Mixed Observability Markov Decision Processes for Overall Network Performance Optimization in Wireless Sensor Networks
Optimizing overall performance of Wireless Sensor Networks (WSNs) is important due to the limited resources available to nodes. Several aspects of this optimization problem have been studied (e.g. improving Medium Access Control (MAC) protocols, routing, energy management) mostly separately, although there is a strong inter-connection between them. In this paper an Artificial Intelligence (AI) based framework is presented to address this problem. Mixed-Observability Markov Decision Processes (MOMDPs) are used to effectively model multiple aspects of WSNs in stochastic environments including MAC in data link layer, routing in network layer, data aggregation, power management, etc.