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Intelligent Sensors in Smart Home and Cities
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Intelligent Sensors in Smart Home and Cities

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 4220

Special Issue Editors

Prof. Dr. Joan Lu

E-Mail Website
Guest Editor
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
Interests: mobile computing; smart home technologies; distributed systems; data science
Dr. Qiang Xu

E-Mail Website
Guest Editor
School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
Interests: theoretic computational modeling in multiple disciplinary applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent sensors are applied for a wide range of applications, e.g., traffic control systems, home electronical facilities, healthcare detective devices, etc. With facilitating AI theory and technologies, big data science, advanced wireless sensor actuators, signal processing, and advanced embedded systems, sensors become smart, powerful, and sustainable technologies that are beneficial to society. 

This Special Issue is to invite scholars submitting high-quality original articles in the area of intelligent sensors with relevant technologies, theories, and applications that are contributed to build up smart homes or smart cities. The research papers may address, but are not limited to, the following: (1) the impact of intelligent sustainable sensor technologies on significant social and economic issues; (2) the latest discoveries and developments in smart sensor systems; and (3) a systematical review of state-of-the-art technologies, with critical analyses of and discussions on the current situation and future directions of intelligent sensor technologies in smart homes and smart cities.

The papers can be expanded from excellent conference articles or written with newly developed research outcomes.

Prof. Dr. Joan Lu
Dr. Qiang Xu
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 submissions that pass pre-check are 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 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.

Keywords

  • decision analytics
  • predictive models
  • spatial analytics
  • risk analytics
  • graph analytics
  • big data
  • analytics
  • sustainable development
  • smart society
  • smart homes
  • smart city/cities
  • AI theory and technologies
  • big data science
  • signal processing
  • sensors
  • computational models for intelligent systems
  • advanced embedded systems

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Published Papers (3 papers)

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Research

27 pages, 7047 KiB  
Article
Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes
by Srivatsa P and Thomas Plötz
Sensors 2024, 24(12), 3944; https://doi.org/10.3390/s24123944 - 18 Jun 2024
Viewed by 670
Abstract
There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, [...] Read more.
There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications. Full article
(This article belongs to the Special Issue Intelligent Sensors in Smart Home and Cities)
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20 pages, 5732 KiB  
Article
Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure
by Hamdy M. Youssef, Radwa Ahmed Osman and Alaa A. El-Bary
Sensors 2024, 24(9), 2761; https://doi.org/10.3390/s24092761 - 26 Apr 2024
Viewed by 941
Abstract
Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the [...] Read more.
Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system’s robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes. Full article
(This article belongs to the Special Issue Intelligent Sensors in Smart Home and Cities)
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16 pages, 1969 KiB  
Article
Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition
by Jingyang Deng, Shuyi Zhang and Jinwen Ma
Sensors 2023, 23(20), 8373; https://doi.org/10.3390/s23208373 - 10 Oct 2023
Cited by 3 | Viewed by 1696
Abstract
Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple [...] Read more.
Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple technical skills of playing badminton, it is usually a challenging task due to the difficulty of feature extraction from the sensor data. As a kind of end-to-end approach, deep neural networks have the capacity of automatic feature learning and extracting. However, most current studies on sensor-based badminton activity recognition adopt CNN-based architectures, which lack the ability of capturing temporal information and global signal comprehension. To overcome these shortcomings, we propose a deep learning framework which combines the convolutional layers, LSTM structure, and self-attention mechanism together. Specifically, this framework can automatically extract the local features of the sensor signals in time domain, take the LSTM structure for processing the badminton activity data, and focus attention on the information that is essential to the badminton activity recognition task. It is demonstrated by the experimental results on an actual badminton single sensor dataset that our proposed framework has obtained a badminton activity recognition (37 classes) accuracy of 97.83%, which outperforms the existing methods, and also has the advantages of lower training time and faster convergence. Full article
(This article belongs to the Special Issue Intelligent Sensors in Smart Home and Cities)
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