LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications

Authors(2) :-Mahmood Hussain Mir, Dr. D. Ravindran

Internet of Things is a technological change which is not only “connected” but it is “smart”, connected is not equal to smart. The switch from connected to smart is the ability of performing analytics at device level, which implies moving beyond, means not just only sensing the data but also processing the data. IoT in “smart” sense also called as Internet of Everything (IoE) or Internet of Anything (IoA). Edge Computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud based systems. The transmission time of cloud based computing is intolerable almost for every smart application. In this paper the existing system is studied and also the drawbacks of system are highlighted and problems associated with it are discussed. Keeping existing system in view a novel and efficient system is proposed, which tries to eliminate the drawbacks of the existing system. The LETISA is based on edge computing, which is a new and emerging technology that brings the services close to the proximity of data sources, as IoT devices are not only used as data gathering devices but are also acting as data consumers. The LETISA improves the efficiency of IoT based applications by deploying some of the computational capabilities at the edge devices. Finally the performance of the system is compared with the existing system, which shows the LETISA can significantly overcome the end-to-end latency.

Authors and Affiliations

Mahmood Hussain Mir
Research Scholar Department of Computer Sciences, St. Joseph's College, Tiruchirappalli, Tamil Nadu, India
Dr. D. Ravindran
Associate Professor Department of Computer Sciences, St. Joseph's College, Tiruchirappalli, Tamil Nadu, India

Cloud Computing, Edge Computing, IoT, LEC, LETISA.

  1. Flavin, Michael. "Free, Simple and Easy to Use: Disruptive Technologies, Disruptive Innovation and Technology Enhanced Learning", Disruptive Technology Enhanced Learning, 2017, pp. 19-52.
  2. Weber, Rolf H., and Romana Weber, “Internet of things. Vol. 12, Springer,     2010.
  3. Shi, Weisong, et al. "Edge computing: Vision and challenges." IEEE Internet of Things Journal, Vol. 3, No. 5, 2016, pp. 637-646.
  4. Mell, Peter, and Tim Grance. "The NIST definition of cloud computing." 2011, pp. 1-7.
  5. Cloud Computing Tutorial for Beginners, https://www.guru99.com/cloud-computing-for-beginners.html.
  6. Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D. S., “Challenges and opportunities in edge computing”, IEEE International Conference, 2016, pp. 20-26.
  7. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S., “Fog computing and its role in the internet of things” In Proceedings of MCC workshop on Mobile cloud computing, 2012, pp. 13-16.
  8. Buyya, Rajkumar, Rajiv Ranjan, and Rodrigo N. Calheiros. "Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services." International Conference on Algorithms and Architectures for Parallel Processing. Springer, 2010, pp. 13-31.
  9. Gong, C., Liu, J., Zhang, Q., Chen, H., & Gong, Z., “The characteristics of cloud computing”, IEEE Conference, 2010, pp. 275-279.
  10. Moreno-Vozmediano, Rafael, Rubén S. Montero, and Ignacio M. Llorente. "Iaas cloud architecture: From virtualized datacenters to federated cloud infrastructures." Computer, Vol. 45, No.12, 2012 pp. 65-72.
  11. Hassanalieragh, Moeen, Alex Page, Tolga Soyata, Gaurav Sharma, Mehmet Aktas, Gonzalo Mateos, Burak Kantarci, and Silvana Andreescu, "Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges." IEEE International Conference, 2015, pp. 285-292.
  12. Shinde, Tejaswinee A., and Jayashree R. Prasad. "IoT based Animal Health Monitoring with Naive Bayes Classification." IJETT, Vol. 4, No. 2, 2017, pp. 1-4.
  13. Tao, Fei, Ying Cheng, Li Da Xu, Lin Zhang, and Bo Hu Li. "CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system." IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, 2014, pp. 1435-1442.
  14. IoT Analytics https://iot-analytics .com/10-internet-of-things-applications/, Accessed on 24-07-2017.
  15. Hou, Enxing, Long Dai, and Zhenwei Wen. "Method, apparatus and electronic device for controlling smart home device." U.S. Patent No. 9,691,272. 27 Jun. 2017.
  16. Chan, M., Estève, D., Fourniols, J. Y., Escriba, C., & Campo, E., “Smart wearable systems: Current status and future challenges”, Artificial intelligence in medicine, Vol.56, No. 3, 2012, pp. 137-156.
  17. Sheng, Zhengguo, Shusen Yang, Yifan Yu, Athanasios Vasilakos, Julie Mccann, and Kin Leung., "A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities." IEEE Wireless Communications, Vol. 20, No. 6, 2013, pp. 91-98.
  18. Kurt, S., Yildiz, H. U., Yigit, M., Tavli, B., & Gungor, V. C., “Packet size optimization in wireless sensor networks for smart grid applications”, IEEE Transactions on Industrial Electronics, Vol. 64, No. 3, 2017, pp. 2392-2401.
  19. Da Xu, Li, Wu He, and Shancang Li. "Internet of things in industries: A survey." IEEE Transactions on industrial informatics, Vol. 10, No. 4, 2014, pp. 2233-2243.
  20. BJ Hubert Shanthan, A. Dalvin Vinoth Kumar, and L. Arockiam. "Filling Fuel Quantity Measurement Systems Using Internet Of Things.", International Journal of Innovative Research and Advanced Studies, Vol. 3, No. 13, 2016, pp. 152-154.
  21. Lin, Zhicheng, Pui-In Mak, and Rui Paulo Martins. "A sub-GHz multi-ISM-band ZigBee receiver using function-reuse and gain-boosted N-path techniques for IoT applications," Ultra-Low-Power and Ultra-Low-Cost Short-Range Wireless Receivers in Nanoscale CMOS. Springer International Publishing, 2016, pp. 81-103.
  22. Dacko, Scott G. "Enabling smart retail settings via mobile augmented reality shopping apps." Technological Forecasting and Social Change, 2016.
  23. Armbrust, Michael, Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M., "A view of cloud computing." Communications of the ACM, Vol.53, No. 4, 2010, pp. 50-58.
  24. Using an IoT gateway to connect the "Things" to the cloud, John Treadway, http://internetofthingsagenda.techtarget.com/feature/Using-an-IoT-gateway-to-connect-the-Things-to-the-cloud. Accessed on 1st July 2017.
  25. Chen, Min, et al. "Cloud-based wireless network: Virtualized, reconfigurable, smart wireless network to enable 5G technologies." Mobile Networks and Applications, Vol. 20, No.6, 2015, pp. 704-712.

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 688-694
Manuscript Number : CSEIT1724178
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Mahmood Hussain Mir, Dr. D. Ravindran, "LETISA: Latency optimal Edge computing Technique for IoT based Smart Applications", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.688-694, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT1724178

Article Preview

Follow Us

Contact Us