A Learning-Based Adaptive Routing for QoS-Aware Data Collection in Fixed Sensor Networks With Mobile Sinks
Routing data from sensor nodes to designated mobile data sinks is a common and challenging task in a wide spectrum of Wireless Sensor Network (WSN) applications and thus becoming an active research area. In this paper, a reinforcement-learning based adaptive routing scheme implemented through Adaptive Critic Design (ACD) is proposed. In this scheme, sensor nodes discover and improve the routes at the time of packets transmission. Decision is made dynamically at each sensor node based on various constraints and environmental conditions considered and multi-objective optimization performed.