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Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks
Heyuan Shi1, Xiaoyu Song2, Ming Gu3
1School of Software, Tsinghua University, Beijing 100084, China. shy15@mails.tsinghua.edu.cn.
Sensors (Basel, Switzerland)
|December 6, 2016
Summary
This study introduces a genetic-based trading scheduling algorithm (GTSA) for vehicular participatory sensing networks (VPSN). The GTSA efficiently maximizes profits for trading platforms by optimizing task selection and participant recruitment.
Area of Science:
- Computer Science
- Network Engineering
- Data Science
Background:
- Vehicular participatory sensing networks (VPSN) are increasingly prevalent, offering significant application potential.
- Existing literature often overlooks the distinct economic roles of task publishers and trading platforms in VPSN.
- Trading platforms aim to maximize profit, not necessarily task quality, by strategically selecting tasks and participants.
Purpose of the Study:
- To address the scheduling problem for trading platforms in vehicular participatory sensing.
- To develop an algorithm that optimizes task selection and participant recruitment for profit maximization.
- To account for the predictable mobility patterns of vehicles in the scheduling process.
Main Methods:
- A genetic-based trading scheduling algorithm (GTSA) was developed.
- The algorithm addresses two key scheduling aspects: task selection and participant recruitment.
- Predictable vehicle mobility was incorporated into the scheduling model.
Main Results:
- The GTSA algorithm was tested using a realistic dataset of taxi trajectories.
- Experimental results demonstrate the algorithm's efficiency.
- The GTSA enables trading platforms to achieve considerable profit in VPSN.
Conclusions:
- The proposed GTSA is an effective solution for the trading platform scheduling problem in VPSN.
- The algorithm successfully balances task selection and participant recruitment for profit.
- VPSN trading platforms can leverage GTSA to enhance their profitability using predictable mobility data.
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