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F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-in-Motion Using MQTT Protocol

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10542))

Abstract

Internet of Things (IoT) evolves very rapidly over time, since everything such as sensors/actuators linked together from around the world with use of evolution of ubiquitous computing through the Internet. These devices have a unique IP address in order to communicate with each other and transmit data with features of wireless technologies. Fog computing or so called edge computing brings all Cloud features to embedded devices at edge network and adds more features to servers like pre-store data of Cloud, fast response, and generate overhasty users reporting. Fog mediates between Cloud and IoT devices and thus enables new types of computing and services. The future applications take the advantage of combing the two concepts Fog and Cloud in order to provide low delay Fog-based and high capacity of storage Cloud-based. This paper proposes an IoT architecture for healthcare network based on Fog to Cloud and Data in Motion (F2CDM). The proposed architecture is designed and implemented over three sites: Site 1 contains the embedded devices layer, Site 2 consists of the Fog network layer, while Site 3 consists of the Cloud network. The Fog layer is represented by a middleware server in Al-Nahrain University with temporary storage such that the data lives inside for 30 min. During this time, the selection of up-normality in behavior is send to the Cloud while the rest of the data is wiped out. On the other hand, the Cloud stores all the incoming data from Fog permanently. The F2CDM works using Message Queue Telemetry Transport (MQTT) for fast response. The results show that all data can be monitored from the Fog in real time while the critical data can be monitored from Cloud. In addition, the response time is evaluated using traffic generator called Tsung. It has been found that the proposed architecture reduces traffic on Cloud network and provides better data analysis.

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References

  1. Rahmani, A.M., Gia, T.N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., Liljeberg, P.: Exploiting smart e-Health gateways at the edge of healthcare internet-of-things: a fog computing approach. Future Gener. Comput. Syst. 78, 641–658 (2017)

    Article  Google Scholar 

  2. Silva, B.N., Khan, M., Han, K.: Internet of things: a comprehensive review of enabling technologies, architecture, and challenges. IETE Tech. Rev. 1–16 (2017). doi:10.1080/02564602.2016.1276416

  3. Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (2014)

    Google Scholar 

  4. Mahmud, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. arXiv preprint arXiv:1611.05539 (2016)

  5. Dastjerdi, A.V., Gupta, H., Calheiros, R.N., Ghosh, S.K., Buyya, R.: Fog computing: principles, architectures, and applications. Distributed, Parallel, and Cluster Computing. arXiv:1601.02752 (2016)

  6. Papadokostaki, K., Mastorakis, G., Panagiotakis, S., Mavromoustakis, C.X., Dobre, C., Batalla, J.M.: Handling big data in the era of internet of things (IoT). In: Mavromoustakis, C.X., Mastorakis, G., Dobre, C. (eds.) Advances in Mobile Cloud Computing and Big Data in the 5G Era. SBD, vol. 22, pp. 3–22. Springer, Cham (2017). doi:10.1007/978-3-319-45145-9_1

    Chapter  Google Scholar 

  7. Leadbetter, A., Smyth, D., Fuller, R., O’grady, E., Shepherd, A.: Where big data meets linked data: applying standard data models to environmental data streams. In: 2016 IEEE International Conference on Big Data (Big Data) (2016)

    Google Scholar 

  8. Gilchrist, A.: The technical and business innovators of the industrial internet. Industry 4.0, pp. 33–64. Apress, Berkeley (2016). doi:10.1007/978-1-4842-2047-4_3

    Chapter  Google Scholar 

  9. Ebbers, M.: 5 Things to Know About Big Data in Motion. IBM (2013)

    Google Scholar 

  10. MQTT (2014). http://mqtt.org/. Accessed 22 Mar 2017

  11. Banks, A., Gupta, R.: MQTT Version 3.1. 1. OASIS standard (2014)

    Google Scholar 

  12. ISO - International Organization for Standardization. ISO/IEC 20922:2016 - Information technology – Message Queuing Telemetry Transport (MQTT) v3.1.1. http://www.iso.org/iso/catalogue_detail.htm?csnumber=69466/. Accessed 22 Mar 2017

  13. Fysarakis, K., Askoxylakis, I., Soultatos, O., Papaefstathiou, I., Manifavas, C., Katos, V.: Which IoT protocol? Comparing standardized approaches over a common M2M application. In: 2016 IEEE Global Communications Conference (GLOBECOM) (2016)

    Google Scholar 

  14. Triawan, M.A., Hindersah, H., Yolanda, D., Hadiatna, F.: Internet of things using publish and subscribe method cloud-based application to NFT-based hydroponic system. In: 2016 6th International Conference on System Engineering and Technology (ICSET) (2016)

    Google Scholar 

  15. Thangavel, D., Ma, X., Valera, A., Tan, H.-X., Tan, C.K.-Y.: Performance evaluation of MQTT and CoAP via a common middleware. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (2014)

    Google Scholar 

  16. Sethi, P., Sarangi, S.R.: Internet of things: architectures, protocols, and applications. J. Electr. Comput. Eng. (2017). Hindawi

    Google Scholar 

  17. Grgic, K., Speh, I., Hedi, I.: A web-based IoT solution for monitoring data using MQTT protocol. In: 2016 International Conference on Smart Systems and Technologies (SST) (2016)

    Google Scholar 

  18. Pulse sensor. https://pulsesensor.com/. Accessed 23 Mar 2017

  19. Tsung. http://tsung.erlang-projects.org/. Accessed 23 Mar 2017

  20. NodeMCU. http://nodemcu.com/index_en.html. Accessed 23 Mar 2017

  21. IPerf. https://iperf.fr/. Accessed 23 Mar 2017

  22. Wireshark. https://www.wireshark.org/. Accessed 23 Mar 2017

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Correspondence to Istabraq M. Al-Joboury or Emad H. Al-Hemiary .

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Al-Joboury, I.M., Al-Hemiary, E.H. (2017). F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-in-Motion Using MQTT Protocol. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (eds) Ubiquitous Networking. UNet 2017. Lecture Notes in Computer Science(), vol 10542. Springer, Cham. https://doi.org/10.1007/978-3-319-68179-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-68179-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68178-8

  • Online ISBN: 978-3-319-68179-5

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