A novel coverage-aware task allocation scheme in Cooperative Mobile Crowd Sensing

Task assignment is a key issue in Mobile Crowd Sensing, which affects the quality and cost of tasks. Most of the existing works mainly consider the scenario where workers can complete tasks independently, ignoring the requirement of cooperation with multiple workers for complex sensing tasks. The quality of the cooperation of workers' teams needs to be evaluated, and the task coverage should be improved in Cooperative Mobile Crowd Sensing. Therefore, in this paper, we first introduce the team cohesion index by analyzing the impact of workers' social networks on tasks and then propose a new measurement method to better evaluate the quality of workers' teamwork. Then, to improve the task coverage, the task assignment is modeled as a multi-constraint optimization problem in view of several factors, including location, worker team cohesion, and skill coverage. The greedy approach and the heuristic genetic algorithm are combined to create the two-stage algorithm known as GGA. Finally, experiments are conducted based on real datasets, which verify the effectiveness of the team cohesion index measurement method and task allocation algorithm GGA.

Automated summary

This paper investigates the task assignment problem in Mobile Crowd Sensing, proposing a team cohesion index to evaluate the quality of workers' teamwork, and improving task coverage through a two-stage algorithm called GGA that combines greedy algorithm and heuristic genetic algorithm.