An Incentive Algorithm for Cross-region Task Allocation based on Worker Coalition Under Mobile Crowdsourcing

Mobile crowdsourcing is rapidly growing with Artificial Intelligent of Things. At the same time, the type and complexity of the tasks requested by the requester change and diversify. Therefore, how to design allocation algorithms for the situation of increasing task complexity is particularly critical. In this paper, to cope with this problem, the idea of worker coalition collaboration and reputation evaluation mechanisms are introduced into it. A two-stage allocation based on same-region and cross-region is performed in the divided regional grid. In the first stage, multi-worker and multi-task allocation is realized by combining the reverse auction theory based on the workers' historical reputation value, which motivates the workers with high reputation value to choose their tasks and contribute data with high sensed quality. The second stage utilizes genetic algorithms to select a coalition of workers for cross-region sensed execution for tasks that do not meet sensed quality requirements. This process will provide additional payoff incentives to compensate for travel costs within the worker coalition, increasing the number of tasks completed and maximizing social welfare. Finally, multiple comparison experiments on the real dataset Yelp are conducted for validation.