scholarly journals An ambient intelligence system for assisted living

2017 ◽  
Author(s):  
Alessandra De Paola ◽  
Pierluca Ferraro ◽  
Salvatore Gaglio ◽  
Giuseppe Lo Re ◽  
Marco Morana ◽  
...  
Keyword(s):  
Assisted Living ◽  
Ambient Intelligence ◽  
Intelligence System

An Ambient Intelligence System to Assist Team Training and Competition in Cycling

2006 ◽  
pp. 97-102 ◽  
Author(s):  
Ingmar Fliege ◽  
Alexander Geraldy ◽  
Reinhard Gotzhein ◽  
Thomas Jaitner ◽  
Thomas Kuhn ◽  
...  
Keyword(s):  
Ambient Intelligence ◽  
Team Training ◽  
Intelligence System

On Dependability Issues in Ambient Intelligence Systems

2011 ◽  
Vol 3 (3) ◽  
pp. 18-27
Author(s):  
Marcello Cinque ◽  
Antonio Coronato ◽  
Alessandro Testa
Keyword(s):  
Assisted Living ◽  
Intelligent Systems ◽  
Ambient Intelligence ◽  
Ambient Assisted Living ◽  
Smart Environments ◽  
Practical Application ◽  
Smart Systems ◽  
Computing Paradigm ◽  
Ambient Intelligent ◽  
Verification Techniques

Ambient Intelligence (AmI) is the emerging computing paradigm used to build next-generation smart environments. It provides services in a flexible, transparent, and anticipative manner, requiring minimal skills for human-computer interaction. Recently, AmI is being adapted to build smart systems to guide human activities in critical domains, such as, healthcare, ambient assisted living, and disaster recovery. However, the practical application to such domains generally calls for stringent dependability requirements, since the failure of even a single component may cause dangerous loss or hazard to people and machineries. Despite these concerns, there is still little understanding on dependability issues in Ambient Intelligent systems and on possible solutions. This paper provides an analysis of the AmI literature dealing with dependability issues and to propose an innovative architectural solution to such issues, based on the use of runtime verification techniques.

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.

An integrated ambient intelligence system for a Smart Lab environment

2020 ◽  
Vol 21 (2) ◽  
pp. 289
Author(s):  
Thanh Pham ◽  
Alaa Sheta ◽  
Dat Do ◽  
Scott A. King
Keyword(s):  
Ambient Intelligence ◽  
Intelligence System

Designing a usable ambient intelligence system

2010 ◽  
Vol 6 (2) ◽  
pp. 189
Author(s):  
Lorna Uden ◽  
Pedro Valderas
Keyword(s):  
Ambient Intelligence ◽  
Intelligence System

scholarly journals Concept of Horticulture Ambient Intelligence System

2015 ◽  
Vol 7 (4) ◽  
pp. 209-2016
Author(s):  
A. Vasilenko ◽  
◽  
M. Ulman ◽  
Keyword(s):  
Ambient Intelligence ◽  
Intelligence System

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.

scholarly journals Data Collection Technology for Ambient Intelligence Systems in Internet of Things

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1846
Author(s):  
Alexander Vodyaho ◽  
Vasiliy Osipov ◽  
Nataly Zhukova ◽  
Vladimir Chernokulsky
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Ambient Intelligence ◽  
Dynamic Structure ◽  
Intelligence System ◽  
Structure State ◽  
And Performance ◽  
Using Data ◽  
Definition Of ◽  
And Behavior

Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance are essential. A novel technology is proposed, which includes the following steps: (1) definition of the data collection (DC) problem (considering the model of the observed system, DC conditions, etc.); (2) DC policy assignment; (3) construction of DC models; (4) evaluation and presentation of the data processing results. The proposed DC technology supports the development of data collecting subsystems in AmIS. Such subsystems provide data that reflect the changes in structure, state, situation, and behavior of participants in their IoT environment in time. Therefore, we show how this “cognitive” function of the DC process increases the intelligence level of IoT environment.

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