Skip to main content
Springer Nature Link
Log in

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

  • Chapter
  • First Online:
Handbook of Smart Cities

  • 1675 Accesses

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].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://en.wikipedia.org/wiki/Prospect_theory

  2. 2.

    http://dublincore.org

  3. 3.

    https://www.w3.org/2004/02/skos

  4. 4.

    http://wiki.dbpedia.org/services-resources/ontology

  5. 5.

    http://schema.org

  6. 6.

    http://lov.okfn.org/dataset/lov/

  7. 7.

    https://www.wikidata.org

  8. 8.

    http://hbase.apache.org

  9. 9.

    http://spark.apache.org

  10. 10.

    https://www.w3.org/TR/prov-dm

  11. 11.

    http://km.aifb.kit.edu/projects/btc-2012

  12. 12.

    http://bio2rdf.org

References

  1. S. E. Koonin, “Urban informatics: Putting big data to work in our cities [online],” http://data-informed.com/urban-informatics-putting-big-data-to-work-in-our-cities/, 2016.

  2. “NYU Center for Urban Science and Progress [online],” http://cusp.nyu.edu/urban-informatics/.

  3. “Beyond smart cities - people first approach [online],” http://www.urbaninformatics.net/.

  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.

    Google Scholar 

  5. “Wikipedia article on Urban Computing [online],” https://en.wikipedia.org/wiki/Urban_computing.

  6. “McKinsey Report [online],” http://mckinseyonsociety.com/emerging-trends-in-urban-informatics/.

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  25. “Ushahidi Platform [online],” https://blog.ushahidi.com/2012/06/05/ushahidi-beijing/.

  26. “Fixmystreet platform [online],” https://www.fixmystreet.com/.

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  31. “Waze traffic navigation app [online],” https://www.waze.com/en-GB/.

  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.

    Google Scholar 

  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.

    Google Scholar 

  34. A. Guttman, R-trees: A dynamic index structure for spatial searching, 1984, vol. 14.

    Article  Google Scholar 

  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.

    Google Scholar 

  36. “Smart waste management [online],” http://www.link-labs.com/smart-waste-management/.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  41. “Waste management using Enevo [online],” https://www.enevo.com/.

  42. “Waste management using SmartBin [online],” https://www.smartbin.com/.

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  60. T. K. Wijaya, M. Vasirani, and K. Aberer, “Crowdsourcing behavioral incentives for pervasive demand response,” Tech. Rep., 2014.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  65. C. B. Ferster and B. F. Skinner, “Schedules of reinforcement,” Appleton-Century-Crofts, 1957.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  73. “Resource Description Framework,” http://www.w3.org/RDF.

  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.

    Article  Google Scholar 

  75. D. Vrandecic and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Comm. of the ACM, vol. 57, no. 10, pp. 78–85, 2014.

    Article  Google Scholar 

  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.

    Google Scholar 

  77. “SPARQL 1.1,” http://www.w3.org/TR/sparql11-query/.

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  93. A. Katib, P. Rao, and V. Slavov, “A tool for efficiently processing SPARQL queries on RDF quads,” in Proc. of the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, Oct. 2017, pp. 1–4, http://ceur-ws.org/Vol-1963/paper472.pdf.

  94. S. K. Madria, “Security and risk assessment in the Cloud,” IEEE Communications Magazine, 2016.

    Google Scholar 

  95. H. Kajino, H. Arai, and H. Kashima, “Preserving worker privacy in crowdsourcing,” Data Mining and Knowledge Discovery, vol. 28, pp. 1314–1335, 2014.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  2. University of Missouri-Kansas, Kansas City, MO, USA

    Praveen Rao

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Kumar Madria

Authors
  1. Anirban Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Praveen Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjay Kumar Madria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay Kumar Madria .

Editor information

Editors and Affiliations

  1. School of Computer Science, McGill University, Montreal, QC, Canada

    Muthucumaru Maheswaran

  2. CIT, UAE University, Al Ain, United Arab Emirates

    Elarbi Badidi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mondal, A., Rao, P., Madria, S.K. (2018). Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities. In: Maheswaran, M., Badidi, E. (eds) Handbook of Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-319-97271-8_4

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97270-1

  • Online ISBN: 978-3-319-97271-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics