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Sensor Networks for Smart Roads

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

Deadline for manuscript submissions: closed (30 May 2018) | Viewed by 75701

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Prof. Dr. Gianluigi Ferrari

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Guest Editor

Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; networking and communication; signal processing; smart systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Barbara Mavì Masini

E-Mail Website
Guest Editor

1. Institute of Electronics Information Engineering and Telecommunications, National Research Council, Bologna, Italy
2. Department of Electrical, University of Bologna, 40136 Bologna, Italy
Interests: connected vehicles; Internet of vehicles (IoV); relay-assisted communications; visible light communication (VLC); 5G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart roads will likely shape the mobility of the future. In particular, smart roads will rely on a successful interaction between smart vehicles and smart infrastructure. Vehicles are becoming more and more important actors in the current industrial revolution, providing new challenging applications, from safety to traffic management and infotainment. Concurrently, governmental entities are placing more and more interest (and financial support) to the design and implementation of smart road infrastructures, able to efficiently interact with smart vehicles (e.g., connected autonomous vehicles). In this challenging scenario, it is expected that accurate traffic and road monitoring, through innovative sensor networks, will play a key role in the deployment of innovative smart roads.

In the last decade, commercial vehicles have witnessed an exponential growth of their sensing, computational, and communication capabilities. By exploiting their sensing and communication capabilities, the vehicles can cooperate to create so-called vehicular sensor networks. Moreover, on-board mobile devices act naturally as sensing devices, owing to a large set of on-board sensors (e.g., inertial, voice, magnetic, video, etc.), with widely available direct Internet connectivity.

Infrastructures are also being provided with connection capabilities: smart road infrastructures include, for example, road side units which can provide wireless access to vehicles and, at the same time, collect relevant information with a large array of sensing capabilities. At the same time, next-generation cellular communication systems, i.e., 5G systems, are expected to provide significant data collection and dissemination capabilities. Overall, road/traffic monitoring sensor networks will likely be directly connected to the Internet, thus enabling the implementation of the Internet of Thing (IoT)-oriented smart roads.

This Special Issue focuses on sensor networking for smart roads and aims at covering cutting-edge research advances in topics covering vehicular sensor networking, sensing technologies, wireless communication technologies challenges, standardization, field trials, and others.

Topics of interest include, but are not limited to the following:

Vehicular sensor networks.
Infrastructure-based sensor networks for road/traffic monitoring.
Connectivity issues in road/traffic sensing.
5G technologies for road/traffic sensing.
11p and 802.11r technologies for road/traffic sensing.
Crowd sensing for vehicular networks.
Internet of Things (IoT)-based smart roads.
Software defined vehicular sensor networks.
High density platooning.
Autonomous intersection management.
Vehicular named data networking.
Heterogeneous vehicular networks.
Large scale simulations of sensor networks for road/traffic monitoring.
Field trials.

Dr. Gianluigi Ferrari
Dr. Barbara Masini
Guest Editor

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Published Papers (12 papers)

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Research

14 pages, 7111 KiB

Open AccessArticle

Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks

by Mariano Gallo and Giuseppina De Luca

Sensors 2018, 18(8), 2640; https://doi.org/10.3390/s18082640 - 12 Aug 2018

Cited by 10 | Viewed by 3985

Abstract

This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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18 pages, 4284 KiB

Open AccessArticle

Optimization of Scanning and Counting Sensor Layout for Full Route Observability with a Bi-Level Programming Model

by Donghui Shan, Xiaoduan Sun, Jianbei Liu and Ming Sun

Sensors 2018, 18(7), 2286; https://doi.org/10.3390/s18072286 - 14 Jul 2018

Cited by 12 | Viewed by 3564

Abstract

Utilizing the data obtained from both scanning and counting sensors is critical for efficiently managing traffic flow on roadways. Past studies mainly focused on the optimal layout of one type of sensor, and how to optimize the arrangement of more than one type of sensor has not been fully researched. This paper develops a methodology that optimizes the deployment of different types of sensors to solve the well-recognized network sensors location problem (NSLP). To answer the questions of how many, where and what types of sensors should be deployed on each particular link of the network, a novel bi-level programming model for full route observability is presented to strategically locate scanning and counting sensors in a network. The methodology works in two steps. First, a mathematical program is formulated to determine the minimum number of scanning sensors. To solve this program, a new ‘differentiating matrix’ is introduced and the corresponding greedy algorithm of ‘differentiating first’ is put forward. In the second step, a scanning map and an incidence matrix are incorporated into the program, which extends the theoretical model for multiple sensors’ deployment and provides the replacement method to reduce total cost of sensors without loss of observability. The algorithm developed at the second step involved in two coefficient matrixes from scanning map and incidence parameter enumerate all possibilities of replacement schemes so that cost of different combination schemes can be compared. Finally, the proposed approach is demonstrated by comparison of Nguyen-Dupuis network and real network, which indicates the proposed method is capable to evaluate the trade-off between cost and all routes observability. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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12 pages, 2412 KiB

Open AccessArticle

Practical Methods for Vehicle Speed Estimation Using a Microprocessor-Embedded System with AMR Sensors

by Vytautas Markevicius, Dangirutis Navikas, Adam Idzkowski, Darius Andriukaitis, Algimantas Valinevicius and Mindaugas Zilys

Sensors 2018, 18(7), 2225; https://doi.org/10.3390/s18072225 - 10 Jul 2018

Cited by 19 | Viewed by 5170

Abstract

The proper operation of computing resources in a microprocessor-embedded system plays a key role in reducing computing time. Processing the variable amount of collected data in real-time improves the performance of a microprocessor-embedded system. In this regard, a vehicle’s speed measurement system is no exception. The computing time for evaluating any speed value is expected to be reduced as much as possible. Four computational methods, including cross-correlation, are discussed. An exemplary pair of recorded signals presenting the change in magnetic field magnitude is analyzed. The sample delay values are compared. The results of the evaluated speed and the execution time of the program code are presented for each method based on a dataset of 200 randomly driven vehicles. The results of the performed tests confirm that the cross-correlation-based methods are not always reliable in situations when the sample size is small, i.e., it is a segment of the impulse response caused by a driving vehicle. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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18 pages, 404 KiB

Open AccessArticle

A Survey on the Roadmap to Mandate on Board Connectivity and Enable V2V-Based Vehicular Sensor Networks

by Barbara M. Masini, Alessandro Bazzi and Alberto Zanella

Sensors 2018, 18(7), 2207; https://doi.org/10.3390/s18072207 - 9 Jul 2018

Cited by 96 | Viewed by 7646

Abstract

Vehicles will soon be connected and will be interacting directly with each other and with the road infrastructure, bringing substantial benefits in terms of safety and traffic efficiency. The past decade has seen the development of different wireless access technologies for vehicle-to-everything (V2X) communications and an extensive set of related use cases have been drafted, each with its own requirements. In this paper, focusing on short-range communications, we analyze the technical and economic motivations that are driving the development of new road users’ connectivity, discussing the international intentions to mandate on board devices for V2X communication. We also go in depth with the enabling wireless access technologies, from IEEE 802.11p to short-range Cellular-V2X and other complementary technologies, such as visible light communication (VLC) and millimeterWaves, up to hybrid communication and 5G. We conclude our survey with some performance comparison in urban realistic scenarios, underlying that the choice of the future enabling technology is not so easy to predict and mostly depends on mandatory laws at the international level. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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12 pages, 383 KiB

Open AccessArticle

On the Coverage of Bus-Based Mobile Sensing

by Pedro Henrique Cruz Caminha, Rodrigo De Souza Couto, Luís Henrique Maciel Kosmalski Costa, Anne Fladenmuller and Marcelo Dias de Amorim

Sensors 2018, 18(6), 1976; https://doi.org/10.3390/s18061976 - 20 Jun 2018

Cited by 22 | Viewed by 5195

Abstract

A cost-effective approach to gather information in a smart city is to embed sensors in vehicles such as buses. To understand the limitations and opportunities of this model, it is fundamental to investigate the spatial coverage of such a network, especially in the case where only a subset of the buses have a sensing device embedded. In this paper, we propose a model to select the right subset of buses that maximizes the coverage of the city. We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and show that it is possible to cover 94% of the same streets if only 18% of buses have sensing capabilities embedded. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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22 pages, 1289 KiB

Open AccessArticle

Cellular-V2X Communications for Platooning: Design and Evaluation

by Giovanni Nardini, Antonio Virdis, Claudia Campolo, Antonella Molinaro and Giovanni Stea

Sensors 2018, 18(5), 1527; https://doi.org/10.3390/s18051527 - 11 May 2018

Cited by 82 | Viewed by 10800

Abstract

Platooning is a cooperative driving application where autonomous/semi-autonomous vehicles move on the same lane in a train-like manner, keeping a small constant inter-vehicle distance, in order to reduce fuel consumption and gas emissions and to achieve safe and efficient transport. To this aim, they may exploit multiple on-board sensors (e.g., radars, LiDARs, positioning systems) and direct vehicle-to-vehicle communications to synchronize their manoeuvres. The main objective of this paper is to discuss the design choices and factors that determine the performance of a platooning application, when exploiting the emerging cellular vehicle-to-everything (C-V2X) communication technology and considering the scheduled mode, specified by 3GPP for communications over the sidelink assisted by the eNodeB. Since no resource management algorithm is currently mandated by 3GPP for this new challenging context, we focus on analyzing the feasibility and performance of the dynamic scheduling approach, with platoon members asking for radio resources on a per-packet basis. We consider two ways of implementing dynamic scheduling, currently unspecified by 3GPP: the sequential mode, that is somehow reminiscent of time division multiple access solutions based on IEEE 802.11p—till now the only investigated access technology for platooning—and the simultaneous mode with spatial frequency reuse enabled by the eNodeB. The evaluation conducted through system-level simulations provides helpful insights about the proposed configurations and C-V2X parameter settings that mainly affect the reliability and latency performance of data exchange in platoons, under different load settings. Achieved results show that the proposed simultaneous mode succeeds in reducing the latency in the update cycle in each vehicle’s controller, thus enabling future high-density platooning scenarios. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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21 pages, 8108 KiB

Open AccessArticle

On the Deployment and Noise Filtering of Vehicular Radar Application for Detection Enhancement in Roads and Tunnels

by Young-Duk Kim, Guk-Jin Son, Chan-Ho Song and Hee-Kang Kim

Sensors 2018, 18(3), 837; https://doi.org/10.3390/s18030837 - 11 Mar 2018

Cited by 24 | Viewed by 6581

Abstract

Recently, radar technology has attracted attention for the realization of an intelligent transportation system (ITS) to monitor, track, and manage vehicle traffic on the roads as well as adaptive cruise control (ACC) and automatic emergency braking (AEB) for driving assistance of vehicles. However, when radar is installed on roads or in tunnels, the detection performance is significantly dependent on the deployment conditions and environment around the radar. In particular, in the case of tunnels, the detection accuracy for a moving vehicle drops sharply owing to the diffuse reflection of radio frequency (RF) signals. In this paper, we propose an optimal deployment condition based on height and tilt angle as well as a noise-filtering scheme for RF signals so that the performance of vehicle detection can be robust against external conditions on roads and in tunnels. To this end, first, we gather and analyze the misrecognition patterns of the radar by tracking a number of randomly selected vehicles on real roads. In order to overcome the limitations, we implement a novel road watch module (RWM) that is easily integrated into a conventional radar system such as Delphi ESR. The proposed system is able to perform real-time distributed data processing of the target vehicles by providing independent queues for each object of information that is incoming from the radar RF. Based on experiments with real roads and tunnels, the proposed scheme shows better performance than the conventional method with respect to the detection accuracy and delay time. The implemented system also provides a user-friendly interface to monitor and manage all traffic on roads and in tunnels. This will accelerate the popularization of future ITS services. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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16 pages, 2226 KiB

Open AccessArticle

Fuzzy Traffic Control with Vehicle-to-Everything Communication

by Muntaser A. Salman, Suat Ozdemir and Fatih V. Celebi

Sensors 2018, 18(2), 368; https://doi.org/10.3390/s18020368 - 27 Jan 2018

Cited by 20 | Viewed by 5484

Abstract

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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16 pages, 5533 KiB

Open AccessArticle

Monitoring Traffic Information with a Developed Acceleration Sensing Node

by Zhoujing Ye, Linbing Wang, Wen Xu, Zhifei Gao and Guannan Yan

Sensors 2017, 17(12), 2817; https://doi.org/10.3390/s17122817 - 5 Dec 2017

Cited by 26 | Viewed by 6322

Abstract

In this paper, an acceleration sensing node for pavement vibration was developed to monitor traffic information, including vehicle speed, vehicle types, and traffic flow, where a hardware design with low energy consumption and node encapsulation could be accomplished. The service performance of the sensing node was evaluated, by methods including waterproof test, compression test, sensing performance analysis, and comparison test. The results demonstrate that the sensing node is low in energy consumption, high in strength, IPX8 waterproof, and high in sensitivity and resolution. These characteristics can be applied to practical road environments. Two sensing nodes were spaced apart in the direction of travelling. In the experiment, three types of vehicles passed by the monitoring points at several different speeds and values of d (the distance between the sensor and the nearest tire center line). Based on cross-correlation with kernel pre-smoothing, a calculation method was applied to process the raw data. New algorithms for traffic flow, speed, and axle length were proposed. Finally, the effects of vehicle speed, vehicle weight, and d value on acceleration amplitude were statistically evaluated. It was found that the acceleration sensing node can be used for traffic flow, vehicle speed, and other types of monitoring. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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15 pages, 586 KiB

Open AccessArticle

Coverage Probability and Area Spectral Efficiency of Clustered Linear Unmanned Vehicle Sensor Networks

by Haejoon Jung and In-Ho Lee

Sensors 2017, 17(11), 2550; https://doi.org/10.3390/s17112550 - 5 Nov 2017

Cited by 6 | Viewed by 4330

Abstract

In this paper, we consider clustered unmanned vehicle (UV) sensor networks for swarm sensing applications in a linear structure such as highway, tunnel, underwater pipelines, power lines, and international border. We assume that the linear UV sensor networks follow Thomas cluster process (TCP), in which the cluster locations are modelled by Poisson point process (PPP), while the cluster members (UVs) are normally distributed around their cluster centers. We focus on communications between UVs within a cluster such as local sensing data transfer or swarm coordination, where multiple UV pairs can share the same frequency band simultaneously. Thus, in the presence of co-channel interference both from the same cluster and the other clusters, we study the coverage and area spectral efficiency of the clustered UV sensor networks in a linear topology. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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25 pages, 805 KiB

Open AccessArticle

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

by Joan Claudi Socoró, Francesc Alías and Rosa Ma Alsina-Pagès

Sensors 2017, 17(10), 2323; https://doi.org/10.3390/s17102323 - 12 Oct 2017

Cited by 68 | Viewed by 8241

Abstract

One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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17 pages, 1298 KiB

Open AccessArticle

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

by Xiaolei Ma, Sen Luan, Bowen Du and Bin Yu

Sensors 2017, 17(10), 2160; https://doi.org/10.3390/s17102160 - 21 Sep 2017

Cited by 19 | Viewed by 5259

Abstract

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. Full article

(This article belongs to the Special Issue Sensor Networks for Smart Roads)

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