Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 57 references
TL;DR
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
Abstract
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.
Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, industrial automation, and Internet of Things (IoT) applications. However, limited energy resources and hotspot formation near the base station significantly reduce network lifetime and communication efficiency. This study proposes a Hotspot-Aware Load-Balanced Clustering and Routing Protocol (HALBCRP) to improve energy utilization and mitigate hotspot formation in WSNs. The proposed protocol integrates energy-aware cluster formation, hotspot-aware cluster head selection, dynamic load balancing, and fault-tolerant multi-hop routing into a unified framework. Sensor nodes are grouped using an energy-distance-based clustering strategy, while cluster heads are selected based on residual energy, connectivity, traffic load, and proximity to the base station. To prevent energy holes and traffic congestion, overloaded cluster heads dynamically redistribute traffic to neighboring cluster heads. The performance of HALBCRP was evaluated using MATLAB and compared with ITSA-UCHSE, HHDAP, Q-DAEER, and IPSO based on energy consumption, average throughput, number of alive nodes, number of dead nodes, and network lifetime. Simulation results show that HALBCRP achieves lower energy consumption (0.605–1.053 J), maintains a higher number of alive nodes throughout the simulation, and attains an average throughput of 28.62 packets per round. Furthermore, the protocol records superior network lifetime values of FND = 800 rounds, HND = 1150 rounds, and LND = 1340 rounds. These results demonstrate that HALBCRP effectively mitigates hotspot formation, balances network load, and significantly extends network lifetime compared with existing approaches.
Okehie Baslem, I. D. Ikpaya, Onyeanusi Ugochukwu Chimobi et al.· International Journal of Tre...· 0 citations
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
S. Priyadarshini, A. T.· International journal of com...· 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
A novel cluster-based routing protocol that integrates a Fungal Growth Optimizer for adaptive cluster head (CH) selection and a Graph Neural Network for inter-cluster routing, which demonstrates FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.
Huang-shui Hu, Shuo Liu, Qier Kang et al.· Symmetry· 1 citation
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) 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
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