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.
Highlights
By leveraging mobile vehicles as basic sensing units which cope through wireless network, vehicular crowdsensing is a new tool for pervasive information collection, sharing, and exploration,[1] which can be used to complete large-scale sensing application at a lower cost
We propose an approximation mechanism called location-based task assignment (LBTA) to solve the above problem, a heuristic algorithm 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 (OPS) algorithm to finish this task
While the vehicle’s time budget constraint and the proportion of different types of tasks are fixed, we vary the number of sensing tasks n from 25 to 55, while the number of mobile vehicles m are fixed from 10 to 30, respectively
Summary
By leveraging mobile vehicles as basic sensing units which cope through wireless network, vehicular crowdsensing is a new tool for pervasive information collection, sharing, and exploration,[1] which can be used to complete large-scale sensing application at a lower cost. Before starting task allocation process, the platform needs to initialize system parameters including geographical locations of vehicles and sensing tasks, number of duplicates for each sensing task, and its corresponding score. The platform generates order allocation among various vehicles and starts a round of task assignment When it is vehicleu’s turn, the platform will plan a sensing path for that vehicle using OPS algorithm described in section ‘‘OPS based on single vehicle’’ and update the number of duplicates for each sensing task. To simulate actual sensing task allocation process, first we set each vehicle’s time budget Tmax to be a random variable T + s, where T is a constant and s subjects to uniform distribution in range [210, 10]. Since we make our target of allocation as maximizing the utility of platform, in steps, we mainly focus on some factors affecting platform utility, which include vehicle’s time budget, number of vehicles, total amount of tasks, and proportion platform utility aosfmtaaxskPs niin=À11dPiffnje=re2n(t1tÀyple)sp. ixWij.e define
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
