Wireless Rechargeable Sensor Networks Research Articles

Towards Perpetual Wireless Rechargeable Sensor Networks with Path Optimization of Mobile Chargers

Towards Perpetual Wireless Rechargeable Sensor Networks with Path Optimization of Mobile Chargers

Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs

This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms are verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.

Combined fuzzy-metaheuristic framework for bridge health monitoring using UAV-enabled rechargeable wireless sensor networks

It is essential to monitor the health of important infrastructure (e.g., bridges) to maintain their functions. Visual inspections have been conventionally dominant in this regard, although they are susceptible to human errors. Wireless sensor networks (WSNs) provide an automated, convenient, and low-cost option for developing bridge health monitoring (BHM) networks. However, the constant use of WSNs for monitoring the structural and environmental health of bridges can pose a serious challenge due to the limited lifetimes of these networks that depend on the battery lifetimes of sensor nodes. This paper proposes a combined fuzzy-metaheuristic framework to maintain the BHM stability by using rechargeable sensors. This framework benefits from metaheuristic methods and the fuzzy logic to match the sensor network configuration management to the specific conditions of each bridge, recognizing that different bridges share very few common characteristics. Every new bridge is unique; hence, it is difficult to design a BHM paradigm that fits the conditions of all bridges. The proposed framework manages the current network configuration concerning the conditions of each bridge. This framework manages the network topology formation, information relay, and recharge by using a multipurpose objective, tuning control parameters, and controlling network activities in an optimization process. Moreover, unmanned aerial vehicles (UAVs) are employed to recharge sensor nodes under the proposed framework strategies to overcome the energy limitation of sensor nodes. The proposed framework is evaluated on three bridge scenarios: Hardanger Bridge, Bergsøysund Bridge, and New Carquinez Suspension Bridge. Compared to common WSN methods, it demonstrated superior performance under various conditions, including the rate of active and inactive nodes, energy efficiency, survival rate, stability, recharge delay, average node energy, recharge requests, and total packets received. The evaluation results demonstrate that the proposed framework significantly surpasses existing methods in terms of WSN performance metrics. The results show that the proposed framework outperforms existing methods by an average of 32.8% for the Hardanger Bridge, 53.2% for the Bergsøysund Bridge, and 31.2% for the New Carquinez Suspension Bridge.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Intelligent Trajectory Design and Charging Scheduling in Wireless Rechargeable Sensor Networks With Obstacles

Intelligent Trajectory Design and Charging Scheduling in Wireless Rechargeable Sensor Networks With Obstacles

Towards Intelligent Decision Making for Charging Scheduling in Rechargeable Wireless Sensor Networks

Towards Intelligent Decision Making for Charging Scheduling in Rechargeable Wireless Sensor Networks

Unveiling Efficient Partial Charging Schedules for Wireless Rechargeable Sensor Networks Using Novel Aquila Optimization Approach

Unveiling Efficient Partial Charging Schedules for Wireless Rechargeable Sensor Networks Using Novel Aquila Optimization Approach

A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC's staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

An Overview of On-Demand Wireless Charging as a Promising Energy Replenishment Solution for Future Wireless Rechargeable Sensor Networks

An Overview of On-Demand Wireless Charging as a Promising Energy Replenishment Solution for Future Wireless Rechargeable Sensor Networks

Performance evaluation of Cuttlefish optimization-based contention control in Wireless Rechargeable Sensor Network

Performance evaluation of Cuttlefish optimization-based contention control in Wireless Rechargeable Sensor Network

Reinforcement learning-based charging cluster determination algorithm for optimal charger placement in wireless rechargeable sensor networks

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Interval many‐objective dynamic charging planning in wireless rechargeable sensor networks

SummaryCharging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many‐objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high‐dimensional multi‐objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I‐SPEA2) that introduces an environmental response mechanism is proposed. I‐SPEA2 treats individual target interval values as ranges of values on a two‐dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

A New Joint Data Collection and Wireless Energy Transfer (SSDCWC) Strategy for Rechargeable Wireless Sensor Network

A New Joint Data Collection and Wireless Energy Transfer (SSDCWC) Strategy for Rechargeable Wireless Sensor Network

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

Efficient Scheduling of Charger-UAV in Wireless Rechargeable Sensor Networks: Social Group Optimization Based Approach

Efficient Scheduling of Charger-UAV in Wireless Rechargeable Sensor Networks: Social Group Optimization Based Approach

CSCT: Charging Scheduling for Maximizing Coverage of Targets in WRSNs

In recent years, wireless rechargeable sensor networks (WRSNs), as a crucial technology in cyber-physical-social systems (CPSSs), have gradually become a hotspot of research, with the development of wireless energy transmission technology. In previous works, the objective is to maximize the survival rate of sensor nodes. However, in this article, we focus on maintaining more targets. First, it details the charging scheduling problem of maximizing coverage of targets (CoT) in on-demand charging architecture of WRSNs. Also, the problem is formalized as a multiple-objective optimization problem, which aims at maximizing the CoT and the energy efficiency simultaneously. After that, the charging scheduling for maximizing coverage of targets (CSCT) scheme is proposed to achieve the above objectives. Then, the problem is reformulated as a Deadline-TSP problem that is NP-hard. To address this problem, we design an energy predictive model and propose the CSCT with an n-path (n-CSCT) scheme that has an O(|N|ⁿ) computational complexity. In addition, the resurrection of sensor nodes is considered in this article. Thus, the n-CSCT with node resurrection (n-CSCT-R) scheme is proposed for this case. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

An Efficient Scheduling Scheme for Semi-On-Demand Charging in Wireless Rechargeable Sensor Networks

An Efficient Scheduling Scheme for Semi-On-Demand Charging in Wireless Rechargeable Sensor Networks