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Towards provenance-based trust-aware model for socio-technically connected self-adaptive system
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Publication Year
2021-07-01
Journal
Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021, pp.761-767
Keyword
Goal modelProvenance modelRequirements engineeringSelf-adaptive systemTrust model
Mesh Keyword
Context dependentCooperative environmentProvenance modelsQuality attributesSelf-adaptive systemTrust evaluationTrust evidencesUnmanned vehicle systems
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsSoftware
Abstract
In a socio-technically connected environment, self-adaptive systems need to cooperate with others to collect information to provide context-dependent functionalities to users. A key component of ensuring safe and secure cooperation is finding trustworthy information and its providers. Trust is an emerging quality attribute that represents the level of belief in the cooperative environments and serves as a promising solution in this regard. In this research, we will focus on analyzing trust characteristics and defining trust-aware models through the trust-aware goal model and the provenance model. The trust-aware goal model is designed to represent the trust-related requirements and their relationships. The provenance model is analyzed as trust evidence to be used for the trust evaluation. The proposed approach contributes to build a comprehensive understanding of trust and design a trust-aware self-adaptive system. In order to show the feasibility of the proposed approach, we will conduct a case study with the crowd navigation system for an unmanned vehicle system.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36679
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115835028&origin=inward
DOI
https://doi.org/10.1109/compsac51774.2021.00108
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9529349
Type
Conference
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020R1F1A1075605).
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Lee, Seok-Won Image
Lee, Seok-Won이석원
Department of Software and Computer Engineering
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