A Perspective View of Bio-Inspire Approaches Employing in Wireless Sensor Networks

In this chapter, we discuss a bio-inspired computational model that utilizes heuristic techniques. This model is robust and possesses optimization capabilities to address obscure and substantiated problems. Swarm intelligence is an integral part of this bio-inspired model, functioning within groups. The nature of these algorithms is non-centralized, drawing inspiration from self-management to solve real-life complex computational problems. Examples include the traveling salesman problem, the shortest path problem, optimal fitness functions, security systems, and the use of optimal computational resources in various areas. The deployment of a Wireless Sensor Network involves a group of sensor nodes, typically implemented at remote locations to observe environmental behaviors. However, these sensor nodes operate on batteries, making replacement or recharge nearly impossible once deployed. Energy is a crucial resource for wireless sensor networks to extend their lifetime. While numerous concepts have been proposed to improve the lifespan of wireless sensor networks, many issues in Wireless Sensor Networks (WSN) are designed as multi-dimensional optimization problems. The bio-inspired model offers a solution to overcome these challenges. Swarm Intelligence proves to be a simple, efficient, and effective computational methodology for addressing various issues in wireless sensor networks, including node localization, clustering, data aggregation, and deployment. The Swarm Intelligence methodology encompasses several algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Reactive Search Optimization (RSO), Fish Swarm Algorithm (FSA), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Differential Evolution (DE). This chapter introduces Swarm Intelligence-based optimization algorithms and explores the impact of PSO in wireless sensor networks.