Aug 2026· International Journal of Electronics and Communication Engineering· 0 citations· 52 references
Abstract
Wireless Sensor Networks (WSNs) use resource-constrained sensor nodes to continuously monitor ambient conditions and serve as data sinks for various Internet of Things (IoT) applications. However, maximizing Energy Efficiency (EE) while maintaining reliable data delivery remains a significant challenge. Existing clustering and routing algorithms struggle with issues such as uneven energy consumption, premature node failures, and poor network performance. Additionally, many existing metaheuristic schemes are not adaptable to dynamic network environments, which leads to ineffective energy management. To address the above issues and enhance the energy efficiency of IoT-based WSN, this research introduced a novel Energy-Aware Cluster-Optimized Intelligent Routing (EACO-IR) protocol. The EACO-IR protocol performs in three stages: stable cluster formation, Cluster Head (CH) selection, and energy-aware route finding. At first, stable clusters are formed using the Hybrid Fuzzy–Density Adaptive Kronecker Clustering (HF-DAC) algorithm. Subsequently, CHs are optimally selected using the Mutation-Enhanced Armadillo–Devil Optimization (MEADO) based on a multi-objective function for the Base Station (BS). Finally, the Multi-level Energy-Aware Attention Transformer- Based Reinforcement Learning is introduced to create intra and inter-cluster data travel ways to minimize communication overhead from SNs to the BS. Experimental results show that the proposed protocol yields an average throughput of 4 Mbps, an average Packet Delivery Ratio (PDR) of 98.71%, and an end-to-end latency of 0.06 seconds, outperforming current state-of-the-art clustering and routing algorithms. Ultimately, this framework establishes a highly adaptable template for deploying self-optimizing, long-lasting IoT architectures capable of supporting real-time data streaming without premature network degradation.
Wireless Sensor Networks (WSNs) are generally constrained by their built-in energy limits, leading to very short operational lifespans. Moreover, the node energy reserves of homogeneous WSNs are far lower than those of heterogeneous WSNs. For this reason, energy-efficient routing is a core optimization direction for this type of network. The core research goal of this paper is to optimize dynamic routing protocols to improve the performance of the classic LEACH protocol in heterogeneous WSNs. We propose to enhance energy efficiency through a real-time adaptive scheme, which relies on two core methods: a cluster head selection mechanism based on the residual energy criterion, and a priority hop count strategy. It is a widely agreed conclusion in the field that hierarchical routing for heterogeneous WSNs outperforms flat routing and location-based routing in both energy efficiency and scalability. This paper also implements three supporting technologies: topology control, adaptive routing, and power management. These technologies reduce energy consumption through multi-hop clustering and optimized relay selection, ultimately supporting more flexible routing strategies, extending the network's operational cycle, and meeting the application requirements in the Internet of Things and information technology fields.
Vishwajit K. Barbudhe, Shruti Dixit· International journal of com...· 0 citations
Wireless sensor networks (WSNs) are increasingly prominent due to their applicability across diverse domains. WSNs represent the future of intelligent sensing, offering robust, flexible, and monitoring solutions to support the proliferation of the Internet of Things (IoT), in applications such as smart cities, autonomous systems, digital twins, bio-integrated sensing, and large-scale climate monitoring. Although numerous clustering-based routing protocols have been proposed, achieving energy-efficient clustering and cluster head (CH) selection remains a significant challenge. Additionally, most of the approaches are either non-adaptive or centralized. Addressing these challenges, this paper proposes a distributed K-means clustering algorithm integrated with a modified low energy adaptive clustering hierarchy-improved energy distance (DK-means-LEACH-IED) protocol for clustering and CH selection. The proposed method starts with a timer-based selection of initial centroid nodes, then applies K-means clustering to those nodes. CHs are then selected using an adaptive, weighted energy-distance function that accounts for nodes’ residual energy and their distance from the cluster centroid. The proposed protocol is implemented in OMNET++ using the Castalia framework, followed by a comprehensive performance evaluation and comparison with LEACH and the centralized Energy-driven K-means-based LEACH routing protocols. The results demonstrate that DK-means-LEACH-IED achieves improved network lifetime, energy balancing, and scalability while maintaining competitive throughput performance compared with existing protocols. The proposed protocol improves network stability during the critical depletion phase by extending the 70% node survival lifetime by up to 14.04% and achieves throughput improvement of up to 49.18% compared with the evaluated benchmark protocols, demonstrating its ability to balance energy utilization and communication efficiency for emerging IoT applications.
Md. Yasin Arafat, M. Drieberg, A. A. Aziz et al.· Engineering Research Express· 0 citations
Wireless sensor networks (WSNs) have gained significant attention in recent research due to their potential in various applications. However, energy conservation remains a critical challenge, as the nodes within these networks operate on non-replaceable batteries. This paper introduces a cluster-based proactive routing protocol designed for three-tier energy-heterogeneous WSNs, aiming to enhance network lifetime and energy efficiency. The proposed protocol incorporates a modified threshold-based cluster head (CH) selection mechanism, which considers both the energy levels and distances of sensor nodes (SNs) relative to the base station (BS). This approach prioritizes nodes with higher energy and closer proximity to the BS, increasing their likelihood of being elected as CHs. Furthermore, this protocol minimizes the formation of unnecessary CHs, particularly for SNs with low remaining energy, thereby reducing energy waste. Moreover, the proposed protocol is modified to address the challenge of long transmission distances for low-power nodes by strategically deploying them in specific network zones, thus further optimizing energy efficiency. Simulation results demonstrate that the modified protocol outperforms existing protocols such as SEP, IMPLEACH, and EEECA-THWSN in terms of total network lifetime, stability period (first node death), half-node death, last node death, throughput, and normalized remaining energy. Numerically, the proposed protocol with normal zone improves network performance, achieving an increase of up to 27% in network lifetime and throughput compared to other protocols.
F. A. Mohamed, E. Hassan, M. Dessouky et al.· Scientific Reports· 0 citations
Wireless Sensor Networks (WSNs) find application in environmental monitoring, industrial automation, health care, and the Internet of Things. However, energy restrictions of nodes and faulty routing mechanisms affect network lifetime and communication. To address these issues, this paper presents an Energy-Efficient Cluster-Based Multi-Hop Routing (E2CMR) protocol, an enhanced version of the LEACH routing protocol. The framework suggests RSSI-based methods for node layering, adaptive selection of cluster heads, and energy-aware multi-hop routing to balance energy consumption. Using residual energy, initial energy, and average network energy, an enhanced threshold function is developed to select an optimal cluster head for efficient rotation. Moreover, energy-aware route establishment reduces transmission distance and communication overhead, thereby enhancing packet delivery performance. The proposed protocol was validated in an NS-2 simulation using 100 similar sensor nodes. The following protocols were compared with ECI-LEACH, ESCH, and the ICSI routing protocol. The experimental results show that E2CMR achieves a maximum throughput of 370 kbps, delaying the first node's death to the 30th round. In the 50th round, it maintains 26% residual energy, routing overhead reduces to 26, and network lifetime improves by 38%. The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
Sushma Priyadarshini, Anuradha T· International journal of com...· 0 citations
Internet of Things (IoT) has transformed modern life by enabling interconnected systems through distributed sensor nodes that collect data from remote locations such as agriculture, wildlife monitoring, and forestry. However, challenges arise due to the limited battery capacity of sensor nodes, affecting network lifetime and energy efficiency. To improve energy conservation and extend network longevity, clustering techniques play a vital role. Although various clustering protocols have been proposed, many still face the "energy-hole" problem caused by inefficient Cluster Head (CH) selection methods. CHs are responsible for managing intra-cluster communication and tend to exhaust energy rapidly, especially those near the base station due to excessive relay traffic. To address this, a Multi-Objective Particle Swarm Optimization (MOPSO) technique is proposed for CH selection to ensure better energy efficiency and intra-cluster distance in IoT-based wireless sensor networks (WSNs). This technique operates in two phases: cluster formation and CH selection. Euclidean distance is used for clustering member nodes, and high-energy nodes are adaptively chosen as CHs using the MOPSO method. Simulations conducted in MATLAB R2019a validate that the proposed MOPSO outperforms existing algorithms such as LEACH, LEACH-FL, LEACH-FC, KM-PSO, EECHS-ARO, HSWO, and EECHIGWO by mitigating premature convergence and enhancing CH selection accuracy. The proposed technique achieves improvements of 10.02% in packet delivery rate and 9.68% in network lifetime. Results indicate that 105 nodes remain active after the final simulation round, with lower average energy consumption of 0.0570 Joules.
Monisha Gupta, Chandrasekar Vadivelraju· Review of Computer Engineeri...· 0 citations
An Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity is proposed.
Dabbeta Ganapathi Dabbeta Ganapathi, Halavath Vijaya Halavath Vijaya, P. K. P Kavitha· International Journal of Sci...· 0 citations