Familiar Paths are the Best: Incentive Mechanism Based on Path-Dependence Considering Space-Time Coverage in Crowdsensing
Deng Li, Chaojie Li, Xiaoheng Deng, Liu Hui, Jiaqi Liu
Abstract
Location Dependent Mobile Crowdsensing (LDMC) often needs to collect data at different time points in various regions to ensure the coverage of sensing data. An incentive mechanism is needed to encourage participants to move to sparse areas and improve coverage. However, there are two problems: 1) most incentive mechanisms assume that the participants can get accurate information about tasks; 2) those mechanisms encourage participants through absolute utility so that the platform can obtain an improvement of incentive effect by increasing the reward. However, nodes usually get inaccurate information in reality. Moreover, behavioral economics finds that decision-making is often affected by relative utility rather than absolute utility. Path-dependence means that choices made on the basis of transitory conditions can persist long after those conditions change, which can solve the above problems. This study uses cognitive bias and the reference effect to explain the principle of path-dependence, and proposes a mechanism called Task Coverage promotion based on Path-dependence (TCPD). TCPD cultivates the cognitive bias of participants, causing an overestimation of expected utility. Then, it sets dynamic reference points to prevent participants from quitting early. The simulation results show that TCPD can improve the coverage and effectiveness of the platform.