Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Sensors

Divya Gupta¹  Shalli Rani¹  Sahil Verma²  Jana Shafi³  Muhammad Fazal Ijaz⁴  Syed Hassan Ahmed⁵ 

[1] Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India;Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India;Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul University, Wadi Ad-Dwasir 11991, Saudi Arabia;Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea;Independent Researcher, Corona, CA 13088, USA;

关键词: information centric networking;    internet of things;    collaborative filtering;    edge cloud;    content caching;   

DOI  :  10.3390/s21165491

来源: DOAJ

【 摘 要 】

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

【 授权许可】

Unknown