Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks
In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCV...
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doaj-10ed374baabd433588b769356a39b2022020-11-24T21:04:31ZengMDPI AGSensors1424-82202017-08-01178188110.3390/s17081881s17081881Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor NetworksPing Zhong0Ya-Ting Li1Wei-Rong Liu2Gui-Hua Duan3Ying-Wen Chen4Neal Xiong5School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaIn wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.https://www.mdpi.com/1424-8220/17/8/1881data collectionwireless chargingnetwork partitionadaptive anchor selection algorithmoptimization function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ping Zhong Ya-Ting Li Wei-Rong Liu Gui-Hua Duan Ying-Wen Chen Neal Xiong |
spellingShingle |
Ping Zhong Ya-Ting Li Wei-Rong Liu Gui-Hua Duan Ying-Wen Chen Neal Xiong Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks Sensors data collection wireless charging network partition adaptive anchor selection algorithm optimization function |
author_facet |
Ping Zhong Ya-Ting Li Wei-Rong Liu Gui-Hua Duan Ying-Wen Chen Neal Xiong |
author_sort |
Ping Zhong |
title |
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks |
title_short |
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks |
title_full |
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks |
title_fullStr |
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks |
title_full_unstemmed |
Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks |
title_sort |
joint mobile data collection and wireless energy transfer in wireless rechargeable sensor networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-08-01 |
description |
In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs. |
topic |
data collection wireless charging network partition adaptive anchor selection algorithm optimization function |
url |
https://www.mdpi.com/1424-8220/17/8/1881 |
work_keys_str_mv |
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