Self-Adaptive Framework with Game Theoretic Decision Making for Internet of Things

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.