Multi-Campaign Oriented Spatial Crowdsourcing

The system throughput and workers' travel distance are two important factors in spatial crowdsourcing and improving one of them usually means sacrificing the other. However, existing works either fail to consider the trade-off between these two factors or resolve their conflicts by simply targeting tasks within a bounding circle for each worker. In this paper, we compromise between the throughput and the distance by formulating these two factors as score terms in the objective function. Apart from that, we study the multi-campaign scenario in our problem, which is not uncommon in practical applications while not yet discussed in existing works. The worker diversity of the campaigns is formulated as another score term in the objective function. The problem of multi-campaign oriented spatial crowdsourcing is to maximize the aforementioned score function. We prove the problem is NP-hard and provide several approximation solutions. Extensive experiments have been conducted to validate the devised solutions.