Abstract
Cloud-assisted body area networks have been the focus of researchers in past years as a response to the development of robust wireless body area networks (WBANs). While software such as Signal Processing in Node Environment (SPINE) provide Application Programming Interfaces (APIs) to manage heterogeneous biomedical sensor networks, others have focused on developing tools that address the issue of sensor connection/control, data receiving, and visualization. However, existing software tools lack sufficient flexibility, scalability, and support for complicated biomedical systems. In this paper, BSNCloud, a cloud-centered heterogeneous and comprehensive wireless body sensor data collection, streaming, and analytics framework is proposed. The system combines the sensor control and data aggregator event detection, real-time data analysis, visualization, and streaming into one Android App and incorporated four key components in the cloud server: data repository, algorithm repository, machine learning engine, and web portal. A prototype has been implemented with preliminary performance evaluation. Results show that the system is promising in its full utilization of the high performance computing power as well as the large volume storage capacity.
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References
Shimmer Enabling Softwares, Available http://www.shimmersensing.com/research-and-education/applications/data-aquisition-software-sensor-systems/
Qualcomm Wearables, Available https://www.qualcomm.com/products/wearables/
Intel IoT Developer Kit, Available https://software.intel.com/en-us/iot/devkit
Samsung Architecture for Multmodal Interactons: An open data platorm for innovaton, Available http://www.samsung.com/us/globalinnovation/innovation_areas/#digital-health
Fortino, G., et al.: BodyCloud: a SaaS approach for community body sensor networks. Future Gener. Comput. Syst. 35, 62–79 (2014)
WebSocket. Available http://www.websocket.org/
Baronti, P., et al.: Wireless sensor networks: a survey on the state of the art and the 802.15. 4 and ZigBee standards. Comput. Commun. 30(7), 1655–1695 (2007)
Bluetooth Low Energy Core Specification Version 4.0. Available http://www.bluetooth.com/English/Technology/Works/Pages/Bluetooth_low_energy_technology.aspx
SPINE Project. Available http://spine.deis.unical.it/
Shimmer Sensing. Available http://www.shimmersensing.com/ (2013)
Kuryloski, P., et al.: DexterNet: an open platform for heterogeneous body sensor networks and its applications. In: 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009)
Fulford-Jones, T., et al.: CodeBlue: an ad hoc sensor network infrastructure for emergency medical care. In: Proceedings of International Workshop on Body Sensor Networks (2004)
Jiang, S., et al.: CareNet: an integrated wireless sensor networking environment for remote healthcare. In: Proceedings of the ICST 3rd international conference on Body area networks, ICST, Tempe, Arizona (2008)
Kurschl, W., Beer, W.: Combining cloud computing and wireless sensor networks. In: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, p. 512–518. ACM, Kuala Lumpur, Malaysia (2009)
Chu, X., Buyya, R.: Service Oriented Sensor Web, in Sensor Networks and Configuration: Fundamentals, Standards, Platforms, and Applications, N.P. Mahalik, Editor. 2007, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 51–74
Fortino, G., Di Fatta, G., Pathan, M., Vasilakos, A.V.: Cloud-assisted body area networks: state-of-the-art and future challenges. Wirel. Networks 20(7), 1925–1938 (2014). https://doi.org/10.1007/s11276-014-0714-1
Li, M., Cao, Y., Prabhakaran, B.: Multi-level sample importance ranking based progressive transmission strategy for time series body sensor data. In: 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM) (2015)
Read, N., et al.: Loss resilient strategy in body sensor networks. In: Proceedigs of 2011 ACM/IEEE International Conference on Body Area Networks (BodyNets 2011, accepted), Beijing, China (2011)
Levis, P., et al.: TinyOS: an operating system for sensor networks. In: Weber, W., Rabaey, J.M., Aarts, E. (eds.) Ambient Intelligence, pp. 115–148. Springer, Berlin, Heidelberg (2005). https://doi.org/10.1007/3-540-27139-2_7
TelosB sensor data sheet. DOI=http://www.willow.co.uk/TelosB_Datasheet.pdf
Repository, UC Irvine Machine Learning Laboratory. Available https://archive.ics.uci.edu/ml
Huang, X., Zhao, T., Cao, Y.: PIR: a domain specific language for multimedia retrieval. In: Proceedings of IEEE International Symposium on Multimedia (ISM 2013), Anaheim, California, USA (2013)
Forum, M.P.I.: MPI: A Message-Passing Interface Standard - Version 2.2 - Sep.4. 2009, Message Passing Interface Forum (2009)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, San Francisco, California, USA (2004)
Acknowledgments
This work was supported by the National Science Foundation, CNS division (Award No. 1626586). We also would like to thank Rittika Shamsuddin and Barbara Mukami Maweu at University of Texas at Dallas for providing the Naïve Bayes fall detection algorithm.
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Li, M., Enkoji, A., Key, M., Marroquin, A., Prabhakaran, B. (2020). BSNCloud: Cloud-Centered Wireless Body Sensor Data Collection, Streaming, and Analytics System. In: Alam, M.M., Hämäläinen, M., Mucchi, L., Niazi, I.K., Le Moullec, Y. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health. BODYNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-64991-3_5
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