A task allocation algorithm for Coverage-Aware Crowdsensing with Data timeliness

Abstract

In the task allocation of mobile crowdsensing (MCS), importance is often attached to the quality of sensing data while the data timeliness is often neglected, which may lead to the slow response of the MCS platform for urgent tasks (such as fire, geological disasters, etc.), thus missing the golden response time. Based on the definition of worker data timeliness and area coverage, a coverage-aware task allocation algorithm (CATA) is proposed in the paper. The CATA algorithm adopts fog nodes as the intermediate layer between MCS platform and participants and tries to both maximize the data timeliness and minimizing the incentive cost. For tasks with given location and crowdsensing range, workers with higher data timeliness and lower bidding are selected from participants according to their data timeliness and virtual credit. In addition, the location privacy of participants is protected by geo-indistinguishability. Results of simulation experiment validate the effectiveness of the proposed algorithm.