A Fuzzy Inference System and Elephant Herding Optimization for Increasing Survivability in Wireless Sensor Network

Abstract

Wireless Sensor Networks (WSNs) are networks of embedded systems that can sense and transmit data about environmental factors. These sensors, sometimes called as sensor nodes, have a number of drawbacks, including limited data processing capabilities and, most critically, low battery energy. As a result, one of the major challenges in WSNs is developing techniques, hence increasing the networks’ survivability. Based on Fuzzy Inference Systems and Elephant Herding Optimization (EHO), this study provides a new way to assist In order to choose the optimum route, multi-path routing protocols are used. The Fuzzy System determines the low survivability level among the nodes that make up the route and is used to determine the degree of route performance. A comparison is made with various Ant colony methods, such as Relay clustering-based algorithms that have already been investigated on the same topic. Our proposed algorithm, EHO, can obtain more consistent and precise locations, according to simulation findings. The EHO algorithm is used to modify the fuzzy system’s rule base in order to improve the route’s identification strategy and network survival. The simulations demonstrated that the approach is beneficial in terms of Network Survivability when compared to alternative methods, the number of receiving data, and the cost of information received.