Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare

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.

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Ambient Intelligence for Smart Home using The Internet of Things

2019 29th International Telecommunication Networks and Applications Conference (ITNAC) ◽

10.1109/itnac46935.2019.9078001 ◽

2019 ◽

Author(s):

Hakilo Sabit ◽

Peter Han Joo Chong ◽

Jeff Kilby

Keyword(s):

Internet Of Things ◽

Smart Home ◽

Ambient Intelligence ◽

The Internet ◽

The Internet Of Things

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A Framework for Energy-Efficiency in Smart Home Environments

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-662-44745-1_23 ◽

2014 ◽

pp. 237-244 ◽

Cited By ~ 1

Author(s):

Raffaele De Rose ◽

Carmelo Felicetti ◽

Cinzia Raso ◽

Alberto Michele Felicetti ◽

Salvatore Ammirato

Keyword(s):

Energy Efficiency ◽

Smart Home ◽

Home Environments ◽

Smart Home Environments

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Initial research for passive and energy efficient building awareness in Bulgaria

MATEC Web of Conferences ◽

10.1051/matecconf/201929201008 ◽

2019 ◽

Vol 292 ◽

pp. 01008

Author(s):

Veneta Yosifova ◽

Rosen Petrov ◽

Milena Haralampieva

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

Smart Home ◽

Innovative Technologies ◽

Market Situation ◽

Energy Efficient Building ◽

Initial Research

The paper observes the newest innovative technologies regarding buildings energy efficiency like passive building and smart home technologies. The market situation in Europe and in Bulgaria for these types of technologies is analyzed. The outcome of the research will serve as a milestone in developing of a methodology for determining Bulgarian society’s awareness and attitude towards their using in home, business and production buildings.

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In search for market-based energy efficiency investment in households: smart home solutions as an option for optimized use of energy and reduction of costs for energy

Energy Procedia ◽

10.1016/j.egypro.2018.07.026 ◽

2018 ◽

Vol 147 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Reinis Aboltins ◽

Dagnija Blumberga

Keyword(s):

Energy Efficiency ◽

Smart Home ◽

Reduction Of Costs

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HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energies ◽

10.3390/en13051097 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1097 ◽

Cited By ~ 4

Author(s):

Isaac Machorro-Cano ◽

Giner Alor-Hernández ◽

Mario Andrés Paredes-Valverde ◽

Lisbeth Rodríguez-Mazahua ◽

José Luis Sánchez-Cervantes ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

Big Data ◽

Energy Consumption ◽

Energy Saving ◽

Smart Home ◽

User Preferences ◽

The Internet ◽

Automation Systems ◽

Home Energy Management

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

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