Aug 2026· International Journal of Wireless and Microwave Technologies· Vol 16, pp. 395-412· 0 citations
TL;DR
This study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach, offering a scalable and energy-efficient solution for large-scale sensor networks.
Abstract
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.
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.
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.
S. Priyadarshini, A. T.· International journal of com...· 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 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
Simulation analyses conducted in NS-3 demonstrate that the proposed integrated, multi-tier optimization framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
Jitendra Kumar, Debasis Mandal· International journal of res...· 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
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