Abstract
With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.
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References
https://www.oica.net/. Accessed 11 Feb 2021
Qi, L., Wang, X., Xu, X., Dou, W., Li, S.: Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing. IEEE Trans. Netw. Sci. Eng. 8(2), 1145–1153 (2020). https://doi.org/10.1109/TNSE.2020.2969489
Xu, X., et al.: Secure service offloading for internet of vehicles in sdn-enabled mobile edge computing. IEEE Trans. Intell. Transp. Syst. 22(6), 3720–3729 (2020). https://doi.org/10.1109/TITS.2020.3034197
Alghamdi, T., Elgazzar, K., Bayoumi, M., Sharaf, T., Shah, S.: Forecasting traffic congestion using arima modeling. In: 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC), pp. 1227–1232 (2019). https://doi.org/10.1109/IWCMC.2019.87666982019
Zheng, L., Yang, J., Chen, L., Sun, D., Liu, W.: Dynamic spatial-temporal feature optimization with eri big data for short-term traffic flow prediction. Neurocomputing 412, 339–350 (2020)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)
Liu, Y., et al.: An attention-based category-aware GRU model for the next POI recommendation. Int. J. Intell. Syst. 36(7), 3174–3189 (2021). https://doi.org/10.1002/int.22412
Filmon, G.H., Mecit, C.: Short-term traffic flow rate forecasting based on identifying similar traffic patterns. Transp. Res. Part C Emerg. Technol. 66, 61–78 (2016)
Liu, Y., Yu, J.J.Q., Kang, J., Niyato, D., Zhang, S.: Privacy-preserving traffic flow prediction: a federated learning approach. IEEE Internet Things J. 7(8), 7751–7763 (2020)
Qi, Y., Hossain, M.S., Nie, J., Li, X.: Privacy-preserving blockchain-based federated learning for traffic flow prediction. Futur. Gener. Comput. Syst. 117, 328–337 (2021)
Qi, L., et al.: Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Trans. Ind. Inform. 17(6), 4159–4167 (2020). https://doi.org/10.1109/TII.2020.3012157
Wang, F., Zhu, H., Srivastava, G., Li, S., Khosravi, M., Qi, L.: Robust collaborative filtering recommendation with user-item-trust records. IEEE Trans. Comput. Soc. Syst. (2020). https://doi.org/10.1109/TCSS.2021.3064213
Li, S., Choo, K.R., Sun, Q., Buchanan, W.J., Cao, J.: IOT forensics: Amazon echo as a use case. IEEE Internet Things J. 6(4), 6487–6497 (2019)
Xu, X., et al.: Service offloading with deep q-network for digital twinning empowered internet of vehicles in edge computing. IEEE Trans. Industr. Inf. (2020). https://doi.org/10.1109/TII.2020.3040180
Wang, F., et al.: 6G-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing. IEEE Internet Things J. 8(7), 5321–5331 (2020). https://doi.org/10.1109/JIOT.2020.3037669
Xu, X., et al.: Edge server quantification and placement for offloading social media services in industrial cognitive IOV. IEEE Trans. Industr. Inf. 17(4), 2910–2918 (2020). https://doi.org/10.1109/TII.2020.2987994
Xu, X., et al.: PDM: privacy-aware deployment of machine-learning applications for industrial cyber-physical cloud systems. IEEE Trans. Industr. Inf. 17(8), 5819–5828 (2020). https://doi.org/10.1109/TII.2020.3031440
Zhang, Z., Cheng, Y., Liu, N.C.: Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories. Scientometrics 101(3), 1679–1693 (2014)
Acknowledgement
This research work was partially supported by the National Natural Science Foundation of China (No. 42050102).
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Qi, L. et al. (2022). Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_78
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DOI: https://doi.org/10.1007/978-981-16-6554-7_78
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