Abstract
Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
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Co-creating digital solutions to city challenges, https://organicity.eu/.
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OrganiCity’s Data Observatory, https://observatory.organicity.eu/.
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For a map view of the whole installed infrastructure, please visit http://maps.smartsantander.eu/.
References
Abeel, T., Peer, Y. V. D., & Saeys, Y. (2009). Java-ML: A machine learning library. Journal of Machine Learning Research, 10(Apr), 931–934.
Aggarwal, C., Ashish, N., & Sheth, A. (2013). The internet of things: A survey from the data-centric perspective. In Managing and mining sensor data (pp. 383–428). Springer, Boston, MA.
Al-Turjman, F., & Alturjman, S. (2018). Confidential smart-sensing framework in the IoT era. The Journal of Supercomputing, 74(10), 5187–5198. https://doi.org/10.1007/s11227-018-2524-1.
Al-Turjman, F., & Alturjman, S. (2018). Context-sensitive access in industrial internet of things (IIoT) healthcare applications. IEEE Transactions on Industrial Informatics, 14(6), 2736–2744. https://doi.org/10.1109/TII.2018.2808190.
Al-Turjman, F., Hasan, M. Z., & Al-Rizzo, H. (2018). Task scheduling in cloud-based survivability applications using swarm optimization in IOT. Transactions on Emerging Telecommunications Technologies 0(0), e3539. https://doi.org/10.1002/ett.3539. https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.3539.
Alabady, S. A., & Al-Turjman, F. (2018). Low complexity parity check code for futuristic wireless networks applications. IEEE Access, 6, 18,398–18,407. https://doi.org/10.1109/ACCESS.2018.2818740.
Alabady, S. A., Al-Turjman, F., & Din, S. (2018). A novel security model for cooperative virtual networks in the IoT era. International Journal of Parallel Programming. https://doi.org/10.1007/s10766-018-0580-z.
Amaxilatis, D., Boldt, D., Choque, J., Diez, L., Gandrille, E., Kartakis S., et al. (2018). Advancing experimentation-as-a-service through urban IoT experiments. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2018.2871766.
Barowy, D. W., Curtsinger, C., Berger, E. D., & McGregor, A. (2016). Automan: A platform for integrating human-based and digital computation. Communications of the ACM, 59(6), 102–109. https://doi.org/10.1145/2927928. https://doi.org/10.1145/2927928.
Bello, J. P., Silva, C. T., Nov, O., DuBois, R. L., Arora, A., Salamon, J., et al. (2018). SONYC: A system for the monitoring, analysis and mitigation of urban noise pollution. CoRR abs/1805.00889. http://arxiv.org/abs/1805.00889.
Bischof, S., Karapantelakis, A., Nechifor, C., Sheth, A. P., Mileo, A., Barnaghi, P. (2014). Semantic modelling of smart city data. Presented at the W3C Workshop on the Web of Things, Berlin.
Chilton, L. B., Little, G., Edge, D., Weld, D. S., & Landay, J. A. (2013). Cascade: Crowdsourcing taxonomy creation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13 (pp. 1999–2008). New York: ACM. https://10.1145/2470654.2466265, URL http://doi.acm.org/10.1145/2470654.2466265.
CitySDK, City service development kit, https://www.citysdk.eu/. Accessed 15 Dec 2018.
CKAN, the open source data portal, http://ckan.org. Accessed 15 Dec 2018.
Deligiannidou, A., Amaxilatis, D., Mylonas, G., & Theodoridis, E. (2016). Knowledge co-creation in the OrganiCity: Data annotation with JAMAiCA. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IOT) (pp. 717–722). https://doi.org/10.1109/WF-IoT.2016.7845492. doi.ieeecomputersociety.org/10.1109/WF-IoT.2016.7845492
FIWARE Orion Context Broker, https://github.com/telefonicaid/fiware-orion. Accessed 15 Dec 2018.
Ganz, F., Barnaghi, P., & Carrez, F. (2016). Automated semantic knowledge acquisition from sensor data. IEEE Systems Journal, 10(3), 1214–1225. https://10.1109/JSYST.2014.2345843.
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N.Y., Huang, R., et al. (2015). Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys, 48, 7:1–7:31.
Guo, K., Lu, Y., Gao, H., & Cao, R. (2018). Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors, 18(5). https://doi.org/10.3390/s18051341. http://www.mdpi.com/1424-8220/18/5/1341.
Hido, S., Tokui, S., & Oda, S. (2013). Jubatus: An open source platform for distributed online machine learning. In NIPS 2013, Workshop on Big Learning: Advances in Algorithms and Data Management, Lake Tahoe, NV
Karkouch, A., Mousannif, H., Moatassime, H.A., & Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57–81. https://doi.org/10.1016/j.jnca.2016.08.002.
Knerr, T. (2006). Tagging ontology - Towards a common ontology for folksonomies. http://code.google.com/p/tagont/, http://tagont.googlecode.com/files/TagOntPaper.pdf.
Lanza, J., Sanchez, L., Gutierrez, V., Galache, J. A., Santana, J. R., Sotres, P., et al. (2016). Smart city services over a future internet platform based on internet of things and cloud: The smart parking case. Energies, 9, 719. https://doi.org/10.3390/en9090719.
Min, W., Yu, L., Yu, L., & He, S. (2018). People logistics in smart cities. Communications of the ACM, 61(11), 54–59. https://doi.org/10.1145/3239546.
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys Tutorials, 20(4), 2923–2960. https://doi.org/10.1109/COMST.2018.2844341.
NGSI v2 Specification, http://fiware.github.io/specifications/ngsiv2/stable/. Accessed 15 Dec 2018.
Puiu, D., Barnaghi, P., Tonjes, R., Kumper, D., Ali, M.I., Mileo, A., et al. (2016). Citypulse: Large scale data analytics framework for smart cities. IEEE Access, 4, 1086–1108. https://doi.org/10.1109/ACCESS.2016.2541999.
Puschmann, D., Barnaghi, P., & Tafazolli, R. (2018). Using LDA to uncover the underlying structures and relations in smart city data streams. IEEE Systems Journal, 12(2), 1755–1766. https://doi.org/10.1109/JSYST.2017.2723818.
Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., et al. (2010). Learning from crowds. Journal of Machine Learning Research, 11, 1297–1322.
Sánchez, L., Muñoz, L., Galache, J.A., Sotres, P., Santana, J.R., Gutiérrez, V., et al. (2014). SmartSantander: IoT experimentation over a smart city testbed. Computer Networks, 61, 217–238.
Santos, P.M., Rodrigues, J.G.P., Cruz, S.B., Lourenco, T., d’Orey, P.M., Luis, Y., et al. (2018). PortoLivingLab: An IoT-based sensing platform for smart cities. IEEE Internet of Things Journal, 5, 523–532.
Socrata: Data-Driven Innovation of Government Programs, https://socrata.com. Accessed 15 Dec 2018.
Sotres, P., Santana, J.R., Sánchez, L., Lanza, J., & Muñoz, L. (2017). Practical lessons from the deployment and management of a smart city internet-of-things infrastructure: The SmartSantander Testbed Case. IEEE Access, 5, 14,309–14,322. https://doi.org/10.1109/ACCESS.2017.2723659.
Sounds of New York City project, https://wp.nyu.edu/sonyc/. Accessed 15 Dec 2018.
Spring Boot framework, http://spring.io/projects/spring-boot. Accessed 15 Dec 2018.
van Kranenburg, R., Stembert, N., Moreno, M.V., Skarmeta, A.F., López, C., Elicegui, I., et al. (2014). Co-creation as the key to a public, thriving, inclusive and meaningful EU IOT. In R. Hervás, S. Lee, C. Nugent, J. Bravo (Eds.), Ubiquitous Computing and Ambient Intelligence (pp. 396–403). Cham: Springer.
Webber, J. (2012). A programmatic introduction to Neo4J. In Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity, SPLASH ’12 (pp. 217–218). New York: ACM. https://doi.org/10.1145/2384716.2384777.
Welinder, P., Branson, S., Belongie, S., & Perona, P. (2010). The multidimensional wisdom of crowds. In Proceedings of the 23rd International Conference on Neural Information Processing Systems, NIPS’10 (Vol. 2) (pp. 2424–2432). New York: Curran Associates Inc.
Acknowledgements
This work has been partially supported by the EU research project OrganiCity, under contract H2020-645198.
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Amaxilatis, D., Mylonas, G., Theodoridis, E., Diez, L., Deligiannidou, K. (2020). LearningCity: Knowledge Generation for Smart Cities. In: Al-Turjman, F. (eds) Smart Cities Performability, Cognition, & Security. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-14718-1_2
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