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

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

In recent times, wireless energy transfer has become an effective solution to charge devices due to its efficiency and reliability. In a typical Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer technique can solve the energy depletion problem with the aid of a Wireless Charging Vehicle (WCV), thereby enabling the network to extend its lifetime. However, sensor nodes in a WRSN still have their energies depleted before it gets replenished by the WCV. In this paper, we proposed a scheme that prioritizes sensor nodes for charging and also developed efficient algorithms to improve on existing charging schemes so as to extend the lifetime of the WRSN. Firstly, an inspection algorithm was developed to visit and inspect sensor nodes in the network so as to determine the sensor nodes to charge. Secondly, a greedy charge algorithm was introduced to ascertain the shortest distance the WCV needs to travel and, lastly, an energy for nodes’ algorithm was proposed to determine the stopping point and when the WCV needs to return to the base station. Simulation experiments were also conducted to determine the performance of our scheme. The simulation experiments revealed that our proposed scheme made significant improvements when compared to other schemes in literature using several metrics.

As the technological breakthrough is made in wireless charging, the wireless rechargeable sensor networks (WRSNs) are finally proposed. In order to reduce the charging completion time, most existing works use the “mobile-then-charge” model—the Wireless charging vehicles (WCV) moves to the charging spot first and then charges nodes nearby. These works often aim to reduce the node’s movement delay or charging delay. However, the charging opportunities during the movement are overlooked in this model because WCV can charge nodes when it goes from one spot to the next. In order to use the charging opportunities, a speed grading method is proposed under the circumstance of variable WCV speed, which transformed the problem of final charging delay into a traveling salesman problem with speed grading. The problem was further solved by linear programming method. The simulation experiments show that, compared with the existing charging methods, the proposed method has a significant improvement in charging delay.

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV’s service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem’s definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Recent breakthrough in the wireless energy transfer have attracted attention of the researchers due to its potential to prolong the lifetime of Wireless Sensor Networks (WSNs) and to eliminate energy bottlenecks. Through wireless charging vehicles sensor node's energy can be replenished periodically by using Wireless Charging Vehicles (WCVs). However, for a large scale WSN, the capacity of WCVs, positioning and coordination between them, and limited resources of WCVs have to be considered to implement a scalable charging system. Additionally, creating an efficient charging route for the WCVs considering the dynamic energy consumption of sensor nodes is also crucial. Since, WCVs have limited capacity and low system resources, the classic TSP-based Optimization algorithms, which requires high-performance computing, are not suitable for them. In this paper, we proposed GHSC (Guided Hilbert for Specified Cluster), which is based on the Hilbert space filling curve. We consider a clustered WSN with multiple WCVs, where each cluster is assigned to a single WCV. GHSC creates a primary charging route by considering a cluster as an unit square and dividing the square into small sub-squares. As the Hilbert curve goes through each sub-squares, the algorithm assigns a rank for each of them based on their position along the curve. The algorithm then map the nodes into vertices of the sub-squares and rank the nodes as well. Once ranking is done, the algorithm creates a charging route based on the node's ranking. This heuristic approach of the GHSC algorithm can find effective charging tours for the WCVs with a time complexity of (O(n log n) + Ω(nk)). Compared to other Global Optimization algorithms, GHSC was able to find tours, which was at most a logarithmic factor longer than the shortest tour.

The limited energy of the sensor nodes (SNs) has always been a major hindrance for wireless sensor networks (WSNs). Wireless charging of the SNs is a promising alternative to solve the energy constraint problem in WSNs. The charging paradigm with such rechargeable SNs is known as wireless rechargeable sensor network (WRSN). While plenty research efforts have been made to improve the charging performance in a WRSN, little has been done to address the scheduling problem of the MCs having limited capacity. By scheduling problem of the MCs, we want to mean that how efficiently the capacity of multiple MCs can be utilized so that minimum number of MCs are used in order to fulfill the energy demands of the SNs. To fill this research lacuna, we propose three novel on-demand charging schemes which are termed as Pcharge, Bcharge, and Fcharge in this paper. The effectiveness of the proposed schemes is validated by simulation results which reveal that the proposed schemes can achieve promising performance in terms of tour length and capacity utilization of the MCs as compared to a state-of-the-art scheme.

With the advancement in the technology of wireless energy transfer based on magnetic resonant coupling, multiple nodes can be charged simultaneously. In this paper, we utilize this new breakthrough to transfer energy to sensor nodes in wireless sensor networks. We carry on the cellular structure in [4] to describe a wireless sensor network (WSN) and employ a wireless charging vehicle (WCV) periodically visiting each cell to charge nodes within it wirelessly. We propose a dynamic optimization model which is practical and feasible to forecast the departure time and charging time in each cell of the WCV. With the target of maximizing the time during which WCV stays at the service station, we develop two constraints as sufficient and necessary conditions to satisfy the energy requirement at each node and present through simulation how to schedule the WCV to accomplish its energy transfer tasks in a real-time WSN.

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.

The wireless power transfer technology in Wireless Sensor Networks (WSNs) lets the sensors replenish their energy through the Mobile Chargers (MCs). The new network is named Wireless Rechargeable Sensor Networks (WRSNs) in the literature. In these networks, the mobile charger scheduling is very important to retrieve the sensors' energy. In some of the proposed approaches, a deterministic and predetermined path is determined for MCs by using the information from the entire network. These approaches are not appropriate for the uncertain real networks in which the network changes over time. For this reason, we propose an online, grid-based and decentralized method. In this method, MCs select some sensors for renewing their energy by considering the status of the grid cells around them. The purpose of our method named Coverage Aware Charging (CAC) is to replenish the power of low-energy sensors as well as keeping a better network coverage. Finally, we evaluate CAC by using comprehensive simulation different situations.

Curently, Wireless Sensor Network (WSN) is applied in many fields such as industrial plants, security, medical, as well as weather and environmental monitoring. The constraint of the use of WSN is the limited power of each sensor node, which means energy consumption efficiency is an important issue in WSN. Routing is a function in WSN which consumes a considerable amount of energy. One of the routing protocol that can increase the energy efficiency of WSN is Low Energy Adaptive Clustering Hierarchy (LEACH). In this research, energy consumption and the number of dead nodes of LEACH are evaluated using MATLAB simulation. The results show that elevation angle and number of clusters affect the energy consumption and number of dead nodes. HAP-WSN system with clustering topology will reduce energy consumption and minimize the number of dead nodes. From a set of simulations, it can be seen that energy consumption for 0° is greater than energy consumption for 10°. It can be also seen that the number of optimum cluster is 4 for both elevation angels.

Wireless charging is a greatly promising technology to resolve the energy limitation problem suffered by wireless sensor networks. In this paper, we propose an efficient mobile energy replenishment scheme based on hybrid mode, namely MERSH, which merges the advantages of the existing online and offline modes to improve the energy supplement efficiency and meanwhile to adapt well to the energy consumption dynamicity of sensors to achieve sustainable operation of wireless sensor networks. In MERSH, nodes are allowed to proactively send charging requests to the mobile charger, called MC, when the residual energy goes down a lower level. With a special-designed low-cost method, MERSH can plan an optimized path for MC every time when it charges the sensors that have sent charging requests. Moreover, to ensure energy supplement and avoid node failure due to the high dynamic energy consumption, MERSH dynamically adjusts the charging duration of each node along the calculated charging path according to its maximum tolerate charging latency and minimum charging wait time. The theoretic analysis shows that MERSH guarantees MC to move along the optimized charging path, which reduces the whole charging cost. Through extensive simulation results, we validate the effectiveness of the proposed MERSH.

In Wireless Sensor Networks (WSNs) localization -the assigning of sufficiently accurate positions to nodes -is frequently required to meaningfully identify the data collected. In Wireless Rechargeable Sensor Networks (WRSNs) -in which nodes are recharged by some external energy source -high accuracy (e.g. smaller than the mean internode distance) is often required to enable charging in a reasonably efficient manner with practical charger power.

Recent advancement in the wireless power transfer technology has motivated the development of a wireless rechargeable sensor network (WRSN). In WRSNs, the formation of an optimal recharging schedule for each wireless charger vehicle is a well known NP-complete problem. To determine the optimal recharging schedule for each wireless charger vehicle, this paper presents a coverage-aware recharge scheduling scheme (CRS) where ACO-based metaheuristic algorithm is employed. In order to provide fast recharging in WRSN, the proposed scheme employs multiple wireless charger vehicles to perform the charging task. Performance analysis of the proposed scheme confirms its superiority in terms of charging latency.