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Crowdsensing and Vehicle-Based Sensing Mobile Information Systems Crowdsensing and Vehicle-Based Sensing Guest Editors: Carlos T. Calafate, Celimuge Wu, Enrico Natalizio, and Francisco J. Martínez Crowdsensing and Vehicle-Based Sensing Mobile Information Systems Crowdsensing and Vehicle-Based Sensing Guest Editors: Carlos T. Calafate, Celimuge Wu, Enrico Natalizio, and Francisco J. Martínez Copyright © 2016 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “Mobile Information Systems.” All articles are open access articles distributed under the Creative Com- mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editor-in-Chief David Taniar, Monash University, Australia Editorial Board Markos Anastassopoulos, UK Michele Garetto, Italy Francesco Palmieri, Italy Claudio Agostino Ardagna, Italy Romeo Giuliano, Italy Jose Juan Pazos-Arias, Spain Jose M. Barcelo-Ordinas, Spain Francesco Gringoli, Italy Vicent Pla, Spain Alessandro Bazzi, Italy Sergio Ilarri, Spain Daniele Riboni, Italy Paolo Bellavista, Italy Peter Jung, Germany Pedro M. Ruiz, Spain Carlos T. Calafate, Spain Dik Lun Lee, Hong Kong Michele Ruta, Italy María Calderon, Spain Hua Lu, Denmark Carmen Santoro, Italy Juan C. Cano, Spain Sergio Mascetti, Italy Stefania Sardellitti, Italy Salvatore Carta, Italy Elio Masciari, Italy Floriano Scioscia, Italy Yuh-Shyan Chen, Taiwan Maristella Matera, Italy Luis J. G. Villalba, Spain Massimo Condoluci, UK Franco Mazzenga, Italy Laurence T. Yang, Canada Antonio de la Oliva, Spain Eduardo Mena, Spain Jinglan Zhang, Australia Jesus Fontecha, Spain Massimo Merro, Italy Jorge Garcia Duque, Spain Jose F. Monserrat, Spain Contents Crowdsensing and Vehicle-Based Sensing Carlos T. Calafate, Celimuge Wu, Enrico Natalizio, and Francisco J. Martínez Volume 2016, Article ID 2596783, 2 pages A Survey on Smartphone-Based Crowdsensing Solutions Willian Zamora, Carlos T. Calafate, Juan-Carlos Cano, and Pietro Manzoni Volume 2016, Article ID 9681842, 26 pages SenSafe: A Smartphone-Based Traffic Safety Framework by Sensing Vehicle and Pedestrian Behaviors Zhenyu Liu, Mengfei Wu, Konglin Zhu, and Lin Zhang Volume 2016, Article ID 7967249, 13 pages A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios Silvia Mirri, Catia Prandi, Paola Salomoni, Franco Callegati, Andrea Melis, and Marco Prandini Volume 2016, Article ID 2821680, 14 pages Human Mobility Modelling Based on Dense Transit Areas Detection with Opportunistic Sensing Fernando Terroso-Sáenz, Mercedes Valdes-Vela, Aurora González-Vidal, and Antonio F. Skarmeta Volume 2016, Article ID 9178539, 15 pages A Safety Enhancement Broadcasting Scheme Based on Context Sensing in VANETs Chen Chen, Hongyu Xiang, Canding Sun, and Qingqi Pei Volume 2016, Article ID 2630439, 17 pages Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing Chunmei Ma, Qing Zhu, Shuang Wu, and Bin Liu Volume 2016, Article ID 2097243, 10 pages AirCache: A Crowd-Based Solution for Geoanchored Floating Data Armir Bujari and Claudio Enrico Palazzi Volume 2016, Article ID 3247903, 12 pages Hindawi Publishing Corporation Mobile Information Systems Volume 2016, Article ID 2596783, 2 pages http://dx.doi.org/10.1155/2016/2596783 Editorial Crowdsensing and Vehicle-Based Sensing Carlos T. Calafate,1 Celimuge Wu,2 Enrico Natalizio,3 and Francisco J. Martínez4 1 Department of Computer Engineering (DISCA), Universitat Politecnica` de Valencia,` Camino de Vera S/N, 46022 Valencia, Spain 2Graduate School of Information Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan 3Heudiasyc UMR CNRS 7253, Sorbonne Universites,´ UniversitedeTechnologiedeCompi´ egne,` CS 60319, 60203 Compiegne` Cedex, France 4Computer Science and System Engineering Department, University of Zaragoza, Escuela Universitaria Politecnica´ de Teruel, Ciudad Escolar s/n, 44003 Teruel, Spain Correspondence should be addressed to Carlos T. Calafate; [email protected] Received 24 October 2016; Accepted 25 October 2016 Copyright © 2016 Carlos T. Calafate et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Improvements in terms of smart device capabilities and of novel applications and architectures for crowdsensing and communication technologies allowed crowdsensing solu- vehicle-based sensing. tions to emerge as a powerful strategy to revolutionize envi- The articles contained in the present issue include both ronment sensing, becoming one of the key elements of future reviews and research studies focused on data sensing and smart cities. In particular, smartphones, smartwatches, and processing in situations where either pedestrians or vehicles other personal gadgets are now endowed not only with act as mobile sensors. significant computing power and different wireless interfaces, As an introduction to the topic, the paper by W. Zamora butalsowithanincreasingnumberofsensorsabletoprovide et al. entitled “A Survey on Smartphone-based Crowdsensing useful information about the user environment, especially Solutions” provides a survey of smartphone-based crowd- when the user is moving around a city. If vehicular mobility sensing solutions that have emerged in the past few years, is adopted, the sensing capabilities can be further increased focusing on different works published in top-ranked journals by connecting smart devices to vehicles using the On Board and conferences. To properly analyze these previous works, Diagnostic Interface (OBD-II) or provided directly by smart they define a reference framework that allows classifying the vehicles through vehicle-to-vehicle (V2V) or vehicle-to- different proposals under study. Globally, the survey provides infrastructure (V2I) communications. In the future, with the usefulinsightintothebroadscopeofthecrowdsensingarea, gradual introduction of autonomous vehicles and unmanned being of interest to both experts and novices. aerial vehicles, many more sensing alternatives are expected. Analyzing and optimizing mobility in the urban domain In parallel to these developments, network infrastructure is one of the most relevant contributions that mobile crowd- has experienced significant improvements in recent years in sensing can offer to smart cities. In particular, the ever- terms of both coverage and performance which, combined increasing capabilities of smartphones and vehicle-mounted with advances in cloud computing, allows transmitting, devices pave the way for gaining meaningful insight into storing, and processing large amounts of data in an efficient urban mobility. In this context, the work by F. Terroso-Saenz´ manner. Altogether, the potential for generating and analyz- etal.entitled“HumanMobilityModellingBasedonDense ing huge amounts of data is remarkable, being able to provide Transit Areas Detection with Opportunistic Sensing” intro- unprecedented information with high levels of spatial and duces a novel approach to leverage such paradigm, allowing timeresolutionaboutanyeventofinterestoccurringinacity. composing a map of Dense Transit Areas (DTAs) within a The purpose of this special issue is to address recent city representing some of its mobility features while pro- advances on mobile sensing, with particular emphasis on tecting the privacy of users. Besides vehicles, pedestrian and 2 Mobile Information Systems multimodal paths would also benefit greatly from context activities using smartphone sensors: walking versus cycling data, as well as the information about the whole experience and driving versus taking the bus. of traveling and wandering the city, including travel planning Overall, we hope that this special issue is able to shed and payments. The work by S. Mirri et al. entitled “A some light on the major developments in the area of crowd- Service-Oriented Approach to Crowdsensing for Accessible sensing and vehicle-based sensing and attract the attention of Smart Mobility Scenarios” introduces a prototype of the the scientific community to undertake further investigations infrastructure and overall architecture proposed to meet leading to the rapid deployment of such novel solutions. these challenges, describing some of the services that can be provided: path recommendations for wheelchair users and Acknowledgments for elderly persons. In addition to mobility optimization, traffic safety is Wewouldliketoexpressourappreciationtoalltheauthors also a critical issue that should be addressed to minimize for their novel contributions and the reviewers for their sup- the chances of accidents. In this context, the work by Z. Liu port and constructive critiques in making this special issue et al. entitled “SenSafe: A Smartphone-Based Traffic Safety possible. Framework by Sensing Vehicle and Pedestrian Behaviors” Carlos T. Calafate proposes a driving behavior detection mechanism that relies Celimuge Wu on smartphones to sense events in the proximity and provide Enrico Natalizio alerts to drivers. Their proposal also includes a low-overhead Francisco J. Mart´ınez approach for fast data broadcasting, along with a collision estimation algorithm to trigger warnings when dangerous situations are detected. Since broadcasting is a key element for security and context awareness in vehicular ad hoc envi- ronments by simplifying direct vehicle-to-vehicle communi- cations,
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