Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Internet of Things (IoT) Infrastructures for Smart Cities Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities

Authors : Anirban Mondal, Praveen Rao, Sanjay Kumar Madria

Published in: Handbook of Smart Cities

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Over the past few decades, the population in the urban areas has been increasing in a dramatic manner. Currently, about 80% of the U.S. population and about 50% of the world’s population live in urban areas and the population growth rate for urban areas is estimated to be over one million people per week [1, 2]. By 2050, it has been predicted that 64% of people in the developing nations and 85% of people in the developed world would be living in urban areas [1, 2]. Such a dramatic population growth in urban areas has been placing demands on urban infrastructure like never before [1].

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Leveraging Cloud Computing and Sensor-Based Devices in the Operation and Management of Smart Systems
next chapter 5G Wireless Micro Operators for Integrated Casinos and Entertainment in Smart Cities
Footnotes
Literature
1.
2.
3.
4.
M. Foth, J. H. Choi, and C. Satchell, “Urban informatics,” in Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, 2011, pp. 1–8.
5.
6.
7.
A. Mondal, P. Rao, and S. K. Madria, “Mobile computing, internet of things, and big data for urban informatics,” in International Conference on Mobile Data Management (MDM), vol. 2, 2016, pp. 8–11.
8.
N. Ferreira, J. Poco, H. T. Vo, J. Freire, and C. T. Silva, “Visual exploration of big spatio-temporal urban data: A study of New York City taxi trips,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2149–2158, Dec. 2013.
9.
J. He, K. Kunze, C. Lofi, S. K. Madria, and S. Sigg, “Towards mobile sensor-aware crowdsourcing: Architecture, opportunities and challenges,” in Proc. DASFAA Workshops, 2014, pp. 403–412.
10.
D. Yang, G. Xue, X. Fang, and J. Tang, “Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing,” in Proceedings of the 18th annual international conference on Mobile computing and networking, 2012, pp. 173–184.
11.
A. Jian, G. Xiaolin, Y. Jianwei, S. Yu, and H. Xin, “Mobile crowd sensing for Internet of Things: A credible crowdsourcing model in mobile-sense service,” in Proceedings of the IEEE International Conference on Multimedia Big Data, 2015, pp. 92–99.
12.
J. M. Hernández-Muñoz, J. B. Vercher, L. Muñoz, J. A. Galache, M. Presser, L. A. H. Gómez, and J. Pettersson, “The Future Internet,” 2011, ch. Smart Cities at the Forefront of the Future Internet, pp. 447–462.CrossRef
13.
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, Sep. 2013.
14.
S. Zygiaris, “Smart city reference model: Assisting planners to conceptualize the building of smart city innovation ecosystems,” Journal of the Knowledge Economy, vol. 4, no. 2, pp. 217–231, Jun 2013.CrossRef
15.
L. Filipponi, A. Vitaletti, G. Landi, V. Memeo, G. Laura, and P. Pucci, “Smart city: An event driven architecture for monitoring public spaces with heterogeneous sensors,” in Fourth International Conference on Sensor Technologies and Applications, 2010, pp. 281–286.
16.
A. Attwood, M. Merabti, P. Fergus, and O. Abuelmaatti, “SCCIR: Smart cities critical infrastructure response framework,” Proceedings of 4th International Conference on Developments in eSystems Engineering, 2011.
17.
N. Zygouras, N. Zacheilas, V. Kalogeraki, D. Kinane, and D. Gunopulos, “Insights on a scalable and dynamic traffic management system,” Proceedings of EDBT, 2015.
18.
T. Mukherjee, D. Chander, A. Mondal, K. Dasgupta, A. Kumar, and A. Venkat, “CityZen: A cost-effective city management system with incentive-driven resident engagement,” in IEEE 15th International Conference on Mobile Data Management, MDM, 2014, pp. 289–296.
19.
S. Basu Roy, I. Lykourentzou, S. Thirumuruganathan, S. Amer-Yahia, and G. Das, “Task assignment optimization in knowledge-intensive crowdsourcing,” The VLDB Journal, vol. 24, no. 4, pp. 467–491, Aug. 2015.
20.
N. Panagiotou, N. Zygouras, I. Katakis, D. Gunopulos, N. Zacheilas, I. Boutsis, V. Kalogeraki, S. Lynch, and B. O’Brien, “Intelligent urban data monitoring for smart cities,” Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 177–192, 2016.CrossRef
21.
G. Suciu, A. Vulpe, S. Halunga, O. Fratu, G. Todoran, and V. Suciu, “Smart cities built on resilient Cloud Computing and secure Internet of Things,” Proceedings of the International Conference on Control Systems and Computer Science, pp. 513–518, 2013.
22.
W. M. da Silva, A. Alvaro, G. H. R. P. Tomas, R. A. Afonso, K. L. Dias, and V. C. Garcia, “Smart cities software architectures: A survey,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing, ser. SAC ’13, 2013, pp. 1722–1727.
23.
E. M. Daly, F. Lecue, and V. Bicer, “Westland row why so slow?: Fusing social media and linked data sources for understanding real-time traffic conditions,” in Proceedings of the ACM International Conference on Intelligent User Interfaces, 2013, pp. 203–212.
24.
H. Khazaei, S. Zareian, R. Veleda, and M. Litoiu, “Sipresk: A big data analytic platform for smart transportation,” First EAI International Summit, pp. 419–430, 2016.
25.
26.
27.
P. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell: rich monitoring of road and traffic conditions using mobile smartphones,” in Proceedings of the 6th ACM conference on Embedded network sensor systems, 2008, pp. 323–336.
28.
M. Jain, A. P. Singh, S. Bali, and S. Kaul, “Speed-breaker early warning system.” in Proc. USENIX/ACM Workshop on Networked System for Developing Regions, 2012.
29.
Y.-c. Tai, C.-w. Chan, and J. Y.-j. Hsu, “Automatic road anomaly detection using smart mobile device,” in conference on technologies and applications of artificial intelligence, 2010.
30.
A. Mondal, A. Sharma, K. Yadav, A. Tripathi, A. Singh, and N. M. Piratla, “RoadEye: A system for personalized retrieval of dynamic road conditions,” in IEEE 15th International Conference on Mobile Data Management, MDM, 2014, pp. 297–304.
31.
32.
A. Biem, E. Bouillet, H. Feng, A. Ranganathan, A. Riabov, O. Verscheure, H. Koutsopoulos, and C. Moran, “IBM Infosphere Streams for scalable, real-time, intelligent transportation services,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2010, pp. 1093–1104.
33.
S. Mathur, T. Jin, N. Kasturirangan, J. Chandrasekaran, W. Xue, M. Gruteser, and W. Trappe, “Parknet: drive-by sensing of road-side parking statistics,” in Proceedings of the 8th international conference on Mobile systems, applications, and services, 2010, pp. 123–136.
34.
A. Guttman, R-trees: A dynamic index structure for spatial searching, 1984, vol. 14.CrossRef
35.
N. Zygouras, N. Panagiotou, N. Zacheilas, I. Boutsis, V. Kalogeraki, I. Katakis, and D. Gunopulos, “Towards detection of faulty traffic sensors in real-time,” CEUR Workshop Proceedings, vol. 1392, 2015.
36.
37.
A. Rovetta, F. Xiumin, F. Vicentini, Z. Minghua, A. Giusti, and H. Qichang, “Early detection and evaluation of waste through sensorized containers for a collection monitoring application,” Waste Management, vol. 29, no. 12, pp. 2939–2949, 2009.CrossRef
38.
F. Vicentini, A. Giusti, A. Rovetta, X. Fan, Q. He, M. Zhu, and B. Liu, “Sensorized waste collection container for content estimation and collection optimization,” Waste Management, vol. 29, no. 5, pp. 1467–1472, 2009.CrossRef
39.
M. A. A. Mamun, M. A. Hannan, A. Hussain, and H. Basri, “Wireless sensor network prototype for solid waste bin monitoring with energy efficient sensing algorithm,” Proceedings of the International Conference on Computational Science and Engineering, pp. 382–387, 2013.
40.
A. Papalambrou, D. Karadimas, J. Gialelis, and A. G. Voyiatzis, “A versatile scalable smart waste-bin system based on resource-limited embedded devices,” Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8, 2015.
41.
42.
43.
D. Karadimas, A. Papalambrou, J. Gialelis, and S. Koubias, “An integrated node for smart-city applications based on active RFID tags; use case on waste-bins,” Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–7, 2016.
44.
A. Medvedev, P. Fedchenkov, A. Zaslavsky, T. Anagnostopoulos, and S. Khoruzhnikov, “Waste management as an iot-enabled service in smart cities,” Proceedings of the Internet of Things, Smart Spaces, and Next Generation Networks and Systems, ruSMART, pp. 104–115, 2015.
45.
T. Anagnostopoulos, A. Zaslavsky, K. Kolomvatsos, A. Medvedev, P. Amirian, J. Morley, and S. Hadjieftymiades, “Challenges and opportunities of waste management in iot-enabled smart cities: A survey,” IEEE Transactions on Sustainable Computing, vol. 2, no. 3, pp. 275–289, 2017.CrossRef
46.
M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda, “PEIR, the personal environmental impact report, as a platform for participatory sensing systems research,” in Proceedings of the 7th international conference on Mobile systems, applications, and services, 2009, pp. 55–68.
47.
P. Shankara, P. Mahanta, E. Arora, and G. Srinivasamurthy, “Impact of internet of things in the retail industry,” in Proceedings of On the Move to Meaningful Internet Systems: OTM Workshops, 2015, pp. 61–65.CrossRef
48.
S. Fosso Wamba, L. A. Lefebvre, Y. Bendavid, and l. Lefebvre, “Exploring the impact of RFID technology and the EPC network on mobile B2B eCommerce: A case study in the retail industry,” in International Journal of Production Economics, vol. 112, 2008, pp. 614–629.CrossRef
49.
H. Belarbi, A. Tajmouati, H. Bennis, and M. El Haj Tirari, “Predictive analysis of Big Data in Retail industry,” in Proceedings of the International Conference on Computing Wireless and Communication Systems, 2016.
50.
R. Vargheese and H. Dahir, “An IoT/IoE enabled architecture framework for precision on shelf availability: Enhancing proactive shopper experience,” in Proceedings of the IEEE International Conference on Big Data, 2014, pp. 21–26.
51.
D. Hicks, K. Mannix, H. M. Bowles, and B. J. Gao, “SmartMart: IoT-based in-store mapping for mobile devices,” in Proceedings of the IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2013, pp. 616–621.
52.
T. Bohnenberger, A. Jameson, A. Krüger, and A. Butz, “Location-aware shopping assistance: Evaluation of a decision-theoretic approach,” in Proceedings of Human Computer Interaction with Mobile Devices, 2002, pp. 155–169.CrossRef
53.
M.-R. Ra, B. Liu, T. F. La Porta, and R. Govindan, “Medusa: A programming framework for crowd-sensing applications,” in Proceedings of the 10th international conference on Mobile systems, applications, and services, 2012, pp. 337–350.
54.
N. Do, C.-H. Hsu, and N. Venkatasubramanian, “CrowdMAC: a crowdsourcing system for mobile access,” in Proceedings of the 13th International Middleware Conference, 2012, pp. 1–20.
55.
C. Miao, W. Jiang, L. Su, Y. Li, S. Guo, Z. Qin, H. Xiao, J. Gao, and K. Ren, “Cloud-enabled privacy-preserving truth discovery in crowd sensing systems,” in Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015, pp. 183–196.
56.
H. Jin, L. Su, B. Ding, K. Nahrstedt, and N. Borisov, “Enabling privacy-preserving incentives for mobile crowd sensing systems,” in Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS), 2016, pp. 344–353.
57.
T. Dimitriou and I. Krontiris, “Privacy-respecting auctions as incentive mechanisms in mobile crowd sensing,” in IFIP International Conference on Information Security Theory and Practice, 2015, pp. 20–35.
58.
Q. Li and G. Cao, “Providing privacy-aware incentives in mobile sensing systems,” IEEE Transactions on Mobile Computing, vol. 15, pp. 1485–1498, 2016.CrossRef
59.
H. Meka, S. K. Madria, and M. Linderman, “Incentive based approach to find selfish nodes in mobile p2p networks,” in Proceedings of the IEEE Performance Computing and Communications Conference (IPCCC), 2012, pp. 352–359.
60.
T. K. Wijaya, M. Vasirani, and K. Aberer, “Crowdsourcing behavioral incentives for pervasive demand response,” Tech. Rep., 2014.
61.
A. Mondal, S. K. Madria, and M. Kitsuregawa, “E-arl: An economic incentive scheme for adaptive revenue-load-based dynamic replication of data in mobile-p2p networks,” Distributed and Parallel Databases, vol. 28, pp. 1–31, 2010.CrossRef
62.
B. Fogg, “A behavior model for persuasive design,” in Proceedings of the 4th International Conference on Persuasive Technology. ACM, 2009, pp. 40:1–40:7.
63.
L. Duan, T. Kubo, K. Sugiyama, J. Huang, T. Hasegawa, and J. Walrand, “Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing,” in 2012 Proceedings IEEE INFOCOM, March 2012, pp. 1701–1709.
64.
D. Easley and A. Ghosh, “Behavioral mechanism design: Optimal crowdsourcing contracts and prospect theory,” in Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015, pp. 679–696.
65.
C. B. Ferster and B. F. Skinner, “Schedules of reinforcement,” Appleton-Century-Crofts, 1957.
66.
M. Karaliopoulos, I. Koutsopoulos, and M. Titsias, “First learn then earn: Optimizing mobile crowdsensing campaigns through data-driven user profiling,” in Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2016, pp. 271–280.
67.
P. Micholia, M. Karaliopoulos, and I. Koutsopoulos, “Mobile crowdsensing incentives under participation uncertainty,” in Proceedings of the 3rd ACM Workshop on Mobile Sensing, Computing and Communication, 2016, pp. 29–34.
68.
L. Pritschet, D. Powell, and Z. Horne, “Marginally significant effects as evidence for hypotheses: Changing attitudes over four decades,” Psychological Science, vol. 27, no. 7, pp. 1036–1042, 2016.CrossRef
69.
F. Ma, Y. Li, Q. Li, M. Qiu, J. Gao, S. Zhi, L. Su, B. Zhao, H. Ji, and J. Han, “Faitcrowd: Fine grained truth discovery for crowdsourced data aggregation,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 745–754.
70.
I. Boutsis, V. Kalogeraki, and D. Guno, “Reliable crowdsourced event detection in smartcities,” in 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC) (SCOPE - GCTC), 2016, pp. 1–6.
71.
A. Mahmood, W. G. Aref, E. Dragut, and S. Basalamah, “The Palm-tree index: Indexing with the crowd,” Proc. DBCrowd, pp. 26–31, 2013.
72.
S. Kumar, S. Madria, and M. Linderman, “M-Grid: a distributed framework for multidimensional indexing and querying of location based data,” Distributed and Parallel Databases, vol. 35, pp. 55–81, 2017.CrossRef
73.
74.
C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann, “DBpedia - a crystallization point for the Web of data,” Journal of Web Semantics, vol. 7, no. 3, pp. 154–165, September 2009.CrossRef
75.
D. Vrandecic and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Comm. of the ACM, vol. 57, no. 10, pp. 78–85, 2014.CrossRef
76.
M. Bermudez-Edo, T. Elsaleh, P. Barnaghi, and K. Taylor, “IoT-Lite: A Lightweight Semantic Model for the Internet of Things,” in 2016 International IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, July 2016, pp. 90–97.
77.
78.
E. I. Chong, S. Das, G. Eadon, and J. Srinivasan, “An efficient SQL-based RDF querying scheme,” in Proc. of the 31st VLDB Conference, 2005, pp. 1216–1227.
79.
D. Abadi, A. Marcus, S. Madden, and K. Hollenbach, “SW-Store: A vertically partitioned DBMS for semantic web data management,” The VLDB Journal, vol. 18, no. 2, pp. 385–406, 2009.CrossRef
80.
T. Neumann and G. Weikum, “The RDF-3X engine for scalable management of RDF data,” The VLDB Journal, vol. 19, no. 1, pp. 91–113, 2010.CrossRef
81.
C. Weiss, P. Karras, and A. Bernstein, “Hexastore: Sextuple indexing for Semantic Web data management,” Proc. VLDB Endow., vol. 1, no. 1, pp. 1008–1019, 2008.CrossRef
82.
M. Atre, V. Chaoji, M. J. Zaki, and J. A. Hendler, “Matrix “Bit” loaded: A scalable lightweight join query processor for RDF data,” in Proc. of the 19th WWW Conference, 2010, pp. 41–50.
83.
M. A. Bornea, J. Dolby, A. Kementsietsidis, K. Srinivas, P. Dantressangle, O. Udrea, and B. Bhattacharjee, “Building an efficient RDF store over a relational database,” in Proc. of 2013 SIGMOD Conference, 2013, pp. 121–132.
84.
P. Yuan, P. Liu, B. Wu, H. Jin, W. Zhang, and L. Liu, “TripleBit: A fast and compact system for large scale RDF data,” Proc. VLDB Endow., vol. 6, no. 7, pp. 517–528, 2013.CrossRef
85.
J. Huang, D. J. Abadi, and K. Ren, “Scalable SPARQL querying of large RDF graphs,” Proc. of VLDB Endow., vol. 4, no. 11, pp. 1123–1134, 2011.
86.
K. Zeng, J. Yang, H. Wang, B. Shao, and Z. Wang, “A distributed graph engine for web scale RDF data,” Proc. VLDB Endow., vol. 6, no. 4, pp. 265–276, 2013.CrossRef
87.
N. Papailiou, D. Tsoumakos, I. Konstantinou, P. Karras, and N. Koziris, “H2RDF+: An Efficient Data Management System for Big RDF Graphs,” in Proc. of the 2014 ACM SIGMOD Conference, Snowbird, Utah, USA, 2014, pp. 909–912.
88.
S. Gurajada, S. Seufert, I. Miliaraki, and M. Theobald, “TriAD: A Distributed Shared-nothing RDF Engine Based on Asynchronous Message Passing,” in Proc. of the 2014 ACM SIGMOD Conference, Snowbird, Utah, USA, 2014, pp. 289–300.
89.
M. Hammoud, D. A. Rabbou, R. Nouri, S.-M.-R. Beheshti, and S. Sakr, “DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication,” Proc. VLDB Endow., vol. 8, no. 6, pp. 654–665, Feb. 2015.
90.
A. Schätzle, M. Przyjaciel-Zablocki, S. Skilevic, and G. Lausen, “S2RDF: RDF Querying with SPARQL on Spark,” Proc. VLDB Endow., vol. 9, no. 10, pp. 804–815, Jun. 2016.
91.
V. Slavov, A. Katib, P. Rao, S. Paturi, and D. Barenkala, “Fast processing of SPARQL queries on RDF quadruples,” in Proc. of WebDB ’14, 2014, pp. 1–6, https://​arxiv.​org/​pdf/​1506.01333v1.pdf.
92.
A. Katib, V. Slavov, and P. Rao, “RIQ: Fast processing of SPARQL queries on RDF quadruples,” Journal of Web Semantics, vol. 37, no. C, pp. 90–111, 2016.CrossRef
93.
94.
S. K. Madria, “Security and risk assessment in the Cloud,” IEEE Communications Magazine, 2016.
95.
H. Kajino, H. Arai, and H. Kashima, “Preserving worker privacy in crowdsourcing,” Data Mining and Knowledge Discovery, vol. 28, pp. 1314–1335, 2014.MathSciNetCrossRef
Metadata
Title
Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities
Authors
Anirban Mondal
Praveen Rao
Sanjay Kumar Madria
Copyright Year
2018
Book
Handbook of Smart Cities

Print ISBN: 978-3-319-97270-1

Electronic ISBN: 978-3-319-97271-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-97271-8_4

Premium Partner

    Image Credits