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.
Abstract
Wireless Sensor Networks (WSNs) are extensively employed in military surveillance, industrial automation, environmental monitoring, and healthcare applications; however, their performance is severely constrained by limited battery energy, restricted computational capability, communication overhead, and dynamic network topology variations. To address these challenges, this work proposes an energy-efficient clustering and routing framework based on Density-Based Adaptive Soft Clustering (DBASC), Adaptive Lotus Effect Optimization Algorithm (ALEOA), and Improved Greylag Goose Optimization (IGGO). Initially, DBASC organizes sensor nodes into adaptive and balanced clusters by considering node density and spatial proximity, thereby reducing redundant communication and improving cluster stability. Subsequently, ALEOA performs optimal cluster head (CH) selection by exploiting the adaptive and self-organizing characteristics inspired by the lotus effect, enabling efficient energy balancing, adaptive CH rotation, and minimized communication overhead under dynamic WSN conditions. Furthermore, IGGO identifies reliable and energy-aware routing paths by utilizing the cooperative navigation and coordinated movement behavior of greylag geese, which supports stable multi-hop communication, rapid convergence, and efficient route exploration between cluster heads and the base station. The proposed hybrid ALEOA-IGGO framework jointly optimizes clustering and routing operations to minimize energy dissipation, improve packet forwarding reliability, and extend network lifetime. Extensive simulation results demonstrate that the proposed approach achieves low energy consumption of 10 mJ, high throughput of 0.95 Mbps, prolonged network lifetime of 8000 rounds, reduced end-to-end delay of 1.3 ms, and a packet delivery ratio of 99%. Comparative analysis further confirms that the proposed framework outperforms several state-of-the-art approaches, including F-PSO, F-GWO, BOA-ACO, OAFS-IMFO, and QPSOFL, in terms of energy efficiency, routing stability, and quality-of-service performance. The obtained results validate that the proposed ALEOA-IGGO framework effectively addresses the critical challenges of energy-aware clustering and reliable routing in large-scale and dynamic WSN environments.
Wireless Sensor Networks (WSNs) play a critical role in various applications, including environmental monitoring, healthcare, and industrial automation. However, these networks face significant challenges related to energy efficiency, fault tolerance, and reliable data transmission, particularly in dynamic environments. Existing clustering and routing techniques often fail to ensure seamless fault tolerance and energy optimization simultaneously. Many traditional approaches lack robust mechanisms to handle Cluster Head (CH) failures, resulting in reduced network stability and shorter operational lifetimes. To address these limitations, this study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach that enhances energy efficiency and network resilience. The methodology involves optimized CH and Backup CH (BKCH) selection, considering parameters such as residual energy, distance to the base station, and network density. Additionally, Ant Colony Optimization (ACO) is employed to dynamically adjust pheromone levels for energy-efficient routing, ensuring reliable intra-cluster and inter-cluster communication. Simulation results demonstrate that the FT-BKCH-ACO approach significantly improves energy consumption by 23.2%, packet delivery ratio by 10.5% and end-to-end delay by 17.8% compared to existing models. The inclusion of backup CHs ensures seamless communication even in the event of node failures, making this method highly suitable for IoT-enabled WSN applications. The proposed approach bridges the gap between fault-tolerant clustering and adaptive routing, offering a scalable and energy-efficient solution for large-scale sensor networks.
Manisha Chandrakar, A. Hasan· International Journal of Wir...· 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
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
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
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