Power Efficient Framework for Resource Utilization in Fog Computing

Abstract

Wireless Sensor Networks (WSNs) are essential for a number of applications, including industrial automation and environmental monitoring. Optimizing power consumption to increase the network's operational efficiency and lifespan is one of the main issues with WSNs. power optimisation techniques in WSNs are compared with an emphasis on routing protocols and energy-saving protocols. Different approaches, including clustering algorithms, nature-inspired optimisation methods, and cross-layer optimisations, have been investigated to achieve power optimisation. The effectiveness of various algorithms, including ant lion optimisation (ALO), k-means clustering, and other conventional techniques, is investigated in this study in terms of lowering energy consumption and extending network lifetime. The proposed study examines how these algorithms affect the various WSN layers, especially the end device layer and the fog layer. By contrasting the proposed algorithm with well-known methods like energy-efficient cross-layer sensing clustering, Distributed and Morphological Operation-based Data Collection Algorithm (DMOA), and trust-based secure routing, a thorough evaluation of the proposed algorithm is carried out. The comparative study reveals the significant improvements made by the proposed power optimisation algorithm through extensive simulations and performance analysis. The outcomes show how effective it is at cutting energy use and improving overall network performance. The study also clarifies the trade-offs between different power optimisation strategies and their effects on network scalability, reliability, and latency. This study adds to the growing body of information on power optimisation for WSNs and offers helpful tips for academics and industry professionals working in the area. The results emphasise the significance of choosing suitable power optimisation methods based on particular application requirements and network characteristics.