Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning
This work addresses energy-efficient and timely status updates for IoT sensors, which is an incremental improvement in resource-constrained sensing networks.
The paper tackles the problem of minimizing the trade-off between age of information and energy consumption in an IoT sensing network with energy harvesting sensors, proposing a reinforcement learning approach that finds an optimal policy without knowing exact battery levels.
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors.