Hybrid Worker Selection for Task Coverage Maximization in Mobile Crowdsensing
Yi Lv, Xin Chen, Peng He, Yaping Cui, Ruyan Wang, Dapeng Wu · 2023
Mobile crowdsensing (MCS) has become an attractive issue in recent years. Most existing researches either select opportunistic sensing or participatory sensing for task execution, which will lead to the problem of restricted task locations or high cost. In this work, we propose a complementary hybrid worker selection method for MCS, where workers complete tasks in different sensing modes, namely opportunistic and participatory sensing. The proposed worker selection method contains two phases. In the opportunistic worker selection phase, an updated iterative algorithm is designed to select a low-cost and high-coverage opportunistic worker set. Specifically, when an opportunistic worker is selected, the algorithm will update the coverage of the remaining candidate opportunistic workers on the sensing task. In the participatory worker selection phase, we design an algorithm that combines group and match to solve the problem of restricted task locations. Specifically, we group the sensing tasks that opportunistic workers have failed to cover and recruit participatory workers to complete the sensing tasks in the groups. Experiments on a real dataset prove that the proposed method outperforms other benchmark methods.