Location-aware Task Assignment and Routing in Mobile Crowd Sensing

With the increasing number of smart devices, selecting proper workers for tasks is a major research challenge in mobile crowd-sensing (MCS) due to the heterogeneity of tasks and workers. In this paper, we formulate the task allocation problem as an optimization problem by taking into account various tasks requirements (i.e., sensor, location accessibility, reliability), and workers specifications (i.e., available sensors set, velocity) as well as task completion order. The objective of this study is to minimize the total distance moved by all workers. Furthermore, a tabu search algorithm is proposed to select workers, which uses asymmetric traveling salesman problem heuristics to find the task completion order. Simulations using real data sets show the performance of our proposed method.