![]() The simulation results show that the method can improve packet loss and energy consumption. In the work of, the authors presented a time division multiple access (TDMA)-based technique to improve WBANs’ reliability and energy efficiency by adaptively synchronizing nodes while tackling channel and buffer status. Additionally, several previous works in the literature were proposed to investigate the energy-saving technologies from the aspects of the media access control (MAC) protocol design, power control, and cross-layer resource allocation strategies to make efforts to prolong the lifetime of WBANs. A comprehensive survey on the major characteristics, research issues, and challenges in WBANs for patient monitoring from a practical design and implementation perspective was provided in the works of. Therefore, designing an energy efficient resource allocation scheme has great significance to WBANs. Meanwhile, it is difficult or inconvenient to replace the battery as these body sensors may be implanted in the human body. In contrast, body sensors are energy-limited owing to the small size. The hub normally has rich resources, such as energy supply, processing capability, and buffer storage. Different from the conventional complex and wired healthcare devices, WBANs typically consist of a number of battery-driven, invasive, and/or non-invasive body sensors and one hub (mobile phone or personal digital assistant (PDA)) with the communication function in the form of wireless. ![]() The specific application scenario of WBANs is to continuously monitor the vital physiological signals of the human body and transmit the real-time sensory data to the users and doctors without any interruptions in their daily lifestyle to realize smart healthcare in the framework of smart cities. Recent advances in sensors and wireless communication technology have resulted in a promising development of wireless body area networks (WBANs). 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. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. 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. 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. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). 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. ![]() 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. ![]() 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. ![]()
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