International Journal of Communication Systems最新文献

{"title":"Optimizing Network Lifetime via Deep Q-Network Driven Intelligent Aggregation in Heterogeneous Sensor Ecosystems","authors":"Prashant Sangulagi,&nbsp;Somnath Biradar,&nbsp;Shilpa P","doi":"10.1002/dac.70569","DOIUrl":"https://doi.org/10.1002/dac.70569","url":null,"abstract":"<div>\u0000 \u0000 <p>The proliferation of heterogeneous sensor nodes in modern wireless sensor networks (WSNs) necessitates a paradigm shift from rigid, static routing regimes toward autonomous, resource-aware operational management. Conventional data aggregation and clustering heuristics frequently fail to mitigate the nonlinear energy depletion profiles and high data redundancies inherent to dense Internet of things (IoT) ecosystems. This paper proposes a deep Q-network–driven intelligent aggregation (DDIA) framework designed to maximize network longevity through self-learning optimization. The DDIA architectural framework incorporates a localized deep Q-network (DQN) agent. This agent dynamically monitors real-time network state vectors—specifically tracking node residual energy, local traffic intensity, and spatial data correlation coefficients. Based on these inputs, the model executes optimized cluster-head selection sequences and multihop routing trajectories. A key innovation of the proposed work is the implementation of bitwise-rotation entanglement (BRE) for data fusion. In contrast to standard arithmetic or statistical aggregation methods, the proposed BRE technique introduces nonlinear circular rotation primitives. This approach simultaneously compresses and encrypts transient data packets. Consequently, the mechanism maintains a minimal computational footprint, rendering it highly compatible with resource-constrained edge-tier hardware. This creates a dynamic entropy model where the aggregation intensity is adaptively tuned by the DQN agent based on information uncertainty and node energy levels. Simulation results involving 500 heterogeneous nodes demonstrate that the DDIA framework extends the first node dead (FND) milestone by 38% and reduces energy consumption per bit (<i>E</i>_<i>bit</i>) by 42% compared with state-of-the-art protocols like HEED and LEACH. The findings validate the framework's efficacy in providing a scalable, secure, and energy-efficient solution for next-generation industrial WSNs.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 13","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QoS-Aware Relay Node Deployment for Wireless Sensor Networks Using a Repair-Based Multiobjective Evolutionary Algorithm","authors":"YongGuk So,&nbsp;KangSa Yun","doi":"10.1002/dac.70561","DOIUrl":"https://doi.org/10.1002/dac.70561","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless sensor network deployment is a fundamental design problem because the physical placement of relay nodes directly affects network connectivity, communication reliability, and end-to-end delay. Although many deployment studies focus on coverage, connectivity, and deployment cost, delay-related quality-of-service (QoS) requirements are often addressed mainly at the routing or protocol stage after the network topology has been fixed. This paper investigates QoS-aware relay node deployment by incorporating probabilistic link reliability, retransmission-induced expected delay, and deadline satisfaction into the deployment optimization stage. The relay deployment problem is formulated as a constrained multiobjective optimization problem that minimizes deployment cost while maximizing the deadline satisfaction ratio (DSR). Rather than replacing the classical NSGA-II algorithm, this work develops a QoS-aware constrained deployment framework tailored to relay placement in wireless sensor networks. NSGA-II is used as the multiobjective search backbone, and problem-specific sequential repair mechanisms are embedded into the search process. Structural feasibility repair first enforces coverage and connectivity requirements, while QoS-oriented delay repair then refines deadline-violating communication paths. Simulation experiments are conducted under uniform, clustered, and obstacle-based deployment scenarios. The proposed framework is compared with greedy relay placement, standard NSGA-II, MOEA/D, and MOPSO. Ablation, scalability, and parameter sensitivity analyses are also performed to examine the contribution of the repair modules and the robustness of the framework. The results show that the repair-guided framework improves deadline satisfaction and Pareto solution quality while maintaining structural feasibility under constrained deployment settings. These findings suggest that deployment-stage QoS optimization is useful for latency-sensitive WSN applications such as industrial monitoring, disaster surveillance, and mission-critical IoT.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Criteria Decision Making Based Routing Optimization for IEEE 802.1 Time-Sensitive Networking","authors":"Selçuk Cevher,&nbsp;Ömer Kağan Demir","doi":"10.1002/dac.70562","DOIUrl":"https://doi.org/10.1002/dac.70562","url":null,"abstract":"<div>\u0000 \u0000 <p>The effective management of physical processes in cyber physical systems such as in-vehicle platforms and industrial automation necessitates a deterministic real-time communication. Although traditional time-sensitive networks provide low-delay data transmissions, there is a need for the real-time variants of IEEE 802.3 Ethernet to satisfy the low-cost and high-bandwidth requirements. IEEE 802.1 Time-Sensitive Networking (TSN) task group is the leading organization that aims to standardize Ethernet-based deterministic communication technologies. In this paper, route planning for <i>Audio–Video Bridging (AVB)</i> streams is modeled as a <i>multi-criteria decision making</i> problem for TSN with mixed criticality support and a <i>K-shortest paths</i> based heuristic approach is proposed to solve this problem. Our heuristic is integrated into metaheuristic approaches relying on <i>Greedy Randomized Adaptive Search Procedure</i> to construct initial solutions, as generating good-quality initial solutions can improve the performance of a metaheuristic by increasing the convergence speed and accuracy of the solution. Experimental results relying on realistic automotive and Industry 4.0 use cases show that our approach significantly improves the schedulability of AVB streams in presence of time-triggered traffic and features a better computational efficiency compared with the other approaches in the literature.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Dragonfly Optimization and Adaptive Long Short-Term Memory Model for Wireless Sensor Network Security Enhancement","authors":"J. T. Anita Rose,&nbsp;S. Radhika,&nbsp;F. Sangeetha Francelin Vinnarasi,&nbsp;A. Chandrasekar","doi":"10.1002/dac.70554","DOIUrl":"https://doi.org/10.1002/dac.70554","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, wireless sensor networks have emerged as a significant technology in industrial monitoring, smart farming, and healthcare systems. Due to their open and distributed nature, wireless sensor networks are highly vulnerable to various attacks and energy constraints. Various artificial intelligence-based techniques have been developed for energy-efficient routing and intrusion detection, but their performances are limited by poor cluster head selection and slower convergence. To address these limitations, this study proposes an attention-based improved dragonfly algorithm with adaptive long short-term memory for effective energy-efficient routing and intrusion detection in wireless sensor networks. The density peak K-means clustering is designed to form stable clusters of sensor nodes according to the density. The improved dragonfly optimization algorithm is established to select the cluster head optimally, which utilizes the global search capability of the multi-verse optimization algorithm with the exploration–exploitation balancing ability of the Lévy flight strategy, thereby reducing the ineffective solutions. Additionally, the multi-level self-attention mechanism is designed to extract the features from the traffic data that capture the short-term, long-term, and local variations. Further, an adaptive long short-term memory model is designed to detect the attacks that dynamically adjust its weights based on network traffic changes, thereby improving the detection performance. Extensive experimental evaluations demonstrate that the proposed framework outperforms existing models with a higher accuracy of 98.4%, a higher packet delivery ratio of 98.2%, highlighting its suitability in secure and energy-efficient wireless sensor network applications.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Wireless Body Area Network Antennas: Design Principles, Taxonomy, and Emerging Challenges","authors":"Ankur Utsav,&nbsp;Ritesh Kumar Badhai,&nbsp;Chandni Bajaj,&nbsp;Sachin Kumar,&nbsp;Bhawna Goyal","doi":"10.1002/dac.70538","DOIUrl":"https://doi.org/10.1002/dac.70538","url":null,"abstract":"<div>\u0000 \u0000 <p>The internet of medical things (IoMT) is a paradigm that allows for contactless body-centric information (BCI), where wireless body area networks (WBANs) are sub-networks in a larger IoMT network that send BCI to the desired professional or healthcare facility for analysis. WBANs are used for a variety of purposes, including health monitoring, medical diagnosis, fitness tracking, and military use. WBANs rely substantially on antennas, which play an important role in ensuring their effectiveness and ubiquity. WBAN antennas are classified based on the mode of communication: in-body, on-body, and off-body. In this paper, the state-of-the-art of implantable and ingestible antennas has been studied for in-body communication antennas. Wearable or textile antenna trends are discussed and analyzed in terms of on-body and off-body communications. WBAN antennas are also studied in terms of structure, materials, characteristics, and fabrication techniques. Also, the design challenges have been addressed with potential solutions.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Secure Routing and Intrusion Detection System Using Genetic Siberian Tiger Optimization-Based Hybrid Deep Learning Approach in MANET","authors":"Pamidi Naga Venkata Siva Kumar,&nbsp;Gnanasambandam Singaravel","doi":"10.1002/dac.70550","DOIUrl":"https://doi.org/10.1002/dac.70550","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile Ad Hoc Networks (MANETs) are self-organizing wireless networks categorized by dynamic topology, high susceptibility to malicious attacks, and decentralized control. Existing studies have mainly addressed secure routing and intrusion detection distinctly, resulting in inadequate adaptability in highly dynamic and resource-constrained environments. To address these limitations, this research proposes a unified secure routing and intrusion detection framework based on Genetic Siberian Tiger Optimization (GSTO) combined with a hybrid deep learning model. The GSTO algorithm accomplishes optimal route selection by considering multiple quality-of-service parameters like node energy, distance, link quality, and trust, thereby confirming secure and energy-efficient communication. For intrusion detection, non-negative matrix factorization (NMF) is used for dimensionality reduction and feature refinement, pursued by a depthwise separable convolutional gated recurrent unit (DWC-GRU) model to efficiently obtain spatial and temporal dependencies in network traffic with condensed computational cost. The incorporation of optimization, feature selection, and lightweight deep learning improves scalability, diminishes false alarm rates, and advances routing consistency. Experimental outcomes on the UNSW-NB15 and NSL-KDD datasets validate that the proposed model attains detection accuracies of 99.55% and 99.64%, correspondingly. Along with condensed false positive rates and lower energy consumption, the proposed model provides a scalable and secure solution for MANET environments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis of NOMA-Based Hybrid Cognitive Network With Rotating UAV for Enhanced Spectrum Efficiency","authors":"Jayanta Kumar Bag,&nbsp;Chanchal Kumar De,&nbsp;Abhijit Chandra","doi":"10.1002/dac.70539","DOIUrl":"https://doi.org/10.1002/dac.70539","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates the performance of a cooperative non-orthogonal multiple access (NOMA)-based hybrid cognitive radio (CR) network aided by rotating unmanned aerial vehicle (UAV). RF energy harvesting, Nakagami-m fading between terrestrial users, and line-of-sight (LOS) linkages between UAV and different users have been taken into account for this proposed CR network. UAV rotates periodically over a circular region of interest (CROI) to locate ideal primary user (PU) bands, which is used by UAV to establish links between secondary users (SU) that experience severe fading on direct connections. All sources and the UAV make sensing judgments and transmit them to the fusion center (FC) in order to collaboratively determine the condition of a PU band using AND rule. According to the fusion center result, SU and UAV make adaptive transition between overlay and underlay protocols in order to improve spectrum efficiency (SE) of the system. The secondary throughput for NOMA-based rotating UAV-equipped CR networks under conditions of imperfect successive interference cancellation (i-SIC) has been evaluated through closed-form mathematical expressions. The proposed system throughput is examined for different network factors, like the radius of the UAV trajectory, height of UAV, and harvesting angle. Lastly, every analytical closed form equation has been validated using the MATLAB simulation testbed.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Deep Reinforcement Learning and Convex Optimization for RIS-Assisted Cell-Free NOMA Networks","authors":"Sinh Cong Lam,&nbsp;Bach Hung Luu,&nbsp;Trong Minh Hoang","doi":"10.1002/dac.70557","DOIUrl":"https://doi.org/10.1002/dac.70557","url":null,"abstract":"<div>\u0000 \u0000 <p>Cell-free networks are a promising technology for 6G mobile systems in which all active access points (APs) coherently convey user signals to increase diversity and improve user performance. The integration of reconfigurable intelligent surfaces (RISs) and non-orthogonal multiple access (NOMA) further enhances spectral and energy efficiency by providing auxiliary reflection paths and power-domain multiplexing. However, the joint optimization of AP activation and power allocation constitutes a mixed-integer non-convex problem with an exponentially growing action space, which is intractable for conventional optimization techniques. To address this challenge, we propose a hybrid framework that combines deep reinforcement learning with convex optimization for cell-free NOMA networks with RIS assistance. In this framework, a double deep Q-network (DQN) with prioritized experience replay selectively activates APs and RIS units, whereas second-order cone programming (SOCP) optimally allocates transmission power for each configuration. This decomposition reduces the action space from exponential to linear complexity, and thus make the learning process tractable. Simulation results demonstrate that the proposed mechanism achieves significant power reduction while maintaining higher QoS satisfaction compared to baseline ones.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Model of Content-Aware Routing and Caching in WSN Using Heuristic Approach–Aided Multiobjective Derivation With Optimized Path","authors":"Impa Bennuru Huliyappa Gowda,&nbsp;Shanthi Pichandi Anandaraj","doi":"10.1002/dac.70546","DOIUrl":"https://doi.org/10.1002/dac.70546","url":null,"abstract":"<div>\u0000 \u0000 <p>A particularly critical and contentious issue in WSNs is energy conservation, as sensors expend its energy across various functions including communication, mobility, clustering, computation, localization, and administration. The primary sources of energy consumption at the sensor node level are categorized into the following three main activities: sensing by the sensor module, processing by the CPU, and transmitting data via the radio link. Intensive sensing activities, demanding computational processes and frequent data transmissions not only deplete the network's lifespan but also compromise the availability of resources for additional tasks. This paper addresses the pivotal challenge of maximizing the lifetime and energy efficiency of WSNs through the deployment of intelligent, content-aware routing and caching algorithms. The proposed solution emphasizes a cluster-based framework for data transmission and processing, aiming to optimize energy utilization across the network. To achieve this, the novel Hybrid Position of Sailfish and Whale Optimization (HPSWO) is engaged to generate the shortest paths. In order to further analyze the effectiveness of routing and caching, constraints like link utilization, traffic demand, hop counts, average cache size, and accumulated link quality are considered for deriving the multiobjective function. At last, the overall routing process of the developed framework is evaluated using various performance metrics to demonstrate its better efficiency.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IF-UAV: An Intelligent Federated Framework for Energy-Efficient Data Collection in UAV-Assisted IoT Systems","authors":"Ali Kadhum M. Al-Qurabat","doi":"10.1002/dac.70553","DOIUrl":"https://doi.org/10.1002/dac.70553","url":null,"abstract":"<div>\u0000 \u0000 <p>Unmanned aerial vehicles (UAVs) have emerged as efficient mobile data collectors capable of mitigating the communication overhead and uneven energy consumption inherent in large-scale wireless sensor networks (WSNs). However, current clustering, cluster-head (CH) selection, and UAV routing strategies typically operate in isolation, limiting adaptability, scalability, and long-term energy efficiency. This paper proposes IF-UAV, an integrated intelligent framework that enables energy-aware clustering, hybrid machine learning–driven CH selection, continuous-action UAV trajectory optimization, and communication-efficient federated data aggregation. First, an enhanced K-Medoids with Adaptive Learning Rate (KMA-LR) algorithm forms energy-balanced clusters that dynamically respond to residual energy gradients. Next, a Hybrid GWO-CNN model combines global metaheuristic search with learned node-suitability predictions for high-accuracy CH selection. For mobility, a Deep Deterministic Policy Gradient (DDPG) agent learns smooth and energy-optimal UAV trajectories that adapt to network conditions in real time. To reduce communication overhead, FedFusion, an edge-assisted federated aggregation scheme using parameterized autoencoders, compresses local cluster information while preserving essential structure. Simulation results demonstrate significant performance gains over LEACH, PEGASIS, FCM, PSO-Clust, and ACO-UAV, including a 40%–60% extension in early stability, over 30% reduction in UAV flight distance, and up to 50% throughput improvement. The findings confirm that tightly integrated clustering, CH selection, learning-based routing, and federated fusion provide a scalable and energy-efficient solution for UAV-assisted IoT deployments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 12","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}