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Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network

Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limite...

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Autores principales: Liu, Haolin, Deng, Qingyong, Tian, Shujuan, Peng, Xin, Pei, Tingrui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068635/
https://www.ncbi.nlm.nih.gov/pubmed/29996557
http://dx.doi.org/10.3390/s18072223
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author Liu, Haolin
Deng, Qingyong
Tian, Shujuan
Peng, Xin
Pei, Tingrui
author_facet Liu, Haolin
Deng, Qingyong
Tian, Shujuan
Peng, Xin
Pei, Tingrui
author_sort Liu, Haolin
collection PubMed
description Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limited traveling distance and speed, not every node that needs to be charged may be serviced in time. Thus, in such scenario, how to make a route plan for the mobile charger to determine which nodes should be charged first is a critical issue related to the network’s Quality of Service (QoS). In this paper, we propose a mobile charger’s scheduling algorithm to mitigate the data loss of network by considering the node’s criticality in connectivity and energy. First, we introduce a novel metric named criticality index to measure node’s connectivity contribution, which is computed as a summation of node’s neighbor dissimilarity. Furthermore, to reflect the node’s charging demand, an indicator called energy criticality is adopted to weight the criticality index, which is a normalized ratio of the node’s consumed energy to its total energy. Then, we formulate an optimization problem with the objective of maximizing total weighted criticality indexes of nodes to construct a charging tour, subject to the mobile charger’s traveling distance constraint. Due to the NP-hardness of the problem, a heuristic algorithm is proposed to solve it. The heuristic algorithm includes three steps, which is spanning tree growing, tour construction and tour improvement. Finally, we compare the proposed algorithm to the state-of-art scheduling algorithms. The obtained results demonstrate that the proposed algorithm is a promising one.
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spelling pubmed-60686352018-08-07 Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network Liu, Haolin Deng, Qingyong Tian, Shujuan Peng, Xin Pei, Tingrui Sensors (Basel) Article Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limited traveling distance and speed, not every node that needs to be charged may be serviced in time. Thus, in such scenario, how to make a route plan for the mobile charger to determine which nodes should be charged first is a critical issue related to the network’s Quality of Service (QoS). In this paper, we propose a mobile charger’s scheduling algorithm to mitigate the data loss of network by considering the node’s criticality in connectivity and energy. First, we introduce a novel metric named criticality index to measure node’s connectivity contribution, which is computed as a summation of node’s neighbor dissimilarity. Furthermore, to reflect the node’s charging demand, an indicator called energy criticality is adopted to weight the criticality index, which is a normalized ratio of the node’s consumed energy to its total energy. Then, we formulate an optimization problem with the objective of maximizing total weighted criticality indexes of nodes to construct a charging tour, subject to the mobile charger’s traveling distance constraint. Due to the NP-hardness of the problem, a heuristic algorithm is proposed to solve it. The heuristic algorithm includes three steps, which is spanning tree growing, tour construction and tour improvement. Finally, we compare the proposed algorithm to the state-of-art scheduling algorithms. The obtained results demonstrate that the proposed algorithm is a promising one. MDPI 2018-07-10 /pmc/articles/PMC6068635/ /pubmed/29996557 http://dx.doi.org/10.3390/s18072223 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Haolin
Deng, Qingyong
Tian, Shujuan
Peng, Xin
Pei, Tingrui
Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title_full Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title_fullStr Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title_full_unstemmed Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title_short Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
title_sort recharging schedule for mitigating data loss in wireless rechargeable sensor network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068635/
https://www.ncbi.nlm.nih.gov/pubmed/29996557
http://dx.doi.org/10.3390/s18072223
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