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Mobile Crowdsensing Task Allocation Model Based on Discrete Particle Swarm Optimization

Mobile crowdsensing (MCS) is a new crowdsourcing model. With the continuous development of MCS, more and more task requesters and workers participate in the MCS, and how to design a reasonable task allocation scheme hasbecome a hot topic of research. In this paper, we investigate the spatiotemporal task allocation problem considering task time constraints and workers’ execution capabilities, and proposean efficient task allocation algorithm based on the discrete particle swarm optimization to maximise social welfare. In order to further optimise the task allocation scheme, a greedy algorithm is introduced to reduce the distance workers have to travel to perform the task and hence the cost of performing the task. Simulation results show that the algorithm is effective in improving social welfare.