| Autor: |
Guihong Chen, Yiju Zhan, Ye Chen, Liang Xiao, Yonghua Wang, Ning An |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 6, Pp 37403-37412 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2850659 |
| Popis: |
Wireless body area networks (WBANs) have to address jamming attacks to support healthcare applications. In this paper, we present a reinforcement learning-based power control scheme for the communication between the in-body sensors and the WBAN coordinator to resist jamming attacks. This scheme applies Q-learning to guide the coordinator to achieve an optimal power control strategy without being aware of the in-body sensor's transmission parameters and the WBAN model of the other sensors in the dynamic anti-jamming transmission. In addition, a transfer learning method is adopted to accelerate the learning speed. Stackelberg equilibria and their existence conditions are deduced in a single time slot to upper bound the performance of the learning-based sensor power control scheme. Simulation results show that the proposed scheme can efficiently increase the utilities and decrease the transmission energy consumptions for the in-body sensors and the WBAN coordinator, and simultaneously reduce the attack possibility of the jammer compared with a standard Q-learning-based sensor power control scheme. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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