Towards a Blockchain Database for Massive IoT Workloads

Abstract

The Internet of Things (IoT) revolution has massively 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 vision paper we propose Triabase, 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. As such, it does not store detailed records on a medium, like blockchains, which is fundamentally very slow due to the expensive verification process. We lay out the primitive architectural blocks of our design, the requirements and the inherent challenges. Triabase employs technical novelties in respect to its consensus protocol, namely the notion of Proof-of-Federated-Learning (PoFL). The Triabase prototype system is implemented in the Hyperledger Fabric blockchain framework, upon which encouraging preliminary findings have been drawn.