基於區塊鏈和智能合約的車網交通事件確認與信賴驗證

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：31

、訪客IP：18.119.119.119

姓名

楊曜宗(Yao-Tsung Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於區塊鏈和智能合約的車網交通事件確認與信賴驗證
(Road Event Validation and Trust Verification based on Blockchain and Smart Contracts for VANETs)

相關論文

★ 無線行動隨意網路上穩定品質服務路由機制之研究	★ 應用多重移動式代理人之網路管理系統
★ 應用移動式代理人之網路協同防衛系統	★ 鏈路狀態資訊不確定下QoS路由之研究
★ 以訊務觀察法改善光突發交換技術之路徑建立效能	★ 感測網路與競局理論應用於舒適性空調之研究
★ 以搜尋樹為基礎之無線感測網路繞徑演算法	★ 基於無線感測網路之行動裝置輕型定位系統
★ 多媒體導覽玩具車	★ 以Smart Floor為基礎之導覽玩具車
★ 行動社群網路服務管理系統－應用於發展遲緩兒家庭	★ 具位置感知之穿戴式行動廣告系統
★ 調適性車載廣播	★ 車載網路上具預警能力之車輛碰撞避免機制
★ 應用於無線車載網路上之合作式交通資訊傳播機制以改善車輛擁塞	★ 智慧都市中應用車載網路以改善壅塞之調適性虛擬交通號誌

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

如何保證交通資訊的正確性是一項重要的安全議題。車載結合公開金鑰基礎架構能確保資訊在傳遞中的安全性，防止來自外部的惡意竄改與偽造，但是卻無法防堵來自內部成員的竄改行為；再者，導入評鑑系統雖然可防堵內賊，但是系統本身的安全性以及車載網路的分散式架構，卻讓評鑑不易進行。而區塊鏈適用於分散式的架構，並且在共識機制中確保了資料的正確性與不可竄改性，恰好可解決這樣的問題。
本論文提出兩段式事件確認的流程，藉由事故地點不同的節點或是道路設施來驗證正確性。設計適用於車載網路的Proof-of-Event共識機制，藉由可動態調整的多數決來判定交通事件的真偽。同時運用深度學習演算法來推估不同情境的多數決門檻值，以增加事件判定的準確率。並且在取得事故範圍內的多數共識後，將證據寫入本地區塊鏈，使資訊便於區域共享，同時保有匿名性。而當資料同步後，再透過智能合約與執法單位進行證據的信賴驗證，以確保全域共享資訊的正確性與增加證據的可信度。
模擬的結果顯示，本論文提出的機制能有效地反饋交通事件。在高速公路熱點，設定的門檻值只要小於60 veh/min，準確度可達97.21%以上；而當惡意節點佔總數為30%的情況下，偽事件的成功率僅有12.23%。由此可證，本論文確實能夠透過共識以確保交通事件的正確性。再加上證據區塊鏈與智能合約的設計，透過假名鑑定合約來過濾偽造事件，將結果發布至全域區塊鏈，以提供一個可追朔事件的驗證與信賴機制，能有效遏止來自外在或內部的事件篡改與偽造行為。

摘要(英)

It is an important security issue on how to guarantee the correctness of traffic information. The public key infrastructure of VANETs ensures secure transmission and prevents malicious tampering and falsification from outsiders, but it does not prevent attacks from insiders. Furthermore, the reputation system can mitigate these internal attacks, but the security of the system itself and the decentralized architecture of VANTs make the implementation difficult. The blockchain can exactly solve these problems. It is suitable for decentralized architectures, assuring both the correctness and tamper resistance of chaining data through a consensus mechanism.
This dissertation proposes a two-way event validation process to verify the correctness of the accident by different surrounding nodes or road facilities. Design a Proof-of-Event consensus protocol for VANETs to decide on the validity of traffic events by dynamically adjusting the majority decision. Moreover, the deep learning algorithm is applied to estimate the majority threshold of different situations to increase the accuracy of event validation. After obtaining the majority of the consensus within the scope of the accident, the evidence is written into the local blockchain, so that the information can be shared in the zone with anonymity. When the data is linked to the global blockchain, the validity of the pseudonyms is verified by the law enforcement agencies via the smart contract to ensure the correctness of the shared information and increase the trustworthiness of the evidence.
The simulation results show the proposed mechanism can effectively feedbacks traffic events. In a freeway hotspot, if the threshold is less than 60 veh/min, the accuracy is over 97.21%. If the percentage of malicious nodes is 30%, the false event success rate is only 12.23%. It can be proved that this dissertation can guarantee the correctness of traffic incidents through the proposed consensus protocol. Coupled with the design of the local evidence blockchain and the smart contract, the falsified events are filtered through the pseudonym identification contract. Then, the results are deployed to the global blockchain. This methodology provides a trust verification for tracking evidence, which can effectively restrain tampering and forgery from external or internal nodes.

關鍵字(中)

★ 區塊鏈
★ 事件確認
★ 信賴驗證
★ Proof-of-Event共識機制
★ 智能合約
★ 車載網路

關鍵字(英)

★ Blockchain
★ event validation
★ trust verification
★ Proof-of-Event consensus
★ Smart Contract
★ VANET

論文目次

摘要 ii
Abstract i
誌謝 i
Table of Contents ii
List of Figures i
List of Tables i
List of Abbreviations i
Explanation of Symbols i
Introduction 1
1.1 Motivation and Scope 1
1.2 Research Problems and Objectives 4
1.3 Contributions 6
1.4 Dissertation Organization 7
Chapter 2. Background and Related Works 8
2.1 Event Definition in Vehicular Ad Hoc Networks 8
2.2 Road Event Detection and Validation 11
2.2.1. Road Event Detection 11
2.2.2. Road Event Validation 15
2.3 Security and Privacy in Vehicular Ad Hoc Networks 28
2.4 Trust Models in Vehicular Ad Hoc Networks 32
2.5 Blockchain in Vehicular Ad Hoc Networks 34
2.5.1. Blockchain Technology 34
2.5.2. Consensus Protocol in Blockchain Applications 37
2.5.3. Blockchain and Consensus Protocols in VANETs 39
2.6 Smart Contracts 43
2.6.1. Bitcoin Scripts 43
2.6.2. Ethereum Smart Contracts 44
Chapter 3. Methodology Overview 46
3.1 Assumptions 46
3.2 System Overview 47
Chapter 4. Procedure of Event Validation 49
4.1 Road Event Detection Approach 49
4.2 Road Event Validation Approach 52
4.3 Two-Pass Road Event Validation Procedure 53
Chapter 5. Event Transaction and Consensus 57
5.1 Problem Definition 57
5.2 Cryptographic Primitives 59
5.3 Event Transaction 60
5.4 Proof-of-Event Consensus 61
5.5 Evidence Verification 62
Chapter 6. Zone-based Blockchain and Global Blockchain 63
6.1 Two Types of Blockchains for Vehicular Ad Hoc Networks 63
6.2 Data Structure of Zone-based Blockchain 64
6.3 Transaction in Zone-based Blockchain 66
6.4 Transaction in Global Blockchain 68
Chapter 7. Smart Contract for Trust Verification 71
7.1 Smart Contract in Global-chain Operation 71
7.2 Pseudonym Verification Contract 72
Chapter 8. Scenarios 74
8.1 Vehicle-to-Vehicle Scenario 74
8.2 Vehicle-to-Infrastructure Scenario 78
Chapter 9. Simulation Results 83
9.1 Traffic Data Exploration (Freeway) 83
9.2 Traffic Data Exploration (Taoyuan City) 90
9.3 Experiment 1: Prediction of Macroscopic Traffic 96
9.3.1. Prediction with Moving Average and Exponential Smoothing 96
9.3.2. Prediction with Neural Network Approaches 100
9.4 Experiment 2: Event Validation 104
9.5 Experiment 3: Event Validation with Internal Attackers 111
9.6 Experiment 4: Comparison of Consensus Performance 113
Chapter 10. Discussions 115
10.1 Event Life Cycle 115
10.2 Privacy in Blockchain for Vehicular Ad Hoc Networks 116
10.3 Consensus Protocols 117
Chapter 11. Conclusion and Future Works 118
References 120
Appendix A: Event Type in Simulation 131
Publication List 132

參考文獻

[1]
B. Beltz, “100+ Car Accident Statistics for 2019,” Safer America, 2018. [Online]. Available: https://safer-america.com/car-accident-statistics

[2]
National Highway Police Bureau, “Traffic Accident MVKT,” Ministry of Interior, Taiwan, ROC, 2019. [Online]. Available: https://www.hpb.gov.tw/p/412-1000-97.php
[3]
Open Data x Open Taoyuan, Taoyuan City Goverment, Taiwan, ROC. (2019) [Online]. Available: http://data.tycg.gov.tw/opendata, Accessed on: Jun. 12, 2019.

[4]
G. Fowler, “Most Common Car Insurance Repair Scams and What You Can Do To Avoid Being a Victim,” AutoInsureSavings, 2019. [Online]. Available: https://www.autoinsuresavings.org/common-car-insurance-repair-scams

[5]
“By the numbers: fraud statistics,” Coalition Against Insurance Fraud, 2019. [Online]. Available: https://www.insurancefraud.org/statistics.htm

[6]
“Staged Crash,” Cathay Inssurance Report, 2015. [Online]. Available: https://carrisk.cathay-ins.com.tw/dr_z_dt.asp?pkey=96

[7]
S. Gillani, F. Shahzad, A. Qayyum, and R. Mehmood, “A survey on security in vehicular ad hoc networks,” in Communication Technologies for Vehicles (Nets4Cars/Nets4Trains 2013), Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2013, vol. 7865, pp. 59-74. doi: 10.1007/978-3-642-37974-1_5.

[8]
B. Mokhtar and M. Azab, “Survey on Security Issues in Vehicular Ad Hoc Networks,” Alexandria Engineering Journal, vol. 54 (4), pp. 1115-1126, Dec. 2015. doi: 10.1016/j.aej.2015.07.011.

[9]
K. Wüst and A. Gervais, “Do you need a Blockchain?,” in Proc. IEEE Crypto Valley Conference on Blockchain Technology (CVCBT), Zug, Switzerland, 2018. pp. 45-54. doi: 10.1109/CVCBT.2018.00011.

[10]
D. Puthal, N. Malik, S. P. Mohanty, E. Kougianos, and C. Yang, “The Blockchain as a Decentralized Security Framework,” in IEEE Consumer Electronics Magazine, vol. 7, no. 2, pp. 18-21, Mar. 2018. doi: 10.1109/MCE.2017.2776459.

[11]
Intelligent Transport Systems (ITS); Users and Applications Requirements; Part 2: Applications and Facilities Layer Common Data Dictionary, document ETSI TS 102 894-2 V1.3.1, Aug. 2018.
[12]
Dedicated Short Range Communications (DSRC) Message Set Dictionary, document SAE J2735, Mar. 2016.
[13]
S. Husain, A. Kunz, A. Prasad, E. Pateromichelakis, K. Samdanis, and J. Song, “The Road to 5G V2X: Ultra-High Reliable Communications,” in Proc. 2018 IEEE Conference on Standards for Communications and Networking (CSCN), Paris, 2018, pp. 1-6. doi: 10.1109/CSCN.2018.8581819.

[14]
Y.-T. Yang and L.-D. Chou, “Position-based adaptive broadcast for inter-vehicle communications,” in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Beijing, May 2008, pp. 410-414. doi: 10.1109/ICCW.2008.83.

[15]
U. Lee and M. Gerla, “A survey of urban vehicular sensing platforms,” Computer Networks, vol 54 (4), pp. 527-554, Mar. 2010, doi: 10.1016/j.comnet.2009.07.011.

[16]
T. Umedu, K. Isu, T. Higashino, and C.-K. Toh, “An intervehicular-communication protocol for distributed detection of dangerous vehicles,” IEEE Transactions on Vehicular Technology, vol. 59, no. 2, pp. 627-637, Feb. 2010. doi: 10.1109/TVT.2009.2035041.

[17]
S. Abdelhamid, H.S. Hassanein, and G. Takahara, “Vehicle as a Mobile Sensor,” Procedia Computer Science, vol. 34, pp. 286-295, 2014. doi: j.procs.2014.07.025.

[18]
J. Guerrero-Ibañez, S. Zeadally, and J. Contreras-Castillo, “Sensor Technologies for Intelligent Transportation Systems,” Sensors 2018, vol. 4, pp. 1212, Apr. 2018. doi: 10.3390/s18041212.

[19]
Intelligent Transport Systems (ITS); Security; Pre-standardization study on pseudonym change management, document ETSI TR 103 415 V1.1.1, 2018.
[20]
M. Raya and J.-P. Hubaux, “Securing Vehicular Ad Hoc Networks,” Journal of Computer Security, vol. 15, no. 1, pp. 39-68, Jan. 2007. doi: 10.3233/JCS-2007-15103.

[21]
X. Yao, X. Zhang, H. Ning, and P. Li, “Using trust model to ensure reliable data acquisition in VANETs,”, Ad Hoc Networks, vol. 55, pp. 107-118, 2017. doi: 10.1016/j.adhoc.2016.10.011.

[22]
N.-W. Lo and H.-C. Tsai, “A Reputation System for Traffic Safety Event on Vehicular Ad Hoc Networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2009, no. 9, 2009. doi: 10.1155/2009/125348.

[23]
F. G. Mármol and G. M. Pérez, “TRIP, a trust and reputation infrastructure-based dissertation for vehicular ad hoc networks,” Journal of Network Computer Applications, vol. 35, pp. 934-941, 2012. doi: 10.1016/j.jnca.2011.03.028.

[24]
M. Raya, P. Papadimitratos, V. D. Gligor, and J.-P. Hubaux, “On Data-level Trust Establishment in Ephemeral Ad Hoc Networks,” in Proc. 27th IEEE INFOCOM, Phoenix, AZ, 2008, pp. 1238-1246. doi: 10.1109/INFOCOM.2008.180.

[25]
W. Li and H. Song, “ART: An attack-resistant trust management scheme for securing vehicular ad hoc networks,” IEEE Transactions on Intelligent Transportation System, vol. 17, no. 4, pp. 960-969, Apr. 2016. doi: 10.1109/TITS.2015.2494017.

[26]
Uber. Uber Technology Inc., 2009. [Online]. Available: https://www.uber.com

[27]
Google Maps. Google, 2005. [Online]. Available: https://maps.google.com

[28]
Motor Vehicle Driver Information Service, Motor Vehicles Office, Taiwan, 2013. [Online]. Available: https://www.mvdis.gov.tw/m3-emv-eng/

[29]
K. Januja, T. M. Sushma, M. Bharathi, and K. H. Arun, “A Survey on VANET Technologies”, International Journal of Computer Applications, vol. 121, no. 18, 2015. doi: 10.5120/21637-4965.

[30]
S. Kumar, L. Shi, N. Ahmed, S. Gil, D. Katabi, and D. Rus, “CarSpeak: a content-centric network for autonomous driving,” in Proc. ACM SIGCOMM Computer Communication Review - Special (SIGCOMM’12), New York, NY, USA, vol. 4 (4), Oct. 2012, pp. 259-270. doi: 10.1145/2377677.2377
724.

[31]
A. Hussein, F. García, J. M. Armingol, and C. Olaverri-Monreal, “P2V and V2P communication for Pedestrian warning on the basis of Autonomous Vehicles,” in Proc. 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016, pp. 2034-2039. doi: 10.1109/ITSC.2016.7795885.

[32]
F.-H. Tseng, J.-H. Hsueh, C.-W. Tseng, Y.-T. Yang, H.-C. Chao, and L.-D. Chou, “Congestion prediction with big data for real-time highway traffic,” IEEE Access, vol. 6, pp. 57311-57323, 2018. doi: 10.1109/ACCESS.2018.2873569.

[33]
M. Hasan, M. A. Orgun, and R. Schwitter, “A survey on real-time event detection from the Twitter data stream,” Journal of Information Science, vol. 44(4), pp. 443-463, 2018. doi: 10.1177/0165551517698564.

[34]
Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service, ETSI EN 302 637-2 V1.4.0, 2018.
[35]
Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 3: Specifications of Decentralized Environmental Notification Basic Service, ETSI EN 302 637-3 V1.2.1, 2014.
[36]
J. Santa, F. Pereñíguez, A. Moragón, and A. F. Skarmeta, “Experimental evaluation of CAM and DENM messaging services in vehicular communications,” Transportation Research Part C: Emerging Technologies, vol. 46, pp. 98-120, Sep. 2014. doi: 10.1016/j.trc.2014.05.006.

[37]
G. Araniti, C. Campolo, M. Condoluci, A. Iera, and A. Molinaro, “LTE for vehicular networking: A survey,” in IEEE Commun. Magazine, vol. 51, pp. 148-157, 2013. doi: 10.1109/MCOM.2013.6515060.

[38]
L. Zhang, D. Gao, C.H. Foh, D. Yang, and S. Gao, “A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs,” International Journal of Distributed Sensor Networks, May 2014. doi: 10.1155/2014/582761.

[39]
W. Zhu and M. Barth, “Vehicle trajectory-based road type and congestion recognition using wavelet analysis,” in: Proc. IEEE Intelligent Transportation Systems Conference, ITSC’06, Paris, France, 2006, pp. 879-884. doi: 10.1109/ITSC.2006.1706855.

[40]
S. Vaqar and O. Basir, “Traffic pattern detection in a partially deployed vehicular ad hoc network of vehicles,” IEEE Wireless Communications, vol. 16 (6), pp. 40-46, 2009. doi: 10.1109/MWC.2009.5361177.

[41]
K. Tawara and N. Mukai, “Traffic signal control by using traffic congestion prediction based on Pheromone model,” in: Proc. IEEE Tools with Artificial Intelligence, Vol. 1, ICTAI’2010, Arras, France, 2010, pp. 27-30. doi: 10.1109/ICTAI.2010.13.

[42]
G. Jiang, S. Niu, A. Chang, Z. Meng, and C. Zhang, “Automatic traffic congestion identification method of expressway based on gain amplifier theory”, in: Proc. Advanced Computer Control, Vol. 2, ICACC’10, Shenyang, China, 2010, pp. 648-651. doi: 10.1109/ICACC.2010.5486725.

[43]
B. Persaud, F. Hall, and L. Hall, “Congestion identification aspects of the McMaster incident detection algorithm,” Transportation Research Record, 1287, pp. 167-175, 1990.
[44]
J. E. Bresenham, “Algorithm for computer control of a digital plotter,” IBM Systems Journal, vol. 4, no. 1, pp. 25-30, 1965. doi: 10.1147/sj.41.0025.

[45]
P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time Analysis of the Multiarmed Bandit Problem,” in Machine Learning, Kluwer Academic Publishers, 2002, vol. 47, pp.235-256. doi: 10.1023/A:1013689704352.

[46]
E. Nathanail, P. Kouros, and P. Kopelias, “Traffic volume responsive incident detection,” Transportation Research Procedia, vol. 25, pp. 1755-1768, 2017. doi: 10.1016/j.trpro.2017.05.136.

[47]
S. Krishnan and M. Chen, “Identifying Tweets with Fake News,” in Proc. 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, USA, 2018, pp. 460-464. doi: 10.1109/IRI.2018.00073.

[48]
M. L. Della Vedova, E. Tacchini, S. Moret, G. Ballarin, M. DiPierro, and L. de Alfaro, “Automatic Online Fake News Detection Combining Content and Social Signals,” in Proc. 2018 22nd Conference of Open Innovations Association (FRUCT), Jyvaskyla, Finland, 2018, pp. 272-279. doi: 10.23919/FRUCT.2018.8468301.

[49]
L. Zhang, D. Gao, W. Zhao, and H.-C. Chao, “A multilevel information fusion approach for road congestion detection in VANETs,” Mathematical and Computer Modelling, vol. 58, no.5-6, pp. 1206-1221, Sep. 2013. doi: 10.1016/j.mcm.2013.02.004.

[50]
R. Weil, J. Wootton, and A. Garcła-Ortiz, “Traffic incident detection: sensors and algorithms,” Mathematical and Computer Modelling, vol. 27 (9-11), pp. 257-291, 1998. doi: 10.1016/S0895-7177(98)00064-8.

[51]
H.-C. Hsiao, A. Studer, R. Dubey, E. Shi, and A. Perrig, “Efficient and secure threshold-based event validation for VANETs,” in Proc. ACM conference on Wireless network security (WiSec), Hamburg, Germany, 2011, pp. 163-174. doi: 10.1145/1998412.1998440.

[52]
P. Clifford and I. A. Cosma, “A Statistical Analysis of Probabilistic Counting Algorithms." Scandinavian Journal of Statistics, vol. 39, no. 1, pp. 1-14, Mar. 2012. [Online]. Available: http://www.jstor.org/stable/41411154

[53]
H. Bloom, “Space/time trade-offs in hash coding with allowable errors,” Communications of the ACM, vol. 13, no. 7, pp. 442-426, Jul. 1970. doi: 10.1145/362686.362692.

[54]
G. Cormode and S. Muthukrishnan, “An improved data stream summary: the count-min sketch and its applications,” Journal of Algorithms, vol. 55, no.1, pp. 58-75, Apr. 2005. doi: 10.1016/j.jalgor.2003.12.001.

[55]
N. Alon, Y. Matias, and M. Szegedy, “The space complexity of approximating the frequency moments,” Journal of Computer System Sciences, vol. 58(1), pp. 137-147, 1999. doi: 10.1006/jcss.1997.1545.

[56]
P. Flajolet and G. N. Martin, “Probabilistic counting algorithms for data base applications,” Journal of Computer and System Sciences, vol. 31, no. 2, pp. 182-209, 1985. doi: 10.1016/0022-0000(85)90041-8.

[57]
Z. Bar-Yossef, T. S. Jayram, R. Kumar, D. Sivakumar, and L. Trevisan, “Counting Distinct Elements in a Data Stream,” in. Randomization and Approximation Techniques in Computer Science (RANDOM 2002), Lecture Notes in Computer Science, vol. 2483. Springer, Berlin, Heidelberg, 2002, pp. 1-10. doi: 10.1007/3-540-45726-7_1.

[58]
W. Guo, Z. Wang, W. Wang, and H. Bubb, “Traffic Incident Automatic Detection Algorithms by Using Loop Detector in Urban Roads,” Recent Patents on Computer Science, vol. 8, no. 1, pp. 41-48, 2015. doi: 10.2174/2213275907666141010214241.

[59]
B. Ghosh, B. Basu, and M. O’Mahony, “Time-Series Modeling For Forecasting Vehicular Traffic Flow in Dublin,” in Proc. 84th Transportation Research Board Annual Meeting, National Research Council, Washington, DC, USA, pp. 05-0464-05-0480, 2015.
[60]
M. Tan, S.-C. Wong, J. Xu, Z. Guan, and P. Zhang, “An Aggregation Approach to Short-Term Traffic Flow Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, pp. 60-69, Mar. 2009. doi: 10.1109/TITS.2008.2011693.

[61]
K. Kumar and V. K. Jain, “Autoregressive integrated moving averages (ARIMA) modelling of a traffic noise time series,” Applied Acoustics, vol. 58, no. 3, pp. 283-294, Nov. 1999. doi: S0003-682X(98)00078-4.

[62]
S.-V. Kumar and L. Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” in European Transport Research Review, Springer, Berlin, Heidelberg, 2015. doi: 10.1007/s12544-015-0170-8.

[63]
N. K. Ahmed, A. F. Atiya, N. El-Gayar, and H. El-Shishiny, “An Empirical Comparison of Machine Learning Models for Time Series Forecasting,” Econometric Reviews, vol. 29, no.5-6, pp. 594-621, Aug. 2010. doi: 10.1080/07474938.2010.481556.

[64]
G. Bontempi, S. B. Taieb, and Y.-A. L. Borgne, “Machine Learning Strategies for Time Series Forecasting,” in Business Intelligence, eBISS 2012. Lecture Notes in Business Information Processing, vol. 138, Springer, Berlin, Heidelberg, 2012, pp. 62-77. doi: 10.1007/978-3-642-36318-4_3.

[65]
T. G. Dietterich, “Machine Learning for Sequential Data: A Review,” Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2002, Lecture Notes in Computer Science, Springer Berlin Heidelberg, vol. 2396, pp. 15-30, Aug. 2002. doi: 10.1007/3-540-70659-3_2.

[66]
Pourya, “Time Series Machine Learning Regression Framework,” Towards Data Science, Apr. 2019. [Online]. Available: https://towardsdatascience.com/time-series-machine-learning-regression-framework-9ea33929009a

[67]
A. Kattan, S. Fatima, and M. Arif, “Time-series event-based prediction: An unsupervised learning framework based on genetic programming,” Information Sciences, vol. 301, pp. 99-123, 2015. doi: 10.1016/j.ins.2014.12.054.

[68]
P.-N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining (2nd Edition), Pearson, 2018.
[69]
M.-L. Han, J. Lee, A.-R. Kang, S. Kang, J.-K. Park, and H.-K. Kim, “A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment,” Internet of Vehicles - Safe and Intelligent Mobility, IOV 2015, Lecture Notes in Computer Science, Springer Cham, vol. 9502, pp. 89-97,. Nov. 2015. doi: 10.1007/978-3-319-27293-1_9.

[70]
K. G. Mehrotra, C. K. Mohan, and H.-M. Huang, “Distance-Based Anomaly Detection Approaches,” Anomaly Detection Principles and Algorithms, Terrorism, Security, and Computation (TESECO). Springer Cham, pp. 33-39, Oct. 2017. doi: 10.1007/978-3-319-67526-8_3.

[71]
J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities”. in Proc. National Academy of Science, USA (PANS), vol. 79, pp. 2554-2558, 1982. doi: 10.1073/pnas.79.8.2554.

[72]
D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in Porc. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, Jun. 2012, pp. 3642-3649. doi: 10.1109/CVPR.2012.6248110.

[73]
mxnet, “Handwritten Digit Recognition”, Accessed: Jun. 20, 2019. [Online]. Available: https://mxnet.incubator.apache.org/versions/master/tutorials/python/mnist.html

[74]
M. Sarigül and M. Avci, “Performance comparision of different momentum techniques on deep reinforcement learning,” in Proc. 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, Poland, 2017, pp. 302-306. doi: 10.1109/INISTA.2017.8001175.

[75]
T. Tieleman and G. Hinton, “Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude”, COURSERA: Neural Networks for Machine Learning, vol. 4, no. 2, pp. 26-31, Oct. 2012.
[76]
J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization”, Journal of Machine Learning Research, vol. 12, pp. 2121-2159, Jul. 2011.
[77]
D. Kingma and J. Ba, “Adam: A method for stochastic optimization”, in Proc. 3rd International Conference for Learning Representations (ICLR 2015), San Diego, USA, May 2015.
[78]
M. Dalto, J. Matuško, and M. Vašak, “Deep neural networks for ultra-short-term wind forecasting,” in Proc. 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Mar. 2015, pp. 1657-1663. doi: 10.1109/ICIT.2015.7125335.

[79]
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Proc. 27th International Conference on Neural Information Processing Systems (NIPS′14), vol. 2. MIT Press, Cambridge, MA, USA, pp. 3104-3112.
[80]
X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 187-197, 2015. doi: 10.1016/j.trc.2015.03.014.

[81]
K. Cho, B. Merriënboer, D. Bahdanau, and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches,” in Proc. 8th Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), pp. 103-111, Oct. 2014. doi: 10.3115/v1/W14-4012.

[82]
Z. Lv, J. Xu, K. Zheng, H. Yin, P. Zhao, and X. Zhou, “LC-RNN: A Deep Learning Model for Traffic Speed Prediction,” in Proc. 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. pp. 3470-3476. doi: 10.24963/ijcai.2018/482.

[83]
Colah’s blog. “Understanding LSTM Networks,” 2015. Access: Jun. 20, 2019. [Online]. Available: https://colah.github.io/posts/2015-08-Understanding-LSTMs/

[84]
Intelligent Transport Systems (ITS); Security; ITS communications security architecture and security management, ETSI TS 102 940 V1.3.1, 2018.
[85]
Intelligent Transport Systems (ITS); Security; Security header and certificate formats, ETSI TS 103 097 V1.3.1, 2017.
[86]
Intelligent Transport Systems (ITS); Security; Threat, Vulnerability and Risk Analysis (TVRA), ETSI TR 102 893 V1.2.1, 2017.
[87]
E. Hamida, H. Noura, and W. Znaidi, “Security of Cooperative Intelligent Transport Systems: Standards, Threats Analysis and Cryptographic Countermeasures,” Electronics, vol. 4, no. 3, pp. 380-423, Jul. 2015. doi: 10.3390/electronics4030380.

[88]
Intelligent Transport Systems (ITS); Intelligent Transport Systems (ITS); Security; Security header and certificate formats, ETSI TS 103 097 V1.3.1, Oct. 2017.
[89]
A.-H. Salem, A. Abdel-Hamid, and M.-A. El-Nasr, “The Case for Dynamic Key Distribution for PKI-Based VANETs,” International Journal of Computer Networks & Communications (IJCNC), vol. 6 no. 1, Jan. 2014. doi: 10.5121/ijcnc.2014.6105.

[90]
R.-W. van der Heijden, S. Dietzel, T. Leinmüller, and F. Kargl, “Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 779-811, 2019. doi: 10.1109/COMST.2018.2873088.

[91]
J. Shao, X. Lin, R. Lu, and C. Zuo, “A Threshold Anonymous Authentication Protocol for VANETs,” IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1711-1720, Mar. 2016. doi: 10.1109/TVT.2015.2405853.

[92]
W. Gao, M. Wang, L. Zhu, and X. Zhang, “Threshold-Based Secure and Privacy-Preserving Message Verification in VANETs,” in Proc. 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Beijing, 2014, pp. 795-802. doi: 10.1109/TrustCom.2014.105.

[93]
Blockchain companies, “Discover 100+ Startups and Companies Pioneering the Blockchain Technology Industry,” Blockchain Technologies, Accessed: Jun. 15, 2019. [Online]. Available: https://www.blockchaintechnologies.com/companies/

[94]
S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic cash system,” Technology Report, 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf

[95]
D. Ongaro and J. Ousterhout, “In Search of an Understandable Consenus Algorithm (Extended Version),” in Proc. USENIX conference on Annual Technical Conference (USENIX ATC), USENIX Association, Berkeley, CA, USA, 2014. pp. 305-320. [Online]. Available: https://raft.github.io/raft.pdf

[96]
A. Goranovic, M. Meisel, L. Fotiadis, S. Wilker, A. Treytl, and T. Sauter, “Blockchain applications in microgrids: An overview of current projects and concepts,” in Proc. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON), 2017. pp. 6153-6158. doi: 10.1109/IECON.2017.82170
69.

[97]
J. Garay and A. Kiayias, “Sok: A consensus taxonomy in the blockchain era,” Cryptology ePrint Archive, Report 2018/754, 2018. [Online]. Available: https://eprint.iacr.org/2018/754.pdf

[98]
P.-K. Sharma, S.-Y. Moon, and J.-H. Park, “Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City,” Journal of Information Processing Systems, vol. 13, no. 1, pp. 184-195, 2017. doi: 10.3745/JIPS.03.0065.

[99]
Z. Yang, K. Yang, L. Lei, K. Zheng, and V.-C. Leung, Blockchain-Based Decentralized Trust Management in Vehicular Networks,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1495-1505, Apr. 2019. doi: 10.1109/JIOT.2018.2836144.

[100]
A. Dorri, M. Steger, S.-S. Kanhere, and R. Jurdak, “BlockChain: A Distributed Solution to Automotive Security and Privacy,” in IEEE Communications Magazine, vol. 55, no. 12, pp. 119-125, Dec. 2017. doi: 10.1109/MCOM.2017.1700879.

[101]
M. Singh and S. Kim, “Blockchain Based Intelligent Vehicle Data Sharing Framework,” arXiv preprint, arXiv:1707.07442, 2017. [Online]. Available: https://arxiv.org/pdf/1708.09721.

[102]
M. Singh and S. Kim, “Trust Bit: Reward-based intelligent vehicle commination using blockchain paper,” in Proc. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 2018, pp. 62-67. doi: 10.1109/WF-IoT.2018.8355227.

[103]
Y.-T. Yang, L.-D. Chou, C.-W. Tseng, F.-H. Tseng, and C.-C. Liu, “Blockchain-based Traffic Event Validation and Trust Verification for VANETs,” IEEE Access, vol. 7, pp. 30868-30877, Mar. 2019. doi: 10.1109/ACCESS.2019.2903202.

[104]
M. Castro and B. Liskov, “Practical Byzantine fault tolerance and proactive recovery,” ACM Transactions on Computer Systems (TOCS), vol. 20, pp. 398-461, 2002. doi: 10.1145/571637.571640.

[105]
Z. Witherspoon’s blog. “A Hitchhiker’s Guide to Consensus Protocols,” Nov. 2017. Access: Mar. 12, 2019. [Online]. Available: https://hackernoon.com/a-hitchhikers-guide-to-consensus-algorithms-d81aae3eb0e3

[106]
Bitcoin. (2018) [Online]. Available: https://bitcoin.org/, Accessed on: Oct. 16, 2018.

[107]
Ethereum. (2018) [Online]. Available: https://www.ethereum.org/, Accessed on: Oct. 16, 2018.

[108]
Litecoin. (2018) [Online]. Available: https://litecoin.org/, Accessed on: Dec. 20, 2018.

[109]
Dogecoin. (2018) [Online]. Avaliable: https://dogecoin.com/, Accessed on: Dec. 20, 2018.

[110]
G. Hileman and M. Rauchs, “2017 Global Cryptocurrency Benchmarking Study,” Apr. 2017. doi: 10.2139/ssrn.2965436.

[111]
Decred. (2018) [Online]. Available: https://decred.org/, Accessed on: Dec. 25, 2018.

[112]
Ethereum 2.0 Specifications. Github.(2018) [Online]. Available: https://github.com/ethereum/eth2.0-specs, Accessed on: Dec. 14, 2018.

[113]
Peercoin. (2019) [Online]. Available: https://peercoin.net/, Accessed on: May. 25, 2019.

[114]
S. Seang and D. Torre, “Proof of Work and Proof of Stake consensus protocols: a blockchain application for local complementary currencies,” 2018. [Online]. Available: https://gdrescpo-aix.sciencesconf.org/
195470/documen

[115]
POA. (2019) [Online]. Available: https://poa.network/, Accessed on: Apr. 1, 2019.

[116]
Kovan Testnet. (2019) [Online]. Available: https://kovan.etherscan.io/, Accessed on: Apr. 2, 2019.

[117]
Y. Yuan and F.-Y. Wang, “Towards blockchain-based intelligent transportation systems,” in Proc. 19th IEEE Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016. pp. 2663-2668. doi: 10.1109/ITSC.2016.7795984.

[118]
Steemit. (2019) [Online]. Available: https://steemit.com/, Accessed on: May 12, 2019.

[119]
EOS. (2019) [Online]. Available: https://eos.io/, Accessed on: May 12, 2019.

[120]
BitShares. (2019) [Online]. Available: https://bitshares.org/, Accessed on: May 13, 2019.

[121]
Hyperledger Fabric. (2019) [Online]. Available: https://www.hyperledger.org/projects/fabric, Accessed on: May 17, 2019.
[122]
Ripple. (2019) [Online]. Available: https://ripple.com/, Accessed on: May 18, 2019.

[123]
Stellar. (2019) [Online]. Available: https://www.stellar.org/, Accessed on: May 19, 2019.

[124]
B. C. Florea, “Blockchain and Internet of Things data provider for smart applications,” in Proc. 2018 7th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 2018, pp. 1-4. doi: 10.1109/MECO.2018.8406041

[125]
IOTA. (2018) [Online]. Available: https://www.iota.org/, Accessed on: Oct. 17, 2018.

[126]
Z. Lu, W. Liu, Q. Wang, G. Qu, and Z. Liu, “A Privacy-Preserving Trust Model Based on Blockchain for VANETs,” in IEEE Access, vol. 6, pp. 45655-45664, 2018. doi: 10.1109/ACCESS.2018.2864189.

[127]
N. Malik, P. Nanda, A. Arora, X. He, and D. Puthal, “Blockchain Based Secured Identity Authentication and Expeditious Revocation Framework for Vehicular Networks,” in Proc. 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 12th IEEE Internal Conference on Big Data Science and Engineering (TrustCom/BigDataSE), New York, NY, 2018. pp. 674-679. doi: 10.1109/TrustCom/BigDataSE.2018.00099.

[128]
J. Kang, Z. Xiong, D. Niyato, D. Ye, D. Kim, and J. Zhao, “Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory,” IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2906-2920, Mar. 2019. doi: 10.1109/TVT.2019.2894944.

[129]
H. Khelifi, S. Luo, B. Nour, H. Moungla, and S.-H. Ahmed, “Reputation-Based Blockchain for Secure NDN Caching in Vehicular Networks,” in Proc. IEEE Conference on Standards for Communications and Networking (CSCN), IEEE, 2018, pp. 1-6. doi: 10.1109/CSCN.2018.8581849.

[130]
R.-W. van der Heijden, F. Engelmann, D. Mödinger, F. Schönig, and F. Kargl, “Blackchain: Scalability for Resource-Constrained Accountable Vehicle-to-X Communication,” in Proc. 1st ACM Workshop on Scalable and Resillient Infrastructures for Distributed Ledgers (SERIAL ’17), New York, NY, USA, 2017. pp. 4-5. doi: 10.1145/3152824.3152828.

[131]
A.-M. Antonopoulos, “Mastering Bitcoin,” O’Reily. Chapter 5, [Online]. Available: https://www.oreilly.com/library/view/mastering-bitcoin/9781491902639/ch05.html

[132]
N. Szabo, “Smart Contracts: Formalizing and Securing Relationships on Public Networks,” First Monday, vol. 2, no. 9-1, Sep. 1997. [Online]. Available: https://journals.uic.edu/ojs/index.php/fm/articl
e/view/548/469.

[133]
S. Burkhard and T. Bocek, “Smart contracts - Blockchains in the wings,” in Digital Marketplaces Unleashed. pp. 169-184, Sep. 2017. doi: 10.1007/978-3-662-49275-8_19.

[134]
A. Juels, A. Kosba, and E. Shi, “The Ring of Gyges: Investigating the Future of Criminal Smart Contracts,” in Proc. of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS’16), ACM, New York, NY, USA, 283-295. doi: 10.1145/2976749.2978362.

[135]
F. Vogelsteller and V. Buterin, “EIP 20: ERC-20 Token Standard,” 2015. [Online]. Available: https://eips.ethereum.org/EIPS/eip-20

[136]
W. Entriken, D. Shirley, J. Evans, and N. Sachs, “EIP 721: ERC-721 Non-Fungible Token Standard,” 2018. [Online]. Available: https://eips.ethereum.org/EIPS/eip-721

[137]
CrypotoKitties. (2019) [Online]. Available: https://www.cryptokitties.co/, Accessed on: Aug. 11, 2019.
[138]
G. A. Carpenter, S. Grossberg, and D. B. Rosen “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,” Neural Networks, vol. 4, pp. 759-771, 1991. doi: 10.1016/0893-6080(91)90056-B.

[139]
G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Transactions on Neural Networks, vol. 3, pp. 698-713, Sep. 1992. doi: 10.1109/72.159059.

[140]
S. Liu and K. G. Paterson, “Simulation-based Selective Opening CCA Security for PKE from Key Encapsulation Mechanisms,” in IACR International Workshop on Public Key Cryptography, Springer, Berlin, Heidelberg, 2015. p. 3-26. doi: 10.1007/978-3-662-46447-2_1.

[141]
A. Casteigts, A. Nayak, and I. Stojmenovic, “Multicasting, Geocasting, and Anycasting in Sensor and Actuator Networks,” in Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scalable Coordination and Data Communication, Wiley, 2010, ch. 5, pp. 127-152. doi: 10.1002/9780470570517.ch5.

[142]
X. Liu, A. Casteigts, N. Goel, A. Nayak, and I. Stojmenovic, “Multiratecast in Wireless Fault Tolerant Sensor and Actuator Networks,” in Proc. 2nd International Conference on Computer Science and its Applications, Jeju, South Korea, 2009. pp. 1-6. doi: 10.1109/CSA.2009.5404301.

[143]
ns-3 Network Simulator. (2018) [Online]. Available: https://www.nsnam.org/, Accessed on: Oct. 16, 2018.
[144]
Simulation of Urban Mobility. (2019) [Online]. Available: http://sumo.sourceforge.net/, Accessed on: Mar. 8, 2019.
[145]
D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, “Recent Development and Applications of SUMO - Simulation of Urban Mobility,” International Journal On Advances in Systems and Measurements, vol. 5 (3&4), pp. 128-138, Dec. 2012.
[146]
go-ethereum. (2019) [Online]. Available: https://github.com/ethereum/go-ethereum/, Accessed on: Mar. 8, 2019.
[147]
Trafﬁc Data Collection System (TDCS). (2018) [Online]. Available: http://tisvcloud.freeway.gov.tw/his
tory/vd/, Accessed on: Oct. 16, 2018.

[148]
V. Buterin, “Log(coins)-sized proofs of inclusion and exclusion for RSA accumulators,” 2018. [Online]. Available: https://ethresear.ch/t/log-coins-sized-proofs-of-inclusion-and-exclusion-for-rsa-accumulators/3839

[149]
A. Marie, “Usage-Based Insurance: What it is, How it Works,” InsuranceHotline, Sep. 2016. [Online]. Available: https://www.insurancehotline.com/usage-based-insurance-what-it-is-how-it-works/

指導教授

周立德(Li-Der Chou)

審核日期

2019-8-22

推文