Multi-task online allocation based on path planning strategy in Spatial crowdsourcing environment

This article takes the spatiotemporal crowdsourcing task allocation in a multi worker and multi task environment as the background, and designs a candidate task set algorithm based on region partitioning model for scenarios where workers can accept multiple tasks at the same time. Firstly, the tasks are stored in different regions, named and indexed using the GEOhash method. Secondly, based on the network maximum cost flow model for task allocation, four strategies are proposed to maximize the utility of online multi task allocation. The results show that the CFTA algorithm out-performs the greedy algorithm and random threshold method in terms of total utility, task allocation success rate, and average worker income. In terms of runtime, the CFTA algorithm can effectively solve the multi worker and multi task allocation problem without sacrificing a small amount of time cost.