Learning-Based Activation of Energy Harvesting Sensors for Fresh Data Acquisition

Abstract

We consider an energy harvesting wireless sensor network (EH-WSN), where each sensor, equipped with the battery, senses its surrounding area. We first define the estimation error of the sensing data at a measuring point, which increases as the distance to the sensor increases and the age of information (AoI) of the data increases. The AoI is the elapsed time since the latest status is generated. We also define the network coverage, which is defined as the area having the estimation errors lower than a target value. As a performance metric, we use the $\alpha$-coverage probability, which is the probability that the network coverage is larger than a threshold $\alpha$. Finally, in order to deal with dynamic and complex environments, we propose a reinforcement learning (RL) based algorithm which determines the activation of the sensors. In simulation results, we show the proposed algorithm achieves higher performance than baselines. In addition, we show the impact of the transmission power and the number of sensors on the $\alpha$-coverage probability.