Location Updating Scheme of Sink Node Based on Topology Balance and Reinforcement Learning in WSN

• Main Authors: Xindi Wang, Qingfeng Zhou, Chunxiao Qu, Gao Chen, Junjuan Xia
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Network scaling; location update; pathfinding; reinforcement learning; wireless sensor network
• Online Access: https://ieeexplore.ieee.org/document/8766792/

This paper proposes a scheme for updating the location of the sink node to balance the network topology when a wireless sensor network (WSN) is scaled up. We divide the proposed location update scheme into two steps, namely, searching the optimal location and designing the pathfinding algorithm. For the former, to find the optimal location of the sink node simply and efficiently, we only consider the information of the expanded longer paths and some key nodes instead of the global information of the entire network, which is easy to implement with a low-computational load. Then, considering the general unattended application scenario, we propose an improved reinforcement learning (RL) algorithm for the sink node to calculate a feasible efficient path, and then the sink node follows the path to reach the optimal location. Finally, through simulations, we demonstrate the optimal position of the sink node in expanded scenarios and successfully let the sink node learn the effective pathfinding method to reach the target position. A large number of simulation results verify the efficiency and effectiveness of our proposed scheme from the perspective of the efficiency of the pathfinding algorithm.