Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services

2025-08-17

AI Summary

In MEC-based IIoT systems, establishing initial trust for homogeneous IoT services remains challenging due to short service interactions, scarce recommendations, and heterogeneous contextual information across edge environments—leading to inconsistent trust assessments and severe data sparsity. To address this, we propose a data-driven, context-aware trust bootstrapping method. Our approach innovatively integrates dynamic context modeling with a cross-MEC knowledge sharing mechanism, enabling robust and transferable trust initialization. Experimental evaluation on real-world datasets demonstrates that the proposed method significantly improves both the accuracy and stability of trust estimation, particularly in highly dynamic, low-interaction industrial edge scenarios. It effectively overcomes key limitations of conventional methods—including poor adaptability to contextual shifts, limited generalizability across edge domains, and vulnerability to data sparsity—thereby advancing trustworthy service orchestration in distributed IIoT infrastructures.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Multiagent Systems: Mechanism Design
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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 via enabling knowledge sharing among different MEC environments within a given MEC topology. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on two real-world datasets suitably 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 bootstrap trustworthiness of services in MEC-based IIoT systems.

Problem

• Lack of prolonged interaction for trust measurement in IIoT
• Inconsistent peer interactions for reliable service recommendations
• Uneven context parameters affecting trust evaluation in MEC

Innovation

• Data-driven trust bootstrapping for IIoT services
• Context-aware approach in MEC environments
• Knowledge sharing to tackle data sparsity