Abstract
Since the advent of deep belief network deep learning technology in 2006, artificial intelligence technology has been utilized in various convergence areas, such as autonomous driving and medical care. Some services requiring fast decision making and action typically work seamlessly with edge computing service model. In autonomous driving of a connected vehicle with vehicle-to-everything (V2X) communication, roadside unit (RSU) acts as an edge computing device and it will make safer service by processing V2X messages fast, sent by vehicles or other devices. IEEE 1609.2 standard provides application message security technology to ensure the security and reliability of vehicle-to-vehicle communication messages. It uses elliptic curve digital signature algorithm (ECDSA) signatures based on the NIST p256 curve for message authenticity. In this paper, we investigate that RSU should be able to verify 3500 ECDSA signatures per second considering the expected maximum number of vehicles on nearby roads (e.g., during rush hour), message transmission rate, and IEEE 802.11p wireless channel capacity. RSU should satisfy this requirement without assistance of hardware-based cryptographic accelerator. For the requirement, we propose a hybrid approach of parallel ECDSA signature verification at high speed by using CPU and GPU, simultaneously. Moreover, we implemented the proposed method in various modern computing environments for RSU and edge computing devices. Through the experiments, we reach the conclusion that GPU can contribute to the required performance of ECDSA signature verification in RSU platform, which could not satisfy the above throughput only with CPU unit. The target platform with Intel Pentium E6500 CPU and GeForce GTX650 GPU can verify 5668 signatures per second with 30% utilization, while CPU in the platform can process only 2640 signatures. Even in a higher-performance edge computing device, we examine experimentally that the performance can be further improved by using the proposed hybrid approach.
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Notes
G is elliptic curve base point and n is integer order of G.
References
Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural comput 18(7):1527–1554
Hinton G, Deng L, Yu D, Dahl GE, Mohamed A-R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97
Graves A, Mohamed A-R, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 6645–6649
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning. ACM, pp 160–167
Manning C, Surdeanu M, Bauer J, Finkel J, Bethard S, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 55–60
Chen C, Seff A, Kornhauser A, Xiao J (2015) Deepdriving: learning affordance for direct perception in autonomous driving. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 2722–2730
Endsley MR (2017) Autonomous driving systems: a preliminary naturalistic study of the Tesla model S. J Cogn Eng Decis Mak 11(3):225–238
Yu N, Yu Z, Gu F, Li T, Tian X, Pan Y (2017) Deep learning in genomic and medical image data analysis: challenges and approaches. J Inf Process Syst 13(2):204–214
Singh J, Singh G, Singh R (2017) Optimization of sentiment analysis using machine learning classifiers. Hum Centric Comput Inf Sci 7(1):32
Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 513–520
Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529
Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39
Finogeev AG, Parygin DS, Finogeev AA (2017) The convergence computing model for big sensor data mining and knowledge discovery. Hum Centric Comput Inf Sci 7(1):11
Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656
Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646
Shah S. How is cloud different from edge in an IoT environment? https://www.collaberatact.com/cloud-different-edge-iot-environment/. Accessed 15 June 2018
Shahzadi S, Iqbal M, Dagiuklas T, Qayyum ZU (2017) Multi-access edge computing: open issues, challenges and future perspectives. J Cloud Comput 6(1):30
Rescorla E, Modadugu N (2012) Datagram transport layer security version 1.2. IETF RFC 6347
Granjal J, Monteiro E, Silva JS (2015) Security for the internet of things: a survey of existing protocols and open research issues. IEEE Commun Surv Tutor 17(3):1294–1312
Keoh SL, Kumar SS, Tschofenig H (2014) Securing the internet of things: a standardization perspective. IEEE Internet Things J 1(3):265–275
Dennis EP. Review of NHTSA proposal to mandate V2V communication for safety. https://www.cargroup.org/wp-content/uploads/2017/03/nhtsa_v2v_nprm_review_car_20dec20161.pdf. Accessed 15 June 2018
DSRC Committee (2016) Dedicated short range communications (DSRC) message set dictionary. SAE Standard J 2735
IEEE Std 1609.2-2016 (2016) Intelligent transportation systems committee and others. IEEE standard for wireless access in vehicular environments–security services for applications and management messages
Kerry CF, Gallagher PD (2013) Digital signature standard (DSS). FIPS PUB 186-4
Knežević M, Nikov V, Rombouts P (2016) Low-latency ECDSA signature verification a road toward safer traffic. IEEE Trans Very Large Scale Integr VLSI Syst 24(11):3257–3267
Choi P, Lee M-K, Kim J-H, Kim DK (2017) Low-complexity elliptic curve cryptography processor based on configurable partial modular reduction over NIST prime fields. IEEE Trans Circuits Syst II Express Briefs 65:1703–1707
Liu Z, Huang X, Hu Z, Khan MK, Seo H, Zhou L (2017) On emerging family of elliptic curves to secure internet of things: ECC comes of age. IEEE Trans Dependable Secure Comput 14(3):237–248
Fernandes B, Rufino J, Alam M, Ferreira J (2018) Implementation and analysis of IEEE and ETSI security standards for vehicular communications. Mob Netw Appl 23(3):469–478
Imem AA (2015) Comparison and evaluation of digital signature schemes employed in NDN network. arXiv preprint arXiv:1508.00184
Dai J, Pu L, Xu K, Meng Z, Liu Z, Zhang L (2017) The implementation and performance evaluation of wave based secured vehicular communication system. In: 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pp 1–5
Chen C, Lee SW, Watson T, Maple C, Lu Y (2017) CAESAR: a criticality-aware ECDSA signature verification scheme with Markov model. In: 2017 IEEE Vehicular Networking Conference (VNC), pp 151–154. IEEE
Stone JE, Gohara D, Shi G (2010) OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73
Park Y-H, Seo S-W (2015) Fast and secure group key dissemination scheme for out-of-range V2I communication. IEEE Trans Veh Technol 64(12):5642–5652
Böhm A, Lidström K, Jonsson M, Larsson T (2010) Evaluating CALM M5-based vehicle-to-vehicle communication in various road settings through field trials. In: 2010 IEEE 35th Conference on Local Computer Networks (LCN). IEEE, pp 613–620
Song Y-S, Choi H-K (2017) Analysis of V2V broadcast performance limit for WAVE communication systems using two-ray path loss model. ETRI J 39(2):213–221
Banani S, Gordon S, Thiemjarus S, Kittipiyakul S (2018) Verifying safety messages using relative-time and zone priority in vehicular ad-hoc networks. Sensors 18(4):1195
Unex. RSU-101. https://unex.com.tw/products/dsrc-v2x/solutions/v2x-hardware-1/v2x-enabling-rsu/detail/rsu-101. Accessed 15 June 2018
Savari. Savari SW-1000 road-side-unit (RSU) datasheet. http://www.econolitegroup.com/wp-content/uploads/2017/06/RSU-Econolite.pdf. Accessed 15 June 2018
NXP. NXP i.MX6 series application processors. https://www.nxp.com/products/processors-and-microcontrollers/applications-processors/i.mx-applications-processors/i.mx-6-processors:IMX6X_SERIES. Accessed 15 June 2018
Hardkernel. ODROID-XU4. http://www.hardkernel.com/main/products/prdt_info.php?g_code=G143452239825. Accessed 15 June 2018
Brown M, Hankerson D, López J, Menezes A (2001) Software implementation of the NIST elliptic curves over prime fields. In: Cryptographers Track at the RSA Conference. Springer, pp 250–265
Avanzi RM (2004) Aspects of hyperelliptic curves over large prime fields in software implementations. In: International Workshop on Cryptographic Hardware and Embedded Systems. Springer, pp 148–162
Bernstein DJ (2006) Curve25519: new Diffie–Hellman speed records. In: International Workshop on Public Key Cryptography. Springer, pp 207–228
Giorgi P, Izard T, Tisserand A (2009) Comparison of modular arithmetic algorithms on GPUs. In: ParCo’09: International Conference on Parallel Computing
Güneysu T, Paar C (2008) Ultra high performance ECC over NIST primes on commercial FPGAs. In: International Workshop on Cryptographic Hardware and Embedded Systems. Springer, pp 62–78
Ma Y, Liu Z, Pan W, Jing J (2013) A high-speed elliptic curve cryptographic processor for generic curves over \(gf(p)\). In: International Conference on Selected Areas in Cryptography. Springer, pp 421–437
Acknowledgements
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (No.B0717-16-0097, Development of V2X Service Integrated Security Technology for Autonomous Driving Vehicle).
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Lee, S., Seo, H., Kwon, H. et al. Hybrid approach of parallel implementation on CPU–GPU for high-speed ECDSA verification. J Supercomput 75, 4329–4349 (2019). https://doi.org/10.1007/s11227-019-02744-6
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DOI: https://doi.org/10.1007/s11227-019-02744-6