Torwards Context-aware Mobile Crowdsensing in Vehicular Social Networks

Driving is an integral part of our everyday lives, and the average driving time of people globally is increasing to 84 minutes everyday, which is a time when people are uniquely vulnerable. A number of research works have identified that mobile crowdsensing in vehicular social networks (VSNs) can be effectively used for many purposes and bring huge economic benefits, e.g., safety improvement and traffic management. This paper presents our effort that toward context-aware mobile crowdsensing in VSNs. First, we introduce a novel application-oriented service collaboration (ASCM) model which can automatically match multiple users with multiple mobile crowdsensing tasks in VSNs in an efficient manner. After that, for users' dynamic contexts of VSNs, we proposes a context information management model, that aims to enable the mobile crowdsensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.