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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications.

Divya Gupta1, Shalli Rani1, Syed Hassan Ahmed2

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven content caching strategy for edge-enabled Information-Centric Networking for the Internet of Things (ICN-IoT). The proposed method enhances cache hit ratio and reduces content retrieval delay and hop count for better user experience.

Keywords:
collaborative filteringcontent cachingedge cloudinformation centric networkinginternet of things

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Edge computing significantly advances Internet of Things (IoT) technology, but traditional network architectures struggle to meet future demands.
  • Information-Centric Networking (ICN) offers a promising solution, leading to the development of ICN-IoT architectures.
  • Efficient in-network caching is crucial for edge-enabled ICN-IoT to enhance Quality of Experience (QoE).

Purpose of the Study:

  • To propose an enhanced content caching strategy for edge-enabled ICN-IoT architectures.
  • To leverage Artificial Intelligence (AI)-based collaborative filtering for intelligent content caching at the edge.
  • To improve traffic management and support heterogeneous IoT environments.

Main Methods:

  • Developed an AI-based collaborative filtering content caching strategy for edge nodes in ICN-IoT.
  • Implemented intelligent caching of content on edge nodes to manage traffic from cloud databases.
  • Evaluated the proposed strategy against benchmark strategies like LCE, LCD, CL4M, and ProbCache.

Main Results:

  • The proposed strategy achieved an average gain of 15% in cache hit ratio.
  • Demonstrated a 12% reduction in content retrieval delay compared to the best benchmark (LCD).
  • Achieved a 28% reduction in average hop count compared to the best benchmark (LCD).

Conclusions:

  • The AI-based collaborative filtering content caching strategy offers superior performance for edge-enabled ICN-IoT networks.
  • The proposed strategy effectively enhances QoE by optimizing content delivery and reducing network latency.
  • This approach provides a valuable contribution to addressing challenges in ICN-IoT content caching and traffic management.