Scheduling with Collaborative Mobile Chargers Inter-WSNs
Mobile charge problem describes a fleet of mobile chargers delivers energy to the sensor nodes periodically in wireless sensor networks (WSNs). In it, every sensor node has an asynchronous rest working time during a charging round. We consider the asynchronous rest working time and give the lower an...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2015-05-01
|
Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1155/2015/921397 |
id |
doaj-80c8683092b64338bc1cb0b6603628b4 |
---|---|
record_format |
Article |
spelling |
doaj-80c8683092b64338bc1cb0b6603628b42020-11-25T03:29:31ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-05-011110.1155/2015/921397921397Scheduling with Collaborative Mobile Chargers Inter-WSNsJidong ZhaoXili DaiXiaomin WangMobile charge problem describes a fleet of mobile chargers delivers energy to the sensor nodes periodically in wireless sensor networks (WSNs). In it, every sensor node has an asynchronous rest working time during a charging round. We consider the asynchronous rest working time and give the lower and upper bounds of the recharging cycle by the suitable total serving rate, to give the definition of time windows for the sensor nodes. In this paper, we model this problem in 2-dimensional WSNs as a vehicle routing problem with time windows (VRPTW). For solving the problem of multiple mobile chargers with different routing paths, we propose to transform the multiple routing problems into a single routing problem, by duplicating the sink into multiple virtual sinks. To optimize the routing path, we propose a local optimization algorithm by considering the collaborative charging among the mobile chargers. Through the simulations, we compare our proposed algorithm with the H η C l u s t e r C h a r g i n g ( β ) algorithm. We demonstrate the advantages of our collaborative scheduling algorithm in this problem.https://doi.org/10.1155/2015/921397 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jidong Zhao Xili Dai Xiaomin Wang |
spellingShingle |
Jidong Zhao Xili Dai Xiaomin Wang Scheduling with Collaborative Mobile Chargers Inter-WSNs International Journal of Distributed Sensor Networks |
author_facet |
Jidong Zhao Xili Dai Xiaomin Wang |
author_sort |
Jidong Zhao |
title |
Scheduling with Collaborative Mobile Chargers Inter-WSNs |
title_short |
Scheduling with Collaborative Mobile Chargers Inter-WSNs |
title_full |
Scheduling with Collaborative Mobile Chargers Inter-WSNs |
title_fullStr |
Scheduling with Collaborative Mobile Chargers Inter-WSNs |
title_full_unstemmed |
Scheduling with Collaborative Mobile Chargers Inter-WSNs |
title_sort |
scheduling with collaborative mobile chargers inter-wsns |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2015-05-01 |
description |
Mobile charge problem describes a fleet of mobile chargers delivers energy to the sensor nodes periodically in wireless sensor networks (WSNs). In it, every sensor node has an asynchronous rest working time during a charging round. We consider the asynchronous rest working time and give the lower and upper bounds of the recharging cycle by the suitable total serving rate, to give the definition of time windows for the sensor nodes. In this paper, we model this problem in 2-dimensional WSNs as a vehicle routing problem with time windows (VRPTW). For solving the problem of multiple mobile chargers with different routing paths, we propose to transform the multiple routing problems into a single routing problem, by duplicating the sink into multiple virtual sinks. To optimize the routing path, we propose a local optimization algorithm by considering the collaborative charging among the mobile chargers. Through the simulations, we compare our proposed algorithm with the H η C l u s t e r C h a r g i n g ( β ) algorithm. We demonstrate the advantages of our collaborative scheduling algorithm in this problem. |
url |
https://doi.org/10.1155/2015/921397 |
work_keys_str_mv |
AT jidongzhao schedulingwithcollaborativemobilechargersinterwsns AT xilidai schedulingwithcollaborativemobilechargersinterwsns AT xiaominwang schedulingwithcollaborativemobilechargersinterwsns |
_version_ |
1724578646322053120 |