Triastore: A Web 3.0 Blockchain Datastore for Massive IoT Workloads

The Internet of Things (IoT) revolution has introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this short paper we propose Triastore, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. Triastore comprises of two internal routines, namely: (i) Proof of Federated Learning (PoFL), which trains in a distributed manner a global model for the ingested data; and (ii) Blockchain Consensus, which commits this generated model data on permissioned blockchain database. We present a detailed explanation of our data ingestion algorithm with relevant examples and carry out an experimental evaluation with image data from MNIST. The evaluation shows that our proposed data ingestion framework retains high levels of accuracy with low loss in data quality.