A location-dependent task assignment mechanism in vehicular crowdsensing. (September 2016)

• Record Type: Journal Article
• Authors: Rui, Lanlan; Zhang, Pan; Huang, Haoqiu; Qiu, Xuesong
• Is Part Of: International journal of distributed sensor networks. Volume 12:Number 9(2016)
• Publication Date: 2016-09
• Subjects: Location-dependent task assignment -- NP-hard -- approximate assignment mechanism -- vehicular crowdsensing; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147716669627
• Languages: English
• ISSNs: 1550-1329

Abstract

The development of modern vehicles equipped with various sensors and wireless communication has been the impetus for vehicular crowdsensing applications, which can be used to complete large-scale and complex social sensing tasks such as monitoring road surfaces condition. However, most of the sensing tasks are closely related with specific location and required to be performed in certain area, and in this article, we have proved these kind of location-based optimal task assignment to be an NP-hard (non-deterministic polynomial-time hard) problem. To solve this challenge, we first establish mathematical model of multi-vehicle collaborative task assignment problem, considering vehicle's time budget constraint, location, and multiple requirements of sensing tasks. And we propose an approximation location-based task assignment mechanism for it, which is composed of two parts: the first part is to determine the allocating order among engaged vehicles and the second part is to schedule optimal sensing path for single vehicle, which in this article we propose an optimal sensing path scheduling algorithm to finish this task. Using Lingo software, we prove the efficiency of the proposed optimal sensing path scheduling algorithm. Extensive simulation results also demonstrate correctness and effectiveness of our approach.