Simulation-based cluster head selection using multi-objective particle swarm optimization for IoT: Trade-off between intra-cluster distance and energy efficiency
Aug 2026· Review of Computer Engineering Research· 0 citations· 46 references
TL;DR
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.
Abstract
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.
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
Wireless sensor networks (WSNs) are crucial in various scientific, industrial and infrastructure monitoring applications, in which the sensing, processing, and communication tasks are performed by the distributed sensors, without the presence of any human beings. WSNs have various drawbacks, among which is the power limitation of sensor nodes, which impacts the life of the network and its reliability, due to their battery power. The main problem is the selection of cluster heads (CHs) as CH selection in conventional protocols may be ad hoc/Random which may cause energy imbalance and network failure, which is very common in conventional protocols. The main idea of this study is to present a multi-cluster WSN model with optimal CH selection (the MO model) in which the CHs are selected by a particle swarm optimization (PSO) algorithm, by solving a multipurpose optimization problem based on three criteria: residual energy, distance between candidate CHs and the base station, and intra-cluster distance. The proposed MO model is tested against three baseline models: single-cluster arbitrary selection (SA), multi-cluster arbitrary selection (MA), and single-cluster optimal selection (SO) and tested over 50, 100 and 200 network sizes. Residual energy, number of alive nodes, total energy consumption, network stability and PSO convergence rate are used to evaluate the performance. In all three sizes of the network, the MO model always consumes less energy than the baseline models (up to 52% in the three networks at 200 nodes, when the number of alive nodes is half that of the MO model at equivalent simulation rounds) and significantly outperforms all three baseline models in terms of normalized size stability (up to 1.00 in the three networks at 100 nodes, compared with at most 0.30 for the baseline models). The obtained results indicate that multi-clustering and joint optimization of CH selection, taking into account multiple energy relevant criteria yields substantial, stable gains in energy-efficiency and life-time of the WSN in comparison to arbitrary CH selection and a single criterion CH selection scheme.
I. Kamil, Goodness Adeleke Adetokun· International Journal of Lat...· 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 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
This WBOAICM scheme uses a fitness function formulated using delay, energy, distance, jitter, and packet forwarding potential to facilitate energy and trustful nodes to be selected as CHs in the clustering process, which minimize energy utilization to extend network lifetime.
Jamuna Rani, D. Santhakumar· 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