy is adopted. Simulation results show that user mobility and Im-SIC have a significant effect on the PA-NOMA strategy for multiple users in terms of sum-rate capacity, average bit error rate, and fairness index.
    Keywords: fairness index; PA-NOMA; imperfect SIC; Rician fading; user mobility; user switching.
    DOI: 10.1504/IJCNDS.2024.10053703
     
  • A search ranking algorithm for web information retrieval   Order a copy of this article
    by Shan Shan Zhi, Huan Huan Wang 
    Abstract: The development of the internet has seen an explosion in the amount of information, which has increased the scope of queries for users but greatly increased the difficulty of searching for valid information. In order to retrieve effective information faster, search ranking algorithms are needed to rank the retrieved information and return it to the user. This paper briefly introduced the RankNet algorithm among web information search ranking algorithms and optimised the loss function to improve its retrieval ranking performance. Simulation tests were carried out with Microsoft public data set MSLR-WEB30K. The improved RankNet algorithm was compared with the ranking support vector machine (SVM) algorithm and the traditional RankNet algorithm. The results showed that as the number of returned retrievals increased, the retrieval ranking performance of all three search ranking algorithms tended to decrease; under the same number of returned retrievals, the improved RankNet algorithm had the best performance.
    Keywords: search ranking; rank learning; RankNet; pairing loss; support vector machine; SVM.
    DOI: 10.1504/IJCNDS.2023.10045845
     
  • Utilisation aware virtual machine selection policy for workload consolidation in cloud data centres   Order a copy of this article
    by Dipak Dabhi, Devendra Thakor 
    Abstract: Large-scale virtualised data centres have been constructed throughout the world in response to the rising demand for service-oriented computing and the expansion of cloud computing technologies. These vast data centres consume a significant amount of power and have a significant carbon footprint, which must be reduced to the greatest extent feasible. The dynamic virtual machine consolidation provided by live migration leads to significant energy savings. However, it also constitutes a violation of the service level agreement (SLA). The process of selecting virtual machine (VM) for migration is critical in the realm of energy-aware cloud computing. This study proposes a novel utilisation aware VM selection (UAVMS) policy that aids in VM selection for migration using server utilisation and skewness value. We use the CloudSim toolkit to build our UAVMS policy and compare its performance with existing methods. The experimental results shows that UAVMS reduces the energy usage and SLA violations.
    Keywords: cloud computing; VM consolidation; VMC; QoS; service level agreement; SLA; VM selection; overload host detection; VM placement; underload host detection.
    DOI: 10.1504/IJCNDS.2023.10046013
     
  • A novel high-efficiency searchable encryption scheme under robot cloud computing environment   Order a copy of this article
    by Zhongli Wang, Aiyun Ju 
    Abstract: This paper proposed a high-efficiency searchable encryption scheme under cloud computing environment. Through the matching calculation of keyword index set and keyword trapdoor generated by the data user, the searchable encrypted mechanism is realised. By utilising the powerful computing resources of the cloud server, the pre-decryption operation is introduced to reduce the computing time cost of the data user. The cloud server does not know anything about the original file in the cloud through a trapdoor corresponding to the user-provided keyword (the trapdoor retrieves files containing a specific keyword from a large number of encrypted files). Security analysis shows that the new scheme cannot leak data privacy information and has stronger security than other state-of-the-art schemes.
    Keywords: searchable encryption; cloud computing; keyword search; pre-decryption operation.
    DOI: 10.1504/IJCNDS.2023.10046559
     
  • Survey on wait-free consensus protocol in distributed systems   Order a copy of this article
    by Radha Rani, Dharmendra Prasad Mahato 
    Abstract: Computer applications are transitioning from centralised to decentralised automation in the modern industrial era. The consensus algorithm is a critical component in decentralised applications. Wait-free consensus is an unresolved issue in asynchronous systems. Deterministic protocols are known to be incapable of solving the wait-free consensus problem. Wait-free consensus protocol implementation occurs when all processors complete their predefined steps regardless of the execution speed of the other processors. Many randomised algorithms for wait-free consensus have been proposed, but no deterministic algorithm is possible. In this paper, we present a survey of wait-free consensus algorithms that have been studied and are currently being used in some well-known applications. There is also discussion of open issues and challenges in deploying various consensus mechanisms. This survey will provide a detailed understanding of the wait-free consensus protocol and will aid in the direction of research in the field of designing consensus algorithms.
    Keywords: distributed systems; consensus; asynchronous model; fault tolerance; consensus; message passing.
    DOI: 10.1504/IJCNDS.2023.10049439
     
  • An energy efficient dynamic small cell on/off switching with enhanced k-means clustering algorithm for 5G HetNets   Order a copy of this article
    by Janani Natarajan, B. Rebekka 
    Abstract: The massive growth in the current and envisaged cellular traffic lead to innovations in 5G heterogeneous networks (HetNets) and implementation technologies. The small cells (SCs) or small base stations (SBS) aided macro base station (MBS) topology in HetNets effectively accomplish capacity growth and spectrum reuse at the cost of network complexity, power consumption and energy efficiency. In this paper, we propose a mechanism to maximise the system energy efficiency jointly by enhanced k-means clustering and dynamic load based SC switching algorithm for HetNets. The clustering algorithm optimises the initial centroids for maximum inter-cluster separation. Within each cluster, SC switching is decided based on a permissible threshold of inter-cluster interference. Further, the intra cluster SC coordination is formulated as a cooperative game for load balancing. Simulation results illustrate performance improvement of the proposed scheme up to 20% in energy efficiency and 16% in system throughput compared to conventional k-means clustering approach.
    Keywords: small cells; heterogeneous networks; HetNets; 5G; cluster; energy efficiency; macro base station; MBS.
    DOI: 10.1504/IJCNDS.2023.10047234