Skip to main content

2022 | OriginalPaper | Buchkapitel

11. Advanced Signal Processing for Autonomous Transportation Big Data

verfasst von : Haibin Lv, Dongliang Chen, Jinkang Guo, Zhihan Lv

Erschienen in: Intelligent Cyber-Physical Systems for Autonomous Transportation

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

To study the advanced signal processing for big data in industrial production, this study builds an advanced signal processing system for industrial big data. Then, it compares and analyzes the simulation performance of Kafka clusters with MapReduce and Spark algorithms, respectively. The results show that for data transmission, when the successful propagation probability is 100% and the λ value is 0.01–0.05, it is closest to the actual result, and the data propagation delay is gradually smallest. Through a comparative analysis of system performance, it is found that compared to MapReduce and Spark algorithms, Kafka clusters require the shortest running time at the same data scale and the same computing nodes. Further analysis of their packet loss rate indicates that as the number of collection points increases, the amount of transmitted data has only increased slightly, but the packet loss rate has not changed significantly. Therefore, this study suggests that the system can reduce the delay of data transmission and the running time significantly, which provides experimental references for later industrial production and development.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Xu, Y., Sun, Y., Wan, J., Liu, X., & Song, Z. (2017). Industrial big data for fault diagnosis: Taxonomy, review, and applications. IEEE Access, 5, 17368–17380.CrossRef Xu, Y., Sun, Y., Wan, J., Liu, X., & Song, Z. (2017). Industrial big data for fault diagnosis: Taxonomy, review, and applications. IEEE Access, 5, 17368–17380.CrossRef
2.
Zurück zum Zitat Psannis, K. E., Stergiou, C., & Gupta, B. B. (2018). Advanced media-based smart big data on intelligent cloud systems. IEEE Transactions on Sustainable Computing, 4(1), 77–87.CrossRef Psannis, K. E., Stergiou, C., & Gupta, B. B. (2018). Advanced media-based smart big data on intelligent cloud systems. IEEE Transactions on Sustainable Computing, 4(1), 77–87.CrossRef
3.
Zurück zum Zitat Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047.CrossRef Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047.CrossRef
4.
Zurück zum Zitat Khan, M., Wu, X., Xu, X., & Dou, W. (2017, May). Big data challenges and opportunities in the hype of Industry 4.0. In: 2017 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. Khan, M., Wu, X., Xu, X., & Dou, W. (2017, May). Big data challenges and opportunities in the hype of Industry 4.0. In: 2017 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.
5.
Zurück zum Zitat Lv, Z., Song, H., Basanta-Val, P., Steed, A., & Jo, M. (2017). Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Transactions on Industrial Informatics, 13(4), 1891–1899.CrossRef Lv, Z., Song, H., Basanta-Val, P., Steed, A., & Jo, M. (2017). Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Transactions on Industrial Informatics, 13(4), 1891–1899.CrossRef
6.
Zurück zum Zitat He, Y., Guo, J., & Zheng, X. (2018). From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5), 120–129.CrossRef He, Y., Guo, J., & Zheng, X. (2018). From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35(5), 120–129.CrossRef
7.
Zurück zum Zitat Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Deen, M. J. (2017). Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2), 790–798.CrossRef Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Deen, M. J. (2017). Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2), 790–798.CrossRef
8.
Zurück zum Zitat Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018). Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access, 6, 17190–17197.CrossRef Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018). Industrial big data analytics for prediction of remaining useful life based on deep learning. IEEE Access, 6, 17190–17197.CrossRef
9.
Zurück zum Zitat Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017).The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0[J]. IEEE Industrial Electronics Magazine, 11(1), 17–27.CrossRef Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017).The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0[J]. IEEE Industrial Electronics Magazine, 11(1), 17–27.CrossRef
10.
Zurück zum Zitat He, M., & He, D. (2017). Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 53(3), 3057–3065.CrossRef He, M., & He, D. (2017). Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 53(3), 3057–3065.CrossRef
11.
Zurück zum Zitat Yan, J., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access, 5, 23484–23491.CrossRef Yan, J., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access, 5, 23484–23491.CrossRef
12.
Zurück zum Zitat Li, P., Chen, Z., Yang, L. T., Gao, J., Zhang, Q., & Deen, M. J. (2018). An incremental deep convolutional computation model for feature learning on industrial big data. IEEE Transactions on Industrial Informatics, 15(3), 1341–1349.CrossRef Li, P., Chen, Z., Yang, L. T., Gao, J., Zhang, Q., & Deen, M. J. (2018). An incremental deep convolutional computation model for feature learning on industrial big data. IEEE Transactions on Industrial Informatics, 15(3), 1341–1349.CrossRef
13.
Zurück zum Zitat Wan, J., Hong, J., Pang, Z., Jayaraman, B., & Shen, F. (2019). IEEE ACCESS Special section editorial: Key technologies for smart factory of Industry 4.0. IEEE Access, 7, 17969–17974.CrossRef Wan, J., Hong, J., Pang, Z., Jayaraman, B., & Shen, F. (2019). IEEE ACCESS Special section editorial: Key technologies for smart factory of Industry 4.0. IEEE Access, 7, 17969–17974.CrossRef
14.
Zurück zum Zitat Mcmahon, P., Zhang, T., & Dwight, R. (2020). Requirements for big data adoption for railway asset management. IEEE Access, 8, 15543–15564.CrossRef Mcmahon, P., Zhang, T., & Dwight, R. (2020). Requirements for big data adoption for railway asset management. IEEE Access, 8, 15543–15564.CrossRef
15.
Zurück zum Zitat Patole, S. M., Torlak, M., Wang, D., & Ali, M. (2017). Automotive radars: A review of signal processing techniques. IEEE Signal Processing Magazine, 34(2), 22–35.CrossRef Patole, S. M., Torlak, M., Wang, D., & Ali, M. (2017). Automotive radars: A review of signal processing techniques. IEEE Signal Processing Magazine, 34(2), 22–35.CrossRef
16.
Zurück zum Zitat Allwood, G., Du, X., Webberley, K. M., Osseiran, A., & Marshall, B. J. (2018). Advances in acoustic signal processing techniques for enhanced bowel sound analysis. IEEE Reviews in Biomedical Engineering, 12, 240–253.CrossRef Allwood, G., Du, X., Webberley, K. M., Osseiran, A., & Marshall, B. J. (2018). Advances in acoustic signal processing techniques for enhanced bowel sound analysis. IEEE Reviews in Biomedical Engineering, 12, 240–253.CrossRef
17.
Zurück zum Zitat Mishra, K. V., Shankar, M. B., Koivunen, V., Ottersten, B., & Vorobyov, S. A. (2019). Toward millimeter-wave joint radar communications: A signal processing perspective. IEEE Signal Processing Magazine, 36(5), 100–114.CrossRef Mishra, K. V., Shankar, M. B., Koivunen, V., Ottersten, B., & Vorobyov, S. A. (2019). Toward millimeter-wave joint radar communications: A signal processing perspective. IEEE Signal Processing Magazine, 36(5), 100–114.CrossRef
19.
Zurück zum Zitat Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019). Big data issues in smart grids: A survey. IEEE Systems Journal, 13(4), 4158–4168.CrossRef Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019). Big data issues in smart grids: A survey. IEEE Systems Journal, 13(4), 4158–4168.CrossRef
20.
Zurück zum Zitat Deutsch, J., & He, D. (2017). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 11–20.CrossRef Deutsch, J., & He, D. (2017). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 11–20.CrossRef
21.
Zurück zum Zitat Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018). Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383–398.CrossRef Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018). Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383–398.CrossRef
22.
Zurück zum Zitat Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access, 6, 32328–32338.CrossRef Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access, 6, 32328–32338.CrossRef
23.
Zurück zum Zitat Rani, S., Ahmed, S. H., Talwar, R., & Malhotra, J. (2017). Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Transactions on Industrial Informatics, 13(4), 1961–1968.CrossRef Rani, S., Ahmed, S. H., Talwar, R., & Malhotra, J. (2017). Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Transactions on Industrial Informatics, 13(4), 1961–1968.CrossRef
24.
Zurück zum Zitat Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247–5261.CrossRef Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247–5261.CrossRef
25.
Zurück zum Zitat Kaur, D., Aujla, G. S., Kumar, N., Zomaya, A. Y., Perera, C., & Ranjan, R. (2018). Tensor-based big data management scheme for dimensionality reduction problem in smart grid systems: SDN perspective. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1985–1998.CrossRef Kaur, D., Aujla, G. S., Kumar, N., Zomaya, A. Y., Perera, C., & Ranjan, R. (2018). Tensor-based big data management scheme for dimensionality reduction problem in smart grid systems: SDN perspective. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1985–1998.CrossRef
26.
Zurück zum Zitat Singh, A., Aujla, G. S., Garg, S., Kaddoum, G., & Singh, G. (2019). Deep-learning-based SDN model for Internet of Things: An incremental tensor train approach. IEEE Internet of Things Journal, 7(7), 6302–6311.CrossRef Singh, A., Aujla, G. S., Garg, S., Kaddoum, G., & Singh, G. (2019). Deep-learning-based SDN model for Internet of Things: An incremental tensor train approach. IEEE Internet of Things Journal, 7(7), 6302–6311.CrossRef
27.
Zurück zum Zitat Aujla, G. S., & Jindal, A. (2020). A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring. IEEE Journal on Selected Areas in Communications, 39(2), 491–499.CrossRef Aujla, G. S., & Jindal, A. (2020). A decoupled blockchain approach for edge-envisioned IoT-based healthcare monitoring. IEEE Journal on Selected Areas in Communications, 39(2), 491–499.CrossRef
28.
Zurück zum Zitat Liu, H., Ong, Y. S., Shen, X., & Cai, J. (2020). When Gaussian process meets big data: A review of scalable GPs. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4405–4423.MathSciNetCrossRef Liu, H., Ong, Y. S., Shen, X., & Cai, J. (2020). When Gaussian process meets big data: A review of scalable GPs. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4405–4423.MathSciNetCrossRef
29.
Zurück zum Zitat Xu, W., Zhou, H., Cheng, N., Lyu, F., Shi, W., Chen, J., & Shen, X. (2017). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5(1), 19–35.CrossRef Xu, W., Zhou, H., Cheng, N., Lyu, F., Shi, W., Chen, J., & Shen, X. (2017). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5(1), 19–35.CrossRef
30.
Zurück zum Zitat Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., & Yang, Q. (2017). Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Transactions on Industrial Informatics, 13(5), 2140–2150.CrossRef Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., & Yang, Q. (2017). Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Transactions on Industrial Informatics, 13(5), 2140–2150.CrossRef
31.
Zurück zum Zitat Zhang, Q., Yang, L. T., Chen, Z., Li, P., & Bu, F. (2018). An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Transactions on Industrial Informatics, 15(4), 2330–2337.CrossRef Zhang, Q., Yang, L. T., Chen, Z., Li, P., & Bu, F. (2018). An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Transactions on Industrial Informatics, 15(4), 2330–2337.CrossRef
32.
Zurück zum Zitat Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2017). Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505–6519.CrossRef Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2017). Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505–6519.CrossRef
33.
Zurück zum Zitat Sharma, S. K., & Wang, X. (2017). Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access, 5, 4621–4635.CrossRef Sharma, S. K., & Wang, X. (2017). Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access, 5, 4621–4635.CrossRef
34.
Zurück zum Zitat Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying fog computing in industrial internet of things and industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674–4682.CrossRef Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying fog computing in industrial internet of things and industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674–4682.CrossRef
Metadaten
Titel
Advanced Signal Processing for Autonomous Transportation Big Data
verfasst von
Haibin Lv
Dongliang Chen
Jinkang Guo
Zhihan Lv
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-92054-8_11

Premium Partner