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Internet of Things for Smart Homes II

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

Deadline for manuscript submissions: closed (30 January 2021) | Viewed by 53442

Special Issue Editors


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Guest Editor
Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Interests: wireless sensor networks; intelligent transportation systems; Internet of things; green communications; fuzzy logic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the improvement or growth of wireless protocols, the development of cloud services, the refinement of low-energy and high-performance technologies, the practice of Artificial Intelligence, and other forms of convergence solutions based the Internet of Things (IoT) paradigm have begun a new era for Smart Homes. Technologies for IoT-oriented Smart Homes include sensors, interfaces, monitors, and appliances networked together to facilitate the automation and local/remote control of the domestic environment. Thanks to the latest Information and Communication Technologies (ICT) and machine learning algorithms, the Smart Home environment is capable of monitoring the welfare and everyday life activities of residents, learning their distinct necessities and habits, with the aim to readjust itself to them, thus enhancing their overall quality of life. Furthermore, Smart Homes can skillfully control the energy consumption of appliances and all other features related to the domestic environment, thus producing a healthier and energy-effective area for their inhabitants. While IoT-oriented Smart Homes can modify how inhabitants interact with the domestic environment, each distinct technology needs different levels of security based on the sensitivity of the controlled system and the information it manages. Smart Homes can be exposed to security threats and privacy breach that stem from current ICT and protocols.

This Special Issue solicits the submission of high-quality and unpublished papers that aim to solve open technical problems and challenges typical of IoT-oriented Smart Homes. The main aim is to integrate novel approaches efficiently, focusing on the performance evaluation and comparison with existing solutions. Both theoretical and experimental studies for typical IoT-oriented Smart Homes scenarios are encouraged. Furthermore, high-quality review and survey papers are also welcomed.

Topics of interest include but are not limited to:

  • Wireless networks for smart homes;
  • Green communications for smart homes;
  • Energy management systems and networks for smart homes;
  • Smart environment monitoring and control;
  • Smart management of home appliances;
  • Innovative applications and services for smart homes;
  • Machine learning methods applied to smart homes;
  • Artificial neural networks for smart home automation;
  • Security and privacy in smart homes;
  • Data integrity, authentication, and access control for smart homes.

Prof. Dr. Giovanni Pau
Prof. Dr. Ilsun You
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

Published Papers (12 papers)

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Research

16 pages, 738 KiB  
Article
Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes
by Yanyan Wang, Lin Wang, Ruijuan Zheng, Xuhui Zhao and Muhua Liu
Sensors 2021, 21(7), 2347; https://doi.org/10.3390/s21072347 - 28 Mar 2021
Cited by 6 | Viewed by 2233
Abstract
In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) [...] Read more.
In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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21 pages, 8205 KiB  
Article
FLEX-IoT: Secure and Resource-Efficient Network Boot System for Flexible-IoT Platform
by Keon-Ho Park, Seong-Jin Kim, Joobeom Yun, Seung-Ho Lim and Ki-Woong Park
Sensors 2021, 21(6), 2060; https://doi.org/10.3390/s21062060 - 15 Mar 2021
Cited by 4 | Viewed by 2479
Abstract
In an internet of things (IoT) platform with a copious number of IoT devices and active variation of operational purpose, IoT devices should be able to dynamically change their system images to play various roles. However, the employment of such features in an [...] Read more.
In an internet of things (IoT) platform with a copious number of IoT devices and active variation of operational purpose, IoT devices should be able to dynamically change their system images to play various roles. However, the employment of such features in an IoT platform is hindered by several factors. Firstly, the trivial file transfer protocol (TFTP), which is generally used for network boot, has major security vulnerabilities. Secondly, there is an excessive demand for the server during the network boot, since there are numerous IoT devices requesting system images according to the variation of their roles, which exerts a heavy network overhead on the server. To tackle these challenges, we propose a system termed FLEX-IoT. The proposed system maintains a FLEX-IoT orchestrater which uses an IoT platform operation schedule to flexibly operate the IoT devices in the platform. The IoT platform operation schedule contains the schedules of all the IoT devices on the platform, and the FLEX-IoT orchestrater employs this schedule to flexibly change the mode of system image transfer at each moment. FLEX-IoT consists of a secure TFTP service, which is fully compatible with the conventional TFTP, and a resource-efficient file transfer method (adaptive transfer) to streamline the system performance of the server. The proposed secure TFTP service comprises of a file access control and attacker deception technique. The file access control verifies the identity of the legitimate IoT devices based on the hash chain shared between the IoT device and the server. FLEX-IoT provides security to the TFTP for a flexible IoT platform and minimizes the response time for network boot requests based on adaptive transfer. The proposed system was found to significantly increase the attack-resistance of TFTP with little additional overhead. In addition, the simulation results show that the volume of transferred system images on the server decreased by 27% on average, when using the proposed system. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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25 pages, 7869 KiB  
Article
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Sensors 2021, 21(5), 1636; https://doi.org/10.3390/s21051636 - 26 Feb 2021
Cited by 176 | Viewed by 9197
Abstract
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of intelligent and smart environments and systems that [...] Read more.
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of intelligent and smart environments and systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Due to the reliance of conventional Machine Learning (ML) techniques on handcrafted features in the extraction process, current research suggests that deep-learning approaches are more applicable to automated feature extraction from raw sensor data. In this work, the generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains. Four baseline LSTM networks are comparatively studied to analyze the impact of using different kinds of smartphone sensor data. In addition, a hybrid LSTM network called 4-layer CNN-LSTM is proposed to improve recognition performance. The HAR method is evaluated on a public smartphone-based dataset of UCI-HAR through various combinations of sample generation processes (OW and NOW) and validation protocols (10-fold and LOSO cross validation). Moreover, Bayesian optimization techniques are used in this study since they are advantageous for tuning the hyperparameters of each LSTM network. The experimental results indicate that the proposed 4-layer CNN-LSTM network performs well in activity recognition, enhancing the average accuracy by up to 2.24% compared to prior state-of-the-art approaches. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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14 pages, 2519 KiB  
Article
Multipath TCP-Based IoT Communication Evaluation: From the Perspective of Multipath Management with Machine Learning
by Ruiwen Ji, Yuanlong Cao, Xiaotian Fan, Yirui Jiang, Gang Lei and Yong Ma
Sensors 2020, 20(22), 6573; https://doi.org/10.3390/s20226573 - 18 Nov 2020
Cited by 8 | Viewed by 7005
Abstract
With the development of wireless networking technology, current Internet-of-Things (IoT) devices are equipped with multiple network access interfaces. Multipath TCP (MPTCP) technology can improve the throughput of data transmission. However, traditional MPTCP path management may cause problems such as data confusion and even [...] Read more.
With the development of wireless networking technology, current Internet-of-Things (IoT) devices are equipped with multiple network access interfaces. Multipath TCP (MPTCP) technology can improve the throughput of data transmission. However, traditional MPTCP path management may cause problems such as data confusion and even buffer blockage, which severely reduces transmission performance. This research introduces machine learning algorithms into MPTCP path management, and proposes an automatic learning selection path mechanism based on MPTCP (ALPS-MPTCP), which can adaptively select some high-quality paths and transmit data at the same time. This paper designs a simulation experiment that compares the performance of four machine learning algorithms in judging path quality. The experimental results show that, considering the running time and accuracy, the random forest algorithm has the best performance in judging path quality. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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28 pages, 6062 KiB  
Article
Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach
by Huan Liu, Shiyong Li and Wei Sun
Sensors 2020, 20(22), 6545; https://doi.org/10.3390/s20226545 - 16 Nov 2020
Cited by 13 | Viewed by 3033
Abstract
Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link [...] Read more.
Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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14 pages, 1451 KiB  
Article
Energy and Transmission Efficiency Enhancement in Passive Optical Network Enabled Reconfigurable Fronthaul Supporting Smart Homes
by Rentao Gu, Gang Wang, Zhekang Li and Yuefeng Ji
Sensors 2020, 20(21), 6245; https://doi.org/10.3390/s20216245 - 2 Nov 2020
Cited by 2 | Viewed by 2027
Abstract
Smart home technologies are growing actively all around the world. As a result, great pressures are imposed on internet of things networks by dynamic traffic and plenty of devices. The passive optical network is considered one of the most promising fronthaul technologies. In [...] Read more.
Smart home technologies are growing actively all around the world. As a result, great pressures are imposed on internet of things networks by dynamic traffic and plenty of devices. The passive optical network is considered one of the most promising fronthaul technologies. In particular, the time and wavelength division multiplexing passive optical network has shown the advantage of high capacity and received attention recently. In support of internet of things networks, the energy and transmission efficiency has emerged as an important issue on the time and wavelength division multiplexing passive optical network enabled fronthaul networks. In this paper, we try to enhance the energy and transmission efficiency of the time and wavelength division multiplexing passive optical network enabled reconfigurable fronthaul. Fronthaul links’ load balancing is also taken into consideration. An integer non-linear programming model is employed to formulate the joint optimization problem. We also provide an adaptive genetic algorithm-based approach with fast convergence. The simulation results show that the active units of fronthaul can be dynamically switched on/off with the traffic variation and a significant energy saving is achieved. In addition, the maximum transmission efficiency increases by 87% with integer non-linear programming method in off-peak periods. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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17 pages, 6840 KiB  
Article
IoT-Based Electricity Bill for Domestic Applications
by Ramón Octavio Jiménez Betancourt, Juan Miguel González López, Emilio Barocio Espejo, Antonio Concha Sánchez, Efraín Villalvazo Laureano, Sergio Sandoval Pérez and Luis Contreras Aguilar
Sensors 2020, 20(21), 6178; https://doi.org/10.3390/s20216178 - 29 Oct 2020
Cited by 6 | Viewed by 5353
Abstract
This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review [...] Read more.
This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review and administrate their energy consumption. Using low-cost sensors and the electronic board Particle® Photon, an energy meter is proposed. The presented prototype is extremely compact and satisfies safety measures to be used by anyone in a domestic installation. The measurement data is displayed and processed in real-time, and an appropriate algorithm determines the accumulated kWh. The energy consumed is displayed using an Html interface of easy interpretation for the customers, given recommendations about their consumption habits and some alarms in case of abnormal or high consumption. As a reinforcement measure for avoiding large consumption bills, the system is programmed to send messages to the user, remembering if the estimated consumption is large. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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16 pages, 3452 KiB  
Article
Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation
by Yang Hu, Diane J. Cook and Matthew E. Taylor
Sensors 2020, 20(21), 6145; https://doi.org/10.3390/s20216145 - 29 Oct 2020
Cited by 2 | Viewed by 2480
Abstract
Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults’ life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning [...] Read more.
Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults’ life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning environment that is designed to help individuals install a SHiB kit. This article presents an experiment examining how smart home sensor installation was affected by knowledge gained from two methods, SHiB CBLE, and a written document. Results show that participants who were trained by the CBLE took significantly (p<0.05) less time in the installation session than those in the control group. The accuracy rate of SHiB kit installation is 78% for the group trained by the CBLE and 77% for the control group. Participants trained by the CBLE showed significantly (p<0.01) higher confidence in the actual installation than those in the control group. These results suggest that having a training before the actual installation will help installers avoid unnecessary work, shorten the installation time, and increase installers’ confidence. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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16 pages, 11055 KiB  
Article
An Efficient Resource Allocation Strategy for Edge-Computing Based Environmental Monitoring System
by Juan Fang, Juntao Hu, Jianhua Wei, Tong Liu and Bo Wang
Sensors 2020, 20(21), 6125; https://doi.org/10.3390/s20216125 - 28 Oct 2020
Cited by 3 | Viewed by 2283
Abstract
The cloud computing and microsensor technology has greatly changed environmental monitoring, but it is difficult for cloud-computing based monitoring system to meet the computation demand of smaller monitoring granularity and increasing monitoring applications. As a novel computing paradigm, edge computing deals with this [...] Read more.
The cloud computing and microsensor technology has greatly changed environmental monitoring, but it is difficult for cloud-computing based monitoring system to meet the computation demand of smaller monitoring granularity and increasing monitoring applications. As a novel computing paradigm, edge computing deals with this problem by deploying resource on edge network. However, the particularity of environmental monitoring applications is ignored by most previous studies. In this paper, we proposed a resource allocation algorithm and a task scheduling strategy to reduce the average completion latency of environmental monitoring application, when considering the characteristic of environmental monitoring system and dependency among task. Simulations are conducted, and the results show that compared with the traditional algorithms. With considering the emergency task, the proposed methods decrease the average completion latency by 21.6% in the best scenario. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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21 pages, 1535 KiB  
Article
Optimization of Mixed Energy Supply of IoT Network Based on Matching Game and Convex Optimization
by Dongsheng Han, Tao Liu and Yincheng Qi
Sensors 2020, 20(19), 5458; https://doi.org/10.3390/s20195458 - 23 Sep 2020
Cited by 3 | Viewed by 1770
Abstract
The interaction capability provided by the Internet of Things (IoT) significantly increases communication between human and machine, changing our lives gradually. However, the abundant constructions of 5G small base stations (SBSs) and large-scaled access of IoT terminal equipment (TE) will surely cause a [...] Read more.
The interaction capability provided by the Internet of Things (IoT) significantly increases communication between human and machine, changing our lives gradually. However, the abundant constructions of 5G small base stations (SBSs) and large-scaled access of IoT terminal equipment (TE) will surely cause a dramatic increase in energy expense costs of a wireless communication system. In this study, we designed a bilateral random model of TE allocation and energy decisions in IoT, and proposed a mixed energy supply algorithm based on a matching game and convex optimization to minimize the energy expense cost of the wireless communication system in IoT. This study divided the problem of minimizing energy expense cost of the system into two steps. First, the random allocation problem of TEs in IoT was modeled to a matching game problem. This step is to obtain the TE matching scheme that minimizes the energy consumption of the whole system on the basis of guaranteeing the quality of service of TEs. Second, the energy decision problem of SBS was modeled into a convex optimization problem. The energy purchase scheme of SBSs with the minimum energy expense cost of the system was obtained by solving the optimal solution of the convex optimization. According to the simulation results, the proposed mixed energy supply scheme can decrease the energy expense cost of the system effectively. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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22 pages, 1363 KiB  
Article
Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach
by Sangyoon Lee and Dae-Hyun Choi
Sensors 2020, 20(7), 2157; https://doi.org/10.3390/s20072157 - 10 Apr 2020
Cited by 82 | Viewed by 8802
Abstract
This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a [...] Read more.
This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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21 pages, 5438 KiB  
Article
A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning
by Yu-Hsiu Lin
Sensors 2020, 20(6), 1649; https://doi.org/10.3390/s20061649 - 16 Mar 2020
Cited by 9 | Viewed by 3903
Abstract
Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a [...] Read more.
Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN’s performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM. Full article
(This article belongs to the Special Issue Internet of Things for Smart Homes II)
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