The week's pick
Reseach Article
Large Data Management in IoT Application for Supply Chain Environment
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 7 |
| Year of Publication: 2018 |
| Authors: Bhavana M. Bachhav, Parikshit N. Mahalle |
10.5120/ijca2018917641
|
Bhavana M. Bachhav, Parikshit N. Mahalle . Large Data Management in IoT Application for Supply Chain Environment. International Journal of Computer Applications. 182, 7 ( Aug 2018), 1-4. DOI=10.5120/ijca2018917641
Abstract
In numerous IoT applications, large number of sensors and data receivers sends information to server. The servers gather information that reaches huge amount in short time. In such cases, IoT application can face the challenge of real time managing and extracting client useful information from the whole data stored on server. The main challenge faced in global supply chain management (SCM) is the development of decision making models as the huge amount of data stored on server. Here the approach is designed considering the sensor data set for analyzing all factors involved, and depending upon the analyzed data prediction of the future trade scenario is done with the help of linear regression. This helps in any manufacturing company for decisions making, systematizing their products/services, managing inventories, Prices of the product and warehousing arrangement.
References
- Catalin Constantin CERBULESCU, Claudia Monica,” Large data management in IoT application ” ,2016 IEEE,978- 1-4673-8606-7.
- Abu-Elkheir, ” Data management for the Internet of Things: Green directions ”, Globecom Workshops (GC Wkshps), 2012 IEEE, Anaheim,CA, pp 386 390, 3-7 Dec. 2012
- ”Data Management for the Internet of Things: Design Primitives and Solution”, Mervat Abu-Elkheir, Mohammad Hayajneh and Najah Abu Ali,Sensors 2013, 13, 15582-15612; doi:10.3390/s131115582 ,14 November 2013 ,ISSN 1424-8220
- Yuchao Zhou, Suparna De ; Wei Wang ; Klaus Moessner, ” Enabling Query of Frequently Updated Data from Mobile Sensing Sources”, Inst. for Commun. Syst., Univ. of Surrey, Guildford, UK, Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on, 19-21 Dec. 2014, Page(s): 946 952, ISBN: 978-1-4799-7980-6
- Xingjun Hao, X. Hao, Peiquan Jin ; Lihua Yue, ” Efficient Storage of Multi-Sensor Object-Tracking Data ”, School of Computer Science and Technology, University of Science and Technology of China, Hefei, IEEE Transactions on Parallel and Distributed Systems (Volume:PP ,Issue: 99), Page(s): 1-5 ISSN : 1045-9219
- Ting Lu, Jun Fang, Cong Liu, ”A Unified Storage and Query Optimization Framework for Sensor Data”, 2015 12th Web Information System and Application Conference (WISA), 11-13 Sept. 2015, 229 -234, ISBN: 978-1-4673-9371-3
- Cooper, J.; James, ” Challenges for database management in the internet of things” , IETE Tech.Rev. 2009, 26, 320329.
- Y. Qin, Q. Z. Sheng, N. J. G. Falkner, S. Dustdar, H. Wang, and A.V. Vasilakos, ”When Things Matter: A Data-Centric View of the Internet of Things,in CoRR, 2014.
- L. Chen, M. Tseng, and X. Lian, ”Development of foundation models for Internet of Things,” Frontiers of Computer Science in China, vol. 4, no.3, pp. 376-385, September 2015.
- K. Aberer, M. Hauswirth, and A. Salehi, ”A middleware for fast and flexible sensor network deployment,” in Proc. 32nd International Conference on Very Large Databases, 2006
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