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A new QoC parameter and corresponding context inconsistency elimination algorithms for sensed contexts and non-sensed contexts

Applied intelligence (Dordrecht, Netherlands), 2022, Vol.52 (1), p.681-698 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-021-02226-4

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  • Title:
    A new QoC parameter and corresponding context inconsistency elimination algorithms for sensed contexts and non-sensed contexts
  • Author: Fan, Shidi ; Xu, Hongji ; Xiong, Hailiang ; Chen, Min ; Liu, Qiang ; Xing, Qinghua ; Li, Tiankuo
  • Subjects: Algorithms ; Ambient intelligence ; Artificial Intelligence ; Computer Science ; Context ; Machines ; Manufacturing ; Mechanical Engineering ; Parameters ; Processes ; Ubiquitous computing
  • Is Part Of: Applied intelligence (Dordrecht, Netherlands), 2022, Vol.52 (1), p.681-698
  • Description: As the key products of ubiquitous computing, context-aware systems have been widely used in many fields such as digital home, smart healthcare and so on. However, in the face of the typical application environment formed by multiple sensors and intelligent devices, the inconsistency of contexts that hinders the normal operation of the systems has become an inevitable and urgent problem that needs to be resolved. In this paper, we propose a new quality of context (QoC) parameter relevance to enrich the comprehensive assessment of the context quality. Moreover, on this basis, we put forward novel context inconsistency elimination algorithms that use multiple QoC parameters and Dempster-Shafer theory to solve the inconsistency problem of sensed contexts and non-sensed contexts, respectively. Experimental analyses from multiple dimensions fully show that the proposed algorithms have obvious advantages over the other algorithms in terms of accuracy, stability, and robustness.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0924-669X
    EISSN: 1573-7497
    DOI: 10.1007/s10489-021-02226-4
  • Source: ProQuest One Psychology
    ProQuest Central
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