International Journal of Communication Networks and Distributed Systems (14 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
   
  
• A comprehensive review of clustering and routing protocols in wireless sensor networks: challenges, trends, and future directions  
  by Md. Kamaruzzaman, Abhijit Chandra 
  Abstract: Wireless sensor networks (WSNs) have emerged as a crucial technology in diverse application domains including environmental monitoring, healthcare and military surveillance etc. Due to the inherent constraints in energy and computational capacity of sensor nodes (SNs), the design of energy efficient communication protocols remains a significant research challenge. Clustering and routing algorithms play a vital role in enhancing the scalability, energy efficiency and lifetime of WSNs. This paper highlights a comprehensive review on clustering and routing algorithms. Clustering techniques are classified based on parameters such as network architecture and energy efficiency; while routing protocols are categorised into data-centric and hierarchical schemes. The paper critically evaluates the strengths, limitations of various algorithms under different network scenarios. In addition, emerging trends such as AI-ML driven approaches and mobility-aware routing have also been discussed. This paper also identifies current research gaps and outlines future directions which provide a valuable reference for researchers.
  Keywords: blockchain integrated routing; energy-aware clustering; hierarchical routing; metaheuristic optimisation; QoS routing; software defined wireless sensor networks; swarm intelligence; wireless rechargeable sensor networks.
  DOI: 10.1504/IJCNDS.2027.10079260
   
  
• Energy efficient fault-tolerant routing framework for wireless sensor networks using modified weighted practical Byzantine fault tolerance algorithm with slot scheduling technique  
  by G. Radhika , N. Radhika  
  Abstract: Heterogeneous wireless sensor networks (HWSNs) are essential for Internet of Things (IoT) applications, enabling real-time environmental monitoring through resource-constrained sensor nodes. However, limited energy resources, inefficient routing, and a lack of adequate fault tolerance compromise network performance and reliability. Hence, this study proposes a multi-stage framework encompassing clustering, routing, scheduling, and fault-tolerance mechanisms. Initially, cluster heads are selected using attribute based clustering to enhance energy efficiency. A Q-learning-based routing algorithm (QLRA) is employed for optimal path selection, while an adaptive code optimisation algorithm (ACOA) identifies suitable forwarder nodes to prevent network isolation. To extend the network’s lifespan, a Slot Scheduling Technique (SST) manages the operational states of the nodes. Furthermore, a modified weighted practical byzantine fault tolerance (MWPBFT) protocol integrated with nature-inspired fringe lizard (NIFL) optimised leader selection enhances reliability by detecting Byzantine and malicious faults. Simulation results reveal that our proposed approach attained the significant improvements compared to existing methods.
  Keywords: fault tolerance; WSNs; wireless sensor networks; MWPBFT; modified weighted practical Byzantine fault tolerance algorithm; NIFL; nature-inspired frilled lizard optimisation; SST; slot scheduling technique; duty cycle mechanism.
  DOI: 10.1504/IJCNDS.2027.10079391
   
  
• Load balancing in cloud computing: challenges, constraints, algorithms, and future research directions  
  by Ravi Gugulothu, Suneetha Bulla, Vijaya Saradhi Thommandru 
  Abstract: This survey critically reviews and classifies existing load-balancing techniques in cloud environments based on their underlying strategies, algorithmic approaches, and optimisation objectives. Furthermore, the strengths and limitations of various algorithms, including round robin, Honeybee foraging, ant colony optimisation (ACO), genetic algorithms, and machine learning-based models are discussed in terms of scalability, adaptability, and convergence behaviour. By identifying the research gaps in current techniques and emphasising the lack of standardised formulations for objective functions and constraints, this survey provides the groundwork for future research in implementing adaptive, robust, and efficient load-balancing methods in cloud computing. In the Meta reinforcement learning-based load balancing approach, the makespan is highly decreased by 19.51%, balanced CPU utilisation by 21.98% and energy consumption by 22.75%. The results effectively proves that the integration of machine learning with optimisation strategies provides more satisfactory outcomes over load balancing in cloud environment.
  Keywords: load balancing; cloud computing; resource allocation; scheduling strategies; performance metrics; optimisation algorithms.
  DOI: 10.1504/IJCNDS.2027.10079607
   
  
• Enhancing cloud data security with multi-block partitioning and hybrid encryption techniques  
  by M. P. Madhura Yadav, Sanjeev Kulkarni 
  Abstract: Cloud computing offers scalable storage for large-scale data; however, protecting sensitive information from unauthorised access in multi-tenant public cloud environments remains a major challenge. This study proposes a hybrid security model integrating multi-block partitioning (MBP) with advanced encryption standard (AES) and Blowfish algorithms to combine AES’s strong security with Blowfish’s processing efficiency. Data are segmented into smaller blocks, each sequentially encrypted by both algorithms, reducing exposure to breaches. The approach is evaluated in a local NetBeans environment using comma-separated values (CSV) datasets ranging from 10 MB to 40 MB. Key metrics, encryption/decryption time, Shannon entropy, and Index of Coincidence, are analysed. Results reveal a favourable balance between performance and randomness, achieving a Shannon entropy of 6.81 and an Index of Coincidence of 0.00246, indicating highly unpredictable ciphertext and enhanced data protection compared to conventional techniques.
  Keywords: hybrid encryption; cloud security; MBP; multi-block partitioning; AES; advanced encryption standard; blowfish algorithm.
  DOI: 10.1504/IJCNDS.2027.10079610