An optimal energy allocation algorithm for energy harvesting wireless sensor networks

Abstract

Abstract-With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by nonrechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. 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 node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.

Leveraging application context for efficient sensing

“…On the other hand, a delay-tolerant application might tolerate loss in data freshness by transmitting less frequently thereby saving more energy. The existing MDP [4] based approach does not allow applications to tune this tradeoff. The control-policy approach [2] considers tradeoff between energy gain and delay of buffered data because a system can save energy while decreasing the number of transmission events.…”

“…Even if the sampling were done optimally, there are still significant challenges with data transmission. The state of the art approaches for determining optimal transmission policy include control-limit policy [2] and MDP based approaches [4]. The control-limit policy does not consider heterogeneous sensors.…”

“…The control-limit policy does not consider heterogeneous sensors. The MDP based approach [4] requires complete knowledge of transition probability of the harvesting level and channel condition variations, which is unrealistic. In addition, both offer limited control over the energy-delay tradeoff and are not suitable for real-time and delay-tolerant applications.…”

Energy cooperation for throughput optimization based on save-then-transmit protocol in wireless communication system

“…How to prolong the lifetime effectively and economically is still an open challenge. However, as a promising technique, energy harvesting has emerged and received significant attention in recent years [1][2][3][4][5][6][7][8]. Unlike the conventional scenario that wireless nodes are going to die if they exhaust their battery energy, the energy harvesting technique makes the node energy inexhaustible by means of recharging the battery from nature (solar cells, water mills, or mechanical vibration absorption devices, etc.).…”

“…Xu et al [4] studied the downlink throughput of a coordinated multipoint enabled cellular network, where the base stations are powered by both the conventional grid and the harvested energy. In [5,6], energy buffer and data buffer were adopted, with which sensor nodes employ management policies to optimize their throughput. A save-then-transmit protocol was proposed in [7,8] to optimize the system performance by finding the optimal save-ratio.…”