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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT.

Mekala, M.S.; Srivastava, Gautam; Park, Ju H.; Jung, Ho-Youl


Gautam Srivastava

Ju H. Park

Ho-Youl Jung


Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches.


MEKALA, M.S., SRIVASTAVA, G., PARK, J.H. and JUNG, H.-Y. 2022. An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT. Digital communications and networks [online], 8(6), pages 900-910. Available from:

Journal Article Type Article
Acceptance Date Jul 8, 2022
Online Publication Date Jul 19, 2022
Publication Date Dec 31, 2022
Deposit Date Jan 31, 2023
Publicly Available Date Jan 31, 2023
Journal Digital communications and networks
Print ISSN 2352-8648
Electronic ISSN 2352-8648
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 8
Issue 6
Pages 900-910
Keywords Edge computing; Adaptive trust weight (ATW) model; Quotient user-centric coeval-learning (QUCL) mechanism; Deep learning; Service reliability
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