AIT  Vol.4 No.3 , July 2014
Managing Computing Infrastructure for IoT Data
ABSTRACT
Digital data have become a torrent engulfing every area of business, science and engineering disciplines, gushing into every economy, every organization and every user of digital technology. In the age of big data, deriving values and insights from big data using rich analytics becomes important for achieving competitiveness, success and leadership in every field. The Internet of Things (IoT) is causing the number and types of products to emit data at an unprecedented rate. Heterogeneity, scale, timeliness, complexity, and privacy problems with large data impede progress at all phases of the pipeline that can create value from data issues. With the push of such massive data, we are entering a new era of computing driven by novel and ground breaking research innovation on elastic parallelism, partitioning and scalability. Designing a scalable system for analysing, processing and mining huge real world datasets has become one of the challenging problems facing both systems researchers and data management researchers. In this paper, we will give an overview of computing infrastructure for IoT data processing, focusing on architectural and major challenges of massive data. We will briefly discuss about emerging computing infrastructure and technologies that are promising for improving massive data management.

Cite this paper
Tyagi, S. , Darwish, A. and Khan, M. (2014) Managing Computing Infrastructure for IoT Data. Advances in Internet of Things, 4, 29-35. doi: 10.4236/ait.2014.43005.
References
[1]   Internet of Things—Strategic Research Roadmap.
http://ec.europa.eu/information_society/policy/rfid/documents/in_cerp.pdf

[2]   Schonfeld, E. (2010) Costolo: Twitter Now Has 190 Million Users Tweeting 65 Million Times a Day.
http://techcrunch.com/2010/06/08/twitter-190-million-users/

[3]   Want, R. (2004) Rfid—A Key to Automating Everything. Scientific American.
http://dx.doi.org/10.1038/scientificamerican0104-56

[4]   Fuhrmann, T. and Harbaum, T. (2003) Using Bluetooth for Informationally Enhanced Environments. Proceedings of the IADIS International Conference e-Society 2003, Lisbon, 2003.

[5]   Adelmann, R., Langheinrich, M. and Floerkemeier, C. (2006) A Toolkit for Bar Code Recognition and Resolving on Camera Phones—Jump Starting the Internet of Things. Workshop Mobile and Embedded Interactive Systems (MEIS 2006) at Informatik.

[6]   Rohs, M. and Gfeller, B. (2004) Using Camera-Equipped Mobile Phones for Interacting Real-World Objects. In: Ferscha, A., Hoertner, H. and Kotsis, G., Eds., Advances in Pervasive Computing, Autrian Computer Society (OCG).

[7]   http://edutechwiki.unige.ch/en/Internet_of_things

[8]   Zikopoulos, P., DeRoos, D., Parasuraman, K., Deutsch, T., Corrigan, D. and Giles, J. (2013) Harness the Power of Big Data McGraw-Hill.

[9]   Spangler, S., Chen, Y., Proctor, L., Lelecu, A., Behal, A., He, B. and Davis, T. (2009) COBRA—Mining Web for Corporate Brand and Reputation Analysis. Web Intelligence and Agent Systems, 7, 243-254.

[10]   Gonzalez, H., Han, J.W., Li, X.L., et al. (2006) Warehousing and Analyzing Massive RFID Data Sets. Proceedings of the 22nd International Conference on Data Engineering, Atlanta, 83-92.

[11]   Li, T.L., Liu, Y., Tian, Y., Shen, S., and Mao, W. (2012) A Storage Solution for Massive IoT Data Based on NoSQL. IEEE International Conference on Green Computing and Communications, 20-23 November 2012, Besancon, 50-57.

[12]   Fan, T.R. and Chen, Y.Z. (2010) A Scheme of Data Management in the Internet of Things. Proceedings of ICNIDC-2010, Beijing, 24-26 September 2010, 110-114.

[13]   Ding, Z.M. and Gao, X. (2012) A Database Cluster System Framework for Managing Massive Sensor Sampling Data in the Internet of Things. Chinese Journal of Computers, 35, 1175-1191.

 
 
Top