Adaptive energy management for solar energy harvesting wireless sensor nodes
Wireless Sensor Networks (WSN) will have a key role in the upcoming era of the Internet of Things (IoT) as they will be forming the basis of communication infrastructure. Energy harvesting has been a widely used instrument for prolonging the battery life and enhancing the quality of service (QoS) of sensor nodes (SN). In this study, we investigate adaptive transmission policies for a solar-powered wireless sensor node which is tasked with sending status updates to a gateway as frequently as possible with energy-neutral operation constraints. On the basis of empirical data, we model the daily variations of the solar energy harvesting process with a Discrete Time Markov Chain (DTMC). When the number of states of the DTMC is increased, the harvesting process is modeled more accurately. Using the DTMC model, we formulate the energy management problem of the WSN node as a Markov Decision Process (MDP); and based on this model, we use the policy iteration algorithm to obtain optimal energy management policies so as to minimize the average Age of Information (AoI) of the corresponding status update system. We validate the effectiveness of the proposed approach using datasets belonging to two different locations with 20 years of solar radiance data.