Mobile Crowd‐Sensing for Smart Cities
Chandreyee Chowdhury, Sarbani Roy · Smart Cities · 2017
This chapter reviews the literature of mobile crowd-sensing (MCS) for smart cities is reviewed thoroughly including motivation, possible applications, and issues that are key to successful deployment of such services. It discusses the challenges of crowd-sensing in the context of smart city followed by a brief overview of existing frameworks. The chapter also discusses the issues regarding task assignment, user profiling and trustworthiness, design of incentive mechanisms, localized analytics, and security and privacy. While crowd-sourcing is aimed to utilize collective intelligence of the crowd to solve complex tasks by breaking them down to smaller tasks, crowd-sensing splits the responsibility of gathering correct information to the crowd. Toward this, a geo-social model of MCS is proposed. This model is based on a distributed architecture for task design, assessment, and execution. McSense is a framework that is also proposed for MCS. This framework talks about monetary or service incentives given to users.