H-Plane: Intelligent Data Management for Mobile Healthcare Applications

详细信息    查看全文

• 关键词：Cloud computing ; Healthcare IoT framework ; Log storage system
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9847
• 期：1
• 页码：283-294
• 全文大小：733 KB
• 参考文献：1.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, pp. 13–16. ACM (2012)
  2.Gupta, T., Singh, R.P., Phanishayee, A., Jung, J., Mahajan, R.: Bolt: data management for connected homes. In: Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation, pp. 243–256. USENIX Association (2014)
  3.Zhang, B., Mor, N., Kolb, J., Chan, D.S., Lutz, K., Allman, E., Wawrzynek, J., Lee, E., Kubiatowicz, J.: The cloud is not enough: saving IoT from the cloud. In: 7th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2015) (2015)
  4.Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRef
  5.Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. comput. 13(18), 1587–1611 (2013). Wiley Online LibraryCrossRef
  6.Bui, N., Zorzi, M.: Health care applications: a solution based on the internet of things. In: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, pp. 131. ACM (2011)
  7.Shafer, I., Sambasivan, R.R., Rowe, A., Ganger, G.R.: Specialized storage for big numeric time series. In: 5th USENIX Workshop on Hot Topics in Storage and File Systems (2013)
  8.Kubiatowicz, J., Bindel, D., Chen, Y., Czerwinski, S., Eaton, P., Geels, D., Gummadi, R., Rhea, S., Weatherspoon, H., Weimer, W., Wells, C.: Oceanstore: an architecture for global-scale persistent storage. ACM Sigplan Not. 35(11), 190–201 (2000)CrossRef
  9.Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Crowley, P., Papadopoulos, C., Wang, L., Zhang, B.: Named data networking. ACM SIGCOMM Comput. Commun. Rev. 44(3), 66–73 (2014)CrossRef
  10.Dilraj, N., Rakesh, K., Rahul, K., Maneesha, R.: A low cost remote cardiac monitoring framework for rural regions. In: 5th EAI International Conference on Wireless Mobile Communication and Healthcare - “Transforming healthcare through innovations in mobile and wireless technologies” (MOBIHEALTH). ACM (2015)
  
• 作者单位：Rahul Krishnan Pathinarupothi (18)
  Bithin Alangot (19)
  Maneesha Vinodini Ramesh (18)
  Krishnashree Achuthan (19)
  P. Venkat Rangan (18)
  
  18. Amrita Center for Wireless Networks and Applications (AmritaWNA), Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham Amrita University, Clappana, India
  19. Amrita Center for Cybersecurity Systems and Networks, Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham Amrita University, Clappana, India
  
• 丛书名：Mobile Web and Intelligent Information Systems
• ISBN：978-3-319-44215-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9847

文摘

We present an intelligent data management framework that can facilitate development of highly scalable and mobile healthcare applications for remote monitoring of patients. This is achieved through the use of a global log data abstraction that leverages the storage and processing capabilities of the edge devices and the cloud in a seamless manner. In existing log based storage systems, data is read as fixed size chunks from the cloud to enhance performance. However, in healthcare applications, where the data access pattern of the end users differ widely, this approach leads to unnecessary storage and cost overheads. To overcome these, we propose dynamic log chunking. The experimental results, comparing existing fixed chunking against the H-Plane model, show 13 %–19 % savings in network bandwidth as well as cost while fetching the data from the cloud.