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Special Issue "Smart Homes: A Prospective of Sensing, Communication, and Automation"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2022 | Viewed by 3275

Special Issue Editors

Dr. Kok Yew Ng
E-Mail Website
Guest Editor
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
Interests: control engineering; fault diagnosis; mobile computing
Dr. Adnan Ahmad Cheema
E-Mail Website
Guest Editor
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
Interests: wireless communication; radio environments; sensing; IoT
Prof. Dr. Dewar Finlay
E-Mail Website
Guest Editor
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
Interests: the application of technology in cardiovascular medicine with a particular focus on computerised ECG analysis
Special Issues, Collections and Topics in MDPI journals
Prof. James McLaughlin
E-Mail Website
Guest Editor
School of Engineering, Ulster University, Newtownabbey BT37 0QB, UK
Interests: sensors, biomedical and diagnostics; healthcare technologies

Special Issue Information

Dear Colleagues,

Despite being conceptualised during the 1980s, the term “Internet of Things” was not coined until 1999. Since then, we have seen exciting and continuous growth in research and development in the field, especially towards the applications of integration with smart sensors in smart homes and equivalent environments. The current situation that is the COVID-19 pandemic has further pushed for more aggressive and innovative research and development of IoT in smart homes for automation and healthcare, amongst others.

The purpose of this Special Issue is to report on the latest research findings on smart homes and the related developments. Papers that are focused on the development and integration of sensors in specific research areas such as (but not limited to) intelligent lighting systems, healthcare and diagnostics, wireless communications, home control and automation, monitoring, and mobile computing, as well as web services and cloud computing, are also welcome.

Dr. Kok Yew Ng
Dr. Adnan Ahmad Cheema
Prof. Dewar Finlay
Prof. Christopher Nugent
Prof. James McLaughlin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart homes
  • Internet of Things
  • intelligent lighting systems
  • mobile computing
  • healthcare and diagnostics
  • wireless sensors
  • home control and automation
  • human factors
  • monitoring
  • web services
  • cloud computing

Published Papers (3 papers)

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Research

Article
Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering
Sensors 2022, 22(11), 4036; https://doi.org/10.3390/s22114036 - 26 May 2022
Viewed by 302
Abstract
With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two [...] Read more.
With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM. Full article
(This article belongs to the Special Issue Smart Homes: A Prospective of Sensing, Communication, and Automation)
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Article
Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart Home
Sensors 2022, 22(11), 3965; https://doi.org/10.3390/s22113965 - 24 May 2022
Viewed by 439
Abstract
This paper presents a wireless kitchen fire prevention system that can detect and notify the fire risk caused by gas stoves. The proposed system consists of two modules. The sensor module detects the concentration of carbon dioxide (CO2) near the gas [...] Read more.
This paper presents a wireless kitchen fire prevention system that can detect and notify the fire risk caused by gas stoves. The proposed system consists of two modules. The sensor module detects the concentration of carbon dioxide (CO2) near the gas stove and transmits the monitoring results wirelessly. The alarm module, which is placed in other places, receives the data and reminds the user of the stove status. The sensor module uses a cost-efficient electrochemical CO2 sensor and embeds an in situ algorithm that determines the status of the gas stove based on the measured CO2 concentration. For the wireless communication between the modules, on-off keying (OOK) is employed, thereby achieving a longer battery lifetime of the alarm module, low cost, and simple implementation. To increase the lifetime further, a wake-up function based on passive infrared (PIR) sensing is employed in the alarm module. Our system can successfully detect the on state of the stove within 40 s and the off state within 200 s. Thanks to the low-power implementation, in situ algorithm, and wake-up function, the alarm module’s expected battery lifetime is extended to about two months. Full article
(This article belongs to the Special Issue Smart Homes: A Prospective of Sensing, Communication, and Automation)
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Article
Design and Implementation of the E-Switch for a Smart Home
Sensors 2021, 21(11), 3811; https://doi.org/10.3390/s21113811 - 31 May 2021
Cited by 2 | Viewed by 1462
Abstract
As the development of systems in smart homes is increasing, it is of ever-increasing importance to have data, which artificial intelligence methods and techniques can apply to recognize activities and patterns or to detect anomalies, with the aim of reducing energy consumption in [...] Read more.
As the development of systems in smart homes is increasing, it is of ever-increasing importance to have data, which artificial intelligence methods and techniques can apply to recognize activities and patterns or to detect anomalies, with the aim of reducing energy consumption in the main home domestic services, and to offer users an alternative in the management of these resources. This paper describes the design and implementation of a platform based on the internet of things and a cloud environment that allows the user to remotely control and monitor Wi-Fi wireless e-switch in a home through a mobile application. This platform is intended to represent the first step in transforming a home into a smart home, and it allows the collection and storage of the e-switch information, which can be used for further processing and analysis. Full article
(This article belongs to the Special Issue Smart Homes: A Prospective of Sensing, Communication, and Automation)
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