Abstract
The edge computing that is an efficient alternative of the cloud computing, for handling of the tasks that are time sensitive, has become has become very popular among a vast range of IOT based application especially in the industrial sides. The huge amount of information flow and the services requisition from the IOT has made the traditional cloud computing incompatible on the time of big data flow. So the paper proposes an enhanced edge model for the by incorporating the artificial intelligence along with the integration of caching to the edge for handling of the big data flow in the applications of the internet of things. The performance evaluation of the same in the network simulator 2 for enormous flow of task that are time sensitive , evinces that the proposed method has a minimized delay compared the traditional cloud computing models.
References
Buyya, Rajkumar, and Satish Narayana Srirama, eds. Fog and edge computing: principles and paradigms. John Wiley & Sons, 2019.
Yousefpour, Ashkan, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P. Jue. "All one needs to know about fog computing and related edge computing paradigms: A complete survey." Journal of Systems Architecture (2019).
Mao, Yuyi, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B. Letaief. "A survey on mobile edge computing: The communication perspective." IEEE Communications Surveys & Tutorials 19, no. 4 (2017): 2322-2358.
Li, He, Kaoru Ota, and Mianxiong Dong. "Learning IoT in edge: Deep learning for the Internet of Things with edge computing." IEEE Network 32, no. 1 (2018): 96-101.
Zeydan, Engin, Ejder Bastug, Mehdi Bennis, Manhal Abdel Kader, Ilyas Alper Karatepe, Ahmet Salih Er, and Mérouane Debbah. "Big data caching for networking: Moving from cloud to edge." IEEE Communications Magazine 54, no. 9 (2016): 36-42.
Dubey, Harishchandra, Jing Yang, Nick Constant, Amir Mohammad Amiri, Qing Yang, and Kunal Makodiya. "Fog data: Enhancing telehealth big data through fog computing." In Proceedings of the ASE bigdata & socialinformatics 2015, p. 14. ACM, 2015.
Dastjerdi, Amir Vahid, and Rajkumar Buyya. "Fog computing: Helping the Internet of Things realize its potential." Computer 49, no. 8 (2016): 112-116.
Ren, Ju, Yi Pan, Andrzej Goscinski, and Raheem A. Beyah. "Edge computing for the internet of things." IEEE Network 32, no. 1 (2018): 6-7.
Taherizadeh, Salman, Andrew C. Jones, Ian Taylor, Zhiming Zhao, and Vlado Stankovski. "Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review." Journal of Systems and Software 136 (2018): 19-38.
Mach, Pavel, and Zdenek Becvar. "Mobile edge computing: A survey on architecture and computation offloading." IEEE Communications Surveys & Tutorials 19, no. 3 (2017): 1628-1656.
Shi, Weisong, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. "Edge computing: Vision and challenges." IEEE Internet of Things Journal 3, no. 5 (2016): 637-646.
Hoang, Dinh Thai, Dusit Niyato, Diep N. Nguyen, Eryk Dutkiewicz, Ping Wang, and Zhu Han. "A Dynamic Edge Caching Framework for Mobile 5G Networks." IEEE Wireless Communications 25, no. 5 (2018): 95-103.
Dai, Yueyue, Du Xu, Sabita Maharjan, Guanhua Qiao, and Yan Zhang. "Artificial intelligence empowered edge computing and caching for internet of vehicles." IEEE Wireless Communications 26, no. 3 (2019): 12-18.
Hu, Long, Yiming Miao, Gaoxiang Wu, Mohammad Mehedi Hassan, and Iztok Humar. "iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing." Future Generation Computer Systems 90 (2019): 569-577.