Markov Decision Process-Based Switching Algorithm for Sustainable Rechargeable Wireless Sensor Networks

In a tree-based wireless sensor network (WSN), a tree structure rooted at sink node is usually created for efficient data collection. Recently, the use of solar harvesting technologies for rechargeable sensor nodes is evolving. Moreover, in a tree-based rechargeable WSN, the nodes that belong to different routes will have different energy dissipation due to unequal harvested-energy and utilized-energy. Network sustainability and energy efficiency are important issues in a tree-based rechargeable sensor network. In this paper, a Markov decision process-based switching algorithm has been designed for a sustainable data collection tree while reducing energy consumption in the network. Furthermore, an analysis of energy consumption has been performed using a real-time sensor traffic pattern. A prediction model has been adopted to estimate the harvesting energy (based on solar power) for the rechargeable sensor nodes. In this paper, the state of each node is defined based on different independent energy levels. The state of each node may change with time depending on harvested-energy and utilized-energy. The proposed Markov decision process approach finds the optimal switching policy for sensor nodes, which switch from one parent to another based on energy levels to preserve sustainability. A detailed theoretical analysis has been performed along with simulation results to show the efficacy of the proposed approach.