Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review-Reference-Cited by-同舟云学术

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

Published:2023 Issue: Volume:11 Page:106803-106832
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lenard Teri¹^ORCID,Collen Anastasija¹^ORCID,Benyahya Meriem¹^ORCID,Nijdam Niels Alexander¹^ORCID,Genge Béla²^ORCID

Affiliation:

1. Centre Universitaire d’Informatique, Geneva School of Economics and Management, University of Geneva, Carouge, Switzerland

2. Department of Electrical Engineering and Information Technology, Faculty of Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureş, Târgu Mureş, Romania

Funder

OPEVA project that has received funding within the Key Digital Technologies Joint Undertaking (KDT JU) from the European Union’s Horizon Europe Programme

Swiss State Secretariat for Education, Research and Innovation

Innosuisse—Swiss Innovation Agency

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or KDT JU

Neither the European Union nor the granting authority can be held responsible for them

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10267920.pdf?arnumber=10267920

Reference210 articles.

1. Trust-based adversary detection in edge computing assisted vehicular networks

2. ATM: An Active-Detection Trust Mechanism for VANETs Based on Blockchain

3. A Wormhole Attack Detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

4. Evaluation of a Reputation Management Technique for Autonomous Vehicles

5. Trust management and evaluation for edge intelligence in the Internet of Things