Machine Learning On Real-Time Data to Enhance Home Automation
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : Sameer Shaikh, Ayyaj Attar, Siddik Pathan, Rukaiya Shaikh
|DOI : 10.14445/22315381/IJETT-V42P202|
Sameer Shaikh, Ayyaj Attar, Siddik Pathan, Rukaiya Shaikh "Machine Learning On Real-Time Data to Enhance Home Automation", International Journal of Engineering Trends and Technology (IJETT), V42(1),6-8 December 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Traditional home automation systems are mostly hard-coded or require manual automation plan generation by users. This requires interaction between a control system (an app) and the user, requiring quite some effort and time to be put in manual planning of the home environment. This project intends to explore combining ML and real-time usage data to generate personalized and time-variant home automation plans. These plans will save the user time and effort, leading to a smoother ML driven home automation experience. We collect streaming usage statistics from smart-home occupants and store it on a centralized server. Simultaneously, we also collect “external” data (which may consist of environmental factors like natural light intensity, wind speed, et cetera) which may influence occupants’ usage behavior. These datasets are combined, with data timestamps as a unique identifying field, into a super-set. It’s then fed into a Machine Learning system to correlate user habits with time of the day and the external factors. The correlation hence established will be updated as new data coming into the system in real time or if it crosses a certain percentile ratio threshold. This correlation will be used to generate personalized automation plans for individual occupants. Hence, by combining real-time usage data from a conventional home automation system and Machine Learning, we will be able to provide smoother and more comfortable environment to the users, as the burden of plan generation will be greatly reduced.
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machine learning, home automation, real-time analytics, simple reflex agent, learning agent, anomaly detection, activity detection.