Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework Pokhrel, Shiva 2022, Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework, Neural Computing and Applications, pp. 1-10, doi: 10.1007/s00521-022-07065-z. Attached Files Name Description MIMEType Size Downloads Title Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework Author(s) Pokhrel, Shiva orcid.org/0000-0001-5819-765X Journal name Neural Computing and Applications Start page 1 End page 10 Total pages 10 Publisher Springer Place of publication Berlin, Germany Publication date 2022 ISSN 0941-0643 1433-3058 Keyword(s) Asynchronous advantage actor critic (A3C) Federated learning (FL) Industry 4.0 Machine learning Semantic communication (SC) Smart manufacturing and automation Transfer learning (TL) Summary AbstractEstablished methods of communication are based mainly on Shannon’s theory of information, which purposefully overlooks semantic elements of communication. The future wireless technology should promise to facilitate many services, based on content, needs, and semantics, precisely customized to network capabilities. This gave rise to significant concern for Semantic Communication (SC), a novel paradigm considering the message’s meaning during transmission. Federated learning (FL) and Asynchronous Advantage Actor Critic (A3C) are the two emerging distributed and artificially intelligent approaches that provide diverse and possibly massive network coverage for data-driven SC solutions of industry 4.0 automation. Although SC is still in an early development stage, FL-empowered architecture has been recognized as one of the most promising solutions to meet the ubiquitous intelligence in the anticipated sixth-generation (6G) networks. This paper identifies industry 4.0 automation needs that drive the convergence of artificial intelligence and 6G for learning from data streams. We develop a novel SC framework based on the FL and A3C networks and discuss its potential along with transfer learning to address most of the new difficulties anticipated in 6G for industrial communication networks. Our proposed framework has been evaluated with extensive simulation results. Language eng DOI 10.1007/s00521-022-07065-z Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 0906 Electrical and Electronic Engineering 1702 Cognitive Sciences HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30166893 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full text (Open Access) Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 20 Apr 2022, 09:58:04 EST Wed, 20 Apr 2022, 14:44:07 EST Mon, 02 May 2022, 15:01:45 EST Mon, 02 May 2022, 15:02:55 EST Thu, 30 Jun 2022, 08:23:31 EST Thu, 30 Jun 2022, 12:14:30 EST Thu, 30 Jun 2022, 12:17:09 EST Thu, 30 Jun 2022, 13:31:14 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 14 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 20 Apr 2022, 09:58:04 EST