DECENT: Deep Learning Enabled Green Computation for Edge Centric 6G Networks

Kashyap, PK; Kumar, S; Jaiswal, A; Kaiwartya, O; Kumar, M; Dohare, U; Gandomi, AH

DECENT: Deep Learning Enabled Green Computation for Edge Centric 6G Networks

Kashyap, PK Kumar, S Jaiswal, A Kaiwartya, O Kumar, M Dohare, U Gandomi, AH

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network and Service Management, 2022, 19, (3), pp. 2163-2177
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Kashyap, PK
dc.contributor.author	Kumar, S
dc.contributor.author	Jaiswal, A
dc.contributor.author	Kaiwartya, O
dc.contributor.author	Kumar, M
dc.contributor.author	Dohare, U
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-21T03:09:01Z
dc.date.available	2023-03-21T03:09:01Z
dc.date.issued	2022-09-01
dc.identifier.citation	IEEE Transactions on Network and Service Management, 2022, 19, (3), pp. 2163-2177
dc.identifier.issn	1932-4537
dc.identifier.issn	1932-4537
dc.identifier.uri	http://hdl.handle.net/10453/167926
dc.description.abstract	Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G networks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Network and Service Management
dc.relation.isbasedon	10.1109/TNSM.2022.3145056
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	DECENT: Deep Learning Enabled Green Computation for Edge Centric 6G Networks
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-01-22T00:00:00+1000Z
dc.date.updated	2023-03-21T03:09:00Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	3

Abstract:

Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G networks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.

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http://hdl.handle.net/10453/167926