NSFC Program - 91218302 & 61527812, National Science and Technology Major Project - 2016ZX01038101, MIIT IT funds (Research and application of TCN key technologies) of China), and National Key Technology RD Program 2015BAG14B01-02
Sensor networks, Learning classifier systems, Production scheduling, Algorithms, Problem solving
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.
Shi, H., Song, X., Gu, M., & Sun, J. (2016). Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks. Sensors, 16(12), 2013.