A LEARNING-BASED ADAPTIVE ROUTING FOR QOS-AWARE DATA COLLECTION IN FIXED SENSOR NETWORKS WITH MOBILE SINKS

摘要

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. Extensive simulations using synthetic network topologies and sink traces are conducted to test the performance of the proposed routing algorithm with the guidance of Design of Experiments (DoE). The results show the proposed scheme is highly robust and adaptive to a variety of situations.