The process used to build an autonomous smart home system based on cyber-physical systems (CPS) principles has recently received increased attention from researchers and developers. However, there are many challenges to be resolved before designing and implementing such a system. In this chapter, the authors present a high-level design approach that simulates a smart home system by implementing three levels of the 5C architecture used in CPS modeling and uses well-known machine learning algorithms to predict future user actions. The simulation demonstrates how users will interact with the smart home system to make more efficient use of resources. The authors also present results from analyzing real-world user data to validate the accuracy of prediction of user actions. This research illustrates the benefits of considering CPS principles when designing a home autonomous system that reliably predicts a user's needs.
Design and Implementation of a Smart Home Router based on Intel Galileo Gen 2
