International Journal of Communication Networks and Distributed Systems (13 papers in press)

Regular Issues

• 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
   
  
• Advances in MEMS technology: an in-depth analysis of evolution, applications, and future directions  
  by Huu Q. Tran, 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
   
  
• AI-based intrusion detection and adaptive access control for enhancing security in 6G network slicing  
  by R. Kanthavel , R. Dhaya  
  Abstract: As 6G networks become a reality, they will certainly usher in new security challenges that will need to be addressed, particularly due to network slicing in multi-tenant environments. In this work, we proposed an AI-based framework consisting of: (1) a deep learning-based intrusion detection system (AI-IDS), and (2) a reinforcement learning-based adaptive access control system (AACS) for slice-level security. The proposed system identifies threats (known and unknown), dynamically develops, and enforces access policies based on normal user behaviour. The framework was evaluated using various benchmark datasets in a simulated 6G slicing environment. Overall, the proposed AI framework achieved ~93% detection accuracy, low false positive rates (~4%), and reasonably rapid response times (~75 ms). Results showed the proposed framework provided higher adaptability and accuracy over traditional fixed security mechanisms.
  Keywords: 6G network slicing; AI-based intrusion detection; AACS; adaptive access control system; RL; reinforcement learning; DL; deep learning; cybersecurity in multi-tenant networks.
  DOI: 10.1504/IJCNDS.2026.10073797
   
  
• A review of machine and deep learning techniques for network intrusion detection  
  by Vasanth Nayak, Sumathi Pawar, B. L. Sunil Kumar 
  Abstract: The rapid development of the Internet and communication technology has led to the expansion of large networks and data. In response to these threats, intrusion detection systems (IDS) were created to protect networks by analysing network traffic to ensure privacy, fairness, and security. The challenge remains correcting, reducing, and identifying new inputs. Recently, IDS based on machine learning (ML) and deep learning (DL) have been offered as effective techniques for detecting network vulnerabilities. This paper provides an overview of IDS and then classifies important ML and DL techniques for developing network-based IDS (NIDS) systems. Furthermore, this paper updates the techniques, evaluation methods, and data selection to reflect the current needs and advances in ML and DL-based NIDS. The limitations of the proposed method are analysed, key research issues are identified, and future research plans for NIDS based on ML as well as DL are provided.
  Keywords: detecting the network anomaly; network based intrusion detection system; deep learning; network security.
  DOI: 10.1504/IJCNDS.2026.10075413
   
  
• A novel approach based on multi-level blockchain framework for securing clustered VANET’s routing from wormhole assault  
  by Shahjahan Ali, Parma Nand, Shailesh Tiwari 
  Abstract: Available research signifies that CB-MAC (Cluster-based Medium Access Control) protocols do good work for managing & controlling Vehicular Ad-hoc Network (VANET), but it wants to ensure improved privacy & security preserving authentication procedure. The VANET is wireless in nature, due to which it is much more sensitive to various security assaults i.e. wormhole, block hole, gray hole etc. The wormhole assault is very severe assault in VANET, which interrupt the routing mechanism of any routing protocol (i.e. AODV). In this research paper a privacy-preserving authentication protocol based on multi-level blockchain is proposed to stave off the wormhole assault from clustered VANET’s routing. Moreover, formation of vehicle registration centres, authentication centres and key creation procedures, are explained thoroughly. From results it is clear that proposed approach is more efficient in terms of storage and time as compare to existed approaches. The proposed approach based on CB-MAC & Blockchain is simulated with the help of SUMO 0.32.0 and NS-2.35 simulators.
  Keywords: VANET; vehicular ad-hoc network; security; wireless; wormhole; routing protocol; blockchain; MAC; medium access control; SUMO 0.32.0; NS-2.35; throughput.
  DOI: 10.1504/IJCNDS.2026.10075417
   
  
• Intelligent early intrusion prediction and route migration framework for secure data transmission  
  by S. Kranthi, M. Kanchana, M. Suneetha 
  Abstract: A method of managing resources for networks that makes use of dynamic software-defined networking (SDN) that allows administrators to regulate resources in real-time. However, traditional models have met challenges, including high packet drop, energy consumption, and low performance. To overcome these challenges, a novel solution called hyena sequence intrusion detection (HSID) is proposed. This involves the creation of required nodes in the network, and the hyena function continually monitors node status. It effectively eliminates high-energy nodes, ensuring the sustainability of the routing process. The framework is implemented and rigorously tested in the Python platform, with a thorough evaluation of network efficiency parameters. In comparison, various metrics are considered, including energy consumption, throughput, packet drop, detection accuracy, and confidentiality rate. The results demonstrate higher performance satisfaction with the proposed model, emphasising its effectiveness in addressing the identified challenges and enhancing overall network security and efficiency.
  Keywords: network efficiency; throughput; detection accuracy; confidentiality rate; network security.
  DOI: 10.1504/IJCNDS.2026.10075420
   
  
• Joint association management and contiguous channel bonding for coexisting high throughput users and legacy users  
  by Babul P. Tewari, Poulomi Mukherjee 
  Abstract: Channel bonding in high throughput (HT) Wi-Fi networks facilitates higher data rate but may restrict the number of non-overlapping channels. This also restricts spatial reuse. The assignment of channels becomes further complicated in coexisting network of HT and legacy g users. In this mixed contention scenario, a judicious bonded channel assignment strategy is proposed to facilitate a high data rate to the HT users and fair service coverage to legacy users. An integrated model based on integer linear programming has been formulated with an elegant greedy approach to address a suitable bonded channel assignment strategy. Extensive simulations were conducted for a comparative analysis with contemporary works, focusing on channel bonding and association management. The results demonstrate that prioritising either bonded channels exclusively or basic 20 MHz channels can result in suboptimal performance. The proposed approach successfully removes constraints on Access Points (APs), enabling them to serve users of different types.
  Keywords: 802.11ac WLAN; channel bonding; heterogeneous users; association management; interference management; legacy users; HT users.
  DOI: 10.1504/IJCNDS.2026.10075455
   
  
• A novel multi-hop distributed clustering-based routing protocol for underwater wireless sensor networks  
  by V. Kiruthiga, V. Narmatha 
  Abstract: Underwater wireless sensor networks (UWSNs) face challenges due to limited battery resources that cannot be easily recharged or replaced. To address this, a novel multi-hop distributed clustering-based routing protocol (MH-DCRP) is proposed. This protocol operates in three phases: Location Broadcasting, Cluster Formation, and Data Forwarding. In the first phase, location data is gathered from neighbouring nodes to form clusters. The second phase optimises cluster formation based on distance and energy. After clusters are formed, cluster heads are selected to route data packets to monitoring stations via sonobuoys. In the final phase, data packets are transmitted to the sonobuoys through intra- and inter-cluster routing. The performance of MH-DCRP is evaluated through simulations in both dense and sparse environments, demonstrating superior results in terms of packet delivery ratio, network lifetime, residual energy, and reduced end-to-end delay compared to existing routing schemes. This makes MH-DCRP a more efficient solution for UWSNs.
  Keywords: UWSNs; underwater wireless sensor networks; multipath distributed routing; broadcasting phase; data transmission phase; group clustering mechanism; performance metrics.
  DOI: 10.1504/IJCNDS.2026.10075502
   
  
• Cooperative spectrum sensing and routing related security vulnerabilities in cognitive radio networks: issues, solutions and future challenges  
  by Prathana Saikia, Sanjib K. Deka, Monisha Devi 
  Abstract: Cognitive Radio (CR) is an emerging solution to spectrum scarcity caused by rapid telecommunication growth. This technology operates in frequency bands unused by primary users (PUs), enhancing overall spectrum utilisation. In cognitive radio networks (CRNs), the unused spectrum bands, also called as spectrum holes, are assigned to secondary users (SUs) to attain effective communication amongst SUs. CRNs spectrum sensing, essential for identifying spectrum gaps, is prone to security risks. Moreover, routing in the network layer relies on accurate physical-layer sensing, which is compromised by these vulnerabilities. Ensuring secure spectrum sensing and routing is vital, as their compromise can affect overall CRN performance. Both traditional wireless network vulnerabilities and CRN-specific vulnerabilities can affect CRNs. In light of this, this paper provides a comprehensive survey of current vulnerability problems and related defences for cooperative spectrum sensing (CSS) and CR routing, as well as important unaddressed research challenges that require further investigation.
  Keywords: CRN; cognitive radio network; CSS; cooperative spectrum sensing; cognitive radio network routing; attacks; security issues.
  DOI: 10.1504/IJCNDS.2026.10076031