Joint Path Planning of Truck and Drones for Mobile Crowdsensing: Model and Algorithm

Drones (also known as unmanned aerial vehicles) has been widely utilized to enhance the performance of mobile crowdsensing (MCS) where the drones can greatly increase the service range by serving as mobile task executors. In this paper, we consider the joint use of truck and drones for effective task execution in the MCS model. In this model, drones are dispatched to depart from the truck, execute one or multiple tasks, and converge with the truck for data collection and battery replacement. In this process, the truck can serve as a mobile drone hub as well as a mobile task executor. The design objective is to minimize the overall cost for truck movement, drone flying, and driver payment in the whole MCS process. We formulate this problem as a mixed integer linear programming (MILP) problem. Due to the NP-hardness of this problem, we propose an efficient algorithm based on variable neighborhood search for efficient joint task assignment and path planning. We conduct numerical experiments and the result demonstrate the effectiveness of the proposed algorithm and the advantage of joint use of truck and drones for mobile crowdsensing.