The rapid development of wireless power transfer technology brings forth innovative vehicle energy solutions and breakthroughs utilizing wireless sensor networks (WSNs). In most existing schemes, wireless rechargeable sensor networks (WRSNs) are generally equipped with one or more wireless charging vehicles (vehicles) to serve sensor nodes (SNs). These schemes solve the energy issue to some extent; however, due to off-road and speed limitations of vehicles, some SNs still cannot be charged in time, negatively affecting the lifetime of the networks. Our work proposes a new WRSN model equipped with one wireless charging drone (drone) with a constrained flight distance coupled with several wireless charging pads (pads) deployed to charge the drone when it cannot reach the subsequent stop. Our model solves this charging issues effectively and overcomes energy capacity limitations of the drone. Thus, a wireless charging pad deployment problem is formulated, which aims to apply the minimum number of pads so that at least one feasible routing path can be established for the drone to reach every SN in a given WRSN. Four feasible heuristics, three based on graph theory and one on geometry, are proposed for this problem. In addition, a novel drone scheduling algorithm, the shortest multi-hop path algorithm, is developed for the drone to serve charging requests with the assistance of pads. We examine the proposed schemes through extensive simulations. The results compare and demonstrate the effectiveness of the proposed schemes in terms of network density, region size and maximum flight distance.

Wireless rechargeable sensor networks (WRSNs) employ wireless charging transfer technology to provide dynamic energy replenishment for sensors. Previous related research considers enhancing the flexibility of WRSNs with the use of wireless charging vehicles (WCVs). Although this research prolongs network lifetime to some extent, there still exist two essential drawbacks: off-road limitation and traveling speed limitation. Benefiting from the intrinsic flexibility, high flight speed, low cost and small size, wireless charging drones (drones) have great potentials to be used to charge sensors in WRSNs. However, suffering from limited battery capacities, it is impossible to use drones for charging sensors in large WRSNs independently. To conquer the drawbacks of WCVs and drones, we propose a novel WRSN model which integrates WCVs, WCV-carried drones and independent drones to construct an efficient novel charging system. The proposed system, which aims to extend network lifetime and to minimize the time of charging scheduling on premise of satisfying charging requests with the cooperation of WCVs and drones. Thus, we develop a novel charging scheduling scheme, which divides the sensing region into three circular sub-regions for scheduling the WCVs or drones collaboratively. Sensor nodes located in the inner ring are charged by independent drones directly from the base station. Nodes located in the middle ring are charged by WCVs, while the outer ring charged by WCV-carried drones. Simulation results show that our scheduling scheme outperforms multiple WCVs which either employ temporal or distantial priority scheme and earliest-deadline first scheme with respect to the lifetime and network charging efficiency.

Centralized baseband architecture is believed to be cost efficient and to offer high network performance. The fronthaul network, connecting the centralized baseband to distributed remote radios, requires high capacity and low latency transport links, due to the stringent specifications of the data interface, known as common public radio interface (CPRI). Today, fronthaul links are mostly based on digital CPRI over fibers, although it is possible to realize CPRI over wireless technology with the drawback of poor bandwidth efficiency. In this paper, we propose an analog wireless fronthaul concept to enable bandwidth efficient and scalable fronthaul networks. The basic idea is to transport narrow band analog radio signals over millimeter-wave bands instead of multi-gigabit digital signals. The phase and frequency impairments introduced by high frequency carriers are effectively reduced using an analog pilot-based mitigation technique. As a proof-of-concept demonstration, an analog fronthaul link is implemented, and the link performance is verified for 20 MHz 64-QAM LTE transmission over 70/80 GHz (E-band). The measured error vector magnitude (EVM) is about 3% with 10% pilot bandwidth overhead.

Wireless rechargeable sensor networks (WRSN) are utilized in environmental monitoring, traffic video surveillance, medical services, etc. In most existing schemes, WRSNs provide sustainable energy for sensor nodes by employing one or more wireless charging vehicles (WCVs). However, two essential drawbacks, regional limitations and traveling speed limitations, constrain these schemes when applied in hostile and large-scale environments. On the other hand, benefiting from the intrinsic flexibility, high flight speed, low cost, and small size of drones, some works have used drones to charge sensor nodes. However, suffering from limited battery capacities, it is also hard to only use drones in large-scale WRSNs. To overcome the drawbacks of WCVs and drones, we proposed a novelty wireless charging system that deploys WCV, WCV-carried drones, and wireless charging pads (pads) in a large-scale wireless sensor network. Based on this new wireless charging system, we first formulated a pad deployment problem for minimizing the total number of pads subject to each sensor in the pad region that only can be charged by drones. In this work, three near-optimal algorithms, i.e., greedy, K-mean, and static, for the pad deployment problem are proposed. Then, to form a sustainable WRSN, we elucidated the collaborative charging scheduling problem with the deadlines of sensors. To guarantee the maximum number of sensors to be charged before the deadlines, we also presented an approximation algorithm to find the collaborative charging scheduling of WCV and WCV-carried drones with the help of pads based on the three deployment pad schemes. Through extensive simulations, we demonstrate the effectiveness of the proposed deployment pad schemes. and that the number of pads obtained by the greedy and K-mean scheme was generally lower than that of the static scheme with respect to network density, WCV region, and flight range. Then, we also examined the proposed collaborative charging scheduling scheme by extensive simulations. The results were compared and showed the effectiveness of the proposed schemes in terms of lifetime, the percentage of nodes being charged in time, the average move time of drones, the percentage of nodes being charged late by the drones, and the charge efficiency of all vehicles under different traffic loads. Related statistical analyses showed that the percentage of nodes being charged in time and the percentage of nodes being charged late based on the greedy and K-mean schemes were slightly better than those of the static scheme, but the charge efficiency of drones of the static scheme was significantly superior to that of the K-mean scheme under a busy network.

Wireless power transfer technique provides a new and promising method for alleviating the limited energy capacity problem, thus receiving much attention. However, previous works usually consider temporal, spatial, or both factors of the current selected node greedily without taking the residual moving distance of the remaining nodes into consideration. Surely, it is not easy to precisely estimate the residual moving distance of the remaining nodes before knowing their exact order in the scheduling path. In this work, the authors are the first to propose the concept of the residual moving distance (cost) and create a mathematical model to roughly estimate the cost of a given node set. Moreover, they design a temporal and spatial priority charging scheduling algorithm with additional considering the global cost (TSPG). Simulation results demonstrate that TSPG outperforms earliest deadline first scheduling algorithm and revised earliest deadline first scheduling algorithm. Moreover, the proposed new model for estimating moving distance in the residual area has all relative error below 9%.

Charging scheduling is an important issue of wireless rechargeable sensor networks. Previous research proposed to optimize the scheduling sequence by considering factors such as distance and remaining working time. However, packets are transmitted to the base station hop by hop, so that the burden on each sensor is not the same. The unbalancing nature of loading should also be taken into account when dealing with charging requests scheduling. In this paper, the authors have found, both through theoretical analysis on hypothetical model and simulation in more realistic environments, that the communication loading of sensors impacts power consumption of sensors in different tiers relative to the base station significantly. Accordingly, the proposed charging scheduling algorithm takes the loading factor into consideration so that sensors closer to the base station may be given higher priority for recharge. The simulation results show that the proposed method can significantly improve the data delivery rate and achieve higher network availability when compared to previous research.