Artificial Intelligence Based Optimal Transmission-Distance Acquirement Mechanism to Address Longevity of Wireless Sensor Networks

Abstract

Prolonging the lifespan of Wireless Sensor networks (WSN) is a subject of concern and research is on to mitigate this problem. Energy efficiency, Energy balance, and Energy depletion minimization are the three parameters that must be properly planned and must be properly evaluated in order to have a prolonged WSN lifespan. A proper transmission strategy must be in place to address this issue. This work suggests a transmission plan based on the optimum distance and uses Ant Colony Optimization (ACO) a Swarm Intelligence methodology that is seen as an Artificial Intelligence technique. There are two concepts introduced for the achievement of local optimal distance and are energy efficiency and balance for most of the distances in the networks of wireless sensors. The scheme to acquire the global optimal distance is to get the reduction in energy depletion for the sensor-based nodes with less depletion of energy and more usage all across the networks. This is done by using the evaluation method of network longevity. The Network model and Algorithmic design with Simulations developed in this work will validate the aim.