A Nash equilibrium based decision-making method for internet of things

Abstract

In recent years, the Internet of Things (IoT) has gained global popularity. IoT can connect several objects and create a dynamic environment; thus, an IoT system must adapt to environmental changes. To adapt to a dynamic environment, an effective decision-making method is required for an IoT system. Game theory is a mathematical method for decision-making among decision makers, and it may be applied to decision-making for an IoT system. In addition, the concept of self-adaptive software may be applied to IoT because such software evaluates and changes its behavior to satisfy its intended use, and the IoT system then makes decisions and adapts to its dynamic environment. In this study, a decision-making method is proposed along with game theoretic decision-making and self-adaptive loop mechanisms for IoT. The proposed method is based on MAPE-K loops, which are general processes used in self-adaptive software with shared knowledge. In addition, Nash equilibrium is applied to extract candidate strategies, which are evaluated for selecting the most optimal solution. The proposed method was implemented as a prototype, and experiments were conducted to evaluate its runtime performance. The results show that the proposed method can be applied to an IoT environment at runtime.