Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services
Prabath Abeysekara, Hai Dong

TL;DR
This paper introduces a data-driven, context-aware trust bootstrapping method for MEC-based industrial IoT systems, addressing key limitations of existing approaches and enhancing trust evaluation through knowledge sharing and real-world data validation.
Contribution
It presents a novel trust bootstrapping approach tailored for MEC-based IIoT, overcoming interaction and data sparsity challenges, validated with real-world datasets.
Findings
Effective trust bootstrap demonstrated on real-world datasets
Improved trust evaluation accuracy in MEC environments
Knowledge sharing enhances trust assessment robustness
Abstract
We propose a data-driven and context-aware approach to bootstrap trustworthiness of homogeneous Internet of Things (IoT) services in Mobile Edge Computing (MEC) based industrial IoT (IIoT) systems. The proposed approach addresses key limitations in adapting existing trust bootstrapping approaches into MEC-based IIoT systems. These key limitations include, the lack of opportunity for a service consumer to interact with a lesser-known service over a prolonged period of time to get a robust measure of its trustworthiness, inability of service consumers to consistently interact with their peers to receive reliable recommendations of the trustworthiness of a lesser-known service as well as the impact of uneven context parameters in different MEC environments causing uneven trust environments for trust evaluation. In addition, the proposed approach also tackles the problem of data sparsity…
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Taxonomy
TopicsCloud Data Security Solutions · IoT and Edge/Fog Computing · Big Data and Digital Economy
