Development of a Smart Home Context-aware Application: A Machine Learning based Approach

Hoque, M. Robiul; Yang, Sung-Hyun

doi:10.14257/ijsh.2015.9.1.23

Cited by 20 publications

(5 citation statements)

References 14 publications

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“…Typically, HVAC systems are produced not only to heat and cool the air but also to draw in and circulate outdoor air in large buildings [266]. Kabir et al [267] present a context-aware application that provides the service according to a predefined preference of a user. They use the KNN classifier to infer the predefined service that will maximize the user's comfort and safety while requiring minimum explicit interaction of the user with the environment.…”

Section: Applications Of Ml-based Context-awarementioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

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Section: Applications Of Ml-based Context-awarementioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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“…After the training phase, the system continuously interprets the user's current situation and generates personalized recommendations. Kabir et al (2015a) presented a context-aware application, which can provide service according to predefined choice of user. It uses Mahalanobis distance based k nearest neighbor's classifier technique for inference of predefined service.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Modular Context-Aware Services Adaptation For A Smart Living Room

Miraoui¹,

El-etriby²,

Tadj³

et al. 2018

AUTOSOFT

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Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done proactively according to the inhabitant's current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.

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“…Other methods draw on more advanced techniques belonging to the domain of supervised machine learning, such as artificial neural networks, genetic algorithms or support vector machines. [152,[162][163][164][165][166] In addition, practical aspects of the design of reasoning engines also include the application of probabilistic methods, such as BNs, HMMs, and the DST, to which reference was made earlier in Section 4.2.4.…”

Section: Practical Implementation Of the Cps Modelmentioning

confidence: 99%

Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies

Podgórski

Majchrzycka

Dąbrowska

et al. 2016

International Journal of Occupational Safety and Ergonomics

106

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Recent developments in domains of ambient intelligence (AmI), Internet of Things, cyber-physical systems (CPS), ubiquitous/pervasive computing, etc., have led to numerous attempts to apply ICT solutions in the occupational safety and health (OSH) area. A literature review reveals a wide range of examples of smart materials, smart personal protective equipment and other AmI applications that have been developed to improve workers' safety and health. Because the use of these solutions modifies work methods, increases complexity of production processes and introduces high dynamism into thus created smart working environments (SWE), a new conceptual framework for dynamic OSH management in SWE is called for. A proposed framework is based on a new paradigm of OSH risk management consisting of real-time risk assessment and the capacity to monitor the risk level of each worker individually. A rationale for context-based reasoning in SWE and a respective model of the SWE-dedicated CPS are also proposed.

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Development of a Smart Home Context-aware Application: A Machine Learning based Approach

Cited by 20 publications

References 14 publications

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

A Hybrid Modular Context-Aware Services Adaptation For A Smart Living Room

Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies

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