Abstract
The fusion of artificial intelligence, ML, and edge computing can promote a smarter version of IOT. We are perching in the fourth industrial revolution where the requirement of the IT industry is growing rapidly. The introduction of cloud has already changed the game of data storage and infrastructure development around the globe. As most of the industrial computation resides on cloud, the requirement of high speed along with high security has become the major concern. The term edge computing is tossed for lowering the internet latency and providing a safer, faster, and less complicated mode of computation.
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References
Stankovski S (2020) The impact of edge computing on industrial automation. In: 19th international symposium INFOTEH-JAHORINA, IEEE
Satyanarayanan M (2016) The emergence of edge computing. Comput Sci Eng 50:30–39
Shi W (2016) Edge computing: vision and challenges. IEEE Internet of Things Journal 3(5):637–646
Kim OTT, Tri ND, Tran NH, Hong CS et al (2015) A shared parking model in vehicular network using fog and cloud environment. In: Network operations and management symposium (APNOMS), IEEE
Truong NB, Lee GM, Ghamri-Doudane Y (2015) Software defined networking-based vehicular ad hoc network with fog computing. In: Integrated network management (IM), IEEE International
Dolui K (2019) Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing. In: JIASI CHEN, deep learning with edge computing: a review, IEEE
Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge
Satyanarayanan M (2017) The emergence of edge computing. Computer (Long Beach Calif) 50:30–39
Bizanis N, Kuipers F (2016) SDN and virtualization solutions for the internet of things: a survey. IEEE Access 4:5591–5606
Li J, Peng M, Cheng A, Yu Y, Wang C (2014) Resource allocation optimization for delay-sensitive traffic in fronthaul constrained cloud radio access networks, IEEE Syst J 1–12
ETSI (2014) Mobile-edge computing introductory technical white paper, white paper, mobile-edge computing industry initiative
Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutorials 19:2322–2358
Nayak SR, Sivakumar S, Bhoi AK, Chae GS, Mallick PK (2021) Mixed-mode database miner classifier: parallel computation of graphical processing unit mining. Int J Electr Eng Educ 0020720920988494
Kaur K, Dhand T, Kumar N, Zeadally S (2017) Container-as-a-service at the SDGE: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wireless Commun 24:48–56
Mishra S, Mishra D, Mallick PK, Santra GH, Kumar S (2021) A novel borda count based feature ranking and feature fusion strategy to attain effective climatic features for rice yield prediction. Informatica 45(1):13–31
Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58
Huang J, Qian F, Gerber A, Mao ZM, Sen S, Spatscheck O (2012) A close examination of performance and power characteristics of 4g LTE networks. In: Proceedings of the 10th international conference on mobile systems, applications, and services. ACM, pp 225–238
Satyanarayanan M, Chen Z, Ha K, Hu W, Richter W, Pillai P (2014) Cloudlets: at the leading edge of mobile-cloud convergence. In: 2014 6th international conference on mobile computing, applications and services (MobiCASE), IEEE, pp 1–9
Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the CC workshop on mobile cloud computing, ACM, pp 13–16
Kistler JJ, Satyanarayanan M (1992) Disconnected operation in the coda file system. ACM Trans Comput Syst 10:3–25
Elijah (2017) Cloudlet-based edge computing. http://elijah.cs.cmu.edu/. Accessed 19 July 2017
Dilley J et al (2002) Globally distributed content delivery. IEEE Internet Comput 6:50–58
O’Regan G (2012) A brief history of computing. Springer, London
Satyanarayanan M (2017) The emergence of edge computing. Computer 50:30–39
Sadeghi A, Sheikholeslami F, Giannakis GB (2018) Optimal and scalable caching for 5G using reinforcement learning of space-time popularities. IEEE J Sel Top Signal Process 12(1):180–190
Cucinotta T et al (2009) A real-time service-oriented architecture for industrial automation. IEEE Trans Industr Inf 5(3):267–277
Stankovski S, Ostojić G, Zhang X (2016) Influence of industrial internet of things on mechatronics. J Mechatron Autom Ident Technol 1(1):1–6
Khana WZ, Ahmed E, Hakak S, Yaqoob I, Ahmede A (2019) Edge computing: a survey. Futur Gener Comput Syst 97:219–235
Buyya R, Srirama SN (eds) (2019) Fog and edge computing: principles and paradigms. Wiley, London
Varghese B, Wang N, Nikolopoulos DS (2017) Feasibility of fog computing. Springer, London
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Singh, A., Kumar, A., Chauhan, B.K. (2022). A Comprehensive Study of Edge Computing and the Impact of Distributed Computing on Industrial Automation. In: Mallick, P.K., Bhoi, A.K., Barsocchi, P., de Albuquerque, V.H.C. (eds) Cognitive Informatics and Soft Computing. Lecture Notes in Networks and Systems, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-16-8763-1_19
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