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Special Issue "Crowd-Sensing and Remote Sensing Technologies for Smart Cities"

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

Deadline for manuscript submissions: closed (31 July 2018).

Special Issue Editor

Dr. Mohamed Bakillah
Website
Guest Editor
1. Senior Advisor of GIS Governmental Center, RAK, United Arab Emirates
2. Associated Senior Researcher, Department of Geomatic Engineering, Laval University, Canada
3. Associated Senior Researcher, GIScience research group, Heidelberg University, Germany
Interests: GIScience; Big Data; volunteered deographic information; semantic interoperability and standards; mobiles; sensor networks; ad hoc networks; data mining; spatiotemporal analysis; reasoning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

During the past decade we have witnessed great development in various types of sensors carried on different platforms varying from handheld devices and physical urban sensors to satellites. This has enabled the collection of a tremendous amount of geo-spatial data from the urban environment. Methods and algorithms are needed to inspect, analyze and apply this big data in a proper and efficient way in everyday applications. Regarding crowd-sensing technology, there has been special concern and attention paid to the reliability, credibility and usability of such volunteered geographic information for being used in projects. It is believed that integrating data sources from traditional wireless sensor networks with emerging mobile crowd sensing, as well as data sets produced by satellites, can further enhance novel large-scale sensing applications and systems into every sector of the economy.

We welcome scholars to share their research on challenges and solutions of Crowd-Sensing and Remote Sensing Technologies for Smart Cities.

Dr. Mohamed Bakillah
Guest Editor

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 2000 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

  • Urban Remote Sensing
  • Mobile crowd sensing
  • Participatory sensing
  • Wireless sensor networks
  • Internet of Things (IoT)
  • Vehicular sensor networks
  • Volunteered Geographic Information (VGI)
  • Location-based social networks (LBSN)
  • Big Data Spatial-temporal analysis
  • GI solutions for Urban Transportation
  • Smart Cities Urban Informatics
  • Intelligent Transportation Systems

Published Papers (12 papers)

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Research

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Open AccessArticle
A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data
Sensors 2018, 18(11), 3794; https://doi.org/10.3390/s18113794 - 06 Nov 2018
Cited by 4
Abstract
In this work, we present a system that generates customized pedestrian routes entirely based on data from OpenStreetMap (OSM). The system enables users to define to what extent they would like the route to have green areas (e.g., parks, squares, trees), social places [...] Read more.
In this work, we present a system that generates customized pedestrian routes entirely based on data from OpenStreetMap (OSM). The system enables users to define to what extent they would like the route to have green areas (e.g., parks, squares, trees), social places (e.g., cafes, restaurants, shops) and quieter streets (i.e., with less road traffic). We present how the greenness, sociability, and quietness factors are defined and extracted from OSM as well as how they are integrated into a routing cost function. We intrinsically evaluate customized routes from one-thousand trips, i.e., origin–destination pairs, and observe that these are, in general, as we intended—slightly longer but significantly more social, greener, and quieter than the respective shortest routes. Based on a survey taken by 156 individuals, we also evaluate the system’s usefulness, usability, controlability, and transparency. The majority of the survey participants agree that the system is useful and easy to use and that it gives them the feeling of being in control regarding the extraction of routes in accordance with their greenness, sociability, and quietness preferences. The survey also provides valuable insights into users requirements and wishes regarding a tool for interactively generating customized pedestrian routes. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing
Sensors 2018, 18(8), 2596; https://doi.org/10.3390/s18082596 - 08 Aug 2018
Cited by 4
Abstract
Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. [...] Read more.
Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client–server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
A Data Model for Using OpenStreetMap to Integrate Indoor and Outdoor Route Planning
Sensors 2018, 18(7), 2100; https://doi.org/10.3390/s18072100 - 30 Jun 2018
Cited by 8
Abstract
With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor [...] Read more.
With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor route planning, in this paper, we focus on the connections inside buildings and propose a data model, which uses OSM primitives (nodes, ways and relations) and tags to capture horizontal and vertical indoor components, as well as the connection between indoor and outdoor environments. A set of new approaches is developed to support indoor modeling and mapping. Based on the proposed data model, we present a workflow that enables automatic generation of a routing graph and provide an algorithm to calculate integrated indoor-outdoor routes. We applied our data model to a set of test cases. The application results demonstrate the capability of our data model in modeling built environments and its feasibility for the integration of indoor and outdoor navigation. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing
Sensors 2018, 18(5), 1566; https://doi.org/10.3390/s18051566 - 15 May 2018
Cited by 1
Abstract
Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance [...] Read more.
Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance is no longer effective. Therefore, developing an approach to evaluate and optimizing users’ service perceptions has become increasingly important. Meanwhile, the development of a new sensing paradigm, mobile crowdsensing (MCS), makes it possible to evaluate and analyze the user’s OTT service perception from end-user’s point of view other than from the network side. In this paper, the key factors that impact users’ end-to-end OTT web browsing service perception are analyzed by monitoring crowdsourced user perceptions. The intrinsic relationships among the key factors and the interactions between key quality indicators (KQI) are evaluated from several perspectives. Moreover, an analytical framework of perceptional degradation and a detailed algorithm are proposed whose goal is to identify the major factors that impact the perceptional degradation of web browsing service as well as their significance of contribution. Finally, a case study is presented to show the effectiveness of the proposed method using a dataset crowdsensed from a large number of smartphone users in a real mobile network. The proposed analytical framework forms a valuable solution for mobile network maintenance and optimization and can help improve web browsing service perception and network quality. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories
Sensors 2018, 18(4), 1261; https://doi.org/10.3390/s18041261 - 19 Apr 2018
Cited by 19
Abstract
Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed [...] Read more.
Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
Assessing and Mapping of Road Surface Roughness based on GPS and Accelerometer Sensors on Bicycle-Mounted Smartphones
Sensors 2018, 18(3), 914; https://doi.org/10.3390/s18030914 - 19 Mar 2018
Cited by 14
Abstract
The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness [...] Read more.
The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness of un-motorable roads, such as pedestrian and bicycle lanes. This hinders many applications related to pedestrians, cyclists and wheelchair users. In recognizing these research gaps, this paper proposes a method for measuring the surface roughness of pedestrian and bicycle lanes based on Global Positioning System (GPS) and accelerometer sensors on bicycle-mounted smartphones. We focus on the International Roughness Index (IRI), as it is the most widely used index for measuring road surface roughness. Specifically, we analyzed a computing model of road surface roughness, derived its parameters with GPS and accelerometers on bicycle-mounted smartphones, and proposed an algorithm to recognize potholes/humps on roads. As a proof of concept, we implemented the proposed method in a mobile application. Three experiments were designed to evaluate the proposed method. The results of the experiments show that the IRI values measured by the proposed method were strongly and positively correlated with those measured by professional instruments. Meanwhile, the proposed algorithm was able to recognize the potholes/humps that the bicycle passed. The proposed method is useful for measuring the surface roughness of roads that are not accessible for professional instruments, such as pedestrian and cycle lanes. This work enables us to further study the feasibility of crowdsourcing road surface roughness with bicycle-mounted smartphones. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
3VSR: Three Valued Secure Routing for Vehicular Ad Hoc Networks using Sensing Logic in Adversarial Environment
Sensors 2018, 18(3), 856; https://doi.org/10.3390/s18030856 - 14 Mar 2018
Cited by 6
Abstract
Today IoT integrate thousands of inter networks and sensing devices e.g., vehicular networks, which are considered to be challenging due to its high speed and network dynamics. The goal of future vehicular networks is to improve road safety, promote commercial or infotainment products [...] Read more.
Today IoT integrate thousands of inter networks and sensing devices e.g., vehicular networks, which are considered to be challenging due to its high speed and network dynamics. The goal of future vehicular networks is to improve road safety, promote commercial or infotainment products and to reduce the traffic accidents. All these applications are based on the information exchange among nodes, so not only reliable data delivery but also the authenticity and credibility of the data itself are prerequisite. To cope with the aforementioned problem, trust management come up as promising candidate to conduct node’s transaction and interaction management, which requires distributed mobile nodes cooperation for achieving design goals. In this paper, we propose a trust-based routing protocol i.e., 3VSR (Three Valued Secure Routing), which extends the widely used AODV (Ad hoc On-demand Distance Vector) routing protocol and employs the idea of Sensing Logic-based trust model to enhance the security solution of VANET (Vehicular Ad-Hoc Network). The existing routing protocol are mostly based on key or signature-based schemes, which off course increases computation overhead. In our proposed 3VSR, trust among entities is updated frequently by means of opinion derived from sensing logic due to vehicles random topologies. In 3VSR the theoretical capabilities are based on Dirichlet distribution by considering prior and posterior uncertainty of the said event. Also by using trust recommendation message exchange, nodes are able to reduce computation and routing overhead. The simulated results shows that the proposed scheme is secure and practical. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques
Sensors 2018, 18(2), 509; https://doi.org/10.3390/s18020509 - 08 Feb 2018
Cited by 14
Abstract
Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project [...] Read more.
Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
Adaptive Sampling for Urban Air Quality through Participatory Sensing
Sensors 2017, 17(11), 2531; https://doi.org/10.3390/s17112531 - 03 Nov 2017
Cited by 5
Abstract
Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most [...] Read more.
Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation
Sensors 2017, 17(11), 2498; https://doi.org/10.3390/s17112498 - 31 Oct 2017
Cited by 14
Abstract
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex [...] Read more.
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle
An Effective Semantic Event Matching System in the Internet of Things (IoT) Environment
Sensors 2017, 17(9), 2014; https://doi.org/10.3390/s17092014 - 02 Sep 2017
Cited by 6
Abstract
IoT sensors use the publish/subscribe model for communication to benefit from its decoupled nature with respect to space, time, and synchronization. Because of the heterogeneity of communicating parties, semantic decoupling is added as a fourth dimension. The added semantic decoupling complicates the matching [...] Read more.
IoT sensors use the publish/subscribe model for communication to benefit from its decoupled nature with respect to space, time, and synchronization. Because of the heterogeneity of communicating parties, semantic decoupling is added as a fourth dimension. The added semantic decoupling complicates the matching process and reduces its efficiency. Our proposed algorithm clusters subscriptions and events according to topic and performs the matching process within these clusters, which increases the throughput by reducing the matching time from the range of 16–18 ms to 2–4 ms. Moreover, the accuracy of matching is improved when subscriptions must be fully approximated, as demonstrated by an over 40% increase in F-score results. This work shows the benefit of clustering, as well as the improvement in the matching accuracy and efficiency achieved using this approach. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Review

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Open AccessReview
The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey
Sensors 2018, 18(8), 2581; https://doi.org/10.3390/s18082581 - 07 Aug 2018
Cited by 10
Abstract
With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the [...] Read more.
With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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