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The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific 90∘ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method.

Wireless rechargeable sensor networks (WRSNs) address the energy scarcity problem in wireless sensor networks by introducing mobile chargers (MCs) to recharge energy-hungry sensor nodes. Scheduling MCs to charge the recharge nodes is the primary focus of the energy replenishment scheme in WRSNs. The performance of the energy replenishment scheme is significantly influenced by the energy level of each node, which is depends on the data collection scheme employed by the network. Consequently, integrating energy replenishment and data collection has become a new concern in WRSN research. However, the MCs’ workload and travel time increase when data collection and energy replenishment are performed simultaneously, leading to an increase in both the node’s charging delay and data collection delay. In this work, our goal is to reduce the delays in data collection and node charging by proposing a new joint energy replenishment and data collection approach. In the proposed approach, certain nodes in the network are selected as data storage nodes to temporarily store all the collected data based on their geographical locations. A special class of MCs, called MCDs (mobile charger and data collectors), is then assigned the responsibility of charging these data storage nodes and collecting the data stored. Afterwards, the task of recharging the remaining network nodes falls to another type of MC. By combining the capabilities of two distinct MC types, the workload and the travel distance of MCs are reduced. When compared to the conventional joint algorithms, the simulation results demonstrate that the proposed approach successfully decreases the delay it takes to gather data and recharge nodes.

A prototype of a mobile electric charging station was developed to simulate the energy supply to a rural medical post. A 20 m2 medical post module was built, divided into two rooms (medical staff room and patient room) and a heater, a freezer, a refrigerator, lights and a personal computer were added inside. The mobile electric charging station was made up of an array of 2.88 kW flexible photovoltaic panels, a 48 V and 19.2 kW·h LiFePO4 battery bank, a charger inverter with a total capacity of 5 kW and a 4 kW electric generator. All of this equipment was placed in an all-terrain pickup truck. Temperature sensors and electrical sensors were installed to evaluate the performance of the prototype in charging and discharging scenarios. Results were obtained according to the operation over 10 months in the city of Arequipa, Peru. The results indicate an indefinite autonomy on clear days, the autonomy varying between 7 and 10 days for a climate with medium cloudiness, and with very cloudy conditions (i.e., with rain), the autonomy is 2 to 3 days. In circumstances of low solar irradiance, the generator had to supply the energy, thereby improving energy autonomy.

Wireless charging is an important issue in wireless sensor networks, since it can provide an emerging and effective solution in the absence of other power supplies. The state-of-the-art methods employ a mobile car and a predefined moving path to charge the sensor nodes in the network. Previous studies only consider a factor of the network (i.e., residual energy of sensor node) as a constraint to design the wireless charging strategy. However, other factors, such as the travelled distance of the mobile car, can also affect the effectiveness of wireless charging strategy. In this work, we study wireless charging strategy based on the analysis of a combination of two factors, including the residual energy of sensor nodes and the travelled distance of the charging car. Firstly, we theoretically analyze the limited size of the sensor network to match the capability of a charging car. Then, the networked factors are selected as the weights of traveling salesman problem (TSP) to design the moving path of the charging car. Thirdly, the charging time of each sensor node is computed based on the linear programming problem for the charging car. Finally, a charging period for the network is studied. The experimental results show that the proposed approach can significantly maximize the lifetime of the wireless sensor network.

Wireless rechargeable sensor networks (WRSNs) are broadly utilized in numerous areas. However, the limited battery capacity of sensor nodes (SNs) is considered as a critical issue. To extend the battery life of SNs, mobile chargers (MCs) equipped with wireless power transfer (WPT) technology have been proposed as a key solution for charging SNs. Using directional antennas to focus energy within a specific area, as opposed to an omnidirectional antenna, increases the energy efficiency of an MC. In this paper, we focus on the travel path charging scheduling problem with a directional MC in on-demand WRSNs. Our goals are to develop a mechanism to reduce the changing delay time and boost the energy efficiency of MC. In this case, the MC receives the charging requests of SNs and responds to them by selecting appropriate stopping points (SPs) and the charging orientation angles in each SP. We propose a mobile directional charging scheduling (MDCS) solution based on a deep reinforcement learning technique. The simulation results demonstrate the superior performance of our method to existing studies in terms of the energy consumption of the MC, the number of dead SNs, and charging delay time.

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm (CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors. Two MCs can adaptively collaboratively charge the sensors based on the energy limit of MCs and the energy demand of sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithm can effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertainty of node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

Provision of energy to wireless sensor networks is crucial for their sustainable operation. Sensor nodes are typically equipped with batteries as their operating energy sources. However, when the sensor nodes are sited in almost inaccessible locations, replacing their batteries incurs high maintenance cost. Under such conditions, wireless charging of sensor nodes by a mobile charger with an antenna can be an efficient solution. When charging distributed sensor nodes, a directional antenna, rather than an omnidirectional antenna, is more energy-efficient because of smaller proportion of off-target radiation. In addition, for densely distributed sensor nodes, it can be more effective for some undercharged sensor nodes to harvest energy from neighboring overcharged sensor nodes than from the remote mobile charger, because this reduces the pathloss of charging signal due to smaller distances. In this paper, we propose a hybrid charging scheme that combines charging by a mobile charger with a directional antenna, and energy trading, e.g., transferring and harvesting, between neighboring sensor nodes. The proposed scheme is compared with other charging scheme. Simulations demonstrate that the hybrid charging scheme with a directional antenna achieves a significant reduction in the total charging time required for all sensor nodes to reach a target energy level.

Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limited traveling distance and speed, not every node that needs to be charged may be serviced in time. Thus, in such scenario, how to make a route plan for the mobile charger to determine which nodes should be charged first is a critical issue related to the network’s Quality of Service (QoS). In this paper, we propose a mobile charger’s scheduling algorithm to mitigate the data loss of network by considering the node’s criticality in connectivity and energy. First, we introduce a novel metric named criticality index to measure node’s connectivity contribution, which is computed as a summation of node’s neighbor dissimilarity. Furthermore, to reflect the node’s charging demand, an indicator called energy criticality is adopted to weight the criticality index, which is a normalized ratio of the node’s consumed energy to its total energy. Then, we formulate an optimization problem with the objective of maximizing total weighted criticality indexes of nodes to construct a charging tour, subject to the mobile charger’s traveling distance constraint. Due to the NP-hardness of the problem, a heuristic algorithm is proposed to solve it. The heuristic algorithm includes three steps, which is spanning tree growing, tour construction and tour improvement. Finally, we compare the proposed algorithm to the state-of-art scheduling algorithms. The obtained results demonstrate that the proposed algorithm is a promising one.

Although wireless sensor networks (WSNs) have a wide range of applications, their efficient utilization is still limited by the sensor node battery life. To overcome this issue, wireless power transfer technology (WPT) has recently been used to wirelessly charge sensor nodes and extend their lifespan. This technique paved the way to develop a wireless rechargeable sensor network (WRSN) in which a mobile charger (MC) is employed to recharge the sensor nodes. Several wireless charging technologies have been proposed in this field, but they are all tied up in two classes: periodic and on-demand strategies. This paper proposes a proactive charging method as a new charging strategy that anticipates the node’s need for energy in advance based on factors such as the node’s remaining energy, energy consumption rate, and the distance to the MC. The goal is to prevent sensor nodes from depleting their energy before the arrival of the MC. Unlike conventional methods where nodes have to request energy, the proactive charging strategy identifies the nodes that need energy before they reach a critical state. Simulation results have demonstrated that the proactive charging approach using a single MC can significantly improve the network lifespan by 500% and outperform the Nearest Job Next with Preemption (NJNP) and First Come First Serve (FCFS) techniques in terms of the number of survival nodes by 300% and 650%, respectively.

Nowadays, wireless energy charging (WEC) is emerging as a promising technology for improving the lifetime of sensors in wireless rechargeable sensor networks (WRSNs). Using WEC, a mobile charger ( MC ) reliably supplies electric energy to the sensors. However, finding an efficient charging schedule for MC to charge the sensors is one of the most challenging issues. The charging schedule depends on remaining energy, geographical and temporal constraints, etc. Therefore, in this article, a novel efficient charging algorithm is proposed, such that the lifetime of the sensors in WRSN are increased. The proposed algorithm uses a multi-node MC that can charge multiple sensors at the same time. In this algorithm, the charging requests of the low-energy sensors are received by the MC . Then, a reduced number of visiting points are determined for the MC to visit them. The visiting points are within the charging range of one or more requesting sensors. Thereafter, an efficient charging schedule is determined using an adaptive fuzzy model. Sugeno-fizzy inference method (S-FIS) is being used as a fuzzy model. It takes remaining energy, node density, and distance to MC , as network inputs for making real-time decisions while scheduling. Through simulation experiments, it is finally shown that the proposed scheme has higher charging performance comparing to base-line charging schemes in terms of survival ratio, energy utilization efficiency, and average charging latency. In addition, ANOVA tests are conducted to verify the reported results.