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Enhanced Alternate Action Recommender System Using Recurrent Patterns and Fault Detection System for Smart Home Users

We present a fault tolerant alternate action recommender system for smart home Internet of Things (IoT) users to enrich the user experience with uninterrupted routines and various methods to achieve the regular routines in the smart home system. Our system takes events data from the smart home IoT devices as input, performs preprocessing using the big data handling techniques to transform it to be applicable to our system, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual user, ensures those frequently used devices are up and running using our fault detection monitoring system, and then finally recommends the alternate possibilities of achieving the deviated actions. Our custom fault detection system is based on various parameters of the IoT devices and context of the smart home users wherein the alternate recommendations given to the user are practical and useful in real time. We validated our system using user trial methods and various validation techniques.