Outfitting humans with on-body/in-body sensor nodes, wireless body area networks (WBANs) are positioned as the key technology to enhance future telehealth service. The newly emerged wireless power transfer (WPT) and energy harvesting (EH) technology provides a potential of continuous power supply for WBANs. Since the radio frequency (RF) signals can carry energy as well as information at the same time, the time switching between the WPT phase and the wireless information transfer (WIT) phase should be carefully scheduled. By considering a telehealth application scenario (in which multiple patients coexist in a ward and each of them is monitored by multiple sensor nodes), this paper proposes to allocate the duty cycles for the WPT and WIT phases and schedule the transmission time for the WIT links in a joint manner. First, a frame structure for simultaneous information and power transfer (SWIPT) is designed over the time-and-spectrum domain. With the aim to satisfy the minimum rate demands of all the sensor nodes, the optimal duty time for the WPT phase and the optimal transmission time for the WIT links are jointly found by using the convex optimization technique. Finally, a fast algorithm is developed to search the optimal solution by introducing an admission control. The simulation results show that the proposed algorithm can effectively exploit the broadcasting property of RF energy radiation. If the network load were controlled below a certain level, the rate demands of all the sensor nodes in the network can be satisfied.

This paper proposes a bargaining game theoretic rate allocation scheme for wireless-powered machine-type communications (MTCs). In the considered body area MTC network (MTCN), a battery-powered user equipment (UE) acting as the MTC gateway (MTCG) is responsible for collecting the information uploaded by in/on body wireless-powered MTC devices (MTCDs). By solving the Nash bargaining solution (NBS) of the proposed cooperative game, the minimum rate requirements of the MTCDs are satisfied. In addition, the network resource can be allocated to the MTCDs in a fair and efficient manner regarding the difference of their channel qualities. In comparison to other traditional resource allocation methods, the simulation results show that the proposed NBS-based method obtains a good tradeoff between the system efficiency and per-node fairness.

Unmanned aircraft vehicle (UAV) -assisted mobile edge computing (MEC) is an effective way to alleviate the lack of energy and computing power of the Internet of Things (IoT) devices in remote areas. This paper considers the competition among multiple aerial service providers (ASPs) to provide UAV-assisted MEC services to multiple ground network operators (GNOs). We first quantify the conflicting interests of the ASPs and GNOs by different profit functions. Then, the system-wide UAV scheduling and resource allocation is formulated as a multi-objective optimization problem, where an ASP aims to reap the most profits from providing MEC services to the GNOs, while a GNO aims to seek the service of a certain ASP to meet its performance requirements. This problem is a mixed-integer nonlinear programming (MINLP), and we propose a matching theory based algorithm to solve it. We first investigate the UAV trajectory planning and resource allocation between a single UAV and a single service area, and solve it using the Lagrange relaxation and successive convex optimization (SCA) methods. According to the obtained results, the GNOs and ASPs are associated in the framework of the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed matching theory based algorithms achieve the considerable performance.

Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.