Model of an intelligent smart home system based on ambient intelligence and user profiling

2021 ◽  
Author(s):  
Igor Đuric ◽  
Dusan Barac ◽  
Zorica Bogdanovic ◽  
Aleksandra Labus ◽  
Bozidar Radenkovic
Keyword(s):  
Smart Home ◽  
Ambient Intelligence ◽  
User Profiling

Cognitive Internet of Everything (CIoE)

2020 ◽  
pp. 1212-1238
Author(s):  
Gopal Singh Jamnal ◽  
Xiaodong Liu ◽  
Lu Fan ◽  
Muthu Ramachandran
Keyword(s):  
Assisted Living ◽  
Smart Home ◽  
Ambient Intelligence ◽  
Clustering Algorithms ◽  
Critical Evaluation ◽  
Internet Of Everything ◽  
Metropolitan Cities ◽  
New Information ◽  
Ambient Intelligent ◽  
Input Dataset

In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.

Ambient Intelligence Smart Home Automation (AMISHA) System

2017 ◽  
Vol 23 (6) ◽  
pp. 5073-5077 ◽  
Author(s):  
Justin Lim Wei Kit ◽  
Manmeet Mahinderjit Singh ◽  
Nurul Hashimah Ahamed Hassain Malim
Keyword(s):  
Smart Home ◽  
Ambient Intelligence ◽  
Home Automation

Cognitive Internet of Everything (CIoE)

2017 ◽  
pp. 277-309 ◽  
Author(s):  
Gopal Singh Jamnal ◽  
Xiaodong Liu ◽  
Lu Fan ◽  
Muthu Ramachandran
Keyword(s):  
Assisted Living ◽  
Smart Home ◽  
Ambient Intelligence ◽  
Clustering Algorithms ◽  
Critical Evaluation ◽  
Internet Of Everything ◽  
Metropolitan Cities ◽  
New Information ◽  
Ambient Intelligent ◽  
Input Dataset

In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.

A Smarter Smart Home: Case Studies of Ambient Intelligence

2013 ◽  
Vol 12 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Stephen Makonin ◽  
Lyn Bartram ◽  
Fred Popowich
Keyword(s):  
Case Studies ◽  
Smart Home ◽  
Ambient Intelligence

scholarly journals Ambient Intelligence: Concepts and applications

2007 ◽  
Vol 4 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Juan Augusto ◽  
Paul Mccullagh
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Knowledge Acquisition ◽  
Public Transport ◽  
Smart Home ◽  
Ambient Intelligence ◽  
Knowledge Engineering ◽  
System Specification ◽  
Vast Amount ◽  
Address System

Ambient Intelligence aims to enhance the way people interact with their environment to promote safety and to enrich their lives. A Smart Home is one such system but the idea extends to hospitals, public transport, factories and other environments. The achievement of Ambient Intelligence largely depends on the technology deployed (sensors and devices interconnected through networks) as well as on the intelligence of the software used for decision-making. The aims of this article are to describe the characteristics of systems with Ambient Intelligence, to provide examples of their applications and to highlight the challenges that lie ahead, especially for the Software Engineering and Knowledge Engineering communities. In particular we address system specification and verification for the former and knowledge acquisition from the vast amount of data collected for the latter. .

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