Abstract
As the most popular consensus algorithm for blockchain, the Proof-of-Work (PoW) is suffering from the inability of handling computing power fluctuations. Meanwhile, PoW consumes a significant amount of energy without producing actual value. To address these issues, this paper proposes a deep learning-based consensus framework called Proof-of-Improvement (PoI), which recycles the energy from mining blocks to improve the blockchain itself. In PoI, a new reward mechanism is used to encourage miners to include the high-accuracy model in their blocks. Then, based on PoI, a difficulty adjustment algorithm is designed. Experiments are done on real-world data and the result shows the proposed algorithm’s proficiency in preserving block time stability with fluctuating hash rates. To the best of the authors’ knowledge, PoI is the first to handle both energy recycling and difficulty adjustment concurrently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Map of coins: BTC Map. https://mapofcoins.com/bitcoin. Accessed 09 May 2023
Baek, U.J., Ji, S.H., Park, J.T., Lee, M.S., Park, J.S., Kim, M.S.: DDoS attack detection on bitcoin ecosystem using deep-learning. In: 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4 (2019)
Baldominos, A., Saez, Y.: Coin.AI: a proof-of-useful-work scheme for blockchain-based distributed deep learning. Entropy 21(8), 723 (2019)
Ball, M., Rosen, A., Sabin, M., Vasudevan, P.N.: Proofs of useful work. Cryptology ePrint Archive (2017)
Benet, J.: IPFS - Content Addressed, Versioned, P2P File System (2014)
Bissias, G., Thibodeau, D., Levine, B.N.: Bonded mining: difficulty adjustment by miner commitment. In: Pérez-Solà, C., Navarro-Arribas, G., Biryukov, A., Garcia-Alfaro, J. (eds.) DPM/CBT -2019. LNCS, vol. 11737, pp. 372–390. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31500-9_24
Chenli, C., Li, B., Shi, Y., Jung, T.: Energy-recycling blockchain with proof-of-deep-learning. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 19–23 (2019)
Daian, P., Eyal, I., Juels, A., Sirer, E.G.: (Short Paper) PieceWork: generalized outsourcing control for proofs of work. In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 182–190. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_11
Feng, W., Cao, Z., Shen, J., Dong, X.: RTPoW: a proof-of-work consensus scheme with real-time difficulty adjustment algorithm. In: 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), pp. 233–240 (2021)
Huang, J., et al.: BlockSense: towards trustworthy mobile crowdsensing via proof-of-data blockchain. IEEE Trans. Mob. Comput. 1–17 (2022)
Kattis, A., Bonneau, J.: Proof of necessary work: succinct state verification with fairness guarantees. Cryptology ePrint Archive (2020)
King, S.: Primecoin: Cryptocurrency with prime number proof-of-work. July 7th 1(6) (2013)
King, S., Nadal, S.: PPCoin: peer-to-peer crypto-currency with proof-of-stake. self-published paper, August 19(1) (2012)
Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Li, B., Chenli, C., Xu, X., Jung, T., Shi, Y.: Exploiting computation power of blockchain for biomedical image segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2802–2811 (2019)
Li, J., Li, N., Peng, J., Cui, H., Wu, Z.: Energy consumption of cryptocurrency mining: a study of electricity consumption in mining cryptocurrencies. Energy 168, 160–168 (2019)
Liu, Y., Lan, Y., Li, B., Miao, C., Tian, Z.: Proof of learning (PoLe): empowering neural network training with consensus building on blockchains. Comput. Netw. 201, 108594 (2021)
Meshkov, D., Chepurnoy, A., Jansen, M.: Short paper: revisiting difficulty control for blockchain systems. In: Garcia-Alfaro, J., Navarro-Arribas, G., Hartenstein, H., Herrera-Joancomartí, J. (eds.) ESORICS/DPM/CBT -2017. LNCS, vol. 10436, pp. 429–436. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67816-0_25
Miller, A., Juels, A., Shi, E., Parno, B., Katz, J.: Permacoin: repurposing bitcoin work for data preservation. In: 2014 IEEE Symposium on Security and Privacy, pp. 475–490. IEEE (2014)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system, p. 9 (2008)
Nerurkar, P.: Illegal activity detection on bitcoin transaction using deep learning. Soft Comput. 27(9), 5503–5520 (2023)
Qu, X., Wang, S., Hu, Q., Cheng, X.: Proof of federated learning: a novel energy-recycling consensus algorithm. IEEE Trans. Parallel Distrib. Syst. 32(8), 2074–2085 (2021)
Shoker, A.: Sustainable blockchain through proof of exercise. In: 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), pp. 1–9. IEEE (2017)
Zhang, S., Ma, X.: A general difficulty control algorithm for proof-of-work based blockchains. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3077–3081 (2020)
Zheng, K., Zhang, S., Ma, X.: Difficulty prediction for proof-of-work based blockchains. In: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5 (2020)
Zhou, F., Huang, Z., Zhang, C.: Carbon price forecasting based on CEEMDAN and LSTM. Appl. Energy 311, 118601 (2022)
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China (Grant No. 2022YFB2701400), in part by the National Natural Science Foundation of China (Grant No. 62132005, 62172162, 62172161, U22B2029, 62272228).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xia, Z. et al. (2023). Mining for Better: An Energy-Recycling Consensus Algorithm to Enhance Stability with Deep Learning. In: Meng, W., Yan, Z., Piuri, V. (eds) Information Security Practice and Experience. ISPEC 2023. Lecture Notes in Computer Science, vol 14341. Springer, Singapore. https://doi.org/10.1007/978-981-99-7032-2_34
Download citation
DOI: https://doi.org/10.1007/978-981-99-7032-2_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7031-5
Online ISBN: 978-981-99-7032-2
eBook Packages: Computer ScienceComputer Science (R0)