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      Special Issue on “Smart Homes”: Editors’ Notes

      editorial

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          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 with new advanced theoretical and technological solutions that enable smart technology diffusion into homes.

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

          Yu-Hsiu Lin, Yu-Chen Hu (2018)
          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%.
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            Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

            Dan Yang, Bin Xu, Kaiyou Rao … (2018)
            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.
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              Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes

              Guanci Yang, Jing Yang, Weihua Sheng … (2018)
              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.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 18 February 2019
                Publication date Collection: February 2019
                Volume: 19
                Issue: 4
                Electronic Location Identifier: 836
                Affiliations
                Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy; a.freddi@ 123456univpm.it (A.F.); s.longhi@ 123456univpm.it (S.L.)
                Author notes
                [* ]Correspondence: a.monteriu@ 123456staff.univpm.it ; Tel.: +39-071-220-4314; Fax: +39-071-220-4224
                Author information
                https://orcid.org/0000-0001-5388-8697
                Article
                Publisher ID: sensors-19-00836
                DOI: 10.3390/s19040836
                PMC ID: 6412805
                PubMed ID: 30781649
                SO-VID: bbdd70a8-7fb2-4032-88b1-228646f9ed35
                Copyright © © 2019 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 12 February 2019
                Date accepted : 13 February 2019
                Categories
                Subject: Editorial

                ScienceOpen disciplines: Biomedical engineering
                Keywords: 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
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: 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

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