Name
Abstract | ||
|---|---|---|
With the development of the Industrial Internet of Things (IIoT) technology, edge computing is a promising area to release the sensing and computing burdens from the overloaded center. However, in edge network scenarios, we cannot trust every node’s output since some nodes can behave maliciously by making use of the properties, such as multiple identities, heterogeneous capabilities, and mobility. In that case, trust management is widely used to solve the problem of network trustworthiness. In this article, we propose a trust evaluation framework by comprehensively considering the nodes’ malicious behaviors and heterogeneous characteristics of edge networks. Under the Bayesian framework, we use the semi-ring theory to dynamically establish mobile-edge nodes’ trust models. First, we calculate the trust value for each node with a different identity (service provider or requester). Then, we propose a security-regarded task allocation mechanism to improve the reliability of selected trusted nodes according to the matched relationship between the service requesters’ expected capability and the providers’ actual capability. Further, we conduct extensive analysis and simulations to evaluate the proposed methods in typical IIoT scenarios. The results show that the proposed method has better immunity to abnormal behaviors, including the noncooperation, malicious feedback, on–off attacks, Sybil attacks, whitewashing attack, malicious access, etc., and has higher scheduling accuracy and controllable time complexity compared to existing methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2022.3179428 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Industrial Internet of Things (IIoT)-based edge computing,malicious behaviors,trust,trust evaluation | Journal | 9 |
Issue | ISSN | Citations |
21 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 28 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingpei Wang | 1 | 0 | 0.68 |
| Mufeng Wang | 2 | 0 | 1.01 |
| Zhenyong Zhang | 3 | 0 | 1.35 |
| Hengye Zhu | 4 | 0 | 0.34 |