Intelligent Technique Based on Enhanced Metaheuristic for Optimization Problem in Internet of Things and Wireless Sensor Network

Abstract

For the last decade, there has been an intensive research development in the area of wireless sensor networks (WSN). This is mainly due to their growing interest in several applications of the Internet of Things (IoT). Several issues are thus discussed such as node localization, a capability that is highly desirable for performance evaluation in monitoring applications. The localization aim is to look for precise geographical positions of sensors. Recently, swarm intelligence techniques are suggested to deal with localization challenge and localization is seen as an optimization problem. In this article, an Enhanced Fruit Fly Optimization Algorithm (EFFOA) is proposed to solve the localization. EFFOA has a strong capacity to calculate the position of the unknown nodes and converges iteratively to the best solution. Distributing and exploiting nodes is a chief challenge to testing the scalability performance. the EFFOA is simulated under variant studies and scenarios. in addition, a comparative experimental study proves that EFFOA outperforms some of the well-known optimization algorithms.