Design and Implementation of Tracking a user’s Behavior in a Smart Home

Abstract

Abstract A smart home is automated structures with control contraptions and presented area; it comes with advanced sensing and automation systems to supply the inhabitants with monitoring and management no matter if they are in the home or outside. In light of the developments that are taking place in the smart home, machine learning, and the needs of the home, which makes the appropriate decisions based on the user’s behavior of the smart home, and the use of development in technology to facilitate the general requirements of people. Modern smart home systems tend to be standalone solutions that focus on lifestyle needs using a smart control system, such as lighting and temperature control. Ubiquitous home expects the emergence where sophisticated (Ambient intelligent) systems monitor and learn user’s behavior and lifestyles and enable the home to predict and respond to all the needs and activities of home occupants. During perception, residents perform their daily routine while the in-house embedded sensors transmit and store the readings in a database. By doing so, the intelligent agent uses these readings to generate knowledge, such as patterns and trends. Thus, it can predict based on the built knowledge to enable the smart home to identify and automate the action that meets the occupants’ aims.