Optimized Spherical Network for Clustering based Fault-Tolerant and Reliable Routing in IoT-Enabled WSNs
Md. Asif, Muthukumar Jaya hari prabha, P. Santhosh Srinivasan, S. Prabagaran · 2024
This research presents an innovative approach to optimize the performance and longevity of Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs). The proposed Osprey-Based Bowerbird Spherical Convolutional Network (OBSCN-GIA) framework addresses the critical challenges of energy consumption and fault tolerance. The OBSCN-GIA technique utilizes the Osprey Optimization Algorithm (OOA) for cluster head selection and cluster construction, ensuring efficient network organization. The Bowerbird Optimization Algorithm (BOA) is integrated to enhance fault tolerance and network survivability. A Spherical Convolutional Network (SCN)-based routing technique is employed to optimize WSN route selection, while the Geyser-Inspired Algorithm (GIA) fine-tunes SCN settings for optimal data transfer. The proposed approach was evaluated against existing models, demonstrating superior performance in terms of survivability, packet delivery ratio, network longevity, energy consumption, delay, and throughput. The OBSCN-GIA achieved a remarkable throughput of 99.94% and an average energy consumption rate of 0.02J for a network of 100 nodes. This research contributes to the development of more efficient and sustainable WSNs, paving the way for the widespread adoption of IoT technologies in various applications.