Mobile Wireless Chargers Research Articles

Adaptive Payoff Balance Among Mobile Wireless Chargers for Rechargeable Wireless Sensor Networks

Adaptive Payoff Balance Among Mobile Wireless Chargers for Rechargeable Wireless Sensor Networks

An envy-free online UAV charging scheme with vehicle-mounted mobile wireless chargers

An envy-free online UAV charging scheme with vehicle-mounted mobile wireless chargers

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.

Mobile Wireless Rechargeable UAV Networks: Challenges and Solutions

Unmanned aerial vehicles (UAVs) can help facilitate cost-effective and flexible service provisioning in future smart cities. Nevertheless, UAV applications generally suffer severe flight time limitations due to constrained onboard battery capacity, causing a necessity of frequent battery recharging or replacement when performing persistent missions. Utilizing wireless mobile chargers, such as vehicles with wireless charging equipment for on-demand self-recharging has been envisioned as a promising solution to address this issue. In this article, we present a comprehensive study of \underline{v}ehicle-assisted \underline{w}ireless rechargeable \underline{U}AV \underline{n}etworks (VWUNs) to promote on-demand, secure, and efficient UAV recharging services. Specifically, we first discuss the opportunities and challenges of deploying VWUNs and review state-of-the-art solutions in this field. We then propose a secure and privacy-preserving VWUN framework for UAVs and ground vehicles based on differential privacy (DP). Within this framework, an online double auction mechanism is developed for optimal charging scheduling, and a two-phase DP algorithm is devised to preserve the sensitive bidding and energy trading information of participants. Experimental results demonstrate that the proposed framework can effectively enhance charging efficiency and security. Finally, we outline promising directions for future research in this emerging field.

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.

Correction to: Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.

Broker-Based Nodes Recharging Scheme for Surveillance Wireless Rechargeable Sensor Networks

In Surveillance Wireless Sensor Networks (SWSN), the nodes are distributed at the borders to detect unauthorized intrusions. In such applications, as the nodes are usually equipped with limited batteries and the network area is harsh to be accessible for humans, energy efficiency is considered as one of the main issues in designing the sensor networks. In the last years, Wireless Rechargeable Sensor Networks (WRSN) provides valuable insights into solving the energy constraint problem in WSNs. In a WRSN, Wireless Mobile Chargers (WMC) are responsible to recharge the batteries of the sensor nodes in order to enhance the operation time of the network. In most of the previous WRSN based schemes, the WMCs recharge the nodes singly and directly, which results in increasing the travel time of WMC and waiting time of sensor nodes. In addition, despite of using WMC, the permanent network lifetime has not been addressed in most of the previous schemes. Therefore, to overcome the limitations of existing literature, this research work focuses on the design and development of Broker-based Nodes Recharging Scheme (BNRS) which significantly contributes to improved lifetime and performance of SWSN. In the proposed scheme, power banks are employed as brokers between sensor nodes and WMC. The main duty of a power bank is to receive energy from WMC and transfer to the sensor nodes. Based on the simulation experiments the proposed BNRS achieves the permanent network operation which results in increasing security in the border of countries.

Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Wireless Mobile Charger Excursion Optimization Algorithm in Wireless Rechargeable Sensor Networks

Employing Wireless Mobile Chargers (WMCs) is a promising technique to improve the sensor network lifetime. In this paper, a Wireless Mobile Charger Excursion Optimization (WMCEO) Algorithm is introduced, which aims to optimize the moving trajectory and charging time of WMC in each sojourn location to achieve the enhanced energy efficiency and network lifetime. Unlike previous works, the whole network is divided into Charging Regions (CRs) to charge the several nodes simultaneously, which results in reducing the charging latency and improving the network performance. Moreover, in the proposed scheme, both the remaining energy of nodes located at each CR and the travel time of WMC issues are taken into account in WMC moving path optimization, which results in improving energy efficiency. Likewise, the remaining energy of the nodes located at the other CRs is considered in charging time optimization, which leads to enhance the network lifetime. Simulation results show that the proposed WMCEO scheme enhances network performance in terms of different network evaluation metrics.

Energy-Efficient Tour Optimization of Wireless Mobile Chargers for Rechargeable Sensor Networks

Energy constraint is one of the main design issues of wireless sensor networks. Over the past few years, the concept of the wireless mobile charger (WMC) has been proposed in order to improve the energy efficiency of the networks. In such schemes, optimizing the moving trajectory and charging time of WMC are considered as two main problems. Several schemes attempt to optimize the moving trajectory of WMC without considering the balanced energy depletion time of nodes, which leads to degrading the energy efficiency and network lifetime. Thus, a route optimization of WMC algorithm is proposed to determine the optimal trajectory of WMC to move along sensor nodes in such a way that the balanced energy depletion time of the nodes is achieved. Moreover, in most of the existing schemes, WMCs charge the nodes as long as their batteries are fully charged. However, not considering the remaining lifetime of the other nodes leads to premature network death and decreases the network performance. Therefore, a charge time optimization of WMC algorithm is proposed to overcome the mentioned problem. Simulation results demonstrate that the proposed scheme remarkably enhance the network performance in terms of different evaluation metrics.

Research on Cost-Balanced Mobile Energy Replenishment Strategy for Wireless Rechargeable Sensor Networks

In order to maximize the utilization rate of the Mobile Wireless Chargers (MWCs) and reduce the recharging delay in large-scale Rechargeable Wireless Sensor Networks (WRSNs), a type of Cost-Balanced Mobile Energy Replenishment Strategy (CBMERS) is proposed in this paper. Firstly, nodes are assigned into groups according to their remaining lifetime, which ensures that only the ones with lower residual energy are recharged in each time slot. Then, to balance energy consumption among multiple MWCs, the moving distance as well as the power cost of the MWC are taken as constraints to get the optimal trajectory allocation scheme. Moreover, by further adjusting the amount of energy being replenished to some sensor nodes, it ensures that the MWC have enough energy to fulfill the recharging task and return back to the base station. Experiment results show that, compared with the Periodic recharging strategy and the Cluster based Multiple Charges Coordination algorithm (C-MCC), the proposed method can improve the recharging efficiency of MWCs by about 48.22% and 43.35%, and the average waiting time of nodes is also reduced by about 55.72% and 30.7%, respectively.

Fair Energy Division Scheme to Permanentize the Network Operation for Wireless Rechargeable Sensor Networks

In the past years, the energy constraint problem is known as a design issue of Wireless Sensor Networks (WSNs) due to equipping the sensor nodes with limited power supplies. During the last few years, Wireless Rechargeable Sensor Networks (WRSNs) have gained researchers attention. In a WRSN, the sensor nodes are equipped with RF circuits, which enables them to receive energy from Wireless Mobile Chargers (WMC). However, most of the existing wireless charging algorithms consider the unlimited power budget for WMCs, which is the opposite of feasibility of a real network environment. Likewise, most of the previous works fail to take full advantage of WMC as it starts recharging the nodes when their energy level reaches a threshold, which leads to increasing the inactive time of WMC. Moreover, although previous works employed WMCs, the network lifetime is limited. However, optimal division of the energy of WMC among nodes can guarantee the perpetual network operation. Therefore, proposing an efficient method that jointly solves these challenges is required. In this paper, a new Fair Energy Division Scheme (FEDS) is presented, which undertakes the permanent network operation by optimizing the energy division at the beginning of each cycle. Simulation results exhibit that FEDS achieves perpetual network lifetime. In addition, the proposed scheme improves energy efficiency, (25%) compared to Uneven Cluster-based Mobile Charging (UCMC) and (75%) compared to Nearest-Job-Next Preemption NJNP; travelling time of WMC, (50%) compared to UCMC and (75%) compared to NJNP. In conclusion, the proposed protocol significantly improves network.

Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning

Generally, mobile wireless sensor network (MWSN) senses the sensitive and typical kind of events in various application areas along with the frequent mobility of sensor nodes as compared to traditional wireless sensor network. Due to mobility feature, MWSN extended the working of WSN. To design MWSN, certain key factors like energy efficiency, mobility, data routing, localization and charging strategy are involved. Mobile sensor nodes consume extra amount of energy due to mobility along with sensing and data routing task which exhaust network lifetime rapidly. In this research study, authors have proposed two wireless chargers (fixed and mobile) for sensor nodes. The main difference between fixed and mobile wireless charger is: fixed wireless charger invites mobile sensor nodes for wireless charging at base station whereas mobile wireless charger travels to the position of mobile sensor nodes for wireless charging. Both chargers fulfilled the charging request of sensor nodes efficiently. This is the first research study to propose such type of charging strategy for mobile wireless sensor network (MWSN) as per the available literature. The RL (reinforcement learning) technique is used here for optimization purpose of charging cost. These wireless chargers simulated in MATLAB enviorment and results showed that there is a significant improvement in network lifetime due to these charging strategies when compared to the existing algorithms.

Wireless Charging Deployment in Sensor Networks.

Charging schemes utilizing mobile wireless chargers can be applied to prolong the lifespan of a wireless sensor network. In considering charging schemes with mobile chargers, most current studies focus on charging each sensor from a single position, then optimizing the moving paths of the chargers. However, in reality, a wireless charger may charge the same sensor from several positions in its path. In this paper we consider this fact and seek to minimize both the number of charging locations and the total required charging time. Two charging plans are developed. The first plan considers the charging time required by each sensor and greedily selects the charging service positions. The second one is a two-phase plan, where the number of charging positions is first minimized, then minimum charging times are assigned to every position according to the charging requirements of the nearby sensors. This paper also corrects a problem neglected by some studies in minimizing the number of charging service positions and further provides a corresponding solution. Empirical studies show that compared with other minimal clique partition (MCP)-based methods, the proposed charging plan may save up to 60% in terms of both the number of charging positions and the total required charging time.

A Joint Energy Replenishment and Data Collection Algorithm in Wireless Rechargeable Sensor Networks

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a ${K}$ -means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs’ current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks

Existing studies on wireless sensor networks (WSNs) have revealed that the limited battery capacity of sensor nodes (SNs) hinders their perpetual operation. Recent findings in the domain of wireless energy transfer (WET) have attracted a lot of attention of academia and industry to cater the lack of energy in the WSNs. The main idea of WET is to restore the energy of SNs using one or more wireless mobile chargers (MCs), which leads to a new paradigm of wireless rechargeable sensor networks (WRSNs). The determination of an optimal order of charging the SNs (i.e., charging schedule) in an on-demand WRSN is a well-known NP-hard problem. Moreover, care must be taken while designing the charging schedule of an MC as requesting SNs introduce both spatial and temporal constraints. In this paper, we first present a Linear Programming (LP) formulation for the problem of scheduling an MC and then propose an efficient solution based on gravitational search algorithm (GSA). Our method is presented with a novel agent representation scheme and an efficient fitness function. We perform extensive simulations on the proposed scheme to demonstrate its effectiveness over two state-of-the-art algorithms, namely first come first serve (FCFS) and nearest job next with preemption (NJNP). The simulation results reveal that the proposed scheme outperforms both the existing algorithms in terms of charging latency. The virtue of our scheme is also proved by the well-known statistical test, analysis of variance (ANOVA), followed by post hoc analysis.

A Novel Data Collection Method with Recharge Plan for Rechargeable Wireless Sensor Networks

Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node‐Gosper island‐based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used mobile wireless chargers to visit and recharge the batteries of sensors located on different levels of node‐Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well‐known data collection methods in terms of energy consumption and that my proposed recharge plan is more efficient than previous approaches in terms of charge path length, number of alive nodes, and traveling efficiency.

Node-Gosper Island-Based Data Collection Method with Recharge Plan for Rechargeable Wireless Sensor Networks

Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node-Gosper island-based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used a mobile wireless charger to visit and recharge the batteries of sensors located on different levels of node-Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well-known data collection methods in terms of energy consumption.

Charging utility maximization in wireless rechargeable sensor networks

Wireless energy transfer as a promising technology provides an alternative solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In this paper, we study replenishing energy on sensors in a WRSN to shorten energy expiration durations of sensors, by employing a mobile wireless charger to replenish sensors dynamically. We first formulate a novel sensor recharging problem with an objective of maximizing the charging utility of sensors, subject to the total traveling distance of the mobile charger per tour and the charging time window of each to-be-charged sensor. Due to the NP-hardness of the problem, we then propose an approximation algorithm with quasi-polynomial time complexity. In spite of the guaranteed performance ratio of the approximate solution, its time complexity is prohibitively high and may not be feasible in practice. Instead, we devise a fast yet scalable heuristic for the problem in response to dynamic energy consumption of sensors in the network. Furthermore, we also consider the online version of the problem where sensor replenishment is scheduled at every fixed time interval. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising.