Predictive Energy-Aware Adaptive Sampling with Deep Reinforcement Learning

Abstract

Energy harvesting can enable wireless smart sensors to be self-sustainable by allowing them to gather energy from the environment. However, since the energy availability changes dynamically depending on the environment, it is difficult to find an optimal energy management strategy at design time. One existing approach to reflecting dynamic energy availability is energy-aware adaptive sampling, which changes the sampling rate of a sensor according to the energy state. This work proposes deep reinforcement learning-based predictive adaptive sampling for a wireless sensor node. The proposed approach applies deep reinforcement learning to find an effective adaptive sampling strategy based on the harvesting power and energy level. In addition, the proposed approach enables predictive adaptive sampling by designing adaptive sampling models that consider the trend of energy state. The evaluation results show that the predictive models can successfully manage the energy budget reflecting dynamic energy availability, maintaining a stable energy state for a up to 11.5% longer time.

Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices

“…In [13], Heo et al implement a predictive energy-aware adaptive sampling method for wireless sensor networks (WSNs). Based on historic recordings of the harvested energy as well as the current battery level, a deep actor-critic reinforcement learning implementation is used to adapt the sampling rate accordingly.…”

“…However, oftentimes the activity with the highest power consumption is sensing, and therefore a low-power self-sustaining IoT sensor node must adapt its sampling frequency to accommodate energy requirements [11]. Adaptive sampling is a well-known technique, which found use also in energy harvesting systems [7,13].…”