Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks
The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publi...
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doaj-ca7faadcb13547168f92e8eeaa7726c02020-11-24T23:31:32ZengMDPI AGSensors1424-82202016-11-011612201310.3390/s16122013s16122013Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing NetworksHeyuan Shi0Xiaoyu Song1Ming Gu2Jiaguang Sun3School of Software, Tsinghua University, Beijing 100084, ChinaDepartment of Electrical & Computer Engineering, Portland State University, P.O. Box 751, Portland, OR 97207-0751, USASchool of Software, Tsinghua University, Beijing 100084, ChinaSchool of Software, Tsinghua University, Beijing 100084, ChinaThe vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.http://www.mdpi.com/1424-8220/16/12/2013participatory sensing networksvehicular sensor networkstasks selectionparticipants recruitmentscheduling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Heyuan Shi Xiaoyu Song Ming Gu Jiaguang Sun |
spellingShingle |
Heyuan Shi Xiaoyu Song Ming Gu Jiaguang Sun Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks Sensors participatory sensing networks vehicular sensor networks tasks selection participants recruitment scheduling |
author_facet |
Heyuan Shi Xiaoyu Song Ming Gu Jiaguang Sun |
author_sort |
Heyuan Shi |
title |
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks |
title_short |
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks |
title_full |
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks |
title_fullStr |
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks |
title_full_unstemmed |
Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks |
title_sort |
task and participant scheduling of trading platforms in vehicular participatory sensing networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-11-01 |
description |
The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN. |
topic |
participatory sensing networks vehicular sensor networks tasks selection participants recruitment scheduling |
url |
http://www.mdpi.com/1424-8220/16/12/2013 |
work_keys_str_mv |
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