Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Internet of Things for Everyday Living: Ubiquitous Intelligence at Scale
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Internet of Things for Everyday Living: Ubiquitous Intelligence at Scale

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 3353

Special Issue Editors

Dr. George Onyango Okeyo

E-Mail Website
Guest Editor
College of Engineering, Carnegie Mellon University Africa, Kigali 514, Rwanda
Interests: data analytics; machine learning; smart environments; object orientation; knowledge modelling; Internet of Things; privacy and trust; context awareness
Dr. Zengwei Zheng

E-Mail Website
Guest Editor
School of Computing and Computational Science, Zhejiang University City College, Hangzhou 310015, China
Interests: edge computing; internet of things
Dr. Diego López-de-Ipiña

E-Mail Website
Guest Editor
DeustoTech, University of Deusto, Avda. Universidades 24, 48007 Bilbao, Spain
Interests: data visualisation; deep learning; generative AI; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Liming Luke Chen

E-Mail Website
Guest Editor
School of Computing, Ulster University, Belfast, UK
Interests: data analytics; artificial intelligence; pervasive computing; user-centred intelligent systems; smart environment; digital health and assisted living
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) continues to receive considerable interest from academic and industry researchers interested its exploitation to address problems encountered in everyday living. With developments in sensing technologies and context awareness, it is possible to achieve ubiquitous intelligence. Humans (and the environment) encounter various challenges at home, in offices, in cities, during transport, or in seeking secure spaces. Daily challenges to which ubiquitous intelligence can be applied include supporting the elderly in assisted living, digital health, health monitoring and medication adherence, security and surveillance, urban computing, disaster management, etc. However, overcoming such challenges requires corresponding developments in sensor networks and technologies, frameworks, and application programming interfaces (APIs) that make it possible to provide services to people in their living, work, leisure, or commuting environments. Various technologies, such as cloud, fog, and edge computing, big data, and analytics, may be exploited to derive scalable solutions using ubiquitous intelligence. Despite advances in scalable technologies, challenges in security and privacy, storage and processing, and efficient processing, etc., should be addressed as well.

This Special Issue aims to publish papers detailing the latest advances in IoT and ubiquitous intelligence in which challenges to context awareness, scalable computing, security and privacy, and storage and processing, including analytics, are addressed.  

Topics of interest include, but are not limited to:

  • Ubiquitous sensing and intelligence;
  • Intelligent/smart object and interaction;
  • Intelligent/smart systems and services;
  • Personalization and social aspects;
  • Smart applications and services in assisted living, digital health, urban computing, smart environments;
  • Privacy modelling and preserving methods;
  • Cyber security, incident detection, and response systems;
  • Cyber physical systems and their safety;
  • Scalable technologies including cloud, edge, and fog computing;
  • High-performance computing for IoT applications;
  • Big data and analytics in IoT and ubiquitous intelligence;
  • Social computing and social internet of things;
  • Human-centric computing and cyber–physical–social systems;
  • Theory, modelling, and methodologies for IoT applications.

Dr. George Onyango Okeyo
Dr. Zengwei Zheng
Dr. Diego López-de-Ipiña
Dr. Liming Luke Chen
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.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1245 KiB  
Article
NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System
by Zengwei Zheng, Shuang Yan, Lin Sun, Hengxin Shu and Xiaowei Zhou
Sensors 2023, 23(5), 2694; https://doi.org/10.3390/s23052694 - 01 Mar 2023
Viewed by 1568
Abstract
Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is [...] Read more.
Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is raised by the car. Regarding the NLOS problem, efforts have been made to mitigate the point-to-point ranging error or to estimate the tag coordinate by neural networks. However, it still suffers from some problems such as low accuracy, overfitting, or a large number of parameters. In order to address these problems, we propose a fusion method of a neural network and linear coordinate solver (NN-LCS). We use two FC layers to extract the distance feature and received signal strength (RSS) feature, respectively, and a multi-layer perceptron (MLP) to estimate the distances with the fusion of these two features. We prove that the least square method which supports error loss backpropagation in the neural network is feasible for distance correcting learning. Therefore, our model is end-to-end and directly outputs the localization results. The results show that the proposed method is high-accuracy and with small model size which could be easily deployed on embedded devices with low computing ability. Full article
(This article belongs to the Special Issue Internet of Things for Everyday Living: Ubiquitous Intelligence at Scale)
►▼ Show Figures

Figure 1

13 pages, 2682 KiB  
Article
A Method of Generating Real-Time Natural Light Color Temperature Cycle for Circadian Lighting Service
by Seung-Taek Oh, Deog-Hyeon Ga and Jae-Hyun Lim
Sensors 2023, 23(2), 883; https://doi.org/10.3390/s23020883 - 12 Jan 2023
Cited by 1 | Viewed by 1304
Abstract
The light intensity and color temperature of natural light periodically change and promote the circadian entrainment of the human body. In addition, the color temperature cycle of natural light that is unique to each region is formed by its location and geographic and [...] Read more.
The light intensity and color temperature of natural light periodically change and promote the circadian entrainment of the human body. In addition, the color temperature cycle of natural light that is unique to each region is formed by its location and geographic and environmental factors, affecting the health of its residents. Research on lighting and construction to provide the color temperature of real-time natural light has continued to provide the beneficial effect of natural indoor lighting. However, lighting technology that provides the real-time color temperature of natural light could not be realized since it is challenging to select a color temperature cycle zone due to abrupt color temperature changes at sunrise and sunset. Such drastic shifts cause an irregular measurement of color temperature over time due to general weather or atmospheric conditions. In a previous study, a method of generating a color temperature cycle using deep learning was introduced, but the performance at the beginning and end of the color temperature cycle was unreliable. Therefore, this study proposes generating a real-time natural light color temperature cycle for the circadian lighting service. The characteristics of the daily color temperature cycle were analyzed based on the measured natural light characteristics database, and a data set for learning was established. To improve the color temperature cycle generation performance, a deep learning (TadGAN) model was implemented by searching for the lowest point of the color temperature at the start and end points of the color temperature cycle and applying the boot and ending datasets to these points. The color temperature cycle zone was accurately detected in real-time in the experiment, and the generation performance of the color temperature cycle was maintained at the beginning and end of the color temperature cycle. The mean absolute error decreased by about 67%, confirming the generation of a more accurate real-time color temperature cycle. Full article
(This article belongs to the Special Issue Internet of Things for Everyday Living: Ubiquitous Intelligence at Scale)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop