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Towards Reliable and Scalable Smart Cities: Internet of Things Meets Big Data and AI

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

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 8998

Special Issue Editors

Dr. Fangyu Li

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: IoT (Internet of Things) & CPS (cyber-physical systems); distributed sensor networks; smart city; smart grids; smart healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Guo

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, College of Engineering, University of North Texas, Denton, TX 76203, USA
Interests: machine learning; bioinformatics; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Sumit Chakravarty

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Kennesaw State University, Marietta, GA, USA
Interests: robot sensing and communication; automoation and control
Special Issues, Collections and Topics in MDPI journals
Dr. Zhipeng Cai

E-Mail Website
Guest Editor
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
Interests: cyber security; big data and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet-of-Things (IoT) offers new opportunities for cities to make citizens live and work in more sustainable, healthy and safe places. Smart cities offer huge potential to revolutionize today's society including areas such as healthcare, energy management, transportation, security and smart buildings, which is built upon an intricate technical infrastructure including smart sensing, IoT devices, networking, edge computing, cloud computing, Big Data, and machine learning. Future smart cities will be not only an intelligent and green living environment but also a human-centered public society. IoT, Big Data and artificial intelligence (AI) are cornerstone technologies enabling reliable and scalable smart cities, and have been interacting with each other into an organic ecosystem. That IoT meets Big Data and AI leads to intelligent IoT devices, while smart sensing will be boosted by AI, and IoT big data mining can also be implemented with AI. The trend of “IoT meetings Big Data and AI” has already cast significant impacts on the aspects characterizing a smart city. Smartphones are able to learn the user patterns; home Wi-Fi router can intelligently detect and locate an intruder; vehicles network and communicate with each other for traffic control; surveillance cameras can perform face recognition effectively and accurately. At the same time, the bright future of smart city has also motivated many scientific and engineering challenges for both academia and industry, in order to fully exploit the capabilities and benefits brought by IoT, Big Data and AI. Some important areas are smart city IoT infrastructure planning; framework, platform, protocol, algorithm for smart sensing and data mining; large scale Big Data analysis; smart city modeling; smart city based healthcare, emergency altering, energy management and so on. This special issue will provide the opportunity for multi-disciplinary researchers and teams to discuss the state-of-the-art from both theoretical and application perspectives.

Scope of the Special Issue：

This special issue aims at bringing the researchers from both academia and industry together to disseminate their recent advances related to the challenges and solutions to enable reliable and scalable smart cities. Relevant topics of interest include, but are not limited to:

  • Smart city architectures, algorithms, and protocols based on IoT, Big Data and AI
  • Smart city scalability analysis based on super large-scale multi-agent IoT system
  • Smart city networking, services and infrastructures and reliability
  • Smart city expandable and accountable IoT architectures
  • Smart environment modeling, monitoring, prediction and analysis
  • Smart city frameworks, platforms
  • Smart city big data, open data, and urban computing
  • Smart utilities, consumption, sensing and IoT
  • Smart communities and neighborhoods
  • Smart city security, safety and privacy modeling
  • Smart transportation system planning, evaluation, and technologies
  • Smart city sewage, water and electricity management
  • Smart city healthcare service monitoring
  • Smart city emergency altering, management and infrastructures
  • Smart city crime watching and alerting systems
  • Smart city education, training and social services
  • Smart home, smart building and social community networks/infrastructure
  • Smart city intelligent decision making based on spatial-temporal city big data fusion
  • Smart city sensing knowledge representation from massive IoT devices

Dr. Fangyu Li
Dr. Xuan Guo
Dr. Sumit Chakravarty
Dr. Zhipeng Cai
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 (4 papers)

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Research

21 pages, 1217 KiB  
Article
Analysis of Spatially Distributed Data in Internet of Things in the Environmental Context
by Leonildo José de Melo de Azevedo, Júlio Cezar Estrella, Alexandre C. B. Delbem, Rodolfo Ipolito Meneguette, Stephan Reiff-Marganiec and Sidgley Camargo de Andrade
Sensors 2022, 22(5), 1693; https://doi.org/10.3390/s22051693 - 22 Feb 2022
Viewed by 2314
Abstract
The Internet of Things consists of “things” made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of [...] Read more.
The Internet of Things consists of “things” made up of small sensors and actuators capable of interacting with the environment. The combination of devices with sensor networks and Internet access enables the communication between the physical world and cyberspace, enabling the development of solutions to many real-world problems. However, most existing applications are dedicated to solving a specific problem using only private sensor networks, which limits the actual capacity of the Internet of Things. In addition, these applications are concerned with the quality of service offered by the sensor network or the correct analysis method that can lead to inaccurate or irrelevant conclusions, which can cause significant harm for decision makers. In this context, we propose two systematic methods to analyze spatially distributed data Internet of Things. We show with the results that geostatistics and spatial statistics are more appropriate than classical statistics to do this analysis. Full article
(This article belongs to the Special Issue Towards Reliable and Scalable Smart Cities: Internet of Things Meets Big Data and AI)
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10 pages, 2131 KiB  
Article
Application of Knowledge Distillation Based on Transfer Learning of ERNIE Model in Intelligent Dialogue Intention Recognition
by Shiguang Guo and Qing Wang
Sensors 2022, 22(3), 1270; https://doi.org/10.3390/s22031270 - 08 Feb 2022
Cited by 6 | Viewed by 2439
Abstract
The ‘intention’ classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question’s intention understanding is the text classification. The transfer learning, such as BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Representation [...] Read more.
The ‘intention’ classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question’s intention understanding is the text classification. The transfer learning, such as BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Representation through Knowledge Integration), has put the text classification task into a new level, but the BERT and ERNIE model are difficult to support high QPS (queries per second) intelligent dialogue systems due to computational performance issues. In reality, the simple classification model usually shows a high computational performance, but they are limited by low accuracy. In this paper, we use knowledge of the ERNIE model to distill the FastText model; the ERNIE model works as a teacher model to predict the massive online unlabeled data for data enhancement, and then guides the training of the student model of FastText with better computational efficiency. The FastText model is distilled by the ERNIE model in chatbot intention classification. This not only guarantees the superiority of its original computational performance, but also the intention classification accuracy has been significantly improved. Full article
(This article belongs to the Special Issue Towards Reliable and Scalable Smart Cities: Internet of Things Meets Big Data and AI)
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14 pages, 1259 KiB  
Article
Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature
by Jingcheng Guo and Aijun Yan
Sensors 2021, 21(23), 7878; https://doi.org/10.3390/s21237878 - 26 Nov 2021
Cited by 1 | Viewed by 1716
Abstract
It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic [...] Read more.
It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic algorithms (GAs) and stochastic configuration networks (SCNs), and the SCN-based incinerator temperature model is obtained simultaneously. Firstly, filter feature selection is realized by calculating the MI value between each feature variable and the incinerator temperature from historical data. Secondly, the fitness function of GAs is defined by the root mean square error of the incinerator temperature obtained by training SCNs, and features obtained by MI methods are searched iteratively to complete the wrapper feature selection, where the SCN-based incinerator temperature prediction model is obtained. Finally, the proposed model is verified by MSW incinerator temperature historical data. The results show that the SCN-based prediction model using the hybrid selection method can better predict the change trend of incinerator temperature, which proves that the SCNs has great development potential in the field of prediction modeling. Full article
(This article belongs to the Special Issue Towards Reliable and Scalable Smart Cities: Internet of Things Meets Big Data and AI)
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13 pages, 1946 KiB  
Article
Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning
by Chenxi Ding and Aijun Yan
Sensors 2021, 21(21), 7356; https://doi.org/10.3390/s21217356 - 05 Nov 2021
Cited by 5 | Viewed by 1489
Abstract
Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration [...] Read more.
Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value. Full article
(This article belongs to the Special Issue Towards Reliable and Scalable Smart Cities: Internet of Things Meets Big Data and AI)
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