An intelligent content prefix classification approach for quality of service optimization in information-centric networking

Cutifa Safitri, Yoshihide Yamada, Sabariah Baharun, Shidrokh Goudarzi, Ngoc quang Nguyen, Keping Yu, Takuro Sato

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

Original languageEnglish
Article number33
JournalFuture Internet
Volume10
Issue number4
DOIs
Publication statusPublished - 2018 Apr 9

Keywords

  • Artificial intelligence (AI)
  • Information-centric networking (ICN)
  • Intelligent classifications
  • Quality of service (QoS)

ASJC Scopus subject areas

  • Computer Networks and Communications

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