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Sensor Networks and Internet of Things for Intelligent Infrastructures in Transport and Energy Systems

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

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 10530

Special Issue Editors

Prof. Dr. Mengchu Zhou

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Guest Editor
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: intelligent automation; robotics; Petri nets; discrete event systems; wireless sensor network; big data; Web service; workflow; energy-efficient systems; semiconductor manufacturing; intelligent transportation; and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaojian Zhu

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Guest Editor
School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
Interests: wireless sensor networks; green communications; machine learning; Internet of Things
Prof. Dr. Zofia Lukszo

E-Mail Website
Guest Editor
Engineering Systems and Services, Delft University of Technology, 2600 GA Delft, The Netherlands
Interests: smart energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

More and more complex infrastructures use the Internet of Things (IoT) and wireless sensor networks (WSNs) to make them intelligent and resilient to environmental changes. These are able to better serve human beings and other entities, but building and operating such intelligent infrastructures requires strong sensing and communication networks. Specially, a large number of low-cost sensors or IoT devices need to be deployed in the environment to sense or observe the objects and events, such that accurate and reliable environment information can be sufficiently obtained and then fed back to intelligent agents and human decision makers. The involved sensing, communication, data processing, and security management pose a deluge of theoretically significant and practically meaningful challenges to researchers in related fields.

The goal of this Special Issue is to present original contributions reporting the latest advances in WSNs and IoT for Intelligent Infrastructure in Energy and Transport sectors and their framework, deployment, scheduling, optimization, and security management. This Special Issue aims to foster the dissemination of high-quality research in terms of theory and practice related to WSNs and IoT for Intelligent Infrastructure. Specific topics of interest include but are not limited to the following:

  • Deployment and scheduling of WSNs and IoT for energy and transport sectors;
  • Middleware and novel network protocols for WSNs and IoT;
  • Security management for WSNs and IoT for energy systems;
  • Blockchain and IoT for energy systems;
  • Data management, big data processing and analytics in WSNs and IoT for intelligent infrastructures;
  • Integration of IoT and cloud/edge computing;
  • Applications of WSNs and IoT to highways, rail and train networks, subways, bridges, traffic control, smart grid, smart building, smart campus, smart community, smart village, and smart city.

Prof. Dr. MengChu Zhou

Prof. Dr. Zofia Lukszo

Dr. Xiaojian Zhu

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

  • wireless sensor networks
  • Internet of Things
  • intelligent infrastructures
  • intelligent transport and energy systems
  • sensing
  • communication
  • data processing
  • blockchain
  • security management

Published Papers (3 papers)

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Research

12 pages, 3374 KiB  
Communication
Wideband Anti-Jamming Based on Free Space Optical Communication and Photonic Signal Processing
by Ben Wu, Yang Qi, Chenxi Qiu and Ying Tang
Sensors 2021, 21(4), 1136; https://doi.org/10.3390/s21041136 - 06 Feb 2021
Viewed by 2806
Abstract
We propose and demonstrate an anti-jamming system to defend against wideband jamming attack. Free space optical communication is deployed to provide a reference for jamming cancellation. The mixed signal is processed and separated with photonic signal processing method to achieve large bandwidth. As [...] Read more.
We propose and demonstrate an anti-jamming system to defend against wideband jamming attack. Free space optical communication is deployed to provide a reference for jamming cancellation. The mixed signal is processed and separated with photonic signal processing method to achieve large bandwidth. As an analog signal processing method, the cancellation system introduces zero latency. The radio frequency signals are modulated on optical carriers to achieve wideband and unanimous frequency response. With wideband and zero latency, the system meets the key requirements of high speed and real-time communications in transportation systems. Full article
(This article belongs to the Special Issue Sensor Networks and Internet of Things for Intelligent Infrastructures in Transport and Energy Systems)
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20 pages, 5601 KiB  
Article
A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss
by Mingyu Gao, Chao Chen, Jie Shi, Chun Sing Lai, Yuxiang Yang and Zhekang Dong
Sensors 2020, 20(17), 4850; https://doi.org/10.3390/s20174850 - 27 Aug 2020
Cited by 11 | Viewed by 2403
Abstract
Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) [...] Read more.
Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) to enhance recognition speed and recognition accuracy. Specifically, GMM is utilized to cluster the prior anchors, which are in favor of reducing the clustering error. Meanwhile, considering the most common issue in supervised learning (i.e., the imbalance of data set categories), the category proportion factor is introduced into Quality Focal Loss, which is referred to as CQFL. Furthermore, a five-scale recognition network with a prior anchor allocation strategy is designed for small target objects i.e., traffic sign recognition. Combining five existing tricks, the best speed and accuracy tradeoff on our data set (40.1% mAP and 15 FPS on a single 1080Ti GPU), can be achieved. The experimental results demonstrate that the proposed method is superior to the existing mainstream algorithms, in terms of recognition accuracy and recognition speed. Full article
(This article belongs to the Special Issue Sensor Networks and Internet of Things for Intelligent Infrastructures in Transport and Energy Systems)
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20 pages, 961 KiB  
Article
Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System
by Prince Waqas Khan and Yung-Cheol Byun
Sensors 2020, 20(15), 4252; https://doi.org/10.3390/s20154252 - 30 Jul 2020
Cited by 26 | Viewed by 4551
Abstract
The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system’s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the [...] Read more.
The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system’s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors’ data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV’s stator temperature. We have also performed an informal analysis to demonstrate the proposed system’s robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs. Full article
(This article belongs to the Special Issue Sensor Networks and Internet of Things for Intelligent Infrastructures in Transport and Energy Systems)
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