Abstract
A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J 15(2), 121–142 (2006)
Araújo, A., Kalebe, R., Giraõ, G., Gonçalves, K., Neto, B., et al.: Reliability analysis of an IoT-based smart parking application for smart cities. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4086–4091. IEEE, Piscataway (2017)
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)
Giang, N.K., Lea, R., Leung, V.C.M.: Exogenous coordination for building fog-based cyber physical social computing and networking systems. IEEE Access 6, 31740–31749 (2018). https://doi.org/10.1109/ACCESS.2018.2844336
Giang, N.K., Lea, R., Blackstock, M., Leung, V.C.M.: Fog at the edge: experiences building an edge computing platform. In: 2018 IEEE International Conference on Edge Computing (EDGE) (2018)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). Preprint arXiv:1704.04861
Huynh, L.N., Lee, Y., Balan, R.K.: DeepMon: mobile GPU-based deep learning framework for continuous vision applications. In: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pp. 82–95. ACM, New York (2017)
Imamura, H., Sato, I., Sugiyama, M.: Analysis of minimax error rate for crowdsourcing and its application to worker clustering model (2018). Preprint arXiv:1802.04551
Kawano, M., Mikami, K., Yokoyama, S., Yonezawa, T., Nakazawa, J.: Road marking blur detection with drive recorder. In: 2017 IEEE International Conference on Big Data (Big Data), Dec 2017, pp. 4092–4097 (2017)
Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., Kawsar, F.: DeepX: a software accelerator for low-power deep learning inference on mobile devices. In: Proceedings of the 15th International Conference on Information Processing in Sensor Networks, p. 23. IEEE Press, Piscataway (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plan. Inference 90(2), 227–244 (2000)
Sugiyama, M., Kawanabe, M.: Machine Learning in Non-stationary Environments: Introduction to Covariate Shift Adaptation. MIT Press, Cambridge (2012)
Toshniwal, A., Taneja, S., Shukla, A., Ramasamy, K., Patel, J.M., Kulkarni, S., Jackson, J., Gade, K., Fu, M., Donham, J., et al.: Storm@ twitter. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 147–156. ACM, New York (2014)
Xin, D., Ma, L., Liu, J., Macke, S., Song, S., Parameswaran, A.: HELIX: accelerating human-in-the-loop machine learning. Proc. VLDB Endow. 11(12), 1958–1961 (2018)
Yonezawa, T., Ito, T., Nakazawa, J., Tokuda, H.: SOXFire: a universal sensor network system for sharing social big sensor data in smart cities. In: Proceedings of the 2nd International Workshop on Smart, p. 2. ACM, New York (2016)
Zaharia, M., Das, T., Li, H., Shenker, S., Stoica, I.: Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. In: HotCloud ’12 (2012)
Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM, New York (2009)
Acknowledgements
This work was supported in part by National Institute of Information and Communications Technology and in part by H2020-EUJ-2016 EU-Japan joint research project, BigClouT (Grant Agreement No 723139).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kawano, M. et al. (2020). CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. In: José, R., Van Laerhoven, K., Rodrigues, H. (eds) 3rd EAI International Conference on IoT in Urban Space. Urb-IoT 2018. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-28925-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-28925-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28924-9
Online ISBN: 978-3-030-28925-6
eBook Packages: EngineeringEngineering (R0)