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Special Issue "IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles"

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

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Prof. Dr. Stefano Giordano
E-Mail Website
Guest Editor
Dipartimento di Ingegneria della Informazione (DII), Università di Pisa, Via G. Caruso 16, 56122 Pisa, Italy
Interests: telecommunication networks; vehicular networks; IoT (Internet of Things) networking
Special Issues and Collections in MDPI journals
Dr. Riccardo Mariani
E-Mail Website
Guest Editor
Riccardo Mariani, NVIDIA, Santa Clara, CA 95050, USA
Interests: functional safety; integrated circuits reliability; IoT (Internet of Things); ISO26262

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Sensors entitled “IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles”.
Application of IoT (Internet of Things) technologies is revolutionizing the mobility of people and also goods (i.e., logistics).
The widespread diffusion of distributed and networked sensing and control systems makes possible the set-up of intelligent transport systems (ITS) that are able to monitor, predict, and manage traffic flows. Considering also the possibility of remote vehicle fleet management, and the availability of a sensorized and 5G-connected infrastructure, it will be possible to reduce pollution emissions, time lost in traffic jams, and the number of people killed or injured in accidents. On the other hand, vehicles for both traffic and private transportation (i.e., automotive, bus, coaches, and trains) will become increasingly automated, connected, and shared.
Key items for such a revolution are sensing systems for machine and infrastructure perception and networking technologies for V2I (vehicle-to-infrastructure), V2V (vehicle-to-vehicle), and V2P (vehicle-to-pedestrians) communication. On the sensing side, new contactless techniques are appearing from physical sensors based on light, electromagnetic, or acoustics waves (i.e., videocameras, Lidar, Radar, and ultrasonics) to social sensors exploiting mobile connectivity and web activity of people. Sensor fusion and data understanding, thanks to the adoption of machine learning (ML) and artificial intelligence (AI) techniques, are new emerging research fields. On the networking side, the advent of the 5G era and of edge/cloud-computing paradigms will present new opportunities in terms of increased data-rate, link density, and quality of service, and reduced latency (i.e., tactile internet) for communication, localization, navigation, and tracking.
Since the mobility of people and goods is a safe critical application, functional safety is also a key technology. Another key technology is the cybersecurity to be integrated, via HW and/or SW solutions, in vehicles, in infrastructure and in relevant interconnections.

The particular topics of interest for this Special Issue include but are not limited to the following:

  • New technologies for sensors and for networking transceivers (RF, photonics, and MEMS/MOEMS);
  • V2X (vehicle to everything) and 5G networking technologies;
  • Advances in social sensors;
  • Advances in physical sensors (lidar, radar, videocameras, ultrasonics);
  • New applications and services for mobility of people and goods and for smart cities infrastructure;
  • Edge and cloud paradigms for sensing systems, localization/navigation, and vehicular networks;
  • Enabling technologies for traffic monitoring and fleet management;
  • Cybersecurity/trust/privacy in IoT sensing systems and vehicular networks;
  • Functional safety and standardization (e.g., ISO26262);
  • Data analytics and machine learning for sensors fusion and vehicular networks;
  • Embedded systems and HPC architectures for sensing and vehicular networks;
  • Modeling, optimization, and performance evaluation of vehicular networks;
  • Simulation of autonomous connected vehicles and mobility traffic;
  • Results from experimental systems and prototypes in industry and academia.

Most of the above topics are also key enabling technologies within industry 4.0 and robotic scenarios, and hence paper submissions related to these domains are also invited. The proposed Special Issue and the relevant topics are part of the activities of the Crosslab Industrial IoT carried out by DII-University of Pisa in the framework of the “Dipartimenti di Eccellenza” project.

Prof. Dr. Sergio Saponara
Prof. Dr. Stefano Giordano
Dr. Riccardo Mariani
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

  • New technologies for sensors and for networking transceivers (RF, photonics, MEMS/MOEMS)
  • V2X and 5G networking technologies
  • Advances in social sensors
  • Advances in physical sensors (lidar, radar, videocameras, and ultrasonics)
  • New applications and services for mobility, logistics and smart cities
  • Edge and cloud paradigms for sensing systems, localization/navigation and vehicular networks
  • Enabling technologies for traffic monitoring and fleet management
  • Cybersecurity/trust/privacy in IoT sensing systems and vehicular networks
  • Functional safety and standardization (e.g., ISO26262)
  • Gas emission reduction and energy efficiency opportunities with traffic and vehicle management
  • Data analytics and machine learning for sensors fusion and vehicular networks
  • Embedded systems and HPC architectures for sensing and vehicular networks
  • Modeling, simulation, optimization, and performance evaluation of vehicular networks and traffic
  • Results from experimental systems and prototypes in industry and academia
  • Applications to vehicles, industry 4.0, robotics, and smart cities

Published Papers (12 papers)

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Editorial

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Editorial
Recent Trends on IoT Systems for Traffic Monitoring and for Autonomous and Connected Vehicles
Sensors 2021, 21(5), 1648; https://doi.org/10.3390/s21051648 - 27 Feb 2021
Viewed by 557
Abstract
This Editorial analyzes the manuscripts accepted, after a careful peer-reviewed process, for the special issue “IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles” of the Sensors MDPI journal.[...] Full article

Research

Jump to: Editorial

Article
Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction
Sensors 2021, 21(2), 629; https://doi.org/10.3390/s21020629 - 18 Jan 2021
Cited by 1 | Viewed by 791
Abstract
Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision [...] Read more.
Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform. Full article
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Article
Performance Evaluation of Attribute-Based Encryption in Automotive Embedded Platform for Secure Software Over-The-Air Update
Sensors 2021, 21(2), 515; https://doi.org/10.3390/s21020515 - 13 Jan 2021
Cited by 3 | Viewed by 605
Abstract
This paper aims to show that it is possible to improve security for over the air update functionalities in an automotive scenario through the use of a cryptographic scheme, called “Attribute-Based-Encryption” (ABE), which grants confidentiality to the software/firmware update done Over The Air [...] Read more.
This paper aims to show that it is possible to improve security for over the air update functionalities in an automotive scenario through the use of a cryptographic scheme, called “Attribute-Based-Encryption” (ABE), which grants confidentiality to the software/firmware update done Over The Air (OTA). We demonstrate that ABE is seamlessly integrable into the state of the art solutions regarding the OTA update by showing that the overhead of the ABE integration in terms of computation time and its storage is negligible w.r.t. the other overheads that are introduced by the OTA process, also proving that security can be enhanced with a minimum cost. In order to support our claim, we report the experimental results of an implementation of the proposed ABE OTA technique on a Xilinx ZCU102 evaluation board, which is an automotive-oriented HW/SW platform that is equipped with a Zynq UltraScale+ MPSoC chip that is representative of the computing capability of real automotive Electronic Control Units (ECUs). Full article
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Article
An Indoor Robust Localization Algorithm Based on Data Association Technique
Sensors 2020, 20(22), 6598; https://doi.org/10.3390/s20226598 - 18 Nov 2020
Cited by 2 | Viewed by 478
Abstract
As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result [...] Read more.
As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms. Full article
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Article
Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model
Sensors 2020, 20(22), 6485; https://doi.org/10.3390/s20226485 - 13 Nov 2020
Cited by 3 | Viewed by 1164
Abstract
In order to improve the traffic in large cities and to avoid congestion, advanced methods of detecting and predicting vehicle behaviour are needed. Such methods require complex information regarding the number of vehicles on the roads, their positions, directions, etc. One way to [...] Read more.
In order to improve the traffic in large cities and to avoid congestion, advanced methods of detecting and predicting vehicle behaviour are needed. Such methods require complex information regarding the number of vehicles on the roads, their positions, directions, etc. One way to obtain this information is by analyzing overhead images collected by satellites or drones, and extracting information from them through intelligent machine learning models. Thus, in this paper we propose and present a one-stage object detection model for finding vehicles in satellite images using the RetinaNet architecture and the Cars Overhead With Context dataset. By analyzing the results obtained by the proposed model, we show that it has a very good vehicle detection accuracy and a very low detection time, which shows that it can be employed to successfully extract data from real-time satellite or drone data. Full article
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Article
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)
Sensors 2020, 20(21), 6402; https://doi.org/10.3390/s20216402 - 09 Nov 2020
Cited by 1 | Viewed by 628
Abstract
Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV [...] Read more.
Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity. Full article
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Article
A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes
Sensors 2020, 20(17), 4824; https://doi.org/10.3390/s20174824 - 26 Aug 2020
Cited by 1 | Viewed by 693
Abstract
The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of [...] Read more.
The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie’s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions. Full article
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Article
Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms
Sensors 2020, 20(12), 3573; https://doi.org/10.3390/s20123573 - 24 Jun 2020
Cited by 3 | Viewed by 924
Abstract
This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave [...] Read more.
This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave (mmW) or Free-Space Optics (FSO). Beam-based V2X communications mainly suffer from blockage and pointing misalignment issues. This work focuses on the latter case, which is addressed by proposing a V2X architecture that enables a sensor-aided beam-tracking strategy to counteract the detrimental effect of vibrations and tilting dynamics. A parallel low-rate, low-latency, and reliable control link, in fact, is used to exchange data on vehicle kinematics (i.e., position and orientation) that assists the beam-pointing along the line-of-sight between V2X transceivers (i.e., the dominant multipath component for mmW, or the direct link for FSO). This link can be based on sub-6 GHz V2X communication, as in 5G frequency range 1 (FR1). Performance assessments are carried out to validate the robustness of the proposed methodology in coping with misalignment induced by vehicle dynamics. Numerical results show that highly directional mmW and/or FSO communications are promising candidates for massive data-rate vehicular communications even in high mobility scenarios. Full article
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Article
Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication
Sensors 2020, 20(11), 3334; https://doi.org/10.3390/s20113334 - 11 Jun 2020
Cited by 5 | Viewed by 1204
Abstract
Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with [...] Read more.
Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. However, this requires large volumes of data to be transmitted and may raise privacy issues. This paper presents a dedicated deep learning detection and tracking algorithms that can be run directly on the camera’s embedded system. This method significantly reduces the stream of data from the cameras, reduces the required communication bandwidth and expands the range of communication technologies to use. Consequently, it allows to use short-range radio communication to transmit vehicle-related information directly between the cameras, and implement the multi-camera tracking directly in the cameras. The proposed solution includes detection and tracking algorithms, and a dedicated low-power short-range communication for multi-target multi-camera tracking systems that can be applied in parking and intersection scenarios. System components were evaluated in various scenarios including different environmental and weather conditions. Full article
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Article
PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction
Sensors 2020, 20(11), 3181; https://doi.org/10.3390/s20113181 - 03 Jun 2020
Cited by 2 | Viewed by 1018
Abstract
Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives [...] Read more.
Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics. Full article
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Article
Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles
Sensors 2020, 20(10), 2961; https://doi.org/10.3390/s20102961 - 23 May 2020
Cited by 4 | Viewed by 1134
Abstract
For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to [...] Read more.
For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed. Full article
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Article
Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO
Sensors 2020, 20(6), 1765; https://doi.org/10.3390/s20061765 - 22 Mar 2020
Cited by 4 | Viewed by 1006
Abstract
A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In [...] Read more.
A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy. Full article
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