Task-Bundling-Based Incentive for Location-Dependent Mobile Crowdsourcing

With the ubiquitous usage of mobile devices, we are witnessing the emergence of commercial crowdsourcing applications that leverage the power of the crowd (workers) to collect massive data. However, the participation unbalance problem commonly occurs in existing location-dependent mobile crowdsourcing applications as workers tend to select nearby tasks while far away tasks are ignored. In this article, we propose a novel task bundling based incentive mechanism that dynamically bundles tasks with different popularity together to solve the participation unbalance problem. We consider the continuous sensing scenarios and categorize tasks into high-popularity (hot) tasks and low-popularity (cold) tasks at each round according to the real-time participation situation of tasks at the last round. We then formulate the task bundling problem as a multi-objective optimization problem, and propose a dynamic task bundling algorithm that dynamically bundles cold tasks with hot tasks at each round. The experimental results demonstrate that the bundling incentive mechanism has a more balanced participation for location-dependent tasks in mobile crowdsourcing systems.