EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0

Internet of Things (IoT) is bringing a revolution in today's world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous, providing autonomy to nodes so that they can communicate with other nodes and exchange information at any time. IoT and healthcare together provide notable facilities for patient monitoring. However, one of the most critical challenges is the identification of malicious and compromised nodes. In this article, we propose a machine learning-based trust management approach for edge nodes to identify nodes with malicious behavior. The proposed mechanism utilizes knowledge and experience components of trust, where knowledge is further based on several parameters. To prevent the successful execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit, and only those nodes that satisfy the threshold value can participate in the network. To validate the performance of the proposed approach, we have performed extensive simulations in comparison with existing approaches. The results show the effectiveness of the proposed approach against several potential attacks.

Automated summary

The Internet of Things (IoT) is revolutionizing the world by making surrounding devices smart and capable of performing daily-life activities with precision. IoT and healthcare collaborate to provide notable facilities in patient monitoring. However, the identification of malicious and compromised nodes remains a critical challenge. This article proposes a machine learning-based trust management approach for edge nodes to detect nodes with malicious behavior. The approach utilizes knowledge and experience components of trust and collects recommendations from edge clouds to evaluate indirect and aggregated trust.