Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks
This work addresses energy efficiency for health monitoring sensor networks, but it appears incremental as it builds on existing RL techniques with minor adaptations.
The paper tackles the problem of energy consumption in wireless sensor networks for health monitoring by proposing a reinforcement learning method that uses weak labels from other sensors to train adaptively, achieving performance enhancement and energy savings in simulated and real-world residential localization scenarios.
As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which continuously performs weak training in an energy-aware system. We motivate this using a realistic example of residential localisation based on Received Signal Strength (RSS). The method is cheap in terms of work-hours, calibration and energy usage. It achieves this by utilising other sensors available in the environment. These other sensors provide weak labels, which are then used to employ the State-Action-Reward-State-Action (SARSA) algorithm and train the model over time. Our approach is evaluated on a simulated localisation environment and validated on a widely available pervasive health dataset which facilitates realistic residential localisation using RSS. We show that our method is cheaper to implement and requires less effort, whilst at the same time providing a performance enhancement and energy savings over time.