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

Deadline for manuscript submissions: closed (20 May 2018) | Viewed by 73838

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Special Issue Editors

Prof. Dr. Sauro Longhi

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Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: modelling, identification and control of linear systems; control of mobile base robots and underwater vehicles; service robots for assistive applications; ambient assisted living; power management in hybrid cars and cooperative control of autonomous agents; robotics; fault diagnosis

Prof. Dr. Andrea Monteriù

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Guest Editor

Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: intelligent household; Internet of Things; interoperability; smart monitoring; smart living; safety; Security; Energy Management; home automation; smart sensors; wireless sensor networks; machine learning; home appliances and devices; digital twin; cyber-physical system; risk assessment; operators’ safety
Special Issues, Collections and Topics in MDPI journals

Dr. Alessandro Freddi

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Guest Editor

Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: fault diagnosis; fault tolerant control; unmanned vehicles; robotics; assistive technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in Information Communication Technologies (ICTs) have made interoperability possible, such that everyday devices at home can be networked to give the inhabitants new and unexpected possibilities. In particular, the high penetration rate of the Internet of Things (IoT) paradigm in household environments allows to introduce the new concept of “smart home”, namely a residence equipped with innovative technological solutions and services for improving residents’ living and security. Smart Home Technologies (SHTs) comprise sensors, monitors, interfaces, appliances and devices networked together to enable automation, as well as localized and remote control of the domestic environment. In this context, thanks to the latest sensor technologies and machine learning algorithms, the domestic technological environment is able to monitor the well-being and daily life activities of inhabitants, and to learn their specific needs and habits, such that to adapt itself to them, and thus improving their overall quality of life. In addition, smart homes can intelligently manage the energy usage of appliances and all other aspects of the domestic environment, thus creating a more comfortable, energy-efficient space for their inhabitants. Smart Home Technologies have also a great impact on home automation, health, independent and assisted living, security, and many others.

This Special Issue is devoted to new research efforts and results, developments and applications in the area of sensors and technologies for intelligent household. The aim is to provide a comprehensive collection of some of the current state-of-the-art technologies within this context, together with new advanced theoretical and technological solutions which enable smart technology diffusion into homes. In this Special Issue, we solicit high-quality contributions with consolidated and thoroughly evaluated application-oriented research results in the area of the Smart Home Technologies that are worthy of archival publication in Sensors.

Prof. Dr. Sauro Longhi
Dr. Andrea Monteriù
Dr. Alessandro Freddi
Guest Editors

Manuscript Submission Information

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Keywords

Smart home technologies
Intelligent household
Internet of Things
Interoperability
Smart monitoring
Smart living
Safety
Security
Energy management
Home automation
Smart sensors
Wireless sensor networks
Machine learning
Home appliances and devices

Published Papers (14 papers)

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Editorial

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4 pages, 164 KiB

Open AccessEditorial

Special Issue on “Smart Homes”: Editors’ Notes

by Alessandro Freddi, Sauro Longhi and Andrea Monteriù

Sensors 2019, 19(4), 836; https://doi.org/10.3390/s19040836 - 18 Feb 2019

Cited by 3 | Viewed by 2515

Abstract

In this editorial, we provide an overview of the content of the Special Issue on “Smart Homes”. The aim of this Special Issue is to provide a comprehensive collection of some of the current state-of-the-art technologies in the context of smart homes, together [...] Read more.

(This article belongs to the Special Issue Smart Homes)

Research

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22 pages, 1331 KiB

Open AccessArticle

A Smart Sensing Architecture for Domestic Monitoring: Methodological Approach and Experimental Validation

by Andrea Monteriù, Mario Rosario Prist, Emanuele Frontoni, Sauro Longhi, Filippo Pietroni, Sara Casaccia, Lorenzo Scalise, Annalisa Cenci, Luca Romeo, Riccardo Berta, Loreto Pescosolido, Gianni Orlandi and Gian Marco Revel

Sensors 2018, 18(7), 2310; https://doi.org/10.3390/s18072310 - 17 Jul 2018

Cited by 44 | Viewed by 6972

Abstract

Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident’s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user’s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user’s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home. Full article

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17 pages, 978 KiB

Open AccessArticle

Integrating Cyber-Physical Systems in a Component-Based Approach for Smart Homes

by Javier Criado, José Andrés Asensio, Nicolás Padilla and Luis Iribarne

Sensors 2018, 18(7), 2156; https://doi.org/10.3390/s18072156 - 04 Jul 2018

Cited by 15 | Viewed by 3860

Abstract

Integration of different cyber-physical systems involves a development process that takes into account some solutions for intercommunicating and interoperating heterogeneous devices. Each device can be managed as a thing within the Internet-of-Things concept by using web technologies. In addition, a “thing” can be managed as an encapsulated component by applying component-based software engineering principles. Based on this context, we propose a solution for integrating heterogeneous systems using a specific component-based technology. Specifically, we focus on enabling the connection of different types of subsystems present in smart home solutions. This technology enables interoperability by applying a homogeneous component representation that provides communication features through web sockets, and by implementing gateways in proprietary network connections. Furthermore, our solution eases the extension of these systems by means of abstract representations of the architectures and devices that form part of them. The approach is validated through an example scenario with different subsystems of a smart home solution. Full article

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16 pages, 2529 KiB

Open AccessArticle

Cloud-Based Behavioral Monitoring in Smart Homes

by Niccolò Mora, Guido Matrella and Paolo Ciampolini

Sensors 2018, 18(6), 1951; https://doi.org/10.3390/s18061951 - 15 Jun 2018

Cited by 28 | Viewed by 4625

Abstract

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques. Full article

(This article belongs to the Special Issue Smart Homes)

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13 pages, 2097 KiB

Open AccessArticle

An IBeacon-Based Location System for Smart Home Control

by Qinghe Liu, Xinshuang Yang and Lizhen Deng

Sensors 2018, 18(6), 1897; https://doi.org/10.3390/s18061897 - 11 Jun 2018

Cited by 27 | Viewed by 4958

Abstract

Indoor location and intelligent control system can bring convenience to people’s daily life. In this paper, an indoor control system is designed to achieve equipment remote control by using low-energy Bluetooth (BLE) beacon and Internet of Things (IoT) technology. The proposed system consists of five parts: web server, home gateway, smart terminal, smartphone app and BLE beacons. In the web server, fingerprint matching based on RSSI stochastic characteristic and posture recognition model based on geomagnetic sensing are used to establish a more efficient equipment control system, combined with Pedestrian Dead Reckoning (PDR) technology to improve the accuracy of location. A personalized menu of remote “one-click” control is finally offered to users in a smartphone app. This smart home control system has been implemented by hardware, and precision and stability tests have been conducted, which proved the practicability and good user experience of this solution. Full article

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22 pages, 1035 KiB

Open AccessArticle

Boosting a Low-Cost Smart Home Environment with Usage and Access Control Rules

by Paolo Barsocchi, Antonello Calabrò, Erina Ferro, Claudio Gennaro, Eda Marchetti and Claudio Vairo

Sensors 2018, 18(6), 1886; https://doi.org/10.3390/s18061886 - 08 Jun 2018

Cited by 30 | Viewed by 4836

Abstract

Smart Home has gained widespread attention due to its flexible integration into everyday life. Pervasive sensing technologies are used to recognize and track the activities that people perform during the day, and to allow communication and cooperation of physical objects. Usually, the available infrastructures and applications leveraging these smart environments have a critical impact on the overall cost of the Smart Home construction, require to be preferably installed during the home construction and are still not user-centric. In this paper, we propose a low cost, easy to install, user-friendly, dynamic and flexible infrastructure able to perform runtime resources management by decoupling the different levels of control rules. The basic idea relies on the usage of off-the-shelf sensors and technologies to guarantee the regular exchange of critical information, without the necessity from the user to develop accurate models for managing resources or regulating their access/usage. This allows us to simplify the continuous updating and improvement, to reduce the maintenance effort and to improve residents’ living and security. A first validation of the proposed infrastructure on a case study is also presented. Full article

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16 pages, 4942 KiB

Open AccessArticle

Accurate Fall Detection in a Top View Privacy Preserving Configuration

by Manola Ricciuti, Susanna Spinsante and Ennio Gambi

Sensors 2018, 18(6), 1754; https://doi.org/10.3390/s18061754 - 29 May 2018

Cited by 9 | Viewed by 2998

Abstract

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive. Full article

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20 pages, 4077 KiB

Open AccessArticle

An Efficient Framework for Development of Task-Oriented Dialog Systems in a Smart Home Environment

by Youngmin Park, Sangwoo Kang and Jungyun Seo

Sensors 2018, 18(5), 1581; https://doi.org/10.3390/s18051581 - 16 May 2018

Cited by 4 | Viewed by 5499

Abstract

In recent times, with the increasing interest in conversational agents for smart homes, task-oriented dialog systems are being actively researched. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a dialog framework that can integrate and manage the entire dialog system. Therefore, in this study, we propose a framework that enables the user to effectively develop an intelligent dialog system. The proposed framework ontologically expresses the knowledge required for the task-oriented dialog system’s process and can build a dialog system by editing the dialog knowledge. In addition, the framework provides a module router that can indirectly run externally developed modules. Further, it enables a more intelligent conversation by providing a hierarchical argument structure (HAS) to manage the various argument representations included in natural language sentences. To verify the practicality of the framework, an experiment was conducted in which developers without any previous experience in developing a dialog system developed task-oriented dialog systems using the proposed framework. The experimental results show that even beginner dialog system developers can develop a high-level task-oriented dialog system. Full article

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23 pages, 4382 KiB

Open AccessArticle

Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes

by Guanci Yang, Jing Yang, Weihua Sheng, Francisco Erivaldo Fernandes Junior and Shaobo Li

Sensors 2018, 18(5), 1530; https://doi.org/10.3390/s18051530 - 12 May 2018

Cited by 36 | Viewed by 6822

Abstract

Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy. Full article

(This article belongs to the Special Issue Smart Homes)

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41 pages, 7608 KiB

Open AccessArticle

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data

by Simona-Vasilica Oprea, Alexandru Pîrjan, George Căruțașu, Dana-Mihaela Petroșanu, Adela Bâra, Justina-Lavinia Stănică and Cristina Coculescu

Sensors 2018, 18(5), 1443; https://doi.org/10.3390/s18051443 - 05 May 2018

Cited by 19 | Viewed by 4662

Abstract

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers. Full article

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13 pages, 4998 KiB

Open AccessArticle

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

by Yu-Hsiu Lin and Yu-Chen Hu

Sensors 2018, 18(5), 1365; https://doi.org/10.3390/s18051365 - 27 Apr 2018

Cited by 55 | Viewed by 5436

Abstract

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%. Full article

(This article belongs to the Special Issue Smart Homes)

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16 pages, 7658 KiB

Open AccessArticle

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

by Bhagya Nathali Silva, Murad Khan and Kijun Han

Sensors 2018, 18(3), 685; https://doi.org/10.3390/s18030685 - 25 Feb 2018

Cited by 35 | Viewed by 5376

Abstract

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism. Full article

(This article belongs to the Special Issue Smart Homes)

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12 pages, 5809 KiB

Open AccessArticle

Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

by Dan Yang, Bin Xu, Kaiyou Rao and Weihua Sheng

Sensors 2018, 18(2), 332; https://doi.org/10.3390/s18020332 - 24 Jan 2018

Cited by 63 | Viewed by 8484

Abstract

Indoor occupants’ positions are significant for smart home service systems, which usually consist of robot service(s), appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans’ position in indoor environments based on passive infrared (PIR) sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization. Full article

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Review

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19 pages, 310 KiB

Open AccessReview

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

by Nancy E. ElHady and Julien Provost

Sensors 2018, 18(7), 1991; https://doi.org/10.3390/s18071991 - 21 Jun 2018

Cited by 33 | Viewed by 4978

Abstract

Ambient Assisted Living (AAL) systems aim to enable the elderly people to stay active and live independently into older age by monitoring their behaviour, provide the needed assistance and detect early signs of health status deterioration. Non-intrusive sensors are preferred by the elderly to be used for the monitoring purposes. However, false positive or negative triggers of those sensors could lead to a misleading interpretation of the status of the elderlies. This paper presents a systematic literature review of the sensor failure detection and fault tolerance in AAL equipped with non-intrusive, event-driven, binary sensors. The existing works are discussed, and the limitations and research gaps are highlighted. Full article

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