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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 (25 May 2023) | Viewed by 37303

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Dr. Giovanni Pau

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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

Dr. Ilsun You

E-Mail Website
Guest Editor

Department of Information Security Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Shinchang-myeon, Asan-si 31538, Choongchungnam-do, Republic of Korea
Interests: 5G security; IoT security; authentication and access control; formal security analysis; mobile internet security
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 on 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 home 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
Dr. Ilsun You
Guest Editors

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Related Special Issue

Internet of Things for Smart Homes (4th Edition) in Sensors

Published Papers (12 papers)

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Research

27 pages, 6022 KiB

Open AccessArticle

A Smart Home Digital Twin to Support the Recognition of Activities of Daily Living

by Damien Bouchabou, Juliette Grosset, Sao Mai Nguyen, Christophe Lohr and Xavier Puig

Sensors 2023, 23(17), 7586; https://doi.org/10.3390/s23177586 - 1 Sep 2023

Cited by 5 | Viewed by 2456

Abstract

One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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23 pages, 19250 KiB

Open AccessArticle

Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe

by Kaveripakam Sathish, Ravikumar CV, Mohd Nadhir Ab Wahab, Rajesh Anbazhagan, Giovanni Pau and Muhammad Firdaus Akbar

Sensors 2023, 23(10), 4844; https://doi.org/10.3390/s23104844 - 17 May 2023

Cited by 18 | Viewed by 2763

Abstract

Underwater Wireless Sensor Networks (UWSNs) have recently established themselves as an extremely interesting area of research thanks to the mysterious qualities of the ocean. The UWSN consists of sensor nodes and vehicles working to collect data and complete tasks. The battery capacity of sensor nodes is quite limited, which means that the UWSN network needs to be as efficient as it can possibly be. It is difficult to connect with or update a communication that is taking place underwater due to the high latency in propagation, the dynamic nature of the network, and the likelihood of introducing errors. This makes it difficult to communicate with or update a communication. Cluster-based underwater wireless sensor networks (CB-UWSNs) are proposed in this article. These networks would be deployed via Superframe and Telnet applications. In addition, routing protocols, such as Ad hoc On-demand Distance Vector (AODV), Fisheye State Routing (FSR), Location-Aided Routing 1 (LAR1), Optimized Link State Routing Protocol (OLSR), and Source Tree Adaptive Routing—Least Overhead Routing Approach (STAR-LORA), were evaluated based on the criteria of their energy consumption in a range of various modes of operation with QualNet Simulator using Telnet and Superframe applications. STAR-LORA surpasses the AODV, LAR1, OLSR, and FSR routing protocols in the evaluation report’s simulations, with a Receive Energy of 0.1 mWh in a Telnet deployment and 0.021 mWh in a Superframe deployment. The Telnet and Superframe deployments consume 0.05 mWh transmit power, but the Superframe deployment only needs 0.009 mWh. As a result, the simulation results show that the STAR-LORA routing protocol outperforms the alternatives. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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16 pages, 3087 KiB

Open AccessArticle

Design and Implementation of a Framework for Smart Home Automation Based on Cellular IoT, MQTT, and Serverless Functions

by Marco Esposito, Alberto Belli, Lorenzo Palma and Paola Pierleoni

Sensors 2023, 23(9), 4459; https://doi.org/10.3390/s23094459 - 3 May 2023

Cited by 10 | Viewed by 6022

Abstract

Smart objects and home automation tools are becoming increasingly popular, and the number of smart devices that each dedicated application has to manage is increasing accordingly. The emergence of technologies such as serverless computing and dedicated machine-to-machine communication protocols represents a valuable opportunity to facilitate management of smart objects and replicability of new solutions. The aim of this paper is to propose a framework for home automation applications that can be applied to control and monitor any appliance or object in a smart home environment. The proposed framework makes use of a dedicated messages-exchange protocol based on MQTT and cloud-deployed serverless functions. Furthermore, a vocal command interface is implemented to let users control the smart object with vocal interactions, greatly increasing the accessibility and intuitiveness of the proposed solution. A smart object, namely a smart kitchen fan extractor system, was developed, prototyped, and tested to illustrate the viability of the proposed solution. The smart object is equipped with a narrowband IoT (NB-IoT) module to send and receive commands to and from the cloud. In order to evaluate the performance of the proposed solution, the suitability of NB-IoT for the transmission of MQTT messages was evaluated. The results show how NB-IoT has an acceptable latency performance despite some minimal packet loss. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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24 pages, 13723 KiB

Open AccessArticle

Visualizing the Landscape of Home IoT Research: A Bibliometric Analysis Using VOSviewer

by Jue Wang and Hak-Seon Kim

Sensors 2023, 23(6), 3086; https://doi.org/10.3390/s23063086 - 13 Mar 2023

Cited by 18 | Viewed by 3821

Abstract

Currently, the internet of things (IoT) is being widely deployed in home automation systems. An analysis of bibliometrics is presented in this work that covers articles that were obtained from the Web of Science (WoS) databases and published between 1 January 2018, and 31 December 2022. With VOSviewer software, 3880 relevant research papers were analyzed for the study. Through VOSviewer, we analyzed how many articles were published about the home IoT in several databases and their relation to the topic area. In particular, it was pointed out that the chronological order of the research topics changed, and COVID-19 also attracted the attention of scholars in the IoT field, and it was emphasized in this topic that the impact of the epidemic was described. As a result of the clustering, this study was able to conclude the research statuses. In addition, this study examined and compared maps of yearly themes over 5 years. Taking into account the bibliometric nature of this review, the findings are valuable in terms of mapping processes and providing a reference point. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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16 pages, 1442 KiB

Open AccessArticle

A Graph-Attention-Based Method for Single-Resident Daily Activity Recognition in Smart Homes

by Jiancong Ye, Hongjie Jiang and Junpei Zhong

Sensors 2023, 23(3), 1626; https://doi.org/10.3390/s23031626 - 2 Feb 2023

Cited by 5 | Viewed by 2222

Abstract

In ambient-assisted living facilitated by smart home systems, the recognition of daily human activities is of great importance. It aims to infer the household’s daily activities from the triggered sensor observation sequences with varying time intervals among successive readouts. This paper introduces a novel deep learning framework based on embedding technology and graph attention networks, namely the time-oriented and location-oriented graph attention (TLGAT) networks. The embedding technology converts sensor observations into corresponding feature vectors. Afterward, TLGAT provides a sensor observation sequence as a fully connected graph to the model’s temporal correlation as well as the sensor’s location correlation among sensor observations and facilitates the feature representation of each sensor observation through receiving other sensor observations and weighting operations. The experiments were conducted on two public datasets, based on the diverse setups of sensor event sequence length. The experimental results revealed that the proposed method achieved favorable performance under diverse setups. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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21 pages, 2999 KiB

Open AccessArticle

Decentralized Policy Coordination in Mobile Sensing with Consensual Communication

by Bolei Zhang, Lifa Wu and Ilsun You

Sensors 2022, 22(24), 9584; https://doi.org/10.3390/s22249584 - 7 Dec 2022

Viewed by 1368

Abstract

In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial–temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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16 pages, 3606 KiB

Open AccessArticle

Research on Performance Optimization and Application in Smart Home for Hyperledger Fabric

by Lanfang Ren, Huachun Zhou, Xiaoyong Hang, Bo Yang and Li Su

Sensors 2022, 22(9), 3222; https://doi.org/10.3390/s22093222 - 22 Apr 2022

Cited by 6 | Viewed by 2654

Abstract

With the popularity of smart home services, smart home devices are also increasing significantly. At the same time, security problems of smart home services are becoming more and more important. With the characteristics of non-tampering and multi-party consensus mechanism, Blockchain technology provides powerful capabilities in security protection. In this paper, we introduce the widely used permission Blockchain Fabric for smart home services. As the high requirements of performance, we firstly study the methods of performance optimization for Fabric. Then, a Fabric-based smart home security control system is designed. Based on this system, the smart home system is able to provide access control and security control for smart home devices. The experimental results show that the system works best when the number of concurrent registrations of smart home device is under 6000. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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12 pages, 932 KiB

Open AccessArticle

A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant

by Shih-wei Liao, Cheng-Han Hsu, Jeng-Wei Lin, Yi-Ting Wu and Fang-Yie Leu

Sensors 2022, 22(5), 1891; https://doi.org/10.3390/s22051891 - 28 Feb 2022

Cited by 2 | Viewed by 2315

Abstract

Almond is an extendible open-source virtual assistant designed to help people access Internet services and IoT (Internet of Things) devices. Both are referred to as skills here. Service providers can easily enable their devices for Almond by defining proper APIs (Application Programming Interfaces) for ThingTalk in Thingpedia. ThingTalk is a virtual assistant programming language, and Thingpedia is an application encyclopedia. Almond uses a large neural network to translate user commands in natural language into ThingTalk programs. To obtain enough data for the training of the neural network, Genie was developed to synthesize pairs of user commands and corresponding ThingTalk programs based on a natural language template approach. In this work, we extended Genie to support Chinese. For 107 devices and 261 functions registered in Thingpedia, 649 Chinese primitive templates and 292 Chinese construct templates were analyzed and developed. Two models, seq2seq (sequence-to-sequence) and MQAN (multiple question answer network), were trained to translate user commands in Chinese into ThingTalk programs. Both models were evaluated, and the experiment results showed that MQAN outperformed seq2seq. The exact match, BLEU, and F1 token accuracy of MQAN were 0.7, 0.82, and 0.88, respectively. As a result, users could use Chinese in Almond to access Internet services and IoT devices registered in Thingpedia. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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19 pages, 2463 KiB

Open AccessArticle

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

by Jongmo Kim and Mye Sohn

Sensors 2022, 22(4), 1545; https://doi.org/10.3390/s22041545 - 17 Feb 2022

Cited by 5 | Viewed by 3264

Abstract

Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber–physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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15 pages, 2862 KiB

Open AccessArticle

Cross-Modal Object Detection Based on a Knowledge Update

by Yueqing Gao, Huachun Zhou, Lulu Chen, Yuting Shen, Ce Guo and Xinyu Zhang

Sensors 2022, 22(4), 1338; https://doi.org/10.3390/s22041338 - 10 Feb 2022

Cited by 2 | Viewed by 2369

Abstract

As an important field of computer vision, object detection has been studied extensively in recent years. However, existing object detection methods merely utilize the visual information of the image and fail to mine the high-level semantic information of the object, which leads to great limitations. To take full advantage of multi-source information, a knowledge update-based multimodal object recognition model is proposed in this paper. Specifically, our method initially uses Faster R-CNN to regionalize the image, then applies a transformer-based multimodal encoder to encode visual region features (region-based image features) and textual features (semantic relationships between words) corresponding to pictures. After that, a graph convolutional network (GCN) inference module is introduced to establish a relational network in which the points denote visual and textual region features, and the edges represent their relationships. In addition, based on an external knowledge base, our method further enhances the region-based relationship expression capability through a knowledge update module. In summary, the proposed algorithm not only learns the accurate relationship between objects in different regions of the image, but also benefits from the knowledge update through an external relational database. Experimental results verify the effectiveness of the proposed knowledge update module and the independent reasoning ability of our model. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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21 pages, 814 KiB

Open AccessArticle

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle

by Shih-Yun Huang, Shih-Syun Chen, Min-Xiou Chen, Yao-Chung Chang and Han-Chieh Chao

Sensors 2022, 22(3), 1140; https://doi.org/10.3390/s22031140 - 2 Feb 2022

Cited by 3 | Viewed by 1959

Abstract

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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28 pages, 1732 KiB

Open AccessArticle

Analytical Modeling for Identification of the Machine Code Architecture of Cyberphysical Devices in Smart Homes

by Igor Kotenko, Konstantin Izrailov and Mikhail Buinevich

Sensors 2022, 22(3), 1017; https://doi.org/10.3390/s22031017 - 28 Jan 2022

Cited by 5 | Viewed by 2241

Abstract

Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64. Full article

(This article belongs to the Special Issue Internet of Things for Smart Homes Ⅲ)

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