Delay- and Incentive-Aware Crowdsensing: A Stable Matching Approach for Coverage Maximization

Abstract

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.