Utility maximization of temporally correlated sensing data in energy harvesting sensor networks

Sensing data collection in energy harvesting sensor networks poses great challenges, since energy generating rates of different sensors vary significantly. Most existing studies on efficient data collection assumed that the sensing data from a sensor is temporally independent. We however notice that such sensing data usually is highly temporally correlated, rather than independent. In this paper, we study the problem of allocating energy and data rates to sensors, and performing sensing data routing in an energy harvesting sensor network for a given monitoring period, such that the utility sum of temporally correlated data collected from sensors in the period is maximized, subject to the temporally spatially varying harvesting energy constraint on each sensor. We then propose a near-optimal algorithm for the data utility maximization problem. We finally evaluate the performance of the proposed algorithm with real solar energy data. Experimental results show that the proposed algorithm is very promising and the utility sum of collected sensing data is up to 10% larger than that by the state-of-the-art.