Aug 2026· Engineering Research Express· Vol 8, pp. 165326· 0 citations· 41 references
Physics
TL;DR
A distributed K-means clustering algorithm integrated with a modified low energy adaptive clustering hierarchy-improved energy distance protocol for clustering and CH selection achieves improved network lifetime, energy balancing, and scalability while maintaining competitive throughput performance compared with existing protocols.
Abstract
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.
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.
P. Kumbhar, A. Naik· International Journal of Ele...· 0 citations
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 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.
Vishwajit K. Barbudhe, Shruti Dixit· International journal of com...· 0 citations
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
Sushma Priyadarshini, A. T· International journal of com...· 0 citations
A cluster-based proactive routing protocol designed for three-tier energy-heterogeneous WSNs, aiming to enhance network lifetime and energy efficiency, and modified to address the challenge of long transmission distances for low-power nodes by strategically deploying them in specific network zones.
F. A. Mohamed, E. Hassan, M. Dessouky et al.· Scientific Reports· 0 citations
Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, smart agriculture, and Internet of Things applications, but their performance is constrained by limited battery capacity, uneven energy consumption, and inefficient routing. To address these issues, this paper proposes THGCDTR-RP, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection. The proposed CH selection strategy jointly considers residual energy, node centrality, intra-cluster compactness, and cluster-size balance, while an energy-aware minimum spanning tree mechanism constructs multi-hop routing paths among CHs and the base station (BS). Extensive MATLAB-based simulations under different network sizes, node densities, and BS locations show that THGCDTR-RP consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO. For example, in the 50×50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$50 \times 50$$\end{document} network size, THGCDTR-RP increases the number of packets received at the BS by 144.4%, 83.3%, 89.7%, 77.4%, and 93.1% compared with LEACH, LPSO, LACO, LGWO, and WOA-P, respectively. It also improves the first-node-death round by 271.5%, 71.9%, 78.2%, 65.2%, and 103.0%, and extends the all-node-death round by 17.78%, 44.46%, 46.63%, 35.22%, and 51.17% over the same baselines, respectively.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
The obtained results validate that the proposed hybrid ALEOA-IGGO framework effectively addresses the critical challenges of energy-aware clustering and reliable routing in large-scale and dynamic WSN environments.
D. Faridha Banu, N. Kumaresan· Scientific Reports· 0 citations