The Curse of Sensing: Survey of techniques and challenges to cope with sparse and dense data in mobile crowd sensing for Internet of Things

Abstract

In this paper we present a survey on mobile crowdsensing (MCS) techniques that have been developed to address the Curse of Sensing problem i.e. propensity of MCS applications to generate sparse or dense data that can lead to significant gaps in the extracted knowledge. In order to do so, we identify features, based on the terminologies used in the literature, in order to develop a clear classifications among MCS and crowdsourcing applications and methods. Subsequently, we propose a taxonomy outlining both factors and objectives that need to be considered in designing MCS systems and have a direct impact on MCS applications’ tendency to fall into the Curse of Sensing. We then evaluate the majority of the research proposed in the field of MCS and addressing the Curse of Sensing problem with reference to the proposed taxonomy. Finally, we highlight the existing gaps in the literature and possible directions for future research.