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

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.

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

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1324
Author(s):  
Cheng Gong ◽  
Chao Guo ◽  
Haitao Xu ◽  
Chengcheng Zhou ◽  
Xiaotao Yuan
Keyword(s):  
Sensor Networks ◽  
Communication Networks ◽  
Energy Supply ◽  
Large Scale ◽  
Joint Optimization ◽  
Second Phase ◽  
Optimization Strategy ◽  
Two Phase ◽  
Game Equilibrium ◽  
Wireless Rechargeable Sensor Networks

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.

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

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.

Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

2015 ◽  
Vol 19 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Yi Li ◽  
◽  
Jun Peng ◽  
Fu Jiang ◽  
Kaiyang Liu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Data Transmission ◽  
Cooperative Spectrum Sensing ◽  
Optimization Method ◽  
Joint Optimization ◽  
Transmission Scheme ◽  
Cognitive Radio Sensor Networks

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

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

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3150 ◽  
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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