Search Results (128)

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article

Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy consumption with deep learning–assisted cluster-head (CH) selection. First, an analytical framework is developed to determine the distance-constrained CH eligibility region and the optimal number of clusters, thereby minimizing redundant transmissions and balancing energy consumption. Then, a genetic algorithm (GA) is used to determine the best cluster head configuration. These configurations are then trained by a ResNet-50 deep network and averaged to reduce noise, allowing for real-time cluster head prediction without repeatedly performing expensive heuristic optimization, resulting in more steady performance. Extensive simulations under various network scales demonstrate that HEOCP extends network lifetime by up to 60% compared with conventional LEACH and GA-based approaches, effectively delaying the first-node death and improving overall energy efficiency. Furthermore, the hybrid GA–ResNet framework exhibits high scalability and computational efficiency, making it suitable for large-scale IoT deployments. The results confirm that integrating analytical energy modeling with deep learning provides a powerful and sustainable paradigm for intelligent energy management in future IoT-enabled WSNs. Full article

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively. Full article

With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a Minimum Connected Dominating Set (MCDS) problem. However, since MCDS is NP-complete on general graphs, existing heuristic and exact algorithms suffer from limited coverage, poor connectivity, and high computational cost. To address these issues, we propose TGN-MCDS, a novel algorithm built upon the Temporal Graph Network (TGN) architecture, which leverages graph neural networks for cluster head selection and efficiently learns time-varying network topologies. The algorithm adopts a multi-objective loss function incorporating coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy to guide model training. Simulation results demonstrate that TGN-MCDS rapidly achieves near-optimal CH sets with full node coverage and strong connectivity. Compared with Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB) methods, TGN-MCDS produces fewer and more stable CHs, significantly improving cluster stability while maintaining high computational efficiency for real-time operations in large-scale FANETs. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

Energy efficiency and prolonged network lifetime remain central challenges in wireless sensor networks (WSNs). Clustering, cluster-head (CH) selection, and routing are key to addressing these issues because they directly affect energy consumption, data delivery, and overall network stability. This paper introduces a novel hybrid protocol, PUMA-GRID, which integrates the recently proposed Puma Optimization Algorithm with a grid-based multi-hop routing framework. Unlike traditional schemes, PUMA-GRID adaptively balances exploration and exploitation during CH selection while learning energy-efficient data-forwarding paths through grid-based routing. This combination improves adaptability, scalability, and load balancing, which distinguishes PUMA-GRID from the primary metaheuristic competitor AEO-GRID, as well as earlier AEO, LEACH, and static PUMA variants. The fitness function for CH election incorporates intra-cluster distance, distance to the base station (BS), and residual energy, with adjustable weights that enable flexible adaptation to different deployment scenarios. Simulation experiments were performed under various BS placements and weight configurations to assess the influence of each factor. The results show that the impact of the weights depends strongly on BS location and that careful tuning is required to balance efficiency and fairness. Across all scenarios, PUMA-GRID demonstrates superior performance compared with LEACH, AEO-based schemes, and other PUMA variants. Overall, PUMA-GRID provides an effective and scalable solution for sustainable, energy-aware operation in WSNs. Full article

Wireless sensor networks (WSNs) have emerged as vital technologies for safety-critical applications due to their flexibility, scalability, and reliability. However, existing models such as LEACH, SEP, and TSEP exhibit limitations in energy efficiency, stability, and adaptability to heterogeneous node conditions. To address these gaps, this research proposes a multilevel heterogeneity-based WSN model that optimizes cluster-head (CH) selection and energy utilization for enhanced network performance. Simulations were conducted in MATLAB under unequal energy level variations and compared with established protocols. Results demonstrate that the proposed model consistently outperforms existing approaches in terms of network lifetime, throughput, and energy efficiency. Statistical analysis reveals a best-case improvement of approximately 9000 rounds and a worst-case gain of about 3000 rounds when four heterogeneity levels are employed, compared to three levels. These findings highlight that both the degree of energy diversity and the distribution of energy nodes across levels are crucial for achieving optimal performance. Overall, the proposed architecture significantly enhances reliability, stability, and energy efficiency, making it well-suited for disaster management and other safety-critical applications. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments. Full article

WSNs are an important component of the Internet of Things (IoT), and the research on their routing protocols has always been a hot topic in academia. However, in ARTFMRs’ collaborative operation along railway lines, there are common problems such as energy holes, high latency, and uneven energy consumption in LWSNs. To address these issues, this paper proposes a genetic algorithm-optimized energy-aware routing protocol (GAECRPQ). Firstly, a non-uniform deployment strategy of three-line isosceles triangles is constructed to enhance coverage and balance node distribution. Secondly, an energy–distance adaptive weighting mechanism based on a genetic algorithm is introduced for cluster head (CH) selection to reduce energy consumption in hotspots and extend the network lifetime. Finally, a task-aware TDMA dynamic time slot allocation method is proposed, which incorporates the real-time task status of ARTFMRs into communication scheduling to achieve priority transmission under latency constraints. The simulation results show, that compared with six unequal clustering protocols—EADUC, EAUCA, EBUC, EEUC, LEACH, and LEACH-C—the three-line isosceles triangle deployment has a wider coverage area, and the GAECRPQ protocol increases the network lifetime by 7.4%, the lifetime by 40%, and reduces the average latency by 55.77%, 53.07%, 47.61%, 39.87%, 52.08%, and 50.48%, respectively. This verifies that GAECRPQ has good performance in terms of network lifetime and energy utilization efficiency, providing a practical solution for the collaborative operation of ARTFMRs in railway maintenance scenarios. Full article

Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided by a dual-metric fitness function combining residual energy and intra-cluster distance. EEL-GA is evaluated against EEL variants using Particle Swarm Optimization (PSO), Differential Evolution (DE), and the Artificial Bee Colony (ABC) across key performance metrics, including energy efficiency, packet delivery, and cluster lifetime. Simulations using real environmental data confirm EEL-GA’s superiority in sustaining energy, minimizing delay, and improving network stability, making it ideal for smart forestry and mission-critical WSN deployments. The model also incorporates environmental dynamics, such as temperature and humidity, enhancing its robustness in real-world applications. These findings support EEL-GA as a scalable, adaptive solution for future energy-aware 3D WSN frameworks. Full article

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by balancing energy consumption among sensor nodes. However, HEED relies on simple heuristics for cluster-head (CH) selection, which may not fully exploit the complex spatiotemporal patterns in node energy and topology. This paper introduces a novel protocol, Deep Learning–Hybrid Energy-Efficient Distributed (DL-HEED), which, for the first time, integrates a Graph Neural Network (GNN) into the clustering process. By leveraging the relational structure of WSNs and a comprehensive set of node and network features—including residual energy, node degree, spatial position, and signal strength—DL-HEED enables intelligent, context-aware, and adaptive CH selection. DL-HEED leverages the relational structure of WSNs through deep learning, enabling more adaptive and energy-efficient cluster head selection than traditional heuristic-based protocols. Extensive simulations demonstrate that DL-HEED significantly outperforms classic HEED achieving up to 60% improvement in the network lifetime and energy efficiency as the network size increases. This work establishes DL-HEED as a robust, scalable, and practical solution for next-generation WSN deployments, marking a substantial advancement in the application of deep learning to resource-constrained IoT environments. Full article

Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be used to optimize network management and mitigate the impact of local topology changes on the entire network during collaborative task execution. To address the issue of cluster structure instability caused by the high mobility and vulnerability to interference in UAV networks, we propose a capacity-constrained weighted clustering algorithm for UAV self-organizing networks under interference. Specifically, a capacity-constrained partitioning algorithm based on K-means++ is developed to establish the initial node partitions. Then, a weighted cluster head (CH) and backup cluster head (BCH) selection algorithm is proposed, incorporating interference factors into the selection process. Additionally, a dynamic maintenance mechanism for the clustering network is introduced to enhance the stability and robustness of the network. Simulation results show that the algorithm achieves efficient node clustering under interference conditions, improving cluster load balancing, average cluster head maintenance time, and cluster head failure reconstruction time. Furthermore, the method demonstrates fast recovery capabilities in the event of node failures, making it more suitable for deployment in complex emergency rescue environments. Full article

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article