A Survey on Sparse Mobile Crowdsensing: Functionalities and Research Issues

Abstract

Mobile Crowdsensing (MCS), also known as participatory sensing, refers to the process of utilizing the collective power of mobile devices (such as smartphones and wearables) to collect and share data about the environment, user behavior, and other phenomena. MCS leverages the ubiquity of mobile devices and their embedded sensors to create dynamic and large-scale sensing networks. Participants willingly contribute data from their devices, which are often equipped with sensors like Global Positioning System (GPS), cameras, accelerometers, and microphones. The collected data is aggregated and analyzed to gain insights into various aspects of urban environments, healthcare, transportation, and more. MCS offers real-time data collection, wide geographical coverage, and the potential for crowd-driven insights. Sparse Mobile Crowdsensing (SMCS) is a specialized variant of MCS that deals with the challenges posed by intermittent and irregular data collection from mobile devices. In SMCS scenarios, participants contribute data sporadically due to factors like device availability, user engagement, or incentives. The sporadic nature of data collection leads to gaps in the dataset, requiring innovative techniques for data imputation, prediction, and analysis. SMCS aims to overcome the limitations of sparse data to still provide valuable insights and meaningful applications. This paper provides a comprehensive and detailed study of the MCS and SMCS framework, the relevant research work carried out in sparse mobile crowdsensing, and at the end, we provide open research problems with the help of future research in this domain.