More Web Proxy on the site http://driver.im/

research-article

An integrated architecture for future studies in data processing for smart cities

Authors:

Cristian Chilipirea,

Andreea-Cristina Petre,

Loredana-Marsilia Groza,

Florin PopAuthors Info & Claims

Microprocessors & Microsystems, Volume 52, Issue C

Pages 335 - 342

https://doi.org/10.1016/j.micpro.2017.03.004

Published: 01 July 2017 Publication History

Abstract

Data processing for Smart Cities become more challenging, facing with different handling steps: data collection from different heterogeneous sources, processing sometimes in real-time and then delivered to high level services or applications used in Smart Cities. Applications used for intelligent transportation systems, crowd management, water resources management, noise and air pollution management, require different data processing techniques. The main subject of this paper is to propose an architecture for data processing in Smart Cities. The architecture is oriented on the flow of data from the source to the end user. We describe seven steps of data processing: collection of data from heterogeneous sources, data normalization, data brokering, data storage, data analysis, data visualization and decision support systems. We consider two case studies on crowd management in smart cities and on Intelligent Transportation Systems (ITS) and we provide experimental highlights.

References

[1]

V. Albino, U. Berardi, R.M. Dangelico, Smart cities: Definitions, dimensions, performance, and initiatives, J. Urban Technol., 22 (2015) 3-21.

[2]

A. Whitmore, A. Agarwal, L. Da Xu, The internet of thingsa survey of topics and trends, Inf. Syst. Front., 17 (2015) 261-274.

Digital Library

[3]

J.M. Batalla, P. Krawiec, Conception of id layer performance at the network level for internet of things, Pers. Ubiquit. Comput., 18 (2014) 465-480.

Digital Library

[4]

J.M. Batalla, M. Gajewski, W. Latoszek, P. Krawiec, C.X. Mavromoustakis, G. Mastorakis, Id-based service-oriented communications for unified access to iot, Comput. Electr. Eng., 52 (2016) 98-113.

Digital Library

[5]

A. Gandomi, M. Haider, Beyond the hype: big data concepts, methods, and analytics, Int. J. Inf. Manag., 35 (2015) 137-144.

Digital Library

[6]

C.S. Raghavendra, K.M. Sivalingam, T. Znati, Wireless Sensor Networks, Springer, 2006.

Digital Library

[7]

H. Ma, D. Zhao, P. Yuan, Opportunities in mobile crowd sensing, Commun. Mag. IEEE, 52 (2014) 29-35.

[8]

G.P. Hancke, G.P. Hancke, The role of advanced sensing in smart cities, Sensors, 13 (2012) 393-425.

[9]

K. Su, J. Li, H. Fu, Smart city and the applications, IEEE, 2011.

[10]

G.K. Still, University of Warwick, 2000.

[11]

H. Du, Z. Yu, F. Yi, Z. Wang, Q. Han, B. Guo, Group mobility classification and structure recognition using mobile devices, 2016.

[12]

D. Kumar, H. Wu, Y. Lu, S. Krishnaswamy, M. Palaniswami, Understanding urban mobility via taxi trip clustering, 2016.

[13]

M.S. Grewal, L.R. Weill, A.P. Andrews, Global Positioning Systems, Inertial Navigation, and Integration, John Wiley & Sons, 2007.

Digital Library

[14]

Y. Chon, S. Kim, S. Lee, D. Kim, Y. Kim, H. Cha, Sensing WiFi packets in the air, 2014.

[15]

M. Dash, K.K. Koo, S.P. Krishnaswamy, Y. Jin, A. Shi-Nash, Visualize peoples mobility - both individually and collectively - using mobile phone cellular data, 2016.

[16]

A.J. Ruiz-Ruiz, H. Blunck, T.S. Prentow, A. Stisen, M.B. Kjaergaard, Analysis methods for extracting knowledge from large-scale wifi monitoring to inform building facility planning, IEEE, 2014.

[17]

L. Vu, K. Nahrstedt, S. Retika, I. Gupta, Joint bluetooth/wifi scanning framework for characterizing and leveraging people movement in university campus, ACM, 2010.

[18]

M. Zhou, Z. Tian, K. Xu, X. Yu, X. Hong, H. Wu, Scanme: location tracking system in large-scale campus wi-fi environment using unlabeled mobility map, Expert Syst. Appl., 41 (2014) 3429-3443.

Digital Library

[19]

B. Bonne, A. Barzan, P. Quax, W. Lamotte, Wifipi: Involuntary tracking of visitors at mass events, IEEE, 2013.

[20]

C.D. C. Chilipirea, M. v. Steen, Filters for wi-fi generated crowd movement data, IEEE, 2015.

[21]

Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, R.P. Martin, Measuring human queues using wifi signals, ACM, 2013.

[22]

L. Schauer, M. Werner, P. Marcus, Estimating crowd densities and pedestrian flows using wi-fi and bluetooth, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2014.

[23]

K. Han, E. Graham, D. Vassallo, D. Estrin, Enhancing motivation in a mobile participatory sensing project through gaming, IEEE, 2011.

[24]

H. Aly, A. Basalamah, M. Youssef, Map++: a crowd-sensing system for automatic map semantics identification, IEEE, 2014.

[25]

M. Faulkner, M. Olson, R. Chandy, J. Krause, K.M. Chandy, A. Krause, The next big one: detecting earthquakes and other rare events from community-based sensors, IEEE, 2011.

[26]

M.-R. Ra, B. Liu, T.F. La Porta, R. Govindan, Medusa: a programming framework for crowd-sensing applications, ACM, 2012.

[27]

I. Carreras, D. Miorandi, A. Tamilin, E.R. Ssebaggala, N. Conci, Matador: mobile task detector for context-aware crowd-sensing campaigns, IEEE, 2013.

[28]

P.P. Jayaraman, C. Perera, D. Georgakopoulos, A. Zaslavsky, Efficient opportunistic sensing using mobile collaborative platform mosden, IEEE, 2013.

[29]

T. Yan, M. Marzilli, R. Holmes, D. Ganesan, M. Corner, mcrowd: a platform for mobile crowdsourcing, ACM, 2009.

[30]

H. Vtj, T. Vainio, E. Sirkkunen, K. Salo, Crowdsourced news reporting: supporting news content creation with mobile phones, ACM, 2011.

[31]

J.M. Batalla, Advanced multimedia service provisioning based on efficient interoperability of adaptive streaming protocol and high efficient video coding, J. Real-Time Image Process., 12 (2016) 443-454.

Digital Library

[32]

T. Xie, X. Qin, Scheduling security-critical real-time applications on clusters, Comput. IEEE Trans., 55 (2006) 864-879.

Digital Library

[33]

J.P. Erickson, G. Coombe, J.H. Anderson, Soft real-time scheduling in google earth, IEEE, 2012.

[34]

B. Sprunt, L. Sha, J. Lehoczky, Aperiodic task scheduling for hard-real-time systems, Real-Time Syst., 1 (1989) 27-60.

[35]

C. Dobre, G. Suciu, C. Chilipirea, C. Gosman, Mobility beyond individualism: an integrated platform for intelligent transportation systems of tomorrow, 2014.

[36]

Y. Kryftis, G. Mastorakis, C.X. Mavromoustakis, J.M. Batalla, E. Pallis, G. Kormentzas, Efficient entertainment services provision over a novel network architecture, IEEE Wireless Commun., 23 (2016) 14-21.

[37]

J.M. Batalla, M. Kantor, C.X. Mavromoustakis, G. Skourletopoulos, G. Mastorakis, A novel methodology for efficient throughput evaluation in virtualized routers, IEEE, 2015.

[38]

C. Barbieru, F. Pop, Soft real-time Hadoop scheduler for big data processing in smart cities, IEEE, 2016.

Cited By

Al-Ali AGupta RZualkernan IDas S(2024)Role of IoT technologies in big data management systemsPervasive and Mobile Computing10.1016/j.pmcj.2024.101905100:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101905
Huda NAhmed IAdnan MAli MNaeem F(2024)Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart CitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122380238:PFOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122380
Zhang LLi YPan YDing L(2024)Advanced informatic technologies for intelligent constructionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109104137:PAOnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.109104
Show More Cited By

An integrated architecture for future studies in data processing for smart cities
1. Computer systems organization
  1. Architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures

Recommendations

Smart Cities: an IoT-centric Approach
IWWISS '14: Proceedings of the 2014 International Workshop on Web Intelligence and Smart Sensing

A number of recent Smart City testbeds and deployments have focused on the use of the Internet of Things (IoT) paradigm and technologies for improving the efficiency of city infrastructures. Building on this work, we have explored the use of IoT hubs as ...
Service-oriented architecture for big data analytics in smart cities
CCGrid '18: Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends ...
Smart Cities, data and right to privacy: a look from the Portuguese and Brazilian experience
ICEGOV '18: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance

With the advent of the New Information and Communication Technologies (ICTs) and the accelerated development of the urban centres, a new debate arises on how to use ICTs to enhance the development of cities and to make them smarter, in the way that ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Microprocessors & Microsystems

Microprocessors & Microsystems Volume 52, Issue C

July 2017

556 pages

ISSN:0141-9331

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2017

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Al-Ali AGupta RZualkernan IDas S(2024)Role of IoT technologies in big data management systemsPervasive and Mobile Computing10.1016/j.pmcj.2024.101905100:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101905
Huda NAhmed IAdnan MAli MNaeem F(2024)Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart CitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122380238:PFOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122380
Zhang LLi YPan YDing L(2024)Advanced informatic technologies for intelligent constructionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109104137:PAOnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.109104
Naghib AJafari Navimipour NHosseinzadeh MSharifi A(2022)A comprehensive and systematic literature review on the big data management techniques in the internet of thingsWireless Networks10.1007/s11276-022-03177-529:3(1085-1144)Online publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1007/s11276-022-03177-5
Makani SPittala RAlsayed EAloqaily MJararweh Y(2022)A survey of blockchain applications in sustainable and smart citiesCluster Computing10.1007/s10586-022-03625-z25:6(3915-3936)Online publication date: 29-May-2022
https://dl.acm.org/doi/10.1007/s10586-022-03625-z
Neilson AIndratmo Daniel BTjandra S(2019)Systematic Review of the Literature on Big Data in the Transportation DomainBig Data Research10.1016/j.bdr.2019.03.00117:C(35-44)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.bdr.2019.03.001
Moustaka VVakali AAnthopoulos L(2018)A Systematic Review for Smart City Data AnalyticsACM Computing Surveys10.1145/323956651:5(1-41)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3239566
Marras MManca MBoratto LFenu GLaniado DChampin PGandon FMédini LLalmas MIpeirotis P(2018)BarcelonaNowCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186983(219-222)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3186983
Morosan APop FGrygiel KCerny TKim SWang W(2017)OCPP security - Neural network for detecting malicious trafficProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/3129676.3129693(190-195)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3129676.3129693

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents