International Journal of Communication Networks and Distributed Systems (14 papers in press)

Regular Issues

• Secure and reliable cooperative spectrum sensing in the presence of massive probabilistic Byzantine attacks  
  by Flavien Donkeng Zemo, Sara Bakkali, Ahmed El Hilali Alaoui  
  Abstract: In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) improves spectrum sensing performance in radio environments subject to fading and shadowing. However, when some secondary users (SUs) share falsified sensing information in CSS through a falsification attack on sensing data (spectrum sensing data falsification attack, SSDF), the sensing performance degrades significantly. This document proposes a new probabilistic SSDF attack model and a new defence strategy based on a robust identification and suppression mechanism against massive SSDF attacks. Simulation results obtained under various massive probabilistic SSDF attack scenarios show that the detection performance of the proposed defence scheme outperforms that of the weighted sequential probability ratio test (WSPRT), sequential probability ratio test (SPRT), and Majority fusion techniques with which comparisons have been made. The proposed defence scheme guarantees a near-zero probability of error whatever the number of attackers and whatever the SSDF attack strategy, which is not the case for WSPRT, SPRT, and Majority rule.
  Keywords: Byzantine attacks; CR; cognitive radio technology; CSS; cooperative spectrum sensing; data fusion; SS; spectrum sensing; WSPRT; weighted sequential probability ratio test.
  DOI: 10.1504/IJCNDS.2026.10069338
   
  
• A traffic classification-based traffic engineering framework in software-defined networking  
  by Chih-Yu Lin, Chien-Cheng Wu, Hong-Yi Huang 
  Abstract: Traffic engineering is used to optimise network performance. Due to the dynamic nature of the network environment, devising an efficient traffic engineering approach attracts more attention in next-generation networks. However, network performance optimisation is challenging due to stringent requirements and the need for global observation within the dynamic network environment. Fortunately, software-defined networking (SDN) can provide an abstract global view of the complete network environment, so we are motivated to leverage the SDN framework to construct next-generation networks. In this study, we propose a traffic engineering framework for SDN. Our proposed framework optimises transmission paths by employing traffic classification techniques. In addition, we divide this framework into three modules that can operate independently. Therefore, compared with conventional methods, our proposed framework shows greater flexibility. The superiority of our approach has been verified through rigorous testing on the Mininet simulator and the Ryu controller. Finally, we firmly believe that our contribution opens new avenues for more efficient and optimised network management.
  Keywords: network performance optimisation; SDN; software-defined networking; traffic classification; traffic engineering; Mininet; Ryu.
  DOI: 10.1504/IJCNDS.2026.10069766
   
  
• DDoS attack detection in the cloud environment using an optimized long-short-term memory with an improved firefly algorithm  
  by M.C. Malini, N. Chandrakala 
  Abstract: Distributed denial of service (DDoS) attacks are the most dangerous types of attacks on cloud computing. For cloud computing technology to be widely used, defenses against these threats must be developed. Hence, this present research work proposes a new detection scheme based on a long short-term memory (LSTM) optimized by an improved firefly algorithm (IFA) called LSTM-IFA. The IFA uses opposition-based learning (OBL) to increase population diversity and the local search algorithm (LSA) for enhancing its exploitation is the second enhancement. The IFA is used to enhance the performance of LSTM by optimizing hyperparameters that produce high detection accuracy with a fast convergence rate. Experimental findings were done over four distinct datasets to evaluate the proposed LSTM-IFA approach which is obtained 98.67 % of average accuracy. The experiment's findings demonstrated that, compared to previous detection techniques, the suggested enhanced LSTM methodology achieved a greater detection rate and accuracy.
  Keywords: cloud computing; DDoS attack detection; long-short term memory; firefly algorithm; LSA; local search algorithm; OBL; opposition-based learning.
  DOI: 10.1504/IJCNDS.2026.10070246
   
  
• Enhancing cloud load balancing using a hybrid binary Kepler-SLAP swarm optimisation method  
  by Nanasaheb Bhausahe Kadu, Mahesh Dattatray Nirmal, Mininath Raosaheb Bendre, Sachin Sampatrao Bhosale, Kalyani Tukaram Bhandwalkar, Nilesh Dilip Gholap 
  Abstract: Cloud computing enables convenient access to computational resources, but managing them efficiently remains a challenge. Load balancing optimises performance and resource usage, but achieving efficiency in large-scale environments is complex. In this paper, the Enhancing Load Balancing Efficiency in Cloud Computing utilising Binary Kepler and Slap Swarm Optimised Approach (ELBF-CC-BK-SSOA) is proposed to overcome the challenges. This research proposes Binary Kepler optimisation as a load balancing solution for cloud computing. Initially, the System Efficiency using Binary Kepler optimisation (BKOA) is used for find best solution to problems. Next, resource allocation using MemeticSalp Swarm Optimisation Algorithm (MSSOA) is employed to distribute available resources. Experimental results demonstrate that ELBF-CC-BK-SSOA outperforms existing methods by achieving 33.13% lower response time, 28.27% higher throughput, 24.12% reduced CPU utilisation and 26.19% increased cost efficiency compared to existing techniques. This highlights models effectiveness in enhancing cloud performance, resource utilisation and reducing operational costs for dynamic cloud environments.
  Keywords: Binary Kepler Optimisation algorithm; Cloud computing; Cloud environment; Memetic Salp Swarm Optimisation Algorithm; Load balancing.
  DOI: 10.1504/IJCNDS.2026.10071423
   
  
• Obstacles avoidance charging schedule for multiple mobile charging vehicles in wireless rechargeable sensor networks  
  by Sk Md Abidar Rahaman, Md Azharuddin, Pratyay Kuila 
  Abstract: The integration of wireless power transfer technology into mobile charging vehicles (MCVs) opens up new possibilities for wirelessly recharging the batteries of sensor nodes (SNs). Thereby, it extends the lifespan of wireless rechargeable sensor networks (WRSNs). However, the task of devising optimal charging schedules with MCVs is challenging, especially with the existence of obstacles. In this article, we introduce an approach to enhance the efficiency of charging operations within WRSNs. The proposed method considers several key factors, such as charging requirements, the presence of multiple MCVs, and the challenges posed by obstacles. Our strategy determines charging sequences based on collective charging preferences. The allocation of charging partitions is facilitated by clustering, leveraging the locations and energy consumption rates of SNs with each MCV in a partition. Furthermore, we employ an obstacle avoidance algorithm to address scheduling issues when obstacles are encountered. Extensive simulations are conducted to validate the proposed methodology.
  Keywords: WRSNs; wireless rechargeable sensor networks; charging scheduling; MCVs; mobile charging vehicles; obstacles.
  DOI: 10.1504/IJCNDS.2026.10071922
   
  
• Advances in MEMS technology: an in-depth analysis of evolution, applications, and future directions  
  by Huu Q. Tran, Samarendra Nath Sur, Sy Ngo 
  Abstract: Micro-electro-mechanical systems (MEMS) have revolutionised technology by integrating microelectronics with mechanical systems to create versatile miniature devices. This review explores MEMS evolution, from early developments to recent advancements. It outlines core principles of MEMS design and fabrication, including lithography, deposition, and etching. The paper examines various MEMS devices - sensors, actuators, resonators, and microfluidic systems - emphasising design considerations, fabrication techniques, and performance metrics. It highlights MEMS applications in healthcare, automotive, aerospace, consumer electronics, and telecommunications, driving innovations in medical diagnostics, environmental sensing, and autonomous technologies. Emerging research on new materials, advanced fabrication methods, and integration with nanotechnology and biotechnology is discussed. Key challenges, such as scalability, reliability, and energy efficiency, are addressed, providing insights into future directions. This article serves as a valuable resource for understanding MEMS history, current state, and future opportunities for researchers and industry professionals.
  Keywords: M2M; machine-to-machine; MEMS; Micro-electro-mechanical systems; nano; RF.
  DOI: 10.1504/IJCNDS.2026.10071937
   
  
• Hybrid optimisation with multi-objective fitness for CH selection and effectual routing in WSN  
  by Asha Rawat, Harsh Namdev Bhor, Mukesh Kalla, Manish Subhash Gardi, Sudhir Ramrao Rangari, Suvarna Prabhuappa Bhatsangave 
  Abstract: The wireless sensor network (WSN) contains a huge number of cost-effective and small energy-constrained nodes for network communication. Moreover, the clustering is considered as a major part of WSNs routing. Hence, this paper develops the Cosine Lotus Effect Algorithm (CLEA) for Cluster Head (CH) selection, and the Fractional Cosine Lotus Effect Algorithm (FCLEA) for routing. Initially, the network simulation is carried out, and the CH is done using the proposed CLEA with the fitness components such as Link Lifetime (LLT), delay, inter, and cluster distance, trust factor, and energy, in which, the Radial Basis Function Network (RBFN) is employed for energy prediction. The proposed FCLEA is utilized for routing, where fitness factors such as energy, delay, distance, and trust factors are utilized. Moreover, the FCLEA-based routing obtained the better average residual energy, distance, and throughput of 1.719 J, 7.068m, and 431.8.
  Keywords: LEO; lotus effect optimisation; WSNs; wireless sensor networks; FC; fractional calculus; SCA; sine cosine algorithm; RBFN; radial basis function network.
  DOI: 10.1504/IJCNDS.2026.10072516
   
  
• Peak-to-average power ratio reduction in F-OFDM system using hybrid deep learning and optimised grey coded partial transmit sequence  
  by G. Shyam Kishore, P. Chandrasekhar 
  Abstract: This work presented an effective peak-to-average power ratio (PAPR) reduction with a hybrid grey code phase factor based partial transmit sequence (PTS) and tone reservation based deep convolutional neural network (CNN) technique (Hybrid Grey-PF-TRDCNN). In the first stage, the tone reservation network TRCNN reserves some of the tones to create the peak cancelling signal. In the second stage, the resultant PAPR reduced signal of stage 1 is further reduced with the Grey code phase factor using PTS. Here, the optimal phase sequence is selected by an efficient beetle swarm optimisation (BSO) technique to reduce the PAPR of the signal. The presented hybrid approach provides PAPR reduction in hybrid frequency-quadrature amplitude modulation (HFQAM) based F-OFDM signal. The MATLAB 2021a working platform with Xilinx 14.5 is used to implement the proposed technique. The experimental outcomes of the suggested strategy are contrasted with those of other existing approaches.
  Keywords: tone reservation; grey code phase factor; PTS; partial transmit sequence; optimisation; deep learning; PAPR reduction; filtered OFDM.
  DOI: 10.1504/IJCNDS.2026.10072552
   
  
• Comparative study of VNS and hybridised PSO for resource allocation in V2X communications  
  by Ibtissem Brahmi, Souhir Elleuch, Emna Hajlaoui, Monia Hamdi, Faouzi Zarai 
  Abstract: Exploration into cooperative intelligent traffic systems has yielded improvements in ground transportations efficiency, safety, and comfort. This work focuses on the resource allocation challenge within Vehicle-to-Everything communications. To address this problem, we suggested and compared the performance of two distinct meta-heuristic algorithms. The first technique, variable neighbourhood search (VNS), belongs to the category of solution-based meta-heuristics. The second technique hybridises particle swarm optimisation (PSO),a population-based meta-heuristic, with a proposed local search approach, trying to leverage the strengths and mitigate the weaknesses of both algorithms. The two proposed methods seek to optimise the systems overall throughput while ensuring minimal latency and reliability for both cellular user equipment (CUEs) and vehicle user equipment (VUEs). The algorithms proposed in this paper improve the system throughput and demonstrate its feasibility and utility for V2X communications.
  Keywords: resource allocation; meta-heuristic algorithms; VNS; variable neighbourhood search; PSO; particle swarm optimisation.
  DOI: 10.1504/IJCNDS.2026.10073223
   
  
• Velocity-based user splitting and resource allocation for downlink fifth generation vehicle-to-everything communication  
  by Amit Kumar, Krishnan B. Iyengar, Raghavendra Pal, Abhilash S. Mandloi 
  Abstract: Fifth Generation (5G) Vehicle-to-Everything (V2X) communication requires efficient allocation of resource blocks (RBs) by a base station (BS) to the vehicles it is serving. To that end, this work presents a resource allocation algorithm that exploits the fact that vehicles in an environment typically have different velocities and velocity distributions. The algorithm uses a Velocity Based User Splitting approach that partitions users into discrete (low/high) velocity categories to utilise the difference in coherence intervals between the different vehicles. The channel conditions of low velocity users remain constant for longer than those of high velocity users, and the algorithm uses this difference to perform optimal RB allocation to maximise capacity. The performance of the algorithm is evaluated with respect to many parameters and factors including transmitted power, number of RBs, and channel conditions. The results are compared against random allocation and simple greedy allocation methods, and show a 5% improvement in low signal-to-noise ratio (SNR) conditions.
  Keywords: 5G; Cellular V2X; RA; resource allocation; downlink orthogonal frequency division multiple access; mobility.
  DOI: 10.1504/IJCNDS.2026.10073265
   
  
• A task scheduling technique in the cloud computing environment based on bacterial colony optimisation  
  by R.M. Aravind, R. Pragaladan 
  Abstract: Cloud computing offers high accessibility, scalability, and flexibility in the modern computing era for various useful applications. Distributing and coordinating tasks to get the best resource usage and prevent overload is a difficult problem. In this research, we suggested a load-balancing (LB) method that minimises makespan and increases resource utilisation by employing novel bacterial colony optimisation (BCO) task scheduling to schedule jobs over the available resources. Aiming to balance load across virtual machines (VMs) according to makespan, cost, and resource utilisation - all of which are constrained by concurrent considerations - the suggested approach also seeks to maximise VM throughput. The CloudSim simulator is used to implement our suggested scheduling strategy. According to the testing results, the algorithms that employed the BCO technique performed better than the other techniques in terms of makespan reduction, low execution time, and average resource usage.
  Keywords: cloud computing; load balancing; BCO; bacterial colony optimisation; task scheduling.
  DOI: 10.1504/IJCNDS.2025.10068708
   
  
• TPOT-IDSDN: an AutoML-based model optimisation for intrusion detection system against cyber threats in software defined-networking  
  by D. Sendil Vadivu, Aswin Valsaraj, Ashwin Santhosh, Kaustub Pavagada, Narendran Rajagopalan 
  Abstract: The architectural shift of software defined networks (SDN) creates new security concerns, necessitating the creation of robust intrusion detection systems (IDS) to protect the network infrastructure. This paper focuses on the essential challenge of selecting classifiers for anomaly-based IDS in an SDN environment. An automated machine learning (AutoML) framework called tree-based pipeline optimisation tool (TPOT) was used to speed up this procedure substantially. TPOT automates model selection and hyperparameter optimisation, to decide a best classifier suited for the given dataset. The TPOT framework selected the ExtraTreesClassifier for multiclass and the XGB stacked with the BernoulliNB classifier for binary class with lower execution time (26.91 s, 11.29 s) and 100% accuracy. A comprehensive examination of standard nine machine learning (ML) classifiers confirmed TPOT has provided the best model. When deployed in the IDS framework of SDN, the selected classifiers showed a 100% detection rate that outperformed other existing approaches.
  Keywords: AutoML; automated machine learning; SDN; software defined network; TPOT; tree-based pipeline optimisation tool; cyber security; intrusion detection systems.
  DOI: 10.1504/IJCNDS.2025.10068807
   
  
• Combining coded computation and reinforcement learning to improve edge computing in heterogeneous clusters  
  by Payam Mohammadi, Farzad Parvaresh, Javad Kazemitabar 
  Abstract: The emergence of Internet of Things (IoT) technology has led to extensive connections among various devices which leads to a production of a large amount of heterogeneous data. While, cloud computing is a suitable and efficient processing model for storing and processing this large data, the demand for real-time and delay-sensitive applications is increasing rapidly. Using only cloud computing cannot address this problem properly, because the network bandwidth is limited. Therefore, edge processing as a new processing model and a cloud computing supplement is proposed which is based on a distributed processing architecture. In the proposed reinforcement learning and distributed coding computing (RLCDC) method we harness the DDPG algorithm to manage resources. Additionally, maximum distance separable (MDS) codes are also used to deal with the remaining processors, and to remove the transmitted packets, as well as increasing the network's operational capacity and throughput.
  Keywords: edge computing; coded computing; reinforcement learning; DDPG; deep deterministic policy gradient; IoT; Internet of Things.
  DOI: 10.1504/IJCNDS.2025.10072629
   
  
• Opposition based learning-lyrebird optimisation approach for optimal path planning in UAV-WSN environment  
  by Nilabh Kumar, Prabhat Kumar 
  Abstract: The rapid advancement in wireless sensor networks (WSNs) has prompted the need for efficient data collection, particularly using unmanned aerial vehicles (UAVs). However, selecting an optimal path for UAVs to collect data from sensor nodes while avoiding obstacles is a significant challenge. Thus, this research introduces a novel meta-heuristic optimisation approach for UAV path planning to address these challenges. Initially, a system model is designed that includes a UAV and a set of sensor nodes randomly deployed within a specified area. The proposed method focuses on UAV path planning using a novel opposition-based learning-lyrebird optimisation approach (OBL-LOA). The proposed approach offers a significant improvement in efficiency and performance for UAV path planning in terms of average flight time (s), network life time (rounds), task completion time, average path length (m), average energy consumption (J), and average data collection efficiency (%) and accomplished 27.5801, 2605.63, 1.03425, 33.716, 0.025, and 0.945 respectively.
  Keywords: path planning; opposition learning; lyrebird optimisation; unmanned automated vehicle; obstacles.
  DOI: 10.1504/IJCNDS.2026.10069337