Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.

Content-centric networking is a new networking paradigm for efficient data distribution in various environments including wireless sensor networks. It is likely that everything which benefits from networking will be connected. Recently, we have seen a huge proliferation of heterogeneous wireless devices, sensors, and distributed applications. These devices may generate a vast amount of time-constrained data. Content-centric networking is greatly useful to increase the data availability and the efficiency of data delivery. Internet of Things will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. In this article, we propose a possible architecture, necessary components, and related mechanisms for efficient data delivery in the wireless Internet of Things environment, which is based on content-centric networking. We also present simulation results to show the feasibility of the proposed architecture.

Vehicular communications is a growing area for communications between vehicle and everything. On the other hand, information-centric networking (ICN) is being recognized as a new paradigm for the future Internet and 5G networks. Content-centric networking (CCN) is a similar approach to ICN. In this paper, we propose an architecture and functional entities for CCN-based vehicle-to-everything (V2X) communications. We concentrate on various V2X communication scenarios and the applicability of CCN in the respective scenarios using proactive caching and optimized routing. We also show that CCN in-network caching can be effective by using the content popularity and the degree of vehicular safety oriented content. In addition, we analyze the performance of the architecture and demonstrate that proactive caching works better than reactive caching.

Information-centric networking (ICN) is being recognized as a useful technology for efficient and fast content retrieval for multimedia applications and will accelerate the change from the traditional host-oriented network architecture to the content-oriented network architecture. In this paper, we propose an efficient content retrieval mechanism based on cluster-based caching in ICN. We provide a solution to the on-path and hash caching problems using the cluster-based ICN to increase the probability of content access and decrease the packet loss ratio. Simulation results are also presented to show that the content transfer time in our cluster-based approach is less than the existing caching mechanisms.