Multi-worker multi-task selection framework in mobile crowd sourcing

摘要

In this paper, we address the problem of multi-worker multi-task allocation for mobile crowd sourcing systems (MCS), known to be hard to solve. The existing solutions for multi-task selection are mainly sequential assignments and/or focus on solely minimizing the traveling distance for the workers. Hence, these solutions fall short in allocating the workers that maximize the Quality of Service (QoS) of the tasks. In this work, we propose a Group-based multi-task Worker Selection (GMWS) model that allocates multiple tasks for workers while maximizing the QoS of the tasks, and minimizing their completion time. The proposed approach relies on - 1) clustering tasks based on their geographic locations using k-mediods algorithm, 2) selecting workers based on genetic algorithm (GA), that assigns a group of workers to a cluster of tasks, and 3) delegating workers to individual tasks within a cluster using tabu search algorithm. Simulations based on real-life dataset show that the proposed model outperforms other benchmarks in terms of the total distance traveled and the QoS achieved.