Adaptive data collection approach for periodic sensor networks

Data collection from unreachable terrain and then transmit the information to the sink is a fundamental task in periodic sensor networks. Energy is a major constraint for this network as the only source of energy is a battery with limited lifetime. Therefore, in order to keep the networks operating for long time, adaptive sampling approach to periodic data collection constitutes a fundamental mechanism for energy optimization. The key idea behind this approach is to allow each sensor node to adapt its sampling rates to the physical changing dynamics. In this way, over-sampling can be minimised and power efficiency of the overall network system can be further improved. In this paper, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. The proposed method was successfully tested in a real sensor data set.