Skip to Main content Skip to Navigation
Journal articles

Lightweight blockchain processing: case study: scanned document tracking on Tezos blockchain

Abstract : To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).
Document type :
Journal articles
Complete list of metadata

https://hal.telecom-paris.fr/hal-03559645
Contributor : Gerard Memmi Connect in order to contact the contributor
Submitted on : Monday, February 7, 2022 - 10:00:41 AM
Last modification on : Tuesday, February 15, 2022 - 3:06:15 PM

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Links full text

Identifiers

Citation

Mohamed Allouche, Tarek Frikha, Mihai Mitrea, Gérard Memmi, Faten Chaabane. Lightweight blockchain processing: case study: scanned document tracking on Tezos blockchain. Applied Sciences, MDPI, 2021, 11 (15), pp.7169:1-7169:17. ⟨10.3390/app11157169⟩. ⟨hal-03559645⟩

Share

Metrics

Record views

22