UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Abstract

Unmanned and aerial systems as interactors among different system components for communications, have opened up great opportunities for truth data discovery in Mobile Crowd Sensing (MCS) which has not been properly solved in the literature. In this paper, an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery (UAV-ITD) scheme is proposed to obtain truth data at low-cost communications for MCS. The main innovations of the UAV-ITD scheme are as follows: (1) UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization (DMF) to discover truth data based on the trust mechanism for an Information Elicitation Without Verification (IEWV) problem in MCS. (2) This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy, which saves more communication costs than most previous data collection schemes, where they collect n or kn data samples. Finally, we conducted extensive experiments to evaluate the UAV-ITD scheme. The results show that compared with previous schemes, our scheme can reduce estimated truth error by 52.25%-96.09%, increase the accuracy of workers’ trust evaluation by 0.68-61.82 times, and save recruitment costs by 24.08%-54.15% in truth data discovery.

Keywords

Unmanned aerial systems / Trust computing / Truth discovery / Deep matrix factorization / Low-cost communications

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