A comprehensive study of fractal clustering and firefly algorithm for WSN Deployment: Implementation and outcomes

Abstract

Wireless sensor networks (WSNs) have been highly utilized and defensible technology in diverse application areas for data gathering from remote and hard-to-approach regions. Wireless Sensor Networks are substantially important for the real-world applications such as environmental monitoring, surveillance, and smart infrastructure. Network coverage, connectivity and energy savings are significant factors in the WSN deployment. Wireless sensor networks (WSNs) undergo a great deal of crucial challenges such as minimize energy consumption, maximize coverage, and network lifetime improvement. Sensor nodes are energy constrained and deployed in resource-constrained environments for many real-world applications. Low energy usage is hence crucial to prolong network life. Meanwhile, to guarantee the performance of a WSN, it is crucial to ensure data transmission with less energy consumption and full coverage. These challenges are the central focus of this work, requiring scalable and efficient deployment strategies. In this paper, a complete survey study on optimization technique for deployment of WSN to improve network performance and resource utilization is offered. The paper also suggests a new algorithm named as Fractal Clustering Based Firefly Deployment Algorithm which is particularly designed for the deployment of sensor nodes deployed in WSNs. The proposed hybridize method uses the principles of fractal clustering and firefly optimization algorithm to make light-weight, energy efficient and enhanced optimized deployment strategy. To start with, the algorithm makes use of a fractal clustering technique to partition an area of interest into regions that have similar attributes. This clustering determines the areas that are needed to have higher sensor node density requirements — regions where events requiring a critical response or data traffic are high. The algorithm represents each cluster by a virtual firefly. The firefly algorithm is a biologically-inspired swarm intelligence optimization technique, inspired by the flashing behavior of fireflies which stochastically moves through input parameter space to find favorable deployment configurations. In this paper, the efficiency of the algorithm is verified by simulating the proposed algorithm using MATLAB2020 and comparing it with other deployment strategies. This analysis shows promising results.