Rechargeable Sensor Networks Research Articles

GA-based Charger Deployment Algorithm in Indoor Wireless Rechargeable Sensor Networks

<p>The advancement of Internet of Things technology has made continuous environmental monitoring a crucial requirement for various applications. To address the issue of the network life cycle, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution. This research is focused on WRSN applications in indoor settings such as the Industrial Internet of Things and indoor greenhouses. In such scenarios, rechargeable wireless sensing devices can gather the required information, reducing equipment costs and eliminating the inconvenience of wired sensors. This study proposes the utilization of a genetic algorithm to optimize the deployment of chargers in indoor environments. Compared to greedy algorithms, this approach can determine the best solution for charger deployment and minimize deployment expenses.</p> <p> </p>

Joint Energy Loss and Time Span Minimization for Energy-Redistribution-Assisted Charging of WRSNs With a Mobile Charger

The use of mobile chargers (MCs) to charge the nodes in wireless rechargeable sensor networks via wireless power transfer (WPT) has attracted much research effort. Existing works mostly concentrate on path planning whereas neglecting the opportunities to improve charging coverage and efficiency by exploiting the energy redistribution (ERD) process among nodes and an MC’s capability of charging multiple nodes simultaneously via WPT. To exploit such opportunities, we study the underlying ERD-assisted MC charge scheduling (ERAMCCS) problem, i.e., to find a charging schedule satisfying the nodes’ energy demands with minimum energy loss and minimum time span. After proving that the problem is NP-hard, we propose a charge scheduling algorithm based on the greedy idea (CSBGI), which provides a solution by decoupling the problem into two subproblems: 1) ERAMCCS-Energy and 2) ERAMCCS-Time, to minimize the energy loss and the time span, respectively. By partitioning the energy loss into transmission energy loss and moving energy loss, we solve the ERAMCCS-Energy problem by minimizing the two parts, respectively, by formulating and solving some linear programming problems and traveling salesman problem problems based on the charging position set. The charging position set is iteratively refined by identifying and removing redundant charging positions. For the ERAMCCS-Time problem, concurrent energy transmission opportunities are exploited to try to minimize the time span of the schedule. We demonstrate some key properties of CSBGI, such as its approximation ratio in terms of energy loss and its time complexity. Testbed experiments and numerical simulations confirm the superiority of CSBGI over typical algorithms.

Multi-UAV WRSN charging path planning based on improved heed and IA-DRL

Aiming at the problem of how to improve the energy utilization rate of UAV (Unmanned Aerial Vehicle) in the charging process of wireless rechargeable sensor network, a charging path planning scheme for multi-UAV wireless rechargeable sensor network based on deep reinforcement learning is proposed. Firstly, the multi-UAV path planning problem is described and a network model is established, and then the network model is optimized by using the improved dynamic clustering algorithm of HEED, and then an intelligent path optimization algorithm based on the intelligent algorithm and deep reinforcement learning IA-DRL is proposed for the problem model. According to this algorithm, the optimal charging path of multiple drones is obtained, and finally the drones charge each node to be charged in the network. The experimental results show, compared with other traditional heuristic methods, IA-DRL has more advantages in solving small and medium-scale UAV path planning problems, and compared with the neural network model without PSO, the minimum AVG performance of IA-DRL is improved by about 3.8% and has a faster convergence speed about 550 episodes.

Hybrid scheduling strategy of multiple mobile charging vehicles in wireless rechargeable sensor networks

Hybrid scheduling strategy of multiple mobile charging vehicles in wireless rechargeable sensor networks

Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks

In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC’s charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes’ energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks.

A Tier-Based Loading-Aware Charging Scheduling Algorithm for Wireless Rechargeable Sensor Networks

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.

A Temporal and Spatial Priority With Global Cost Recharging Scheduling in Wireless Rechargeable Sensor Networks

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%.

An Opportunistic Charger Recollection Algorithm for Wireless Rechargeable Sensor Networks

Wireless rechargeable sensor networks (WRSNs) have received a lot of attention due to the development of wireless charging technology. Recently, a new solution of wireless charging vehicle (WCV) for WRSNs with separable charger array equipped with multiple chargers was suggested. By this method, each charger can be unloaded to serve one sensor, while the WCV can work in a very efficient way because it needs not to stay on site and can continue to perform its assigned task. But this solution created a new problem that is how to recollect these chargers for reusing when their charging services are finished. In previous research, however, the recollecting strategy has seldom been considered. In this work, an effectively opportunistic charger recollection algorithm (OCRA) are proposed. Simulation results indicate that OCRA has outperformed previous algorithms in many aspects.

Trajectory optimization of laser-charged UAV to minimize the average age of information for wireless rechargeable sensor network

This paper considers the laser-charged Unmanned Aerial Vehicle (UAV) assisted Wireless Rechargeable Sensor Network (WRSN), where rechargeable sensors are deployed in surveillance environment to monitor information, a UAV is not only used as aerial wireless mobile collector for gathering data from sensors but also used as mobile charger to replenish energy for sensors, Laser Beam Directors (LBDs) are uniformly deployed in the monitoring environment to charge UAV by emitting laser beams. In such network, we study the average Age of Information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors such that all data of the network are transported to the base station and the remaining energy of any sensor exceeds a certain threshold. We prove that the AoIO problem is NP-hard. To solve the AoIO problem, we first study the Total Flight Time Minimizing of UAV (TFTM) problem, which aims at finding an optimal charging solution of UAV to minimize the flight time of UAV based on the order of sensors visited by UAV. Then we prove that the TFTM problem is also NP-hard. Afterwards, we propose a heuristic algorithm to solve the TFTM problem by optimizing flight path, data collection, energy power transfer and laser charging of UAV. Based on the solution for the TFTM problem, we propose an approximation algorithm to solve the AoIO problem. Finally, we conduct extensive simulation experiments to verify the effectiveness of the proposed algorithm.

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

An extremely high number of geographically dispersed, energy‐limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network’s lifespan. In order to compensate for the sensor nodes’ energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network’s lifetime in such a situation is difficult. In this paper, a demand‐based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K‐means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes’ batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster’s center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient‐based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network’s lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively.

Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node gets sorted using the K-means technique, in which the data gets distributed into various clusters. The mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor point. The position of the anchor points is calculated based on the energy distribution using the base station. In this case, the network will act as a spare MC, so that one of the two MCs will run out of energy before reaching the BS. After the current tours of the two MCs terminate, regression analysis for energy prediction initiates, enabling the updating of anchor points in the upcoming round. Based on the findings of the regression-based energy prediction model, the recommended algorithm could effectively refill network energy.

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

An efficient data aggregation with optimal recharging in wireless rechargeable sensor networks

An efficient data aggregation with optimal recharging in wireless rechargeable sensor networks

Dynamic Charging Strategy Optimization for UAV-Assisted Wireless Rechargeable Sensor Networks Based On Deep Q-network

Dynamic Charging Strategy Optimization for UAV-Assisted Wireless Rechargeable Sensor Networks Based On Deep Q-network

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Route selection strategy with minimised mobile charger in wireless rechargeable sensor networks

Corrections to “Multiple Mobile Charger Charging Strategy Based on Dual Partitioning Model for Wireless Rechargeable Sensor Networks”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , we did not check the article carefully enough, which resulted in writing errors in some parts of the paper. In <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table 1</xref> of the original article, we incorrectly wrote the default charging efficiency of MC as 300 mJ/s, but the correct charging efficiency is 3000 mJ/s. Also, in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 8</xref> of the original article, we incorrectly wrote the coordinate range of X-axis as 50 mJ/s–500 mJ/s, while the correct coordinate range is 500 mJ/s–5000 mJ/s. These errors are writing errors and have no impact on the core content of the article, such as experimental methods and conclusions.

DMCP: A Distributed Mobile Charging Protocol in Wireless Rechargeable Sensor Networks

On-demand charging of sensor nodes (SNs) in wireless rechargeable sensor networks has garnered immense attention. Existing works with multiple mobile chargers (MCs) overlooked the benefits of partial charging and distributed control in large-scale wireless rechargeable sensor networks. In addition, most of them have not considered the complete heterogeneity of SNs’ energy profiles and idleness of the MCs. In this article, we address the aforesaid issues and present a game theory based distributed mobile charging protocol, called DMCP. We formulate the mobile charging problem as a series of repeated games played among the MCs and represent a game as a 0-1 integer linear programming to maximize the total profit of the MCs. In each game, DMCP dynamically determines the factor of charging of the energy-deficit SNs based on their relative criticality and selects the next-to-be-recharged SNs using an efficient bi-objective payoff function. In addition, it utilizes an innovative strategy and a self-enforcing agreement to promote cooperation among the MCs. Through extensive simulations and hypothesis testing, we show that DMCP reduces the charging delay up to 44.04% and enhances the charging coverage and survival rate up to 45.12% and 49.97%, respectively.

An energy urgency priority based mobile charging scheme in Wireless Rechargeable Sensor Network

Recently, the use of Wireless Charging Vehicles (WCVs) to replenish energy for multiple nodes simultaneously has been a research hot spot in Wireless Rechargeable Sensor Network (WRSN). However, existing solutions either ignore the energy demand and its urgency, or only consider them partially in some stages, not during the whole process. There is no an overall solution in which distance and energy information with multiple metrics such as residual energy and demand urgency are jointly considered throughout the whole procedure. To address this problem, an Energy Urgency Priority based Mobile Charging Scheme (EUP-MCS) is proposed. Firstly, the WRSN is clustered by the algorithm of Residual Energy Priority based K-Means (REP-Kmeans++) which brings nodes with less residual energy closer to cluster center. Secondly, the WCV’s optimal docking site at each cluster is selected by the algorithm of Gradient Descent based Anchor Point Selection (GD-APS) according to the nodes’ energy demand weight. Finally, the WCV’s access path is determined by the algorithm of Energy Urgency Priority based Ant Colony System (EUP-ACS) which prioritizes the clusters with high energy demand urgency. Extensive simulation experiments show that REP-Kmeans++ reduces the intra-cluster charging time by 6.4% compared to K-Means, and GD-APS reduces the intra-cluster charging time by 16.5% compared to traditional methods. More comparison experiments reveal that our EUP-MCS scheme outperforms other solutions such as Energy-aware Anchor Point Selection (EAPS), Collaborative Charging Algorithm based on Network Density Clustering (CCA-NDC) and Modified MAX–MIN ant system and Equalization Strategy (MMES) in terms of total traveling distance, death node number, average residual energy and stability.

RS-STQ: Recharge Scheduling Algorithm for Maximizing Spatial and Temporal Data Accuracy

With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, most of the existing studies focused on improving the charging efficiency or minimizing the latency, while very few studies improved the data accuracy of the network. This article proposes an efficient-energy recharging schedule for MC, aiming to maximize the data accuracy of the given network. Initially, the data accuracy of each sensor is measured by considering the spatial and temporal qualities. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate; therefore, this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy and recharged sensor’s spatial quality contributions.

On the Global Maximization of Network Lifetime in Wireless Rechargeable Sensor Networks

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.