An efficient fuzzy hyper-edge clustering and popularity-based caching scheme for CCN-enabled IoT networks.

Content-Centric Networking (CCN) has emerged as the most convenient architecture for efficient traffic management in contrast to the IP-based Internet. The in-network caching characteristic of CCN reduces server load and traffic in the network. Furthermore, it enhances end-user Quality-of-Service (QoS) by reducing content retrieval delay. Towards this, the proposed research focuses on the in-network caching capability of CCN-enabled IoT networks to improve content distribution and reduction of load from servers. In CCN-enabled IoT networks, content caching can be performed in any node of the fog layer that exists between the cloud server and IoT devices. To effectively utilize the available caching resources, it is crucial to determine the suitable fog node during content placement decisions. In this direction, a novel fuzzy hyper-edge clustering and content popularity-based caching scheme is proposed for CCN-based IoT networks. The proposed fuzzy clustering scheme dynamically partitions the network into overlapping clusters based on node connectivity. The proposed scheme overcomes the limitations of existing techniques where the number of clusters needs to be fixed initially. The proposed scheme considers the cluster information and content access frequency parameters for content placement decisions. Using the proposed heuristics, the scheme cooperatively caches the popular contents in the fog nodes near IoT devices. The performance of the proposed strategy is examined using extensive simulations on a realistic network configuration. Experiments are performed on the standard Abilene topology, and performance is measured using metrics such as cache hit ratio, average network hop count, and average network delay on cache sizes 50 and 100. The simulation results are recorded at 1, 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 Simulation Time Units (STU). The results show that the proposed caching solution outperforms recent state-of-the-art techniques such as LCE, PDC, CPNDD, HPHC, and CSDD, making it suitable for CCN-enabled IoT networks. [ABSTRACT FROM AUTHOR]