Dynamic Scheduling for Minimizing Age Penalty in Resource-Constrained Classified WBANs with Energy Harvesting

• Published in: IEEE sensors journal Vol. 23; no. 19; p. 1
• Main Authors: Xin, Ge, Hu, Fengye, Ling, Zhuang, Na, Shun, Jin, Chi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: age of information (AoI); Algorithms; Body area networks; Constraints; Dynamic scheduling; Dynamic stability; Energy harvesting; Heuristic algorithms; Lyapunov optimization; Optimization; Penalty function; Protocols; Resource management; RF signals; Scheduling; Sensor systems; Sensors; Time Division Multiple Access; WBANs
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3285248

With the increasing demand for real-time health monitoring applications, the timeliness and accuracy of sampling information in wireless body area networks (WBANs) are the focus of current research. In this paper, we apply a general age penalty function, developed from the age of information (AoI), to express the level of dissatisfaction with data staleness in classified WBANs with bidirectional energy and information transmission system. The sensors in classified WBANs are grouped according to the type of sampled information and send the updated information to the access point (AP) by time division multiple access (TDMA). To minimize the weighted sum age penalty function of the system, we construct an optimization problem subject to the average AoI and transmit power constraints for each sensor. To solve this optimization problem with the non-convex structure, we apply the Lyapunov optimization theory to transform the original problem into solving the stability of the system and propose a dynamic scheduling algorithm based on drift-plus-penalty function (DS-DPP). For acquiring the optimal solution, we first obtain the transmission probability of the sensor based on the set throughput threshold and develop a multivariable joint optimization algorithm to maximize the transmission probability. Then, based on the maximum transmission probability obtained, a node selection algorithm is proposed. The simulation results show that under the same conditions, the DS-DPP algorithm reduces the age penalty function of the system by about 61.5% and 8.9%, respectively, compared with the Random scheduling and the Fair scheduling algorithms.