Feature Interaction in Smart Environments

Abstract

The Internet of Things (IoT) connects many devices daily together in the same environment. Each device may follow the set of rules from a static environment. A static environment is usually controlled by an expert who knows all the necessary rules to provide this environment. The violation of one rule can cause a feature interaction. A feature interaction occurs when two or more devices generate instability in an environment. In a dynamic environment like IoT, devices' inclusion, and exclusion make it impossible for an expert to maintain all these rules up-to-date. It is necessary to provide an automatic solution to avoid violating these rules and maintain the environment's good performance. Thus, this work introduces a new approach to detect a feature interaction in dynamic environments automatically. Almost all previous work provide static rules defined by an expert in a controlled environment to detect an interaction. However, this is not possible in dynamic environments because of the number of device interactions and the number of device connections in/out, which grow exponentially in IoT environments. We started with a lightweight systematic review to better position our research, and then we identified one gap to provide our solution. Thus, our method learns to detect the interactions based on data analysis and then automatically predict the device detections in IoT environments. Datasets were manually annotated. Experiments were performed, and results provide evidence that automatic detection of a set of device interactions is possible in similar or either in complementary domains.