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

In this paper, we are going to discuss a bio-inspired computational model, this model uses heuristic techniques, is robust, and has the optimization capability to solve obscure and substantiated problems. Swarm intelligence is part of a bio-inspired model basically it deals in groups. The natures of these algorithms are non-centralized; self-manage inspired to solve real-life complex computational problems such as traveling salesman problems, shortest path problems, optimal fitness functions, security systems, use of optimal computational resources, and many more areas. Deployment of a Wireless Sensor Network contains a group of sensor nodes, basically, nodes are implemented at remote locations to sensor environment behaviors but sensor nodes are operated by batteries and once deployed almost impossible to replace or recharged. Energy is an important fuel for wireless sensor networks to increase the lifetime of the network. The research proposed many concepts to improve the life span of wireless sensor networks but many issues in WSN are designed as multi-dimensional optimization. Overcoming these issues can be handled through the bio-inspired model. Swarm Intelligence (SI) is simple, efficient computationally, and effective in many wireless sensor network issues such as node localization, clustering, data aggregation, and deployment. Swarm Intelligence methodology contains many algorithms such as ACO, PSO, RSO, FSA, and some other GA, BFA, and DE. This introduces swarm intelligence-based optimization algorithms and also finds the impact of PSO in the wireless sensor network.