Abstract
The increasing use of smart devices connected to the Internet has driven the technological industry and academia to propose applications that allow us to live in a more secure and autonomous way. However, technological advancement has been accompanied by increasingly demanding time requirements, such as fast processing, low latency, and presentation of data within acceptable times. Therefore, we propose in this article a model and computational architecture for a distributed IoT environment with fog computing configuration for a healthcare application. The goal of our proposal is to provide the correct use of specialized tools so that it is possible to indicate a time constraint and thus process and present the data near real-time. We carried out the implementation of the proposed architecture and generated preliminary results that presented reports, through graphs and tables, of the current situation of the assisted user, as well as generating alerts of abnormal situations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avro: Apache avro (2020). https://avro.apache.org. Accessed July 2020
Bhargava, K., McManus, G., Ivanov, S.: Fog-centric localization for ambient assisted living. In: International Conference on Engineering, Technology and Innovation, pp. 1424–1430. IEEE (2017)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, p. 13 (2012)
Buttazzo, G.C.: Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, vol. 24. Springer, Boston (2011)
Dai, D., Li, X., Wang, C., Sun, M., Zhou, X.: Sedna: a memory based key-value storage system for realtime processing in cloud. In: IEEE International Conference on Cluster Computing Workshops, pp. 48–56 (2012)
Gomes, E., Dantas, M., Plentz, P.: A real-time fog computing approach for healthcare environment. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 85–95. Springer (2018)
Gomes, E., Dantas, M.A., de Macedo, D.D., De Rolt, C., Brocardo, M.L., Foschini, L.: Towards an infrastructure to support big data for a smart city project. In: International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 107–112. IEEE (2016)
Gomes, E.H., Dantas, M.A., Plentz, P.D.: A proposal for a healthcare environment with a real-time approach. Int. J. Grid Util. Comput. 11(3), 398–408 (2020)
Gomes, E.H., Plentz, P.D., Rolt, C.R.D., Dantas, M.A.: A survey on data stream, big data and real-time. Int. J. Networking Virtual Organ. 20(2), 143–167 (2019)
Grafana: Grafana labs (2020). https://grafana.com. Accessed July 2020
Influxdb: Influx data (2020). https://www.influxdata.com. Accessed July 2020
Iorga, M., Feldman, L., Barton, R., Martin, M.J., Goren, N., Mahmoudi, C.: Draft SP 800-191, The NIST Definition of Fog Computing. NIST Special Publication 800, March 2017
Kafka: Apache kafka (2020). http://kafka.apache.org. Accessed July 2020
Kononenko, O., Baysal, O., Holmes, R., Godfrey, M.W.: Mining modern repositories with elasticsearch. In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 328–331 (2014)
Lai, X., Liu, Q., Wei, X., Wang, W., Zhou, G., Han, G.: A survey of body sensor networks. Sensors 13(5), 5406–5447 (2013)
Linkedin: Kafka monitor (2020). https://github.com/linkedin/kafka-monitor. Accessed July 2020
MQTT: Mqtt. http://mqtt.org/ (2020). Accessed July 2020
Mshali, H., Lemlouma, T., Moloney, M., Magoni, D.: A survey on health monitoring systems for health smart homes. Int. J. Ind. Ergon. 66, 26–56 (2018)
Nandyala, C.S., Kim, H.K.: From cloud to fog and IoT-based real-time U-healthcare monitoring for smart homes and hospitals. Int. J. Smart Home 10(2), 187–196 (2016)
Nguyen Gia, T., et al.: Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Gener. Comput. Syst. 93, 198–211 (2019)
Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput. Surv. 50(3), 1–43 (2017)
Prometheus: Prometheus (2020). https://prometheus.io. Accessed July 2020
Safaei, A.A.: Real-time processing of streaming big data. Real-Time Systems (2016)
Safaei, A.A.: Real-time processing of streaming big data. Real Time Syst. 53(1), 1–44 (2017)
Sponsored, D.C., Foundation, N.S.: NSF Workshop Report on Grand Challenges in Edge Computing (2016)
Stankovic, J.A.: Misconceptions about real-time computing: a serious problem for next-generation systems. Computer 21(10), 10–19 (1988)
Verma, P., Sood, S.K.: Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet Things J. 5(3), 1789–1796 (2018)
Vilela, P.H., Rodrigues, J.J., Solic, P., Saleem, K., Furtado, V.: Performance evaluation of a Fog-assisted IoT solution for e-Health applications. Future Gene. Comput. Syst. 97, 379–386 (2019)
Volpato, F., Da Silva, M.P., Gonçalves, A.L., Dantas, M.A.R.: An autonomic QoS management architecture for software-defined networking environments. In: IEEE Symposium on Computers and Communications, pp. 418–423. IEEE (2017)
Wang, X.: The architecture design of the wearable health monitoring system based on internet of things technology. Int. J. Grid Util. Comput. 6(3–4), 207–212 (2015)
Acknowledgement
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gomes, E., Zanatta, R., Plentz, P., De Rolt, C., Dantas, M. (2021). An Approach of Time Constraint of Data Intensive Scalable in e-Health Environment. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-61105-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61104-0
Online ISBN: 978-3-030-61105-7
eBook Packages: EngineeringEngineering (R0)