Wireless Rechargeable Sensor Networks Research Articles

EMPC: Energy-Minimization Path Construction for data collection and wireless charging in WRSN

Sensing data collection and energy supplement are key issues of Wireless Rechargeable Sensor Network (WRSN). Using mobile vehicles to collect data and supplement energy can not only effectively reduce the node communication energy consumption, but also ensure the continuity of network operation. We propose an energy-minimization path construction algorithm based on dual-function vehicles for data collection and wireless charging in order to minimize the network energy consumption. The algorithm consists of three phases: adaptive network partition, anchor selection, and dual-function vehicle path construction. A partitioning algorithm based on a minimum spanning tree is proposed to divide the network into several regions in the adaptive network partition phase. Anchor selection phase is used to obtain data collection points in each region. The path construction phase is designed to construct a vehicle mobile path with anchors and charging nodes. Finally, experiments show that the algorithm can not only effectively reduce network energy consumption, but also prolong network lifetime and increase collected data amount.

Implementing MRCRLB technique on modulation schemes in wireless rechargeable sensor networks

As battery technology has not advanced as quickly as other technologies like semiconductor technology, energy efficiency is still one of the primary concerns in Wireless Sensor Networks (WSNs). One of the finest Wireless Energy Transfer (WET) technique is Magnetic Resonant Coupling (MRC) used to re-energize the nodes in WSNs. Recent advancements in MRC show that more than one node can be re-charged at the same time. This paper aims to exploit multiple nodes WET to overcome the issues pertaining to energy in Wireless Sensor Network (WSN). For transferring energy to various static sensor nodes deployed in a network, Unmanned Aerial Vehicle (UAV) is considered which travels inside a network and performs a process of re-charging. Further, WSNs being most acceptable face issues like: battery constraint, and congestion. Therefore, in order to address such issues, we have proposed MRCRLB (Magnetic Resonant Coupling Based Recharging and Load Balancing) scheme. Then, finally the proposed model is tested on various modulation schemes at the physical layer of WSNs to find out most appropriate modulation scheme type for lifetime extension model (MRCRLB).

Modeling and Optimizing Method for Charging Route Planning of Wireless Rechargeable Sensor Network

Wireless rechargeable sensor network consists of a data center, several sensors and one or more mobile chargers. In order to make the wireless rechargeable sensor network (wrsn) work normally, the mobile charger needs to charge the sensor regularly to avoid its power below the threshold. The energy consumption of mobile charger will be caused when charging sensor nodes and on the way of charging sensor. In this paper, how to reasonably plan the charging route of the mobile charger and reduce the energy consumption of the mobile charger on the road are modeled. By comparing the Monte Carlo optimization simulated annealing algorithm with the improved genetic algorithm, the TSP traveling salesman problem with the minimum energy consumption of the mobile charger on the road is solved.

Minimum battery capacity planning in wireless rechargeable sensor networks

This paper studies the problem of the minimum battery capacity required for the normal operation of each sensor when the charging path of the mobile charger is determined. When the parameters of the wireless rechargeable sensor network are determined, the aim is to minimize the battery capacity required by each sensor, and to ensure the permanent operation of the wireless rechargeable sensor network with the minimum sensor energy consumption. By analyzing the actual working conditions of mobile charger, a linear programming model with the goal of minimizing the battery capacity of each sensor is established, Lingo is used to solve the model, and the results are verified by computer simulation. The results show that the computer simulation results are basically consistent with the model solution results, which proved the reliability of the model.

Shortest path planning for mobile chargers

This paper studies the shortest path that a mobile charger should travel when charging a wireless rechargeable sensor network. Under the condition that the latitude and longitude of each node in the wireless rechargeable sensor network are known, we combine the actual working conditions of the mobile charger to establish a TSP problem model and use two improved modern optimization algorithms and three common optimization algorithms to solve. By comparing the results, we conclude that the improved genetic algorithm can obtain the shortest path length of 11485 m with the least number of iterations. The improved genetic algorithm has better applicability to the problem in this background.

Bus network assisted drone scheduling for sustainable charging of wireless rechargeable sensor network

Wireless Rechargeable Sensor Network (WRSN) is largely used in monitoring of environment and traffic, video surveillance and medical care, etc., and helps to improve the quality of urban life. However, it is challenging to provide the sustainable energy for sensors deployed in buildings, soil or other places, where it is hard to harvest the energy from environment. To address this issue, we design a new wireless charging system, which levers the bus network assisted drone in urban areas. We formulate the drone scheduling problem based on this new wireless charging system to minimize the total time cost of drone subject to all sensors can be charged under the energy constraint of drone. Then, we propose an approximation algorithm DSA for the energy tightened drone scheduling problem. To make the tasks of WRSN sustainable, we further formulate the drone scheduling problem with deadlines of sensors, and present the approximation algorithm DDSA to find the drone schedule with the maximal number of sensors charged by the drone before deadlines. Through the extensive simulations, we demonstrate that DSA can reduce the total time cost by 84.83% compared with Greedy Replenished Energy algorithm, and uses at most 5.98 times of the total time cost of optimal solution on average. Then, we also demonstrate that DDSA can increase the survival rate of sensors by 51.95% compared with Deadline Greedy Replenished Energy algorithm, and can obtain 77.54% survival rate of optimal solution on average.

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.

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack-Defense Game Model.

Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment.

Hybrid Cyber Petri net Modelling, Simulation and Analysis of Master-Slave Charging for Wireless Rechargeable Sensor Networks.

Wireless charging provides continuous energy for wireless sensor networks. However, it is difficult to replenish enough energy for all sensor nodes with fixed charging alone, and even more unrealistic to charge a large number of nodes within a short time via mobile charging. In order to overcome the above weaknesses, this paper firstly puts forward a Master-Slave Charging mode for the WRSN (Wireless Rechargeable Sensor Network), where fixed charging is the master mode and mobile charging is the slave mode, respectively. However, Master-Slave Charging is a typical hybrid system involving discrete event decision and continuous energy transfer. Therefore, the Hybrid Cyber Petri net system is proposed to build a visual specification with mathematical expression of Master-Slave Charging. Moreover, wireless charging in the WRSN is modeled and evaluated from the perspective of a hybrid system for the first time. Furthermore, a greedy-genetic algorithm is proposed to obtain the deployment of fixed chargers and the path planning of a mobile charger, by maximizing the actual electric quantity of the master charging problem and minimizing the mobile charger’s travelling path of the slave charging problem. Finally, the simulation results confirm and verify the Hybrid Cyber Petri net model for Master-Slave Charging. It is worth noting that the proposed model in this paper is highly adaptable to various charging modes in the WRSN.

Importance-Different Charging Scheduling Based on Matroid Theory for Wireless Rechargeable Sensor Networks

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

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

Charging path planning algorithm based on multi-parameters in underwater wireless rechargeable sensor network

UWSN (Underwater wireless sensor network) is a significant part of marine environment stereo monitoring system, and the energy problem has always been an important constraint and challenge that limits its wide application. Due to the development of wireless charging technology and intelligent mobile nodes, the energy problem in UWSN has been solved. The underwater sensor network using wireless charging method is called UWRSN (underwater wireless rechargeable sensor network). In wireless rechargeable sensor networks, one of the core issues is the design of the charging planning. In this paper, a charging path planning algorithm based on multi-parameters is proposed for the on-demand charging system of UWRSN. The algorithm plans the charging path for the underwater MC (mobile charger) according to the parameters such as the remaining power, position, and power consumption speed of the underwater sensor node, so as to minimize the number of network dead nodes, improve network energy efficiency, and extend life of the network. Simulation results show that the algorithm has good performance in terms of charging efficiency, extending network life and other performance indicators.

Coverage-Aware Recharging Scheduling Using Mobile Charger in Wireless Sensor Networks

Energy recharging in wireless rechargeable sensor networks (WRSNs) has acquired much attention in recent years. In literature, many recharging path construction algorithms have been proposed. Most of them considered that all sensors are equally important and designed algorithms to increase the number of recharged sensors or decrease the path length of the mobile charger. However, different sensors have different coverage contributions. Recharging the sensors with larger coverage contribution can achieve better surveillance quality. The proposed recharging scheduling algorithm is divided into three phases, including the Initialization, Recharging Scheduling and Path Construction Phases. In the second phase, this paper proposed two recharging scheduling algorithms, namely the Cost-Effective (CE) algorithm and Cost-Effective with Considerations of Coverage and Fairness ( C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F) algorithm. The proposed two algorithms construct paths for the mobile charger and select the recharging sensors based on the higher weight in terms of larger coverage contribution and smaller path cost. Performance results show that the CE and C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F algorithms yield better performance in terms of the fairness of recharging, recharging stability and coverage ratio, as compared with the existing studies.

无线可充电传感器网络中多MCV充电路径优化

无线可充电传感器网络中多MCV充电路径优化

Link Scheduling in Rechargeable Wireless Sensor Networks With Imperfect Battery and Memory Effects

This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects , and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/discharge cycles by up to 47.41% (45.67)% when nodes have a leak (leak-free) battery. Between leak-free and leak battery scenarios, using the former produces up to 51.46% fewer cycles than the latter.

A New Topology Control Algorithm in Software Defined Wireless Rechargeable Sensor Networks

In wireless networks, the network coverage and sustainable operations are closely interlinked. These are the most critical problems in any wireless sensor networks (WSNs), which are based on software defined networks. However, in previous literature, these problems are always considered separately. Consequently, these problems are not addressed in an efficient manner. In this work, we focus on new network structures known as software defined wireless rechargeable sensor networks (SDWRSNs) to ensure long-term operations and full coverage of the network simultaneously. In this work, we propose the least nodes deployment and charging algorithm (LNDCA) based on the homology theory. In the proposed LNDCA, the SDN controller requests the mobile chargers to replenish the energy for the node with the lowest energy. Additionally, the algorithm fully covers the whole network by using minimum number of nodes and ensures continuous operations in the network. The simulation results and analysis conducted in this work show that the proposed algorithm performs well in terms of energy consumption, coverage, and sustainable operations.

Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short‐term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load‐adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high‐capacity smartphone chargers for sensor nodes for charging. Nearly all R‐WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting‐based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node &amp; mobile charger. Here, the collecting &amp; processing of data are performed through the big data collection in WSNs. The R‐WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks

The advent of mobile charging with wireless energy transfer technology has perpetuated the omnipresent wireless rechargeable sensor networks. The existing literature finds that on-demand recharging of the sensor nodes (SNs) can significantly improve the charging performance while using multiple charging vehicles (MCVs) and multi-node charging model. Most of the existing schemes ignore the heterogeneous rate of SNs’ energy consumption, partial charging of the SNs, and joint optimization of multiple network attributes and thus these schemes are deprived from the benefits of prolonging network lifetime to its maximum extent. To this end, this paper addresses all the aforementioned issues and proposes an on-demand multi-node charging scheme for the SNs following a partial charging model. The working of the proposed scheme is twofold. First, charging schedules of the MCVs are generated through optimal halting points by integrating non-dominated sorting genetic algorithm (NSGA-II) and multi-attribute decision making (MADM) approach. Then the charging time at each halting point is decided for the SNs with the help of a partial charging timer. We carry out extensive simulations on the proposed scheme and the results are compared with some existing schemes using various performance metrics. The results confirm the superiority of the proposed scheme over the existing ones.

A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security.

With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number . Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and . Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of and , the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost.