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The integration of Internet of Things (IoT) devices in e-learning systems necessitates robust, scalable, and secure authentication procedures to provide dependable sharing of academic records among remote educational institutions. Conventional centralized systems experience scalability limitations, singular points of failure, and heightened susceptibility to hackers, especially in resource-limited IoT settings. This paper presents a decentralized authentication framework based on Hedera Hashgraph and Knowledge Graphs (KGs) to tackle these issues. The architecture incorporates a GAN-based cryptography module for the generation of dynamic symmetric keys, enhancing resistance against predictive and inference-based attacks. Knowledge Graphs facilitate semantic validation of identification features and improve interoperability among institutions via the Hedera Consensus Service (HCS). The quantitative assessment indicates that the Hedera + KG + GAN model attains a 17.1% increase in throughput, an 11–12% reduction in processing time, up to a 20% decrease in execution time for substantial data volumes, a 6–15% decline in energy consumption, and an approximate 23% reduction in authentication delay during periods of high network utilization relative to the leading competing frameworks. The suggested method provides a scalable, safe, and semantically enriched authentication mechanism for IoT-enabled e-learning ecosystems, creating a solid foundation for next-generation decentralized educational platforms.
Luo et al. (Fri,) studied this question.