Using Minimum Mobile Chargers to Keep Large-Scale Wireless Rechargeable Sensor Networks Running Forever

Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy supply, and then extends the lifetime of WSN.

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Minimizing the number of wireless charging PAD for unmanned aerial vehicle–based wireless rechargeable sensor networks

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211055958 ◽

2021 ◽

Vol 17 (12) ◽

pp. 155014772110559

Author(s):

Yingjue Chen ◽

Yingnan Gu ◽

Panfeng Li ◽

Feng Lin

Keyword(s):

Sensor Networks ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Large Scale ◽

Base Station ◽

Sensor Nodes ◽

Wireless Charging ◽

Aerial Vehicles ◽

Wireless Rechargeable Sensor Networks ◽

Aerial Vehicle

In wireless rechargeable sensor networks, most researchers address energy scarcity by introducing one or multiple ground mobile vehicles to recharge energy-hungry sensor nodes. The charging efficiency is limited by the moving speed of ground chargers and rough environments, especially in large-scale or challenging scenarios. To address the limitations, researchers consider replacing ground mobile chargers with lightweight unmanned aerial vehicles to support large-scale scenarios because of the unmanned aerial vehicle moving at a higher speed without geographical limitation. Moreover, multiple automatic landing wireless charging PADs are deployed to recharge unmanned aerial vehicles automatically. In this work, we investigate the problem of introducing the minimal number of PADs in unmanned aerial vehicle–based wireless rechargeable sensor networks. We propose a novel PAD deployment scheme named clustering-with-double-constraints and disks-shift-combining that can adapt to arbitrary locations of the base station, arbitrary geographic distributions of sensor nodes, and arbitrary sizes of network areas. In the proposed scheme, we first obtain an initial PAD deployment solution by clustering nodes in geographic locations. Then, we propose a center shift combining algorithm to optimize this solution by shifting the location of PADs and attempting to merge the adjacent PADs. The simulation results show that compared to existing algorithms, our scheme can charge the network with fewer PADs.

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A Type of Annulus-Based Energy Balanced Data Collection Method in Wireless Rechargeable Sensor Networks

Sensors ◽

10.3390/s18093150 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3150 ◽

Cited By ~ 1

Author(s):

Chao Sha ◽

Qin Liu ◽

Si-Yi Song ◽

Ru-Chuan Wang

Keyword(s):

Energy Consumption ◽

Sensor Networks ◽

Data Collection ◽

Large Scale ◽

Energy Hole ◽

Node Scheduling ◽

Wireless Rechargeable Sensor Networks ◽

Energy Consumption Optimization ◽

Balance Energy ◽

Simulation Results

With the increasing number of ubiquitous terminals and the continuous expansion of network scale, the problem of unbalanced energy consumption in sensor networks has become increasingly prominent in recent years. However, a node scheduling strategy or an energy consumption optimization algorithm may be not enough to meet the requirements of large-scale application. To address this problem a type of Annulus-based Energy Balanced Data Collection (AEBDC) method is proposed in this paper. The circular network is divided into several annular sectors of different sizes. Nodes in the same annulus-sector form a cluster. Based on this model, a multi-hop data forwarding strategy with the help of the candidate cluster headers is proposed to balance energy consumption during transmission and to avoid buffer overflow. Meanwhile, in each annulus, there is a Wireless Charging Vehicle (WCV) that is responsible for periodically recharging the cluster headers as well as the candidate cluster headers. By minimizing the recharging cost, the energy efficiency is enhanced. Simulation results show that AEBDC can not only alleviate the “energy hole problem” in sensor networks, but also effectively prolong the network lifetime.

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Collaborative mobile charging policy for perpetual operation in large-scale wireless rechargeable sensor networks

Neurocomputing ◽

10.1016/j.neucom.2017.02.094 ◽

2017 ◽

Vol 270 ◽

pp. 137-144 ◽

Cited By ~ 4

Author(s):

Zhigang Chen ◽

Xuehan Chen ◽

Deyu Zhang ◽

Feng Zeng

Keyword(s):

Sensor Networks ◽

Large Scale ◽

Wireless Rechargeable Sensor Networks

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DEVELOPING IMPROVED ENERGY-BASED NODE CHARGE REPLENISHMENT MODEL IN LARGE-SCALE WIRELESS RECHARGEABLE SENSOR NETWORKS

Journal of Critical Reviews ◽

10.31838/jcr.07.14.06 ◽

2020 ◽

Vol 7 (14) ◽

Keyword(s):

Sensor Networks ◽

Large Scale ◽

Wireless Rechargeable Sensor Networks

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Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

10.20944/preprints202009.0626.v1 ◽

2020 ◽

Author(s):

Cheng Gong ◽

Chao Guo ◽

Haitao Xu ◽

Chengcheng Zhou ◽

Xiaotao Yuan

Keyword(s):

Sensor Networks ◽

Communication Networks ◽

Energy Supply ◽

Large Scale ◽

Optimization Method ◽

Joint Optimization ◽

Second Phase ◽

Optimization Strategy ◽

Game Equilibrium ◽

Wireless Rechargeable Sensor Networks

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

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LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l2627.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 3543-3550

Keyword(s):

Sensor Networks ◽

Network Lifetime ◽

Large Scale ◽

Sensor Nodes ◽

Small Scale ◽

Resource Limitations ◽

Wireless Rechargeable Sensor Networks ◽

Battery Capacity ◽

Multiple Charging ◽

Load Balanced

In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

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