Name
Abstract | ||
|---|---|---|
New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/APSEC.2017.18 | 2017 24th Asia-Pacific Software Engineering Conference (APSEC) |
Keywords | Field | DocType |
VSN,Trust Management,Vehicular Cloud,Performance Evaluation | Public key infrastructure,Information system,Social network,Authentication,Computer security,Computer science,Data sharing,Real-time computing,Public-key cryptography,Vehicular ad hoc network,Cloud computing | Conference |
ISSN | ISBN | Citations |
1530-1362 | 978-1-5386-3682-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biling Lin | 1 | 0 | 0.34 |
| Xiao Chen | 2 | 11 | 2.34 |
| Liang Min Wang | 3 | 48 | 14.76 |