Secure and Efficient Context Data Collection Using Content-Centric Networking
Context data collection is a fundamental and important process for realizing context-aware recommender or personalization systems. The existing context data collection approaches are based-on traditional TCP/IP that has several disadvantages such as lack of mobility and security. On the other hand, Content-Centric Networking (CCN) provides advantages in terms of mobility, security, and bandwidth efficiency compared to TCP/IP. In this paper, the authors propose a secure and efficient context data collection and provision approach based on CCN. Simulation results show that this approach can reduce bandwidth consumption by 52.7% - 98.9% in comparison to a TCP/IP-based one.