College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia.
Sensors (Basel). 2019 Dec 19;20(1):44. doi: 10.3390/s20010044.
Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.
无线体域网 (WBAN) 作为一种连续监测人体生理信号的有前途的技术，已经引起了业界和学术界的极大关注。由于 WBAN 中的传感器通常由电池驱动，且不方便充电，因此需要一种节能的资源分配方案来延长网络的使用寿命，同时保证 WBAN 固有服务质量 (QoS) 的严格要求。作为解决能效问题的一种可行替代方案，具有从环境源中收集能量能力的能量收集 (EH) 技术可以减少对电池供电的依赖。因此，在本文中，我们研究了用于能量收集的 WBAN（EH-WBAN）的资源分配问题。我们的目标是通过联合考虑传输模式、中继选择、分配时隙、传输功率以及每个传感器的能量约束，最大限度地提高 EH-WBAN 的能量效率。鉴于 EH-WBAN 的特点，我们将能量效率问题表述为具有离散时间和有限状态马尔可夫决策过程 (DFMDP)，其中分配策略决策由一个没有完整和全局网络信息的中心做出。由于问题的复杂性，我们提出了一种改进的 Q 学习 (QL) 算法来获得最佳的分配策略。数值结果验证了所提出方案的有效性以及所提出的改进 Q 学习 (QL) 算法的低计算复杂度。
