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

Abstract

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.

Citation

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: https://doi.org/10.1016/j.dcan.2022.07.004

• 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
• DOI: https://doi.org/10.1016/j.dcan.2022.07.004
• Keywords: Edge computing; Adaptive trust weight (ATW) model; Quotient user-centric coeval-learning (QUCL) mechanism; Deep learning; Service reliability
• Public URL: https://rgu-repository.worktribe.com/output/1867355