Main Article Content
Abstract
Blockchain platforms for peer-to-peer energy trading often add a digital twin that forecasts each meter's generation and demand. In most designs the twin only raises alarms, while validators are still chosen by stake or computation. The proposed scheme, PoET²S, incorporates the twin's evidence into validator selection: a node's weight is a stake-and-energy term multiplied by its Twin-Trust Score raised to γ = 2.5, so nodes that contradict the physical forecast quickly lose committee seats. A discrete-event simulator compares PoET²S (500 nodes, 15% Byzantine, 30 paired seeds) with seven classical baselines and three design points from recent work: a binary veto, an additive energy-and-trust score, and a linear (γ = 1) reputation weight. Detection is a near-tie: PoET²S catches 91.0% of attackers and the veto 91.7%. PoET²S raises more false alarms per catch by default (13.9 against 9.7) but matches the veto's catch rate at equal false-alarm cost. The malicious committee share is 10.8% under the veto, 3.8% under the linear weight, and 2.6% under PoET²S, and on 100 seeds the gain over the linear weight is 37% (Holm-adjusted p = 7.2 × 10⁻⁹). At 45% Byzantine nodes, the one-third fault bound fails in 64.4% of veto blocks and 2.4% of PoET²S blocks. Simulated commit latency (30.2 ms) is higher than for Proof of Stake and Delegated Proof of Stake, lower than for Practical Byzantine Fault Tolerance, and flat up to 4,000 nodes; an optional fast path lowers it to 26.8 ms. Against Sybil attacks, top-K selection needs certified meter identities.
Keywords
Article Details
References
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- Seed PoET²S Linear Veto Additive
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References
E. Mengelkamp, J. Gärttner, K. Rock, S. Kessler, L. Orsini, and C. Weinhardt, “Designing microgrid energy markets: A case study: The Brooklyn Microgrid,” Appl. Energy, vol. 210, pp. 870–880, Jan. 2018, doi: 10.1016/j.apenergy.2017.06.054.
T. Sousa, T. Soares, P. Pinson, F. Moret, T. Baroche, and E. Sorin, “Peer-to-peer and community-based markets: A comprehensive review,” Renew. Sustain. Energy Rev., vol. 104, pp. 367–378, Apr. 2019, doi: 10.1016/j.rser.2019.01.036.
M. Andoni, V. Robu, D. Flynn, S. Abram, D. Geach, D. Jenkins, P. McCallum, and A. Peacock, “Blockchain technology in the energy sector: A systematic review of challenges and opportunities,” Renew. Sustain. Energy Rev., vol. 100, pp. 143–174, Feb. 2019, doi: 10.1016/j.rser.2018.10.014.
A. Q. Al-Shetwi, I. E. Atawi, M. A. El-Hameed, and A. Abuelrub, “Digital twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration: Advancements, applications, key players, challenges and future perspectives in modernising sustainable grids,” IET Smart Grid, vol. 8, no. 1, art. no. e70026, 2025, doi: 10.1049/stg2.70026.
G. Liang, J. Zhao, F. Luo, S. R. Weller, and Z. Y. Dong, “A review of false data injection attacks against modern power systems,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1630–1638, Jul. 2017, doi: 10.1109/TSG.2015.2495133.
R. K. Suggala, J. Kumar, P. Jain, and B. K. Kumar, “Blockchain technology for digital twin security in smart grids using interpretable generalized additive neural networks,” Peer-to-Peer Netw. Appl., vol. 18, no. 4, art. no. 172, 2025, doi: 10.1007/s12083-025-01929-6.
A. K. Dutta, M. Anjum, H. Min, Y. I. Daradkeh, J. T. Seo, and S. Shahab, “Digital twin-assisted blockchain IoT security model using contrastive and causal learning techniques,” Sci. Rep., vol. 16, art. no. 15732, Apr. 2026, doi: 10.1038/s41598-026-47104-6.
N. Weerasinghe, R. Mishra, P. Porambage, M. Liyanage, and M. Ylianttila, “Proof-of-Monitoring (PoM): A novel consensus mechanism for blockchain-based secure service level agreement management,” IEEE Trans. Netw. Service Manag., vol. 20, no. 3, pp. 2783–2803, Sep. 2023, doi: 10.1109/TNSM.2023.3234862.
O. A. Saleh and M. Cevik, “Blockchain-integrated secure authentication framework for smart grid IoT using energy-aware consensus mechanisms,” Sensors, vol. 25, no. 21, art. no. 6622, 2025, doi: 10.3390/s25216622.
W. Tushar, T. K. Saha, C. Yuen, D. Smith, and H. V. Poor, “Peer-to-peer trading in electricity networks: An overview,” IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3185–3200, Jul. 2020, doi: 10.1109/TSG.2020.2969657.
E. Mengelkamp, B. Notheisen, C. Beer, D. Dauer, and C. Weinhardt, “A blockchain-based smart grid: Towards sustainable local energy markets,” Comput. Sci. Res. Dev., vol. 33, no. 1–2, pp. 207–214, 2018, doi: 10.1007/s00450-017-0360-9.
Y. Xiao, N. Zhang, W. Lou, and Y. T. Hou, “A survey of distributed consensus protocols for blockchain networks,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1432–1465, 2020, doi: 10.1109/COMST.2020.2969706.
M. Castro and B. Liskov, “Practical Byzantine fault tolerance,” in Proc. 3rd USENIX Symp. Operating Systems Design and Implementation (OSDI), New Orleans, LA, USA, 1999, pp. 173–186. [Online]. Available: https://pmg.csail.mit.edu/papers/osdi99.pdf
Z. Li, J. Kang, R. Yu, D. Ye, Q. Deng, and Y. Zhang, “Consortium blockchain for secure energy trading in industrial Internet of Things,” IEEE Trans. Ind. Informat., vol. 14, no. 8, pp. 3690–3700, Aug. 2018, doi: 10.1109/TII.2017.2786307.
J. Kang, Z. Xiong, D. Niyato, D. Ye, D. I. Kim, and J. Zhao, “Toward secure blockchain-enabled Internet of Vehicles: Optimizing consensus management using reputation and contract theory,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 2906–2920, Mar. 2019, doi: 10.1109/TVT.2019.2894944.
X. Deng, J. Li, L. Shi, K. Wei, M. Ding, Y. Shao, W. Chen, and S. Jin, “Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks,” Sci. China Inf. Sci., vol. 67, no. 11, art. no. 219303, 2024, doi: 10.1007/s11432-024-4159-2.
G. E. Suh and S. Devadas, “Physical unclonable functions for device authentication and secret key generation,” in Proc. 44th ACM/IEEE Design Automation Conf. (DAC), San Diego, CA, USA, 2007, pp. 9–14, doi: 10.1145/1278480.1278484.
V. Costan and S. Devadas, “Intel SGX explained,” IACR Cryptology ePrint Archive, Rep. 2016/086, 2016. [Online]. Available: https://eprint.iacr.org/2016/086
S. Pinto and N. Santos, “Demystifying Arm TrustZone: A comprehensive survey,” ACM Comput. Surv., vol. 51, no. 6, art. no. 130, 2019, doi: 10.1145/3291047.
J. Van Bulck, M. Minkin, O. Weisse, D. Genkin, B. Kasikci, F. Piessens, M. Silberstein, T. F. Wenisch, Y. Yarom, and R. Strackx, “Foreshadow: Extracting the keys to the Intel SGX kingdom with transient out-of-order execution,” in Proc. 27th USENIX Security Symp., Baltimore, MD, USA, Aug. 2018, pp. 991–1008. [Online]. Available: https://www.usenix.org/conference/usenixsecurity18/presentation/bulck
S. Matetic, M. Ahmed, K. Kostiainen, A. Dhar, D. Sommer, A. Gervais, A. Juels, and S. Capkun, “ROTE: Rollback protection for trusted execution,” in Proc. 26th USENIX Security Symp., Vancouver, BC, Canada, Aug. 2017, pp. 1289–1306. [Online]. Available: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/matetic
S. Tan, D. De, W.-Z. Song, J. Yang, and S. K. Das, “Survey of security advances in smart grid: A data driven approach,” IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 397–422, 2017, doi: 10.1109/COMST.2016.2616442.
J. R. Douceur, “The Sybil attack,” in Proc. 1st Int. Workshop Peer-to-Peer Systems (IPTPS), ser. Lecture Notes in Computer Science, vol. 2429. Berlin, Germany: Springer, 2002, pp. 251–260, doi: 10.1007/3-540-45748-8_24.
A. Hahn, R. K. Thomas, I. Lozano, and A. Cardenas, “A multi-layered and kill-chain based security analysis framework for cyber-physical systems,” Int. J. Crit. Infrastruct. Prot., vol. 11, pp. 39–50, Dec. 2015, doi: 10.1016/j.ijcip.2015.08.003.
S. W. Roberts, “Control chart tests based on geometric moving averages,” Technometrics, vol. 1, no. 3, pp. 239–250, 1959, doi: 10.1080/00401706.1959.10489860.
Pecan Street Inc., “Dataport,” Austin, TX, USA. [Online]. Available: https://www.pecanstreet.org/dataport/ (accessed Sep. 2026).
S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht, “Smart*: An open data set and tools for enabling research in sustainable homes,” in Proc. KDD Workshop on Data Mining Applications in Sustainability (SustKDD), Beijing, China, Aug. 2012. [Online]. Available: https://lass.cs.umass.edu/papers/pdf/sustkdd12-smart.pdf
J.-P. Martin and L. Alvisi, “Fast Byzantine consensus,” IEEE Trans. Dependable Secure Comput., vol. 3, no. 3, pp. 202–215, Jul.–Sep. 2006, doi: 10.1109/TDSC.2006.35.
P. Kuznetsov, A. Tonkikh, and Y. X. Zhang, “Revisiting optimal resilience of fast Byzantine consensus,” in Proc. ACM Symp. Principles of Distributed Computing (PODC), Jul. 2021, pp. 343–353, doi: 10.1145/3465084.3467924.
Y. Gilad, R. Hemo, S. Micali, G. Vlachos, and N. Zeldovich, “Algorand: Scaling Byzantine agreements for cryptocurrencies,” in Proc. 26th Symp. Operating Systems Principles (SOSP), Shanghai, China, 2017, pp. 51–68, doi: 10.1145/3132747.3132757.
G. F. Riley and T. R. Henderson, “The ns-3 network simulator,” in Modeling and Tools for Network Simulation, K. Wehrle, M. Güneş, and J. Gross, Eds. Berlin, Germany: Springer, 2010, pp. 15–34, doi: 10.1007/978-3-642-12331-3_2.
Seed PoET²S Linear Veto Additive
A. Varga and R. Hornig, “An overview of the OMNeT++ simulation environment,” in Proc. 1st Int. Conf. Simulation Tools and Techniques (SIMUTools), Marseille, France, 2008, art. no. 60, doi: 10.4108/ICST.SIMUTOOLS2008.3027.
M. E. Crovella and A. Bestavros, “Self-similarity in World Wide Web traffic: Evidence and possible causes,” IEEE/ACM Trans. Netw., vol. 5, no. 6, pp. 835–846, Dec. 1997, doi: 10.1109/90.650143.
D. J. Bernstein, N. Duif, T. Lange, P. Schwabe, and B.-Y. Yang, “High-speed high-security signatures,” J. Cryptogr. Eng., vol. 2, no. 2, pp. 77–89, 2012, doi: 10.1007/s13389-012-0027-1.
G. Danezis, L. Kokoris-Kogias, A. Sonnino, and A. Spiegelman, “Narwhal and Tusk: A DAG-based mempool and efficient BFT consensus,” in Proc. 17th European Conf. Computer Systems (EuroSys), Rennes, France, 2022, pp. 34–50, doi: 10.1145/3492321.3519594.
F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bull., vol. 1, no. 6, pp. 80–83, Dec. 1945, doi: 10.2307/3001968.
S. Holm, “A simple sequentially rejective multiple test procedure,” Scand. J. Statist., vol. 6, no. 2, pp. 65–70, 1979. [Online]. Available: https://www.jstor.org/stable/4615733
B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. New York, NY, USA: Chapman & Hall/CRC, 1993, doi: 10.1201/9780429246593.
V. Christodoulou and Y. Bi, “A combination of CUSUM-EWMA for anomaly detection in time series data,” in Proc. IEEE Int. Conf. Data Science and Advanced Analytics (DSAA), Paris, France, 2015, pp. 1–8, doi: 10.1109/DSAA.2015.7344899.
Z.-G. Zhou and P. Tang, “Improving time series anomaly detection based on exponentially weighted moving average (EWMA) of season-trend model residuals,” in Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Beijing, China, 2016, pp. 3414–3417, doi: 10.1109/IGARSS.2016.7729882.
C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications (Lecture Notes in Economics and Mathematical Systems, vol. 186). Berlin, Germany: Springer, 1981, doi: 10.1007/978-3-642-48318-9.
M. Yin, D. Malkhi, M. K. Reiter, G. G. Gueta, and I. Abraham, “HotStuff: BFT consensus with linearity and responsiveness,” in Proc. ACM Symp. Principles of Distributed Computing (PODC), Toronto, ON, Canada, 2019, pp. 347–356, doi: 10.1145/3293611.3331591.
E. Buchman, J. Kwon, and Z. Milosevic, “The latest gossip on BFT consensus,” arXiv:1807.04938, 2018, doi: 10.48550/arXiv.1807.04938.