Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks

Data gathering in wireless sensor networks typically involves two steps: data sensing and data transmission, which dominate the energy consumption of each sensor. In Rechargeable Sensor Networks (RSNs), in order to optimize data gathering, energy should be carefully allocated to data sensing and data transmission due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the optimal data transmission. In this paper, we strive to optimize data gathering by jointly considering data sensing and transmission. To this end, we first design a Balanced Energy Allocation Scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then we propose a Distributed Sensing Rate and Routing Control (DS2RC) algorithm to jointly optimize data sensing and transmission, while guaranteeing network fairness. In DS2RC, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. We theoretically prove the optimality and the convergence of the proposed algorithms. Extensive simulations are performed to demonstrate the efficiency of BEAS and DS2RC by comparing with existing algorithms.