Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
Abstract
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.
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
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) 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
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.
Md. Yasin Arafat, M. Drieberg, A. A. Aziz et al.· Engineering Research Express· 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
This research proposes Multi-Objective Di-Strategy GrayLag Goose Optimization (MO-DSGGO) to optimize energy efficiency and reduce delay via effective clustering and routing in WSN that demonstrates more reliable and efficient network utilization.
E. Madhankumar, K. Selvaraj, Dae-Ki Kang· Scientific Reports· 0 citations