.png){kind=icon}
Return
Data-Driven Trust Prediction in Mobile Edge Computing-Based IoT Systems
.png){kind=icon}
OA
AI
DOI：10.1109/TSC.2021.3121879.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a data-driven distributed machine learning approach to scalably predict the trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing (MEC)-based IoT systems. The proposed approach formulates training distributed trust prediction models within an MEC-based IoT system as a Network Lasso problem. We then introduce a variant of the Stochastic Alternating Method of Multipliers framework enriched with the ability for feature selection at each MEC layer. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on three real-world datasets adjusted to exhibit the context-dependent trust information accumulated in MEC environments within a given MEC topology. The experimental results affirmed the effectiveness of our approach and its suitability to predict trustworthiness of IoT services in MEC-based IoT systems.
Keywords:
Internet of Things
Predictive models
Computational modeling
Topology
Training
Stress
Data models
Trust
mobile edge computing
machine learning
Journal
IF:
5.8
Papers:
2.2K
Citations:
6.5K
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveEffect of Chemical Treatments on the Physical Properties of Non-woven Jute/PLA Biocomposites.png)
BioResources
OAAI
BioResourcesIF0
OAAIShareSave
View more