Incentive Distributed Knowledge Graph Market for Generative Artificial Intelligence in IoT

Generative artificial intelligence (GAI) models are pretrained using extensive public data. However, in the Internet of Things (IoT) domain, distributed and heterogeneous data from end devices lacks contextual relations, which impairs IoT domain GAI training efficiency and leads to inaccurate applications. Nowadays, knowledge graph (KG) offers an effective solution for enhanced performance and interpretability of GAI. Nevertheless, specific data collection and KG creation in IoT scenarios still pose quality and cost challenges for GAI developers. Therefore, designing an effective IoT KG collection and trading strategy is needed to provide reliable knowledge support for IoT GAI from professional data providers. Currently, establishing fair KG pricing, the effective construction of knowledge relations in edge IoT scenarios and preventing data providers from accessing private information remain significant open issues for IoT KG trading. To address these issues, we propose an incentive distributed IoT knowledge market framework to facilitate effective and secure KG trading for IoT GAI. Specifically, first, we design a utility incentive and GAI demand-driven KG pricing strategy, which establishes a three-layer game with trading participants and KG embedding utility function to obtain fair pricing. Second, we devise a smart contract based distributed knowledge aggregation method, which provides collaborative IoT KG relation creation. Third, we propose a privacy-preserving KG construction scheme via homomorphic encryption that achieves consensus of encrypted KG to prevent knowledge providers from accessing IoT privacy. Finally, experimental results of real KG verify the KG-enhanced GAI and proposed market availability.