Abstract
Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.
Highlights
In recent years, due to the massive increase in sensor-rich mobile devices, mobile crowdsensing (MCS) [1] has emerged as a new way of sensing, which relies on a crowd of personal mobile phones, tablet computers, and other smart gadgets to perform large-scale tasks
(ii) To solve route planning and task assignment problem (RPTSP), we propose a multi-vehicleassisted multi-unmanned aerial vehicles (UAVs) path planning algorithm (MVP), which maximizes the profits of the MCS carrier with a response time constraint (iii) Extensive experiments are conducted, and the results show that MVP outperforms the baseline algorithm
We propose an efficient algorithm called MVP to address the multi-vehicle-assisted multi-UAVs path planning problem in MCS
Summary
Due to the massive increase in sensor-rich mobile devices, mobile crowdsensing (MCS) [1] has emerged as a new way of sensing, which relies on a crowd of personal mobile phones, tablet computers, and other smart gadgets to perform large-scale tasks. Wireless Communications and Mobile Computing vehicles can visit the customers to deliver parcels These works are inappropriate for the vehicle-drone cooperative sensing problem studied in this paper, wherein a vehicle is only used to transport UAVs. In the meantime, only few researches studied the routing of the vehicle-drone cooperative sensing system [11, 12]. We propose an efficient heuristic allocation algorithm EHA to determine the paths of multiple vehicles, which solves the task assignment problem and multivehicle path planning problem. Our goal is to maximize the profits of the MCS carrier with a time budget by globally optimizing the assignment of tasks and the route of vehicles and UAVs. Besides, we hope that the number of employed ground vehicles can be minimized. (i) We introduce multiple vehicles to the vehicle-drone cooperative sensing system and formalize the joint route planning and task assignment problem (RPTSP).
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