Data Quality in Mobile Crowd Sensing Systems: Challenges and Perspectives

Abstract

Mobile Crowd Sensing (MCS) has become a new sensing paradigm, which leverages on the ubiquity of mobile devices with advanced multi-modal sensing features in conjunction with human intelligence so as to cost-efficiently monitor and analyze large-scale phenomena. MCS core characteristic is user involvement in data collection, processing, analysis and sharing. Due to the opt-in nature of MCS systems, a number of critical concerns is raised that should be efficiently addressed so as to enable MCS unimpeded advancement. Data reported by users could be unintentionally inaccurate and/or deliberately falsified. Thus, ensuring data quality and integrity in MCS constitutes one of its key challenges. This paper firstly discusses on the data quality challenge and identifying its interdependencies with different underlying key issues. Subsequently, we comprehensively survey representative reputation-based trust establishment mechanisms proposed in related research literature in the context of MCS, as a potential solution to the data quality problem. Their distinct features are analyzed and their relative merits and weaknesses are identified and highlighted. Finally, we discuss on design aspects of reputation mechanisms and provide guidelines and future research directions.