Jul 2026· International Journal of Communication Systems· Vol 39· 0 citations· 67 references
TL;DR
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.
Abstract
Internet of things (IoT) facilitates large‐scale data exchange and pervasive connectivity between heterogeneous sensor nodes during its adoption in intelligent decision making and automation applications. Energy and secure cluster‐based routing protocols in IoT are essential for establishing green communication because they minimize energy consumption, prolong network lifetime, and improve data security by selecting potential cluster heads (CHs). These clustering‐based routing protocols implemented using meta‐heuristic swarm intelligent algorithms determine superior near‐optimal solutions, which help in achieving the necessitated degree of quality of service (QoS) in IoT networks. In this paper, Wolf‐Bird Optimization Algorithm–based Intelligent Clustering Mechanism (WBOAICM) is proposed for constructing optimal clusters, which minimize energy utilization to extend network lifetime. 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. It uses the merits of Wolf‐Bird Optimization Algorithm (WBOA), which establishes a better balance between the exploration and exploitation phases such that superior CHs are selected in the network. It then uses Multipolar Fuzzy N‐Soft Preference Ranking Organization Method for Enrichment Evaluations model for determining sensor nodes' trust using direct and indirect interaction to prevent malevolent nodes from being selected as CHs. The simulation results of this proposed WBOAICM scheme under different rounds sustained 15.64%, a greater number of operating IoT nodes. The results confirmed minimized energy consumption of 21.72% and improved packet delivery rate of 19.54% under different base station locations and network area.
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
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.
Monisha Gupta, Chandrasekar Vadivelraju· Review of Computer Engineeri...· 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
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
Energy efficiency remains a major challenge in IoT-enabled wireless sensor networks because sensor nodes operate with limited battery capacity and are often deployed in environments where battery replacement is impractical. Existing clustering and routing protocols frequently optimize cluster-head selection and routing separately, leading to uneven energy consumption, premature node failure, increased routing overhead, and reduced network reliability. This paper proposes an Adaptive Multi-Agent Reinforcement Learning-Assisted Hybrid Bat-Artificial Bee Colony (MARL-BA-ABC) framework for joint cluster-head selection and routing optimization in IoT-enabled wireless sensor networks. The proposed framework combines the Bat Algorithm for local exploitation, the Artificial Bee Colony algorithm for global exploration, and Multi-Agent Reinforcement Learning for adaptive routing. Cluster-head selection and routing are jointly optimized using residual energy, communication distance, traffic load, node density, and link quality. A multi-objective optimization model is formulated to minimize energy consumption, end-to-end delay, routing overhead, and load imbalance while improving packet delivery ratio, residual energy preservation, and network lifetime. Simulation results show that the proposed MARL-BA-ABC framework outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA), and Artificial Bee Colony (ABC), achieving a First Node Death of 2200 rounds, residual energy of 1.32 J, packet delivery ratio of 98.1%, throughput of 410 kbps, and average end-to-end delay of 7.4 ms.
H. S. Mohammed, Poria Pirozmand, S. Memon et al.· Italian National Conference...· 0 citations