An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network

The fast development of Internet of Things (IoT) has greatly driven the development of mobile crowdsensing vehicular sensor network (CVSN). A lot of fascinating big data-based applications have been developed such as traffic management, health monitoring, and smart city. How to effectively collect enough data while not increasing too much redundancy is still a challenging problem in the big data application for CVSN. In this paper, a data relay mule-based collection scheme (DRMCS) is proposed to improve the quality of service (QoS). Comparing with the previous researches, the innovation of DRMCS is as follows: First, a data collection framework which considers the sensing task completion rate, redundancy rate and delay is proposed. Second, the micro mobile data center (MMDC) is proposed to solve the problem of connecting the huge number of intelligent sensing devices with data centre. Third, a MMDC selection strategy based on simulated annealing algorithm is proposed by DRMCS to improve the data collection performance. Compared with traditional vehicular network opportunistic communication without data relay mule (OCDRM), the sensing task completion rate of DRMCS has been improved by 78.6%.