Abstract

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.

Highlights

  • In recent years, a huge number of smart devices and sensors were deployed in the different areas of networks (e.g., smart health care system, vehicle to everything (V2X) communications, autonomous driving, industries, and smart home) to sense the real-time situation of the corresponding deployed environment and to collect the raw data [1,2]

  • We proposed an architecture based on the coexistence of information-centric networking (ICN) and the types of edge computing (EC) to

  • We proposed a new mechanism to identify the popular content in the network and proposed tier-based architecture

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Summary

Introduction

A huge number of smart devices and sensors were deployed in the different areas of networks (e.g., smart health care system, vehicle to everything (V2X) communications, autonomous driving, industries, and smart home) to sense the real-time situation of the corresponding deployed environment and to collect the raw data [1,2]. Augmented reality (AR), virtual reality (VR) applications, online gaming, body area network (BAN) [3] devices and a lot of other real-time application-oriented sensors may require a very low latency communication environment. Another alarming and important trend is the exponential increase of the IoT devices and the predicted data volume from those devices by 2020 will be 2.3 trillion gigabytes at each day by Mckinsey Global.

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