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An energy efficient cooperative optimal harvesting algorithm for Mobile Sensor Networks

We research into using mobile sensor networks to harvest physical quantities that emanate from sources and are distributed in space in hazardous environments. Examples are temperature, toxic emissions and pollutions. Mobile sensors are more advantageous than static sensors in many ways such as easy re-deployment and environmental friendliness. However, they are usually deployed at low node densities with equally spaced nodes. As a result, the reconstructed distribution maps are highly distorted. Our approach attacks the problem from the source, by mobilizing the sensors to harvest data with high information content cooperatively and intelligently. As a result, we achieve 3.6 times in error reduction relative to an equally distributed grid. Moreover, in comparison with the random waypoint and Broyden-Fletcher-Goldfarb-Shanno methods, we achieve at least 60% more reduction in energy usage by consuming 70% lesser energy. Finally, our approach has a resource utilization efficiency of 50 times that of static sensors.