International Journal of Communication Networks and Distributed Systems (18 papers in press)

Regular Issues

• 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
   
  
• 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
   
  
• Lightweight blockchain-integrated IoT framework: a layered architecture for enhanced security and privacy  
  by Sumita Kumar, Vidhate Amarsinh, Puja Padiya 
  Abstract: The Internet of Things (IoT) enables devices embedded with sensors and communication capabilities to continuously sense and transmit environmental data, enhancing automation, efficiency, and quality of life. However, large-scale IoT deployments face significant challenges related to security, privacy, and cyber threats. This paper proposes a generic lightweight blockchain-based framework to strengthen IoT security and privacy. The framework distributes blockchain operations across multiple IoT layers and employs elliptic curve cryptography (ECC) for lightweight encryption and digital signatures, along with streamlined consensus mechanisms suitable for resource-constrained environments. A DAG based lightweight structure, fine-tuned load balancing, and memory pooling improve performance and resilience against attacks. Smart contracts deployed at the dew and cloudlet layers enable real-time operations and adaptability in dynamic IoT scenarios. The framework is validated against major cyber threats, including intrusion, man-in-the-middle, replay, and eavesdropping attacks. Performance evaluations based on throughput, latency, and scalability confirm its effectiveness for secure, scalable, and high-performance IoT deployments.
  Keywords: IoT; Internet of Things; blockchain; smart contract; cryptography; security; privacy.
  DOI: 10.1504/IJCNDS.2026.10076366
   
  
• Trust-aware buffer management for reliable communication in opportunistic IoT networks using indirect evidence and congestion prediction  
  by S. Gopinathan, S. Babu 
  Abstract: The Opportunistic Internet of Things Network (OppIoTNet) is unsteady and is prone to inactivity, resembling poor communication. Due to the vitality of this network, it is crucial to introduce an approach for effective buffer management by identifying the congestion points in the network. This paper presents a trustaware buffer management system for reliable communication in OppIoTNets. The system has three fundamental elements: evidence creation, blackhole detection and buffer management. It actively detects the message forwarding patterns to build trust evidence, which are categorised as cooperative or malicious with the Incremental 1D Transformer (I1D-XFormer). This real-time model uses attention mechanisms to learn temporal trends and filters out the untrustworthy nodes efficiently. Also, the system uses an extended Kalman filter with nonlinear operation (EKF-NLF) to anticipate the possible congestion points prior to contacts. The given system has an accuracy of 99.95 and F-measure, recall, and precision of 0.9996, 0.9995, and 0.9997, respectively.
  Keywords: indirect evidence; blackhole detection; buffer management; congestion prediction; I1D-XFormer; incremental 1D transformer; EKF-NLF; extended Kalman filter with nonlinear function.
  DOI: 10.1504/IJCNDS.2026.10076982
   
  
• Research on cross layer robustness of coupled urban public utility networks  
  by Lv Jiao , Liu Yan , Bektur Azimov , Yang Haojie , Geng Peng , Zhao Xiaoyan  
  Abstract: This study investigates cascading failure risks in interdependent urban infrastructure by developing a multi-layer economic coupling model that integrates power, gas, water, metro, and heating systems. A comparative framework is established to examine robustness variations between large and small/medium cities based on coupling strength differentials. Vulnerability is assessed under three attack strategies random, betweenness-based, and weighted out-degree-based with cascading impacts quantified by the proposed dynamic propagation loss index (DPLI). Results demonstrate that weighted out-degree attacks induce 43.92% greater systemic degradation than betweenness-based strategies (p < 0.01). The local degree allocation (LDA) protection strategy improves network survivability by 14.32% over Resource Allocation (RA) under targeted attacks. An adaptive edge-enhancement strategy incorporating real-time network dynamics further enhances chained fault containment by 9.9% compared to static models. These findings provide theoretical foundations and practical guidelines for resilient urban infrastructure design under diverse attack scenarios.
  Keywords: cross layer robustness; complex networks; public utility networks; coupling; robustness; protection strategies.
  DOI: 10.1504/IJCNDS.2026.10076997
   
  
• Analysis of channel model for stability control of wireless communication system for connected vehicles in smart cities  
  by Xiaohui Zhang, Shiqing Wang 
  Abstract: Although the wireless communication technology of the Internet of Vehicles provides convenience for improving traffic efficiency and security, there are still problems such as multi-user communication interference, and resource allocation strategy delay in complex and dynamic environments. Therefore, this study proposes a spectrum sensing module based on deep Q-network (DQN) and mobile network, which combines near end strategy optimisation and carrier aggregation module of long short-term memory (LSTM) network. In the experiment, the interference intensity increased from -10 dB to 20 dB, and the error rate of this model was almost close to 10^-10 under high interference intensity. Under the conditions of a signal-to-noise ratio (SNR) of 10 dB and 30 users, the spectral utilisation rates of the research model on three datasets were 92.56%, 90.8%, and 93.24%. The results demonstrate that the model improves the stability and reliability of the vehicle networking wireless communication system in complex environments, while keeping accuracy.
  Keywords: vehicle to network; channel model; stability control; error rate; reinforcement learning; neural network.
  DOI: 10.1504/IJCNDS.2026.10077579
   
  
• A QoS-aware task scheduling strategy for heterogeneous cloud environments via hybrid parallel PSO and deep reinforcement learning  
  by Santanu Dam, Gopa Mandal, Abir Chattopadhyay, Kousik Dasgupta 
  Abstract: Cloud computing offers a range of virtualised services to end users via its service providers or CSPs. Cloud Service Providers must ensure uninterrupted service. Simultaneous tasks arising from the Cloud of Things (CoT), Internet of Things (IoT), edge, and fog devices must be managed in real-time and within a designated timescale. A proficient scheduling strategy is essential to coordinate multiple tasks within the allotted timeframe to address this problem. The implementation of deep reinforcement learning (DRL) has significant promise and underscores its effectiveness in developing an optimised scheduling approach. This work presents a job scheduling strategy utilising a deep reinforcement learning algorithm alongside a modified particle swarm optimisation technique. This establishes a new standard for intelligent, energy-efficient, and high-performance scheduling of cloud tasks. It addresses the difficulty of attaining an ideal equilibrium between workload and enhanced accuracy while maintaining a high standard of service quality.
  Keywords: quality of service; task scheduling; cloud computing; DRL; deep reinforcement learning; CoT; Cloud of Things.
  DOI: 10.1504/IJCNDS.2027.10077676
   
  
• Enhancing intrusion detection system performance under imbalanced data conditions using a hybrid deep learning framework  
  by Ahmad Farid Aseel, Amir Hosein Keyhanipour 
  Abstract: The proliferation of sophisticated cyberattacks necessitates robust Network Intrusion Detection Systems (NIDS) to safeguard network integrity. However, a critical challenge lies in the inherent class imbalance of network traffic data, where attack instances are significantly outnumbered by normal traffic. This paper presents a novel deep learning-based approach to mitigate this challenge and enhance NIDS performance. We propose a framework utilizing Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to address data imbalance. The core strategy involves consolidating all attack classes into a single category, effectively reducing bias towards the dominant normal traffic class. The efficacy of the proposed model is evaluated using established benchmark datasets, UNSW-NB15 and CICIDS2017. The results demonstrate noticeable improvements in overall intrusion detection accuracy. This research offers a promising contribution to the field of network security by introducing a deep learning-based solution that effectively addresses data imbalance and fosters the development of more robust NIDS.
  Keywords: intrusion detection; deep learning; imbalanced data; network security; data imbalance; cyber security.
  DOI: 10.1504/IJCNDS.2027.10077735
   
  
• Hybrid dropped-weight LSTM-DQN for adaptive FQAM modulation in MIMO-filtered OFDM systems  
  by G. Shyam Kishore, P. Chandra Sekhar 
  Abstract: The proposed paper presents another AM method to multiple input multiple output filtered orthogonal frequency division multiplexing (MIMO-F-OFDM) systems, which uses the hybrid approach utilising a combination of dropped weight long short-term memory (WLSTM) and deep Q-network (DQN). The hybrid WLSTM-DQN neural architecture can select robust and dynamic modulation with a high degree of accuracy in determining signal-noise ratio (SNR) in both cases of uncertain channel conditions. To enhance further the system adaptability, a multi-layer Sparse Bayesian learning (MSBL) method, is adopted to determine imprecise information of the channel, whereas the improved pelican optimisation algorithm (IPOA) is used to select the optimal modulation mode. The different frequency and quadrature amplitude modulation (FQAM) schemes are used to evaluate the proposed approach. It is revealed through performance analysis that the performance of the hybrid model is enormously improved in terms of spectral efficiency and low bit error rate (BER), thus confirming that the hybrid model is efficient in multifaceted communications.
  Keywords: deep learning; DQN; deep Q-network; adaptive modulation; filtered orthogonal frequency division multiplexing; channel estimation; mode selection; multi input multi output.
  DOI: 10.1504/IJCNDS.2027.10078441
   
  
• 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 utilised for routing, where fitness factors such as energy, delay, distance, and trust factors are utilised. Moreover, the FCLEA-based routing obtained the better average residual energy, distance, and throughput of 1.719 J, 7.068 m, 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
   
  
• 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 transportation's efficiency, safety, and comfort. This work focuses on the resource allocation challenge within Vehicle-to-Everything (V2X) communications. To address this problem, we suggested and compared the performance of two distinct meta-heuristic algorithms. The first technique, variable neighborhood 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 system's 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 neighborhood search; PSO; particle swarm optimisation.
  DOI: 10.1504/IJCNDS.2026.10073223
   
  
• 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 behavior. 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
   
  
• 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
   
  
• 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
   
  
• Network intrusion detection enhancement system through machine learning algorithms  
  by Nour Kadhim Hadi 
  Abstract: In today's digitally driven landscape, network security is critical as decision-making relies heavily on reliable, high-quality data. This study utilises the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to improve traffic classification and data quality evaluation through state-of-the-art techniques. By employing advanced methods such as feature engineering, ensemble learning, and machine learning (ML) algorithms, the research enhances classification precision and accuracy. The primary goal is to develop an optimised classifier capable of detecting anomalous network traffic indicative of security intrusions. Using a preprocessed dataset, the study identifies significant features and optimises hyperparameters to refine performance. A decision tree classifier was evaluated using unseen data, integrating stages of data preparation, anomaly detection, training, and visualisation. The resulting model demonstrated high effectiveness for intrusion detection systems, achieving an accuracy of 95.36%, alongside 97% precision, a 95% F1-score, and 95% recall. This comprehensive approach ensures robust detection of potential network threats.
  Keywords: machine learning; intrusion detection; decision tree; NSL-KDD; feature importance; performance metrics.
  DOI: 10.1504/IJCNDS.2026.10076268
   
  
• 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
   
  
• A comprehensive review of MIMO-NOMA systems for performance enhancement in future wireless network: current status and open research challenges  
  by G. Prasanna Kumar, K. Ramesh Chandra, Kothapalli Phani Varma, S.V. Kiranmayi Sridhara, Abdul Rahaman Shaik 
  Abstract: Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G and beyond 5G mobile networks. Integration of NOMA with multiple input multiple output (MIMO) technology improves spectral efficiency and user services. This paper provides a comprehensive review of the MIMO-NOMA system in future wireless networks. Initially, the basic concepts of the MIMO-NOMA system are explained, then reviewed single and multi-user MIMO-NOMA systems along with their limitations. The other enabling technologies of the MIMO-NOMA system, such as backscatter communication, mobile edge computing, intelligent reflecting surfaces (IRSs), integrated terrestrial satellite networks, and underwater communication, were also investigated. Finally, discussed future research directions of the proposed review, such as integration of better signal processing techniques, optimal resource management, analyse security and privacy in MIMO, etc.
  Keywords: NOMA; non-orthogonal multiple access; MIMO; multiple input multiple output; TDMA; time division multiple access; FDMA; frequency division multiple access; CDMA; code division multiple access; NAICS; network-assisted interference cancellation and suppression.
  DOI: 10.1504/IJCNDS.2026.10076427