Quantile context-aware social IoT service big data recommendation with D2D communication

Yifan Yang, Jie Xu, Zichuan Xu, Pan Zhou, Tie Qiu

Research output: Contribution to journalArticle

Abstract

With the rapid development of the Internet-of-Things (IoT) networks, millions of IoT services provided through wireless networks are waiting for people's exploration. Such a large number of heterogeneous IoT services produce huge amounts of data in almost real time, known as big data, many of which cannot be measured or quantified. Hence, a recommended system that aims to deal with the unquantifiable big data is urgently needed. To solve the problem, we propose a novel quantile contextual tree-based multiarmed bandits algorithm to support the large-scale recommendation with both quantifiable and unquantifiable data. Furthermore, the high failure rate of communication has a serious influence on the recommendation accuracy of our system with the widely used D2D technology in today's IoT network. To improve recommendation accuracy under the D2D communication, we take into account the feedback of historical service receivers and the historical successful delivery rate (SDP) of data transmission at the same time for the service recommendation system. We give theoretical analysis to prove a sublinear bound of the regret. Numerical experiments with tremendously large data sets show that we can balance the regret with the system time cost and guarantee a high SDP.

Original languageEnglish (US)
Article number9032093
Pages (from-to)5533-5548
Number of pages16
JournalIEEE Internet of Things Journal
Volume7
Issue number6
DOIs
StatePublished - Jun 2020
Externally publishedYes

Keywords

  • Big data
  • D2D communication
  • Online learning
  • Quantile contextual bandit
  • Recommended system
  • Social Internet of Things (IoT)

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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