Distributed adaptive sampling by rechargeable sensor nodes with limited battery capacity

Energy harvesting is a promising technology for extending the lifetime of sensor networks with the restrictions of limited battery energy. One of the main challenging issues is to maximize the sampling rates of all sensor nodes. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize overall network utility. To solve the problem, we firstly propose an adaptive energy allocation scheme for each node to manage its energy use in an efficient way. Then we develop a distributed sampling rate control (DSRC) algorithm to obtain the optimal sampling rate. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are performed to demonstrate the efficiency of our algorithm.