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Special Issue "Convergence of Intelligent Sensing, Networking, and Computing Technologies for ITS"

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

Deadline for manuscript submissions: closed (20 February 2021).

Special Issue Editors

Dr. Celimuge Wu
E-Mail Website
Guest Editor
Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
Interests: vehicular networks; IoT; big data; computational intelligence; ITS
Special Issues and Collections in MDPI journals
Prof. Dr. Yusheng Ji
E-Mail Website
Guest Editor
Information Systems Architecture Research Division, National Institute of Informatics, Tokyo, Japan
Interests: Network architecture; Resource management; Mobile edge computing
Dr. Soufiene Djahel
E-Mail Website
Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
Interests: ITS; wireless networking; network security; connected and autonomous vehicles
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Future vehicular Internet-of-Things (IoT) systems feature a larger number of devices and multiaccess environments where different types of wireless spectrums should be efficiently utilized. At the same time, novel services, such as cooperative autonomous driving and intelligent transport systems (ITS) that demand unprecedented high accuracy, ultra-low latency, and large bandwidth, are emerging. These services have an extreme variance in their resource demand with respect to time, location, context, as well as individual patterns. Current vehicular IoT systems, such as autonomous driving, only consider the intelligence of a single vehicle agent with little attention given to the cooperation/coordination among them, and therefore, a limitation exists. In order to realize a more intelligent transport system, collaboration between different vehicles and smart road infrastructure (e.g., roadside units, traffic light controllers etc.) should be promoted  and  utilized efficiently. In addition, an efficient perception, and computing should be integrated as well to meet the above services constraints. It is envisioned that the integration of intelligent sensing, networking, and computing technologies will reach the level of true agility in ITS.

Recently, artificial-intelligence-based approaches have been attracting great interest in achieving intelligence in computer systems. However, due to the dynamic characteristics of a vehicular environment, conducting efficient learning in such environment is also a challenging scientific problem. This Special Issue focuses on solutions that can synergistically leverage techniques and insights from the domains of sensing, networking, computing, and Artificial Intelligence (AI) technologies to resolve the challenges in ITS, thereby significantly advancing the state-of-the-art in perception, networking, computing, and applications of ITS. Prospective authors are invited to submit original manuscripts that advance the state-of-the-art in topics including, but not limited to:

  • 5G/Beyond 5G and ITS;
  • AI-based approaches for intelligent perception;
  • Applications/use cases highlighting the potential of edge-enabled vehicular networks;
  • Cloud/edge computing architecture for ITS;
  • Collaborative learning in vehicular environment;
  • Collaborative sensing, networking, and computing for ITS;
  • Intelligent networking for ITS;
  • Intelligent vehicular networking solutions in the Beyond 5G era;
  • Machine and deep learning for ITS;
  • Relaying and routing for vehicular IoT;
  • Resource allocation for vehicular IoT;
  • Tactile Internet applications for ITS;
  • Security and privacy for ITS.

Dr. Celimuge Wu
Prof. Yusheng Ji
Dr. Soufiene Djahel
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 papers will be 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 2200 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

  • ITS
  • connected and autonomous vehicles
  • artificial intelligence
  • vehicle sensing
  • vehicular networks
  • vehicular edge computing
  • vehicular big data

Published Papers (6 papers)

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Open AccessArticle
Standards-Compliant Multi-Protocol On-Board Unit for the Evaluation of Connected and Automated Mobility Services in Multi-Vendor Environments
Sensors 2021, 21(6), 2090; https://doi.org/10.3390/s21062090 - 17 Mar 2021
Viewed by 366
Abstract
Vehicle-to-everything (V2X) communications enable real-time information exchange between vehicles and infrastructure, which extends the perception range of vehicles beyond the limits of on-board sensors and, thus, facilitating the realisation of cooperative, connected, and automated mobility (CCAM) services that will improve road safety and [...] Read more.
Vehicle-to-everything (V2X) communications enable real-time information exchange between vehicles and infrastructure, which extends the perception range of vehicles beyond the limits of on-board sensors and, thus, facilitating the realisation of cooperative, connected, and automated mobility (CCAM) services that will improve road safety and traffic efficiency. In the context of CCAM, the successful deployments of cooperative intelligent transport system (C-ITS) use cases, with the integration of advanced wireless communication technologies, are effectively leading to make transport safer and more efficient. However, the evaluation of multi-vendor and multi-protocol based CCAM service architectures can become challenging and complex. Additionally, conducting on-demand field trials of such architectures with real vehicles involved is prohibitively expensive and time-consuming. In order to overcome these obstacles, in this paper, we present the development of a standards-compliant experimental vehicular on-board unit (OBU) that supports the integration of multiple V2X protocols from different vendors to communicate with heterogeneous cloud-based services that are offered by several original equipment manufacturers (OEMs). We experimentally demonstrate the functionalities of the OBU in a real-world deployment of a cooperative collision avoidance service infrastructure that is based on edge and cloud servers. In addition, we measure end-to-end application-level latencies of multi-protocol supported V2X information flows to show the effectiveness of interoperability in V2X communications between different vehicle OEMs. Full article
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Open AccessArticle
An Intelligent Data Uploading Selection Mechanism for Offloading Uplink Traffic of Cellular Networks
Sensors 2020, 20(21), 6287; https://doi.org/10.3390/s20216287 - 04 Nov 2020
Viewed by 469
Abstract
Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or [...] Read more.
Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is (56.54×107), and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and (6.79×103). Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost. Full article
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Open AccessArticle
An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks
Sensors 2020, 20(20), 5902; https://doi.org/10.3390/s20205902 - 19 Oct 2020
Viewed by 463
Abstract
With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing [...] Read more.
With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing packet loss in transmission. However, existing researches on NC do not consider the influence of the rapid quality change of wireless links on NC schemes, which poses a great challenge to dynamically adjust the coding rate according to the variation of link quality in vehicle-to-cloud multipath transmission in order to avoid consuming unnecessary bandwidth resources and to increase network throughput. Therefore, we propose an Adaptive Network Coding (ANC) scheme brought by the novel integration of the Hidden Markov Model (HMM) into the NC scheme to efficiently adjust the coding rate according to the estimated packet loss rate (PLR). The ANC scheme conquers the rapid change of wireless link quality to obtain the utmost throughput and reduce the packet loss in transmission. In terms of the throughput performance, the simulations and real experiment results show that the ANC scheme outperforms state-of-the-art NC schemes for vehicular wireless multipath transmission in vehicular IoT systems. Full article
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Open AccessArticle
W-GPCR Routing Method for Vehicular Ad Hoc Networks
Sensors 2020, 20(12), 3406; https://doi.org/10.3390/s20123406 - 16 Jun 2020
Cited by 2 | Viewed by 878 | Retraction
Abstract
The high-speed dynamics of nodes and rapid change of network topology in vehicular ad hoc networks (VANETs) pose significant challenges for the design of routing protocols. Because of the unpredictability of VANETs, selecting the appropriate next-hop relay node, which is related to the [...] Read more.
The high-speed dynamics of nodes and rapid change of network topology in vehicular ad hoc networks (VANETs) pose significant challenges for the design of routing protocols. Because of the unpredictability of VANETs, selecting the appropriate next-hop relay node, which is related to the performance of the routing protocol, is a difficult task. As an effective solution for VANETs, geographic routing has received extensive attention in recent years. The Greedy Perimeter Coordinator Routing (GPCR) protocol is a widely adopted position-based routing protocol. In this paper, to improve the performance in sparse networks, the local optimum, and the routing loop in the GPCR protocol, the Weighted-GPCR (W-GPCR) protocol is proposed. Firstly, the relationship between vehicle node routing and other parameters, such as the Euclidean distance between node pairs, driving direction, and density, is analyzed. Secondly, the composite parameter weighted model is established and the calculation method is designed for the existing routing problems; the weighted parameter ratio is selected adaptively in different scenarios, so as to obtain the optimal next-hop relay node. In order to verify the performance of the W-GPCR method, the proposed method is compared with existing methods, such as the traditional Geographic Perimeter Stateless Routing (GPSR) protocol and GPCR. Results show that this method is superior in terms of the package delivery ratio, end-to-end delay, and average hop count. Full article
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Open AccessLetter
Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems
Sensors 2020, 20(20), 5818; https://doi.org/10.3390/s20205818 - 15 Oct 2020
Cited by 1 | Viewed by 425
Abstract
In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). We herein describe a joint cooperative positioning and warning (JCPW) system based on angle information. In this system, we first [...] Read more.
In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). We herein describe a joint cooperative positioning and warning (JCPW) system based on angle information. In this system, we first design the sequential task allocation of cooperative positioning (CP) warning and the related frame format of the positioning packet. With the cooperation of RSUs, multiple groups of the two-dimensional angle-of-departure (AOD) are estimated and then transformed into the vehicle’s positions. Considering the system computational efficiency, a novel AOD estimation algorithm based on a truncated signal subspace is proposed, which can avoid the eigen decomposition and exhaustive spectrum searching; and a distance based weighting strategy is also utilized to fuse multiple independent estimations. Numerical simulations prove that the proposed method can be a better alternative to achieve sub-lane level positioning if considering the accuracy and computational complexity. Full article
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Open AccessLetter
Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation
Sensors 2020, 20(19), 5560; https://doi.org/10.3390/s20195560 - 28 Sep 2020
Cited by 1 | Viewed by 602
Abstract
Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties [...] Read more.
Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated. Full article
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