TY - GEN
T1 - Self-Adaptive Framework with Game Theoretic Decision Making for Internet of Things
AU - Lee, Euijong
AU - Kim, Young Gab
AU - Seo, Young Duk
AU - Baik, Doo Kwon
N1 - Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2016-0-00498, User behavior pattern analysis based authentication and anomaly detection within the system using deep learning techniques) and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00756, Development of interoperability and management technology of IoT system with heterogeneous ID mechanism).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/2/22
Y1 - 2019/2/22
N2 - The Internet of Things (IoT) connects several objects within environments that dynamically change, and so requirements may be added and changed at runtime. Therefore, requirements may be satisfied at dynamic change. Self-adaptive software can alter their behavior to satisfy requirements in dynamic environments. In this perspective, the concept of self-adaptive software is suitable for IoT environments. In this study, a self-adaptive framework is proposed for decision making in IoT environments at runtime. The framework includes finite-state machine model designs and game theoretic decision-making methods to extract efficient strategies. The framework is implemented as a prototype, and experiments are performed to evaluate runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime.
AB - The Internet of Things (IoT) connects several objects within environments that dynamically change, and so requirements may be added and changed at runtime. Therefore, requirements may be satisfied at dynamic change. Self-adaptive software can alter their behavior to satisfy requirements in dynamic environments. In this perspective, the concept of self-adaptive software is suitable for IoT environments. In this study, a self-adaptive framework is proposed for decision making in IoT environments at runtime. The framework includes finite-state machine model designs and game theoretic decision-making methods to extract efficient strategies. The framework is implemented as a prototype, and experiments are performed to evaluate runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime.
KW - Finite-state machine
KW - Game theory
KW - Internet of Things
KW - Nash equilibrium
KW - Self-adaptive software
UR - http://www.scopus.com/inward/record.url?scp=85063195791&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2018.8650165
DO - 10.1109/TENCON.2018.8650165
M3 - Conference contribution
AN - SCOPUS:85063195791
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 2092
EP - 2097
BT - Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Region 10 Conference, TENCON 2018
Y2 - 28 October 2018 through 31 October 2018
ER -