ADMITS: Architecting Distributed Monitoring and Analytics in IoT-based Disaster Scenarios
Resumo
The ADMITS project aims to develop algorithms, protocols and architectures to enable a distributed computing environment to provide support for monitoring, failure detection, and analytics in IoT disaster scenarios. We face a context where, every year, millions of people are affected by natural and man-made disasters, whereby governments all around the world spend huge amounts of resources on preparation, immediate response, and reconstruction. Recently, the Internet of Things (IoT) paradigm has been extensively used for efficiently managing disaster scenarios, such as volcanic disasters, floods, forest fire, land- slides, earthquakes, urban disasters, industrial and terrorists attacks, and so on. However, in a disaster scenario the communication/processing infrastructure and the devices themselves may fail, producing either temporary or permanent network partitions and loss of information. Moreover, it is expected that in the years to come, IoT will generate large amounts of data, making processing and analysis challenging in time-critical applications. Considering such challenges, ADMITS targets the development of a architecture in which IoT, Fog, and Cloud computing technologies participate to provide required capabilities for IoT data analytics, real-time stream processing, and failure monitoring for environments potentially subject to disasters. In this positional paper, we discuss the motivation, objectives, architecture, research challenges (and how to overcome them) and initial efforts for the ADMITS project.
Palavras-chave:
IoT, monitoring, data analytics, disaster scenarios
Referências
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Stolpe, M. (2016). The internet of things: Opportunities and challenges for distributed data analysis. SIGKDD Explor. Newsl., 18(1):15–34.
Uddin, M. Y. S., Nelson, A., Benson, K., Wang, G., Zhu, Q., Han, Q., Alhassoun, N., Chakravarthi, P., Stamatakis, J., Hoffman, D., et al. (2016). The scale2 multi-network architecture for iot-based resilient communities. In Smart Computing (SMARTCOMP), 2016 IEEE International Conference on, pages 1–8. IEEE.
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Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., and Petitjean, F. (2016). Characterizing concept drift. Data Min. Knowl. Discov., 30(4):964–994.
Yang, L., Yang, S., and Plotnick, L. (2013). How the internet of things technology en- hances emergency response operations. Technological Forecasting and Social Change, 80(9):1854 – 1867. Planning and Foresight Methodologies in Emergency Preparedness and Management.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4):2347–2376.
Bacciu, D. (2016). Unsupervised feature selection for sensor time-series in pervasive computing applications. Neural Comput. Appl., 27(5):1077–1091.
Buddhika, T. and Pallickara, S. (2016). Neptune: Real time stream processing for in- ternet of things and sensing environments. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 1143–1152.
Cardellini, V., Grassi, V., Lo Presti, F., and Nardelli, M. (2017). Optimal operator replica- tion and placement for distributed stream processing systems. SIGMETRICS Perform. Eval. Rev., 44(4):11–22.
Cardellini, V., Lo Presti, F., Nardelli, M., and Russo Russo, G. (2018). Towards hierarchi- cal autonomous control for elastic data stream processing in the fog. In Heras, D. B., Bouge ́, L., Mencagli, G., Jeannot, E., Sakellariou, R., Badia, R. M., Barbosa, J. G., Ricci, L., Scott, S. L., Lankes, S., and Weidendorfer, J., editors, Euro-Par 2017: Par- allel Processing Workshops, pages 106–117, Cham. Springer International Publishing.
Chandra, T. D. and Toueg, S. (1996). Unreliable failure detectors for reliable distributed systems. Journal of the ACM (JACM), 43(2):225–267.
Chiang, M. and Zhang, T. (2016). Fog and iot: An overview of research opportunities. IEEE Internet of Things Journal, 3(6):854–864.
Cisco (2015). Fog computing and the internet of things: Extend the cloud to where the things are. Cisco White Paper.
Faria, E. R., Gonc ̧alves, I. J., Carvalho, A. C., and Gama, J. a. (2016). Novelty detection in data streams. Artif. Intell. Rev., 45(2):235–269.
Furquim, G., Jalali, R., Pessin, G., Pazzi, R. W., Ueyama, J., et al. (2018). How to improve fault tolerance in disaster predictions: a case study about flash floods using iot, ml and real data. Sensors, 18(3):907.
Gama, J. (2010). Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 1st edition.
Gama, J. a., Zliobaite ̇, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Comput. Surv., 46(4):44:1–44:37.
Guha-Sapir, D., Hoyois, P., and Below, R. (2016). Annual disaster statistical review 2016: The numbers and trends. Brussels: CRED.
Gutie ́rrez, M., Gregorio-Godoy, P., Pe ́rez del Pulgar, G., Mun ̃oz, L. E., Sa ́ez, S., and Rodr ́ıguez-Pato ́n, A. (2017). A new improved and extended version of the multicell bacterial simulator gro. ACS Synthetic Biology, 6(8):1496–1508.
Hidalgo, N., Wladdimiro, D., and Rosas, E. (2017). Self-adaptive processing graph with operator fission for elastic stream processing. Journal of Systems and Software, 127:205 – 216.
Jang, S. S., Oishi, K. T., Egbert, R. G., and Klavins, E. (2012). Specification and simula- tion of synthetic multicelled behaviors. ACS Synthetic Biology, 1(8):365–374.
Kreps, J. (2014). Questioning the lambda architecture. https://www.oreilly. com/ideas/questioning-the-lambda-architecture.
Morales, J., Rosas, E., and Hidalgo, N. (2014). Symbiosis: Sharing mobile resources for stream processing. In 2014 IEEE Symposium on Computers and Communications (ISCC), volume Workshops, pages 1–6.
Platform, C. D. (2018). http://www.dojot.com.br/.
Rauniyar, A., Engelstad, P., Feng, B., and Thanh, D. V. (2016). Crowdsourcing-based disaster management using fog computing in internet of things paradigm. In 2nd IEEE International Conference on Collaboration and Internet Computing, CIC 2016, Pitts- burgh, PA, USA, November 1-3, 2016, pages 490–494.
Ray, P. P., Mukherjee, M., and Shu, L. (2017). Internet of things for disaster management: State-of-the-art and prospects. IEEE Access, 5:18818–18835.
Rosas, E., Hidalgo, N., Gil-Costa, V., Bonacic, C., Marin, M., Senger, H., Arantes, L., Marcondes, C., and Marin, O. (2016). Survey on simulation for mobile ad-hoc communication for disaster scenarios. Journal of Computer Science and Technology, 31(2):326–349.
Rossetto, A., Geyer, C., Arantes, L., and Sens, P. (2018). Impact fd: An unreliable failure detector based on process relevance and confidence in the system. Computer Journal, To be published.
Shcherbakov, M. V., Brebels, A., Shcherbakova, N., Kamaev, V., Gerget, O., and Devy- atykh, D. (2017). Outlier detection and classification in sensor data streams for proac- tive decision support systems. Journal of Physics: Conference Series, 803(1):012143.
SigFox (2018). https://www.sigfox.com/.
Siozios, K., Anagnostos, D., Soudris, D., and Kosmatopoulos, E. (2018). IoT for Smart Grids: Design Challenges and Paradigms. Power Systems. Springer International Publishing.
Stolpe, M. (2016). The internet of things: Opportunities and challenges for distributed data analysis. SIGKDD Explor. Newsl., 18(1):15–34.
Uddin, M. Y. S., Nelson, A., Benson, K., Wang, G., Zhu, Q., Han, Q., Alhassoun, N., Chakravarthi, P., Stamatakis, J., Hoffman, D., et al. (2016). The scale2 multi-network architecture for iot-based resilient communities. In Smart Computing (SMARTCOMP), 2016 IEEE International Conference on, pages 1–8. IEEE.
Venkatesan, R. and Er, M. J. (2016). A novel progressive learning technique for multi- class classification. Neurocomputing, 207:310–321.
Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., and Petitjean, F. (2016). Characterizing concept drift. Data Min. Knowl. Discov., 30(4):964–994.
Yang, L., Yang, S., and Plotnick, L. (2013). How the internet of things technology en- hances emergency response operations. Technological Forecasting and Social Change, 80(9):1854 – 1867. Planning and Foresight Methodologies in Emergency Preparedness and Management.
Publicado
30/06/2020
Como Citar
PASQUINI, Rafael et al.
ADMITS: Architecting Distributed Monitoring and Analytics in IoT-based Disaster Scenarios. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 11-20.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11207.
