Time Estimation for a New Block Generation in Blockchain-Enabled Internet of Things

The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-tomachine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time. © 2023 IEEE.