An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

 

 

Abstract

Wireless sensor networks (WSNs) are ad-hoc networks in which sensors, that are designed to relay data back to sink nodes and/or Base Stations, are deployed in an area and may be configured in real time. Sensors, however, have limited energy supplies and are often left untouched after deployment, thus making battery replacement very difficult or even impossible. Therefore, energy should be efficiently conserved to extend the WSNs lifetime. One of the existing solutions is to deploy multiple sinks, more capable nodes in comparison to sensors, in the network to increase the coverage area and shorten the communication distance between sensors and sinks. However, this raises the issue concerning which sensors should bind to which sinks in order to avoid overloading particular sinks. In this paper, we devise a Genetic Algorithm based approach to solve the problem of balancing the load of sensors amongst sinks in a multi-sink WSN, while ensuring that the best routes to sinks are found for the sensors that cannot directly reach a sink. We evaluate the performance of our approach and compare it to an existing one using the network simulator NS-2 through measuring several metrics such as the variance of remaining energy among sinks, and energy consumption in sinks. The obtained results show that the proposed approach promising.

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