Self-Learning-Based Data Aggregation Scheduling Policy in Wireless Sensor Networks

Abstract

The problems of reducing the transmission delay and maximizing the sensor lifetime are always hot research topics in the domain of wireless sensor networks (WSNs). By excluding the influence of routing protocol on the transmission direction of data packets, the MAC protocol which controls the time point of transmission and reception is also an important factor on the communication performance. Many existing works attempt to address these problems by using time slot scheduling policy. However, most of them exploit the global network knowledge to construct a stationary scheduling, which violates the dynamic and scalable nature of WSNs. In order to realize the distributed computation and self-learning, we propose to integrate the Q-learning into the exploring process of an adaptive slot scheduling with high efficiency. Due to the convergence nature, the scheduling quickly approaches an approximate optimal sequence along with the execution of frames. By conducting the corresponding simulations, the feasibility and the high efficiency of the proposed method can be validated.