Special Issue "Big Data Challenges in Smart Cities"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Robert Laurini

Knowledge Systems Institute, Chicago, IL, USA; Institut National des Sciences Appliquées de Lyon, INSA Lyon, France
Website | E-Mail

Special Issue Information

Dear Colleagues,

Each day, local authorities are collecting zillions of bytes of data and they urgently long whether those data can be useful in decision-making. The so-called big data are coming from various sources, such as from real-time sensors for air pollution, traffic management and energy management, video-surveillance, administrative forms, GIS 2D or 3D data, GPS tracks, aerial photos, videos from drones, etc., without forgetting crowdsourcing for VGI and public participation.

For local administrators and elected officials in smart cities, the optimal use of their big data is very important, since ICT must not be the only the main resource, but rather the overall core of their smart governance.

Various challenges are emerging: How to structure big data? How to combine them efficiently? How to query them? How to extract knowledge? How to extract salient features, determining patterns and trends? How to combine them with deep learning? How to visualize them? How to integrate them into urban dashboards? How to preserve privacy? What are the best strategies for storing them? Surely many other challenges will appear.

In this Special Issue, we are especially interested in original papers dealing with these aspects, and/or describing novel experiences, as well as enriching big data theories with geographic aspects.

Prof. Dr. Robert Laurini
Guest Editor

Manuscript Submission Information

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Keywords

  • big data
  • smart cities
  • smart governance
  • urban knowledge extraction
  • geographic knowledge

Published Papers (3 papers)

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Research

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Open AccessArticle An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
Received: 27 November 2018 / Revised: 15 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract
Mobile network topology changes dynamically over time because of the high velocity of vehicles. Therefore, the concept of the data dissemination scheme in a VANET environment has become an issue of debate for many research scientists. The main purpose of VANET is to
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Mobile network topology changes dynamically over time because of the high velocity of vehicles. Therefore, the concept of the data dissemination scheme in a VANET environment has become an issue of debate for many research scientists. The main purpose of VANET is to ensure passenger safety application by considering the critical emergency message. The design of the message dissemination protocol should take into consideration effective data dissemination to provide a high packet data ratio and low end-to-end delay by using network resources at a minimal level. In this paper, an effective and efficient adaptive probability data dissemination protocol (EEAPD) is proposed. EEAPD comprises a delay scheme and probabilistic approach. The redundancy ratio (r) metric is used to explain the correlation between road segments and vehicles’ density in rebroadcast probability decisions. The uniqueness of the EEAPD protocol comes from taking into account the number of road segments to decide which nodes are suitable for rebroadcasting the emergency message. The last road segment is considered in the transmission range because of the probability of it having small vehicle density. From simulation results, the proposed protocol provides a better high-packet delivery ratio and low-packet drop ratio by providing better use of the network resource within low end-to-end delay. This protocol is designed for only V2V communication by considering a beaconless strategy. the simulations in this study were conducted using Ns-3.26 and traffic simulator called “SUMO”. Full article
(This article belongs to the Special Issue Big Data Challenges in Smart Cities)
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Open AccessArticle Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs
Received: 19 September 2018 / Revised: 11 November 2018 / Accepted: 12 December 2018 / Published: 14 December 2018
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Abstract
Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has
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Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s. Full article
(This article belongs to the Special Issue Big Data Challenges in Smart Cities)
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Review

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Open AccessReview Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review
Received: 15 June 2018 / Revised: 5 July 2018 / Accepted: 18 July 2018 / Published: 24 July 2018
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Abstract
Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms
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Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images. Full article
(This article belongs to the Special Issue Big Data Challenges in Smart Cities)
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