Stable Task Allocation in Mobile Crowdsensing: An Interruption-Driven Approach

Kaimin Wei, Guozi Qi, Lin Cui, Jinpeng Chen, Xiaohui Chen, Ke Xu

Abstract: In mobile crowdsensing, task interruptions can cause failures and reduce system stability. Despite the significance of this issue, few studies have addressed task allocation under interruptions. To bridge this gap, we propose IT-STA, an interruption-based stable task allocation algorithm that reallocates interrupted tasks to improve completion rates and maintain system stability. First, an efficient detection mechanism is designed to promptly identify interrupted tasks, ensuring timely intervention. Second, a distributed reallocation strategy is developed to assign interrupted tasks to suitable participants, leveraging a novel individual migration strategy that enables parallel coordination among nodes, ensuring efficient global matching and avoiding suboptimal solutions. Experimental results demonstrate IT-STA’s superiority over baselines in task allocation stability and performance.

External IDs:dblp:journals/tnsm/WeiQCCCX25