A Survey on Mobile crowd sensing using MCS task allocation & incentives

Abstract

This paper first defines a novel spatial-temporal coverage metric, k-depth coverage, for mobile crowd sensing (MCS) quandaries. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings amassed in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and k-depth coverage objectives constraints. iCrowd first prognosticates the call and mobility of mobile users predicated on their historical records, then it culls a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data amassment goals—i.e., Goal. 1 near-maximal k-depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined k-depth coverage goal. We evaluated iCrowd extensively utilizing an immensely colossal-scale authentic-world dataset for these two data accumulation goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher k-depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same k-depth coverage constraint.