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Special Issue "Sensor Technology for Smart Homes"

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

Deadline for manuscript submissions: 15 July 2020.

Special Issue Editors

Dr. Juan Ye
Website
Guest Editor
School of Computer Science, University of St Andrews, UK
Interests: Privacy; Activity Recognition; Wearable computing
Special Issues and Collections in MDPI journals
Dr. Michael O'Grady
Website
Guest Editor
Prof. Dr. Oresti Banos
Website
Guest Editor
Computational Behaviour Modelling Research Centre for Information and Communications Technology, University of Granada (UGR), 18071 Granada, Spain
Interests: wearable, ubiquitous, and mobile computing; artificial intelligence; data mining; digital health
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the relentless advance in sensing technologies, we are witnessing an increasing number of sophisticated smart home deployments covering a wide spectrum of applications including  health monitoring and home automation. However, the inherent complexity of real-world deployment is significantly challenging current smart home systems; such complexity includes the inherently imperfect nature of sensing technologies, the dynamic nature of human behaviour, and the unpredictability of situations or events occurring in the home. Various problems accrue as a result of such complexity; these include  decreased accuracies in recognising human activities over time and subsequent degradation of the performance of smart home systems with negative implications for user experience.

The objective of this Special Issue is to present the state-of-the-art in sensing technologies for the smart home; document realistic experiences of long-term, real-world smart home deployments; explore novel intelligent algorithms to discover and adapt smart home systems to changes in human daily routines and other contexts; and present new research challenges and opportunities in the smart home domain.

Original, high-quality contributions from both academia and industry are sought. Manuscripts submitted for review should not have been published elsewhere or be under review by other journals or peer-reviewed conferences.

Topics of interest include, but are not limited to, the following:

  • Novel sensing technologies for smart homes;
  • Standardisation initiatives applicable to smart homes;
  • Internet of Things approaches for smart homes;
  • Privacy, security, and data management within smart homes;
  • User experiences of smart home technologies;
  • Human activity recognition, both predictive and in near real time;
  • Interaction design and novel user interfaces for smart homes;
  • Approaches to modelling computational and social intelligence within smart homes;
  • Novel applications and services for the smart home;
  • Smart homes and their inter-relationship with smart cities.
The editors are particularly interested in receiving submissions that consider the following issues:
  • The lifelong learning of occupant behaviour in smart home systems;
  • Human-centred learning in smart home systems;
  • Methodologies for benchmarking smart home platforms and services;
  • Ethical and privacy-preservation approaches to smart homes.

Dr. Juan Ye
Dr. Michael O'Grady
Dr. Oresti Banos
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 papers will be 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 2000 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

  • Sensing technologies
  • Intelligence systems
  • Context reasoning
  • Intelligent user interfaces
  • Smart environments

Published Papers (7 papers)

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Research

Open AccessArticle
Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things
Sensors 2020, 20(3), 620; https://doi.org/10.3390/s20030620 - 22 Jan 2020
Abstract
The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the [...] Read more.
The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor
Sensors 2020, 20(2), 398; https://doi.org/10.3390/s20020398 - 10 Jan 2020
Abstract
This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of [...] Read more.
This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research
Sensors 2019, 19(20), 4565; https://doi.org/10.3390/s19204565 - 21 Oct 2019
Cited by 1
Abstract
Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the [...] Read more.
Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions
Sensors 2019, 19(16), 3621; https://doi.org/10.3390/s19163621 - 20 Aug 2019
Abstract
Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper [...] Read more.
Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
A Multi-Agent Gamification System for Managing Smart Homes
Sensors 2019, 19(5), 1249; https://doi.org/10.3390/s19051249 - 12 Mar 2019
Cited by 6
Abstract
Rapid development and conducted experiments in the field of the introduction the fifth generation of the mobile network standard allow for the flourishing of the Internet of Things. This is one of the most important reasons to design and test systems that can [...] Read more.
Rapid development and conducted experiments in the field of the introduction the fifth generation of the mobile network standard allow for the flourishing of the Internet of Things. This is one of the most important reasons to design and test systems that can be implemented to increase the quality of our lives. In this paper, we propose a system model for managing tasks in smart homes using multi-agent solutions. The proposed solution organizes work and distributes tasks to individual family members. An additional advantage is the introduction of gamification, not only between household members, but also between families. The solution was tested to simulate the entire solution as well as the individual components that make up the system. The proposal is described with regard to the possibility of implementing smart homes in future projects. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study
Sensors 2019, 19(5), 983; https://doi.org/10.3390/s19050983 - 26 Feb 2019
Cited by 11
Abstract
Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given [...] Read more.
Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Open AccessArticle
A Human–Machine Interface Based on Eye Tracking for Controlling and Monitoring a Smart Home Using the Internet of Things
Sensors 2019, 19(4), 859; https://doi.org/10.3390/s19040859 - 19 Feb 2019
Cited by 6
Abstract
People with severe disabilities may have difficulties when interacting with their home devices due to the limitations inherent to their disability. Simple home activities may even be impossible for this group of people. Although much work has been devoted to proposing new assistive [...] Read more.
People with severe disabilities may have difficulties when interacting with their home devices due to the limitations inherent to their disability. Simple home activities may even be impossible for this group of people. Although much work has been devoted to proposing new assistive technologies to improve the lives of people with disabilities, some studies have found that the abandonment of such technologies is quite high. This work presents a new assistive system based on eye tracking for controlling and monitoring a smart home, based on the Internet of Things, which was developed following concepts of user-centered design and usability. With this system, a person with severe disabilities was able to control everyday equipment in her residence, such as lamps, television, fan, and radio. In addition, her caregiver was able to monitor remotely, by Internet, her use of the system in real time. Additionally, the user interface developed here has some functionalities that allowed improving the usability of the system as a whole. The experiments were divided into two steps. In the first step, the assistive system was assembled in an actual home where tests were conducted with 29 participants without disabilities. In the second step, the system was tested with online monitoring for seven days by a person with severe disability (end-user), in her own home, not only to increase convenience and comfort, but also so that the system could be tested where it would in fact be used. At the end of both steps, all the participants answered the System Usability Scale (SUS) questionnaire, which showed that both the group of participants without disabilities and the person with severe disabilities evaluated the assistive system with mean scores of 89.9 and 92.5, respectively. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article

Tentative Title : Evaluation of MAC Protocols for Vital Signs Monitoring within Smart Home Environment

Authors and Affiliations:
Amjad Gawanmeh (Corresponding author): Department of Electrical and Computer Engineering, Khalifa University, Abu DHabi, UAE and Department of Electrical and Computer Engineering, Concordia; University, Montreal Canada Negar Mohammadi-Koushki; Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran


Ahmad Alomari: Department of Software Engineering, Universite du Quebec, Ecole de Technologie; Superieure, Montreal, Canada, and kSecureOps inc., Montreal, Canada

Abstract: One of the appealing applications of smart home is remote monitoring of vital signs, such as heart and respiratory, which has gained more attention during the last two decades. The recent developments in IoT, sensing devices, and smart technologies, increased the demand for such technology due to the need for devices that can continuously monitor physiological signals and offer further analysis and interpretations.

Real-time monitoring systems record, measure, and monitor the vital signs activity for several reasons, such as early detection of heart problems, patient watching, athlete’s issues, etc. This requires the use of medium access control protocol in order to resolve problems such as the correlation of traffic, heavy collisions, and free channel recognition. In this paper, a fuzzy Analytic Hierarchy Process (AHP) based algorithm is proposed for the performance analysis of several MAC protocols that are intended for monitoring medical signal in the human body, such as vital signals. Five different protocols were analyzed according to several performance metrics using a quantitative ranking based on the proposed algorithm. The work concludes by suggesting a new protocol that combines the two most efficient ones and taking advantage of the features of each.

 
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