Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain
Abstract
:Featured Application
Abstract
1. Introduction
- methodological level: conception, specification and implementation of an on-chain/off-chain load-balancing solution, thus making it possible for the intimately constrained computing, storage and software resources of any Blockchain to be abstractly extended by general-purpose computing machine resources;
- experimental level: specifying an experimental testbed and carrying out the underlying experiments for achieving the proof-of-concepts for complex Blockchain application execution (namely visual fingerprinting) on lightweight computing resources (namely, a multiprocessor ARM embedded platform, integrated into a Raspberry Pi);
- applicative level: the methodological framework developed in this study makes it possible for a Blockchain of an arbitrarily large number of nodes to be deployed over any combination of computing resources, from cloud servers and PCs to Raspberry Pi; in this way, even low resource devices (Raspberry Pi) can host up to nine nodes executing complex applications (namely visual fingerprinting).
2. State-of-the-Art
2.1. Blockchain at a Glance
2.2. Video Fingerprinting Basic Concepts
2.3. Multimedia Multiprocessor Embedded Architecture
3.2. New Smart Contract Workflow of On-Chain/Off-Chain Balancing
4. Experimental Set-Up
4.1. Hardware/Software Experimental Platform
4.2. On-Chain/Off-Chain Load Balancing Code
4.3. Fingerprinting Method and Database
5. Experimental Results
6. Discussion
- the Smart contract code is slightly modified so as to cope with the new applicative logic (yet the functions related to the on-chain/off-chain balancing are unchanged);
- the Secure REST Connector is not expected to suffer any modification;
- of course, the off-chain code will be completely changed so as to correspond to the new application.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Nakamoto, S. Bitcoin Open-Source Implementation of P2P Currency; P2P Foundation: Amsterdam, The Netherlands, 2009. [Google Scholar]
- Available online: www.powercompare.co.uk/bitcoin/ (accessed on 10 May 2021).
- Silva, T.B.; Morais, E.S.; Almeida, L.F.F.; Rosa Righi, R.; Alberti, A.M. Blockchain and Industry 4.0: Overview, Convergence, and Analysis. In Blockchain Technology for Industry 4.0. Blockchain Technologies; Rosa Righi, R., Alberti, A., Singh, M., Eds.; Springer: Singapore, 2020. [Google Scholar]
- Issaoui, Y.; Khiat, A.; Bahnasse, A.; Ouajji, H. Smart logistics: Study of the application of Blockchain technology. Procedia Comput. Sci. 2019, 160, 266–271. [Google Scholar] [CrossRef]
- Torky, M.; Hassanein, A.E. Integrating Blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Comput. Electron. Agric. 2020, 178, 105476. [Google Scholar] [CrossRef]
- Alonso, S.G.; Arambarri, J.; López-Coronado, M.; de la Torre Díez, I. Proposing New Blockchain Challenges in eHealth. J. Med. Syst. 2019, 43, 64. [Google Scholar] [CrossRef] [PubMed]
- Chen, Q.; Srivastava, G.; Parizi, R.M.; Aloqaily, M.; Al Ridhawi, I. An incentive-aware Blockchain-based solution for internet of fake media things. Inf. Process. Manag. 2020, 57, 102370. [Google Scholar] [CrossRef]
- Jiang, S.; Li, Y.; Lu, Q.; Hong, Y.; Dabo, G.; **ong, Y.; Wang, S. Policy assessments for the carbon emission flows and sus-tainability of Bitcoin Blockchain operation in China. Nat. Commun. 2021, 12, 1938. [Google Scholar] [CrossRef]
- Garboan, A.; Mitrea, M. Live camera recording robust video fingerprinting. Multimed. Syst. 2016, 22, 229–243. [Google Scholar] [CrossRef]
- Dorri, A.; Kanhere, S.S.; Jurdak, R.; Gauravaram, P. LSB: A Lightweight Scalable Blockchain for IoT security and anonymity. J. Parallel Distrib. Comput. 2019, 134, 180–197. [Google Scholar] [CrossRef]
- Mingxiao, D.; **aofeng, M.; Zhe, Z.; **angwei, W.; Qijun, C. A review on consensus algorithm of Blockchain. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; pp. 2567–2572. [Google Scholar] [CrossRef]
- Hellani, H.; Sliman, L.; Hassine, M.B.; Samhat, A.E.; Exposito, E.; Kmimech, M. Tangle the Blockchain: Toward IOTA and Blockchain Integration for IoT Environment. In Hybrid In-Telligent Systems. HIS 2019; Advances in Intelligent Systems and Computing; Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M., Eds.; Springer: Cham, Switzerland, 2021; Volume 1179. [Google Scholar] [CrossRef]
- Frikha, T.; Chaabane, F.; Aouinti, N.; Cheikhrouhou, O.; Ben Amor, N.; Kerrouche, A. Implementation of Blockchain Con-sensus Algorithm on Embedded Architecture. Secur. Commun. Netw. 2021, 2021, 9918697. [Google Scholar] [CrossRef]
- Frikha, T.; Chaari, A.; Chaabane, F.; Cheikhrouhou, O.; Zaguia, A. Healthcare and Fitness Data Management Using the IoT-Based Blockchain Platform. J. Healthc. Eng. 2021, 2021, 9978863. [Google Scholar] [CrossRef] [PubMed]
- Durand, A.; Hébert, G.; Toumi, K.; Memmi, G.; Anceaume, E. The StakeCube Blockchain: Instantiation. In Proceedings of the 2020 Second International Conference on Blockchain Computing and Applications (BCCA), Antalya, Turkey, 2–5 November 2020. [Google Scholar]
- Available online: https://entethalliance.org/wp-content/uploads/2018/05/EEA-Architecture-Stack-Spring-2018.pdf (accessed on 21 June 2021).
- Su, X.; Huang, T.; Gao, W. Robust video fingerprinting based on visual attention regions. In Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, 19–24 April 2009; Volume 109, pp. 1525–1528. [Google Scholar] [CrossRef]
- Lee, S.; Yoo, C.D. Robust video fingerprinting for content-based video identification. IEEE Trans. Circuits Syst. Video Technol. 2008, 18, 938–988. [Google Scholar] [CrossRef] [Green Version]
- Douze, M.; Gaidon, A.; Jegou, H.; Marszałek, M.; Schmid, C. INRIA-LEAR’s Video Copy Detection System. TRECVID, 2008. Available online: https://hal.inria.fr/inria-00548664/ (accessed on 28 July 2021).
- Haji Rassouliha, A.; Taberner, A.J.; Nash, M.P.; Nielsen, P.M.F. Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Process. Image Commun. 2018, 68, 101–119. [Google Scholar] [CrossRef]
- Available online: https://www.intel.com/content/www/us/en/programmable/products/fpga/features/stx-power-about.html (accessed on 12 May 2021).
- Zynq. Zynq Ultrascale+ mpsoc. 2018. Available online: https://www.xilinx.com/support/documentation/user_guides/ug1213-zynq-migration-guide.pdf (accessed on 7 July 2021).
- Irgens, P.; Bader, C.; Lé, T.; Saxena, D.; Ababei, C. An efficient and cost effective FPGA based implementation of the Viola-Jones face detection algorithm. HardwareX 2017, 1, 68–75. [Google Scholar] [CrossRef]
- Gao, F.; Huang, Z.; Wang, S.; Ji, X. Optimized parallel implementation of face detection based on embedded heterogeneous many-core architecture. Int. J. Pattern Recognit. Artif. Intell. 2017, 31, 1756011. [Google Scholar] [CrossRef]
- Zhao, X.; Liang, X.; Zhao, C.; Tang, M.; Wang, J. Real-Time Multi-Scale Face Detector on Embedded Devices. Sensors 2019, 19, 2158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Raspberrypi. Raspberry Pi 3 Model b+. 2018. Available online: https://www.raspberrypi.org/products/raspberry-pi-4-model-b/ (accessed on 1 July 2021).
- Dolas, P.R.; Ghogare, P.; Kshirsagar, A.; Khadke, V.; Bokefode, S. Face Detection and Recognition Using Raspberry Pi. In Techno-Societal 2020; Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B., Eds.; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Laila, U.; Khan, M.A.; Shaikh, M.K.; bin Mazhar, S.A.; Mehboob, K. Comparative analysis for a real time face recognition system using Raspberry Pi. In Proceedings of the 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (IC-SIMA), Putrajaya, Malaysia, 28–30 November 2017; pp. 1–4. [Google Scholar] [CrossRef]
- Shah, A.A.; Zaidi, Z.A.; Chowdhry, B.S. Daudpoto Real time face detection/monitor using raspberry pi and MATLAB. In Proceedings of the 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, 12–14 October 2016; pp. 1–4. [Google Scholar] [CrossRef]
- Tezos. Available online: https://Tezos.com/ (accessed on 11 May 2021).
- “What Is Tezos,” Blockgeeks. Available online: https://blockgeeks.com/guides/what-is-Tezos/ (accessed on 11 May 2021).
- Goodman, L. Tezos—A Self-Amending Crypto-Ledger White Paper. 2014. Available online: https://academy.bit2me.com/wp-content/uploads/2021/04/tezos-whitepaper.pdf (accessed on 28 July 2021).
- Investopedia. Available online: https://www.investopedia.com/terms/b/blockchain.asp (accessed on 19 December 2019).
PC | Raspberry Pi 3 | |
---|---|---|
1 | 7 ms | 264 ms |
10 | 42.2 ms | 320 ms |
50 | 69.1 ms | 637 ms |
100 | 103 ms | 960 ms |
500 | 375 ms | 3.65 s |
1000 | 704 ms | 6.92 s |
5000 | 3.41 s | ---- |
10,000 | 6.78 s | ---- |
11,557 | 7.77 s | ---- |
PC | Raspberry Pi 3 | |
---|---|---|
Bake | 0.994 s | 2.216 s |
Coin transfer | 1.261 s | 4.575 s |
Typecheck | 0.505 s | 3.167 s |
Deploy a Smart contract | 1.836 s | 7.263 s |
PC | Raspberry Pi | |
---|---|---|
Time | 13.6 s | 23.7 s |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Allouche, M.; Frikha, T.; Mitrea, M.; Memmi, G.; Chaabane, F. Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain. Appl. Sci. 2021, 11, 7169. https://doi.org/10.3390/app11157169
Allouche M, Frikha T, Mitrea M, Memmi G, Chaabane F. Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain. Applied Sciences. 2021; 11(15):7169. https://doi.org/10.3390/app11157169
Chicago/Turabian StyleAllouche, Mohamed, Tarek Frikha, Mihai Mitrea, Gérard Memmi, and Faten Chaabane. 2021. "Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain" Applied Sciences 11, no. 15: 7169. https://doi.org/10.3390/app11157169