Wireless Sensor networks (WSNs) is collection of various sensor devices and used to capture the environment conditions. Node deployment, limited energy capacity, location of sensor devices, Quality of Services (QoS) and data aggregation are the critical design challenges in WSNs. To overcome these design challenges in WSNs, many techniques have proposed. Swarm Intelligence (SI) is one of the most appropriate techniques to overcome the design challenges in WSNs.SI shows a current computational and behavioral similarity for taking care of disseminated issues. Initially took its motivation from the biological illustrations gave by social insects like ants, termites, honey bees, wasp and bee. In this paper, implement performance analysis of many SI techniques such that Ant Colony Optimization (ACO), Elephant swarm Optimization (ESO), Hnee based optimization (HBO), Particle Swarm Optimization (PSO) and Modified Artificial Bee Colony (MABC) to improve the WSNs lifespan.

Wireless Sensor networks (WSNs), Swarm Intelligence (SI), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), Modify Artificial Bee Colony (IABC)

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