TY - GEN
T1 - Towards context-aware mobile crowdsensing in vehicular social networks
AU - Hu, Xiping
AU - Leung, Victor C.M.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/7
Y1 - 2015/7/7
N2 - 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 crowd sensing 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 crowd sensing in VSNs. First, we introduce a novel application-oriented service collaboration (ASCM) model which can automatically match multiple users with multiple mobile crowd sensing 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 crowd sensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.
AB - 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 crowd sensing 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 crowd sensing in VSNs. First, we introduce a novel application-oriented service collaboration (ASCM) model which can automatically match multiple users with multiple mobile crowd sensing 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 crowd sensing applications to autonomously match appropriate service and information with different users (requesters and participants) in crowdsensing.
KW - Context-aware
KW - Mobille crowdsensing
KW - Vehicular social networks
UR - http://www.scopus.com/inward/record.url?scp=84941212995&partnerID=8YFLogxK
U2 - 10.1109/CCGrid.2015.155
DO - 10.1109/CCGrid.2015.155
M3 - Conference contribution
AN - SCOPUS:84941212995
T3 - Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
SP - 749
EP - 752
BT - Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Y2 - 4 May 2015 through 7 May 2015
ER -