A blockchain datastore for scalable IoT workloads using data decaying

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 paper we introduce Triabase, a novel permissioned blockchain system architecture that applies data decaying concepts to cope with scalability issues in regards to blockchain consensus and storage efficiency. For blockchain consensus, we propose the Proof of Federated Learning (PoFL) algorithm which exploits data decaying models as Proof-of-Work. For storage efficiency, we exploit federated learning to construct data postdiction machine learning models to minimize the storage of bulky data on the blockchain. We present a detailed explanation of our system architecture as well as the implementation in the Hyperledger fabric framework. We use our implementation to carry out an experimental evaluation with telco big data at scale showing that our framework exposes desirable qualities, namely efficient consensus at the blockchain layer while optimizing storage efficiency.