SCIENCE@home

An adaptive framework for applying machine learning in smart spaces

Sachin Bhardwaj,Keon-Myung Lee,Jee-Hyong Lee

Published 2019 in ACM Symposium on Applied Computing

ABSTRACT

A smart space 1 is a physical environment that contains cooperating nodes which continuously and autonomously monitor their surroundings. The environment can interact with users and adapt their behaviors to enhance user experiences using semantic reasoning. Such semantic reasoning is based on information gathered and shared either from the physical environment (e.g., via sensors) or from the Internet (e.g., via user profiles). The nodes share knowledge to adapt their behaviors using semantic reasoning in a smart space. On the other hand, machine learning is a promising tool to generate or enhance knowledge for nodes' adaptations. In this paper, we propose a semantic learning component in a comprehensive smart space architecture to generate knowledge on stored semantics for nodes' adaptations. For this purpose, we propose an adaptive framework which includes machine learning techniques in the component for nodes' behaviors. Moreover, an example use-case is presented using the K-nearest neighbor algorithm. Further, two use-cases are discussed in support of the proposed framework. Finally, we address the further work to be studied.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    ACM Symposium on Applied Computing

  • Publication date

    2019-04-08

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1145/3297280.3297405
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-36 of 36 references · Page 1 of 1

CITED BY