In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs’ assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the endto- end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.

The Internet of Bodies (IoB) is an imminent extension to the vast Internet of things domain, where interconnected devices (e.g., worn, implanted, embedded, swallowed, etc.) located in-on-and-around the human body form a network. Thus, the IoB can enable a myriad of services and applications for a wide range of sectors, including medicine, safety, security, wellness, entertainment, to name but a few. Especially considering the recent health and economic crisis caused by novel coronavirus pandemic, a.k.a. COVID-19, the IoB can revolutionize today’s public health and safety infrastructure. Nonetheless, reaping the full benefit of IoB is still subject to addressing related risks, concerns, and challenges. Hence, this survey first outlines the IoB requirements and related communication and networking standards. Considering the lossy and heterogeneous dielectric properties of the human body, one of the major technical challenges is characterizing the behavior of the communication links in-on-and-around the human body. Therefore, this paper presents a systematic survey of channel modeling issues for various link types of human body communication (HBC) channels below 100 MHz, the narrowband (NB) channels between 400 MHz and 2.5 GHz, and ultra-wideband (UWB) channels from 3 to 10 GHz. After explaining bio-electromagnetics attributes of the human body, physical and numerical body phantoms are presented along with electromagnetic propagation tool models. Then, the first-order (i.e., path loss, shadowing, multipath fading) and the second-order (i.e., delay spread, power delay profile, average fade duration, level crossing rate, etc.) channel statistics for NB and UWB channels are covered with a special emphasis on body posture, mobility, and antenna effects. For the HBC channels, three different coupling methods are considered: capacitive, galvanic, and magnetic. Based on these coupling methods, four different channel modeling methods (i.e., analytical, numerical, circuit, and empirical) are investigated, and electrode effects are discussed. Lastly, interested readers are provided with open research challenges and potential future research directions.