Advanced SearchSearch Tips
Handling Streaming Data by Using Open Source Framework Storm in IoT Environment
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Handling Streaming Data by Using Open Source Framework Storm in IoT Environment
Kang, Yunhee;
  PDF(new window)
To utilize sensory data, it is necessary to design architecture for processing and handling data generated from sensors in an IoT environment. Especially in the IoT environment, a thing connects to the Internet and efficiently enables to communicate a device with diverse sensors. But Hadoop and Twister based on MapReduce are good at handling data in a batch processing. It has a limitation for processing stream data from a sensor in a motion. Traditional streaming data processing has been mainly applied a MoM based message queuing system. It has maintainability and scalability problems because a programmer should consider details related with complex messaging flow. In this paper architecture is designed to handle sensory data aggregated The designed software architecture is used to operate an application on the open source framework Storm. The application is conceptually used to transform streaming data which aggregated via sensor gateway by pipe-filter style.
IoT;Sensory Data;Open Source Framework;Storm;Pipe-and-Filter;
 Cited by
Luigi Atzori, Antonio Iera, and Giacomo Morabito, "The Internet of Things: A survey," Computer Networks: The International Journal of Computer and Telecommunications Networking, Vol.54, No.15, pp.2787-2805, 2010.

Yunhee Kang, "New Approach to the Platform for the Application Development on the Internet of Things Environment," Journal of Platform Technology, Vol.3, No.1, pp.21-27, 2015.

Z.(G.) Guo, R. Singh, and M. Pierce, "Building the PolarGrid Portal Using Web 2.0 and OpenSocial," The International Conference for High Performance Computing, Networking, Storage and Analysis (SC'09), Porland, OR, ACM Press, p.5, 2009.

Yunhee Kang, "Enabling BigData Platform for MapReduce Applications in the Science Cloud," Journal of Platform Technology, Vol.2, No.1, pp.42-52, 2014.

W. Gropp and E. Lusk, "Goals guiding design: PVM and MPI," Cluster Computing, 2002. Proceedings. 2002 IEEE International Conference on, pp.257-265, 2002, doi: 10.1109/CLUSTR.2002.1137754.

J. Dean and S. Ghemawat, "Mapreduce: Simplified Data Processing on Large Clusters," Communications of the Acm, Vol.51, pp.107-113, Jan., 2008.

J. Dean and S. Ghemawat, "MapReduce: A Flexible Data Processing Tool," Communications of the Acm, Vol.53, pp. 72-77, Jan., 2010.

Hadoop [Internet],

J. Ekanayake et al., "Twister: A Runtime for Iterative MapReduce," The First International Workshop on MapReduce and its Applications (MAPREDUCE'10) - HPDC2010, 2010.

Yunhee Kang and Y. B. Park, "The performance evaluation of k-means by two MapReduce frameworks, Hadoop vs. Twister," 2015 International Conference on Information Networking (ICOIN), Cambodia, pp.405-406, 2015.

Yunhee Kang and Geoffrey C. Fox, "Performance Evaluation of MapReduce Applications on Cloud Computing Environment, FutureGrid," FGIT-GDC, pp.77-86, 2011.

Curry Edward, "Message-Oriented Middleware," in Middleware for Communications, ed. Qusay H Mahmoud, 1-28. Chichester, England: John Wiley and Sons, 2004.

S. Pallickara and G. Fox, "NaradaBrokering: a distributed middleware framework and architecture for enabling durable peer-to-peer grids," the Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware, Rio de Janeiro, Brazil, 2003.

Apache Storm [Internet],

J. Carlson, "Redis in Action," 1st ed., New York: Manning, 2013.

J. Pokorny, "NoSQL databases: a step to database scalability in web environment," in Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, 2011, pp.278-283.

M. Shaw and D. Garlan, "Software Architecture: Perspectives on an Emerging Discipline," Prentice-Hall, 1996.