Nonlinear Fuzzy Optimization Technique for WSN Based on Quadratic Programming

Abstract

The wireless sensor network (WSN) is a network of intelligent sensor nodes that are linked to a base station (BS) to receive and deliver data packets from one node to another by providing environmental information. BS acts as a router, gathering information from sensor nodes and routing it to the appropriate destination node. Because the WSN is scattered in nature, its parameters change regularly, dependent on the time period. It generates several sorts of noise and interference. As a result, network life span and network metrics are deteriorating. As a result, an effective optimization technique that intelligently models the network is required. In this study, a fuzzy nonlinear optimization approach is provided for improving network life span and network metrics. The fuzzy nonlinear optimization technique used in this paper is a hybrid of the fuzzy inference system and quadratic programming. Quadratic programming is concerned with linear and nonlinear formulations that are based on objective functions and constraints. With the use of a fuzzy membership function, fuzzy logic is used to estimate and decrease uncertainty and imprecise information efficiently. To forecast the improvement in metrics, the suggested strategy is simulated and evaluated in the LINGO optimization software.