Deep Reinforcement Learning-Based Resource Scheduling Strategy for Reliability-Oriented Wireless Body Area Networks

• Main Authors: Xu, Y.-H (Author), Yong, Y.-T (Author), Yu, G. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Series: IEEE Sensors Letters
• Subjects: Air traffic control; Computation complexity; Deep learning; deep reinforcement learning; Global network information; Learning algorithms; Markov Decision Processes; Markov processes; Optimization problems; Reinforcement learning; Reliability; reliable transmission; resource scheduling; Resource-scheduling; Scheduling; Scheduling decisions; Sensor networks; Time slot allocation; Wearable sensors; Wireless body area network; wireless body area networks (WBANs); Wireless local area networks (WLAN)
• Online Access: View Fulltext in Publisher; View in Scopus

 Reliability is a critical factor in designing of wireless body area networks. In this letter, we propose a resource scheduling strategy and solving an optimization problem to maximize the reliability of the transmission of emergency-critical sensory data. We jointly consider transmission mode, relay selection, time slot allocation, and transmit power of each body sensor and formulating the scheduling problem to be a Markov decision process. In this strategy, the scheduling decision is made by each body sensor that do not have complete and global network information. Owning to the formulated problem is nonconvex and the high computation complexity, we propose a deep reinforcement learning algorithm to solve the problem. Numerical results reveal that the proposed strategy is capacity of guaranteeing the reliability of transmission with an acceptable convergence speed. © 2017 IEEE.