Mobile Crowdsensing Model: A survey

Abstract

Mobile Crowdsensing (MCS) is a community detection method in which a person selects a large group of individuals with mobile devices capable of detecting the physical environment and performing various sensing tasks. Thanks to the growth of the Internet of Things, it has recently become the most used paradigm to retrieve sensing data from a dynamic environment due to the users’ mobility and involvement. Indeed, compared to other sensing methods, MCS offers extensive coverage and more precise sensing performance. Optimized with specific models and parameters, it can effectively address challenges and limitations often encountered in traditional methods. To fully leverage the benefits of MCS, an in-depth understanding of its components is essential. This ensures the development of efficient strategies that aptly address the inherent challenges of MCS. Much research has converged on topics such as task allocation, incentivization, and privacy concerns. However, this has inadvertently led to confusion due to varied interpretations of models and overlapping terminology, leaving gaps in knowledge and understanding for newcomers. Our work addresses these gaps by providing a comprehensive representation of the MCS model, seeking to unify the prevailing terminologies.