Abstract-Different from a traditional wireless sensor network (WSN) powered by nonrechargeable batteries, the energy management policy of a rechargeable WSN needs to take into account the process of energy harvesting. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor aims to maximize the expected total amount of data transmitted until the sensor stops functioning subject to timevarying energy harvesting rate, energy availability in the battery, data availability in the data buffer, and channel fading. Since the lifetime of the sensor is a random variable, we formulate the energy allocation problem as an infinite-horizon Markov decision process (MDP), and propose an optimal energy allocation (OEA) algorithm using the value iteration. We then consider a special case with infinite data backlog and prove that the optimal transmission energy allocation (OTEA) policy is monotonic with respect to the amount of battery energy available. Finally, we conduct extensive simulations to compare the performance of our OEA algorithm, OTEA algorithm, the finite-horizon transmission energy allocation (FHTEA) algorithm extended from [2], and the finite-horizon OEA (FHOEA) algorithm from [1]. Simulation results show that the OEA algorithm transmits the largest amount of data, and the OTEA algorithm can achieve a near-optimal performance with low computational complexity. Index Terms-Energy harvesting, Markov decision process (MDP), resource allocation, wireless sensor networks (WSNs).
REF studies the energy allocation problem for sensing and transmission in an energy harvesting sensor node, which takes into account both the data sensing energy consumption and the finite capacity of the data buffer.
Joint Energy Allocation for Sensing and Transmission in Rechargeable Wireless Sensor Networks
IEEE Transactions on Vehicular Technology
IEEE Transactions on Vehicular Technology
Computer Science