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Evidence theory‐based framework for improving automation in home automation system
Author(s) -
Chemoun Karima,
Gilg Marc,
Laghrouche Mourad,
Lorenz Pascal
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3791
Subject(s) - automation , process (computing) , computer science , home automation , data science , reliability (semiconductor) , process automation system , risk analysis (engineering) , engineering , telecommunications , mechanical engineering , medicine , power (physics) , physics , quantum mechanics , operating system
Summary An easy and better way of life is the topic of the day and stays an important theme for the future. New researches focusing on this aspect are increasingly emerging. In recent years, improving automation in home automation systems and making homes even smarter seem to be a major issue. Automation of daily life functioning of home devices makes life easier for inhabitants, and then saves them from manual adjustments. Home automation systems encompass different communication and networking technologies. They are embedded with sensor networks that perceive information about the environment and occupants. The collected data can mainly be processed to infer sequential patterns defining the home behavior. However, the imperfection, particularly uncertainty of sensor readings, is a significant factor to consider. Also, time is an important aspect when reasoning about home behavior, as each pattern (scenario) performs at a given time throughout the day. Evidence theory is recently gaining great attention. It is suitable for dealing with imperfection of sensor data and for including temporal features into the reasoning process to discover the appropriate pattern to launch. This paper presents a new framework based on evidence theory with the inclusion of time features, for improving automation in home automation systems. The effectiveness of the proposed approach is evaluated by conducting experiments on synthetic dataset. The results show the efficiency of the discovery process when multiple working sensors are available. The results demonstrate also that reliability and accuracy of the process improve significantly when temporal information is provided.