Reinforcement Learning-Based Sensor Access Control for WBANs
| Autor: | Yiju Zhan, Liang Xiao, Geyi Sheng, Guihong Chen, Yonghua Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
game theory
reinforcement learning Dynamic network analysis General Computer Science Transmission delay Computer science Real-time computing 02 engineering and technology 01 natural sciences Wireless body area networks 0202 electrical engineering electronic engineering information engineering Reinforcement learning Wireless General Materials Science business.industry Quality of service 010401 analytical chemistry General Engineering access control 020206 networking & telecommunications Energy consumption Transmitter power output 0104 chemical sciences Transmission (telecommunications) Bit error rate lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Access time Communication channel |
| Zdroj: | IEEE Access, Vol 7, Pp 8483-8494 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Wireless body area networks that support fast-growing healthcare applications have to control the access of the sensors in the dynamic network and channel states. In this paper, we propose a sensor access control scheme based on reinforcement learning that enables the coordinator to choose the access time and transmit power of the sensors based on the state that consists of the signal-to-interference plus noise ratio, the transmission priority, the battery level, and the transmission delay of the sensors. This scheme is proved to improve the quality-of-service, save the energy consumption of the sensors, and enhance the transmission reliability. The simulation results show that this scheme reduces the bit error rate, saves the energy consumption, decreases the transmission delay, and increases the overall utility of the sensors compared with the benchmark schemes. |
| Databáze: | OpenAIRE |
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