A location-dependent task assignment mechanism in vehicular crowdsensing

Lanlan Rui, Pan Zhang, Haoqiu Huang and Xuesong Qiu

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 9, 1550147716669627

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.

Keywords: Location-dependent task assignment; NP-hard; approximate assignment mechanism; vehicular crowdsensing (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147716669627 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:9:p:1550147716669627

DOI: 10.1177/1550147716669627

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().