Abstract
With the increasing popularity of Radio Frequency Identification (RFID) technology, indoor applications based on RFID trajectory data analysis are becoming more and more extensive, such as personnel location, tracking, and heat map analysis. The effectiveness of indoor applications relies greatly on high-quality trajectory data. However, due to the constraints of the device and environment, RFID readers will miss reading data in real-world practice, which leads to a large number of indoor trajectories that are incomplete. To enhance trajectory data and support indoor applications more efficiently, many trajectory recovery methods to infer trajectories in free space have been proposed. However, existing methods cannot achieve automated inference and have low accuracy in inferring indoor trajectories. In this paper, we propose an Indoor Trajectory Automatic Recovery framework, ITAR, to recover missing points in indoor trajectories. ITAR adopts a sequence-to-sequence learning architecture to generate complete trajectories. We first construct a directed graph for each trajectory and use a graph neural network to capture complex location transition patterns. Then, we propose a multi-head attention mechanism to capture long-term correlations among trajectory points to improve performance. We conduct extensive experiments on synthetic and real datasets, and the results show that ITAR is superior in performance and robustness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baba, A.I., Jaeger, M., Lu, H., Pedersen, T.B., Ku, W.S., **e, X.: Learning-based cleansing for indoor RFID data. In: Proceedings of the 2016 International Conference on Management of Data, pp. 925–936 (2016)
Baba, A.I., Lu, H., Pedersen, T.B., **e, X.: Handling false negatives in indoor RFID data. In: 2014 IEEE 15th International Conference on Mobile Data Management, vol. 1, pp. 117–126. IEEE (2014)
Baba, A.I., Lu, H., **e, X., Pedersen, T.B.: Spatiotemporal data cleansing for indoor RFID tracking data. In: 2013 IEEE 14th International Conference on Mobile Data Management, vol. 1, pp. 187–196. IEEE (2013)
Derakhshan, R., Orlowska, M.E., Li, X.: RFID data management: challenges and opportunities. In: 2007 IEEE International Conference on RFID, pp. 175–182. IEEE (2007)
Fazzinga, B., Flesca, S., Furfaro, F., Parisi, F.: Cleaning trajectory data of RFID-monitored objects through conditioning under integrity constraints. In: EDBT, pp. 379–390 (2014)
Fazzinga, B., Flesca, S., Furfaro, F., Parisi, F.: Offline cleaning of RFID trajectory data. In: Proceedings of the 26th International Conference on Scientific and Statistical Database Management, pp. 1–12 (2014)
Fazzinga, B., Flesca, S., Furfaro, F., Parisi, F.: Interpreting RFID tracking data for simultaneously moving objects: an offline sampling-based approach. Expert Syst. Appl. 152, 113368 (2020)
Feng, J., et al.: DeepMove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 1459–1468 (2018)
Feng, Y., Huang, W., Wang, S., Zhang, Y., Jiang, S.: Detection of RFID cloning attacks: a spatiotemporal trajectory data stream-based practical approach. Comput. Netw. 189, 107922 (2021)
Floerkemeier, C., Lampe, M.: Issues with RFID usage in ubiquitous computing applications. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 188–193. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24646-6_13
Gu, Yu., Yu, G., Chen, Y., Ooi, B.C.: Efficient RFID data imputation by analyzing the correlations of monitored objects. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 186–200. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00887-0_15
Hu, K.F., Li, L., Lu, Z.P.: AgCleaning: a track data filling algorithm based on movement recency for RFID track data. In: Applied Mechanics and Materials, vol. 490, pp. 1330–1337. Trans Tech Publ (2014)
Huang, W., Zhang, Y., Feng, Y.: ACD: an adaptable approach for RFID cloning attack detection. Sensors 20(8), 2378 (2020)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in Adam (2017)
Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Morzy, M.: Prediction of moving object location based on frequent trajectories. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 583–592. Springer, Heidelberg (2006). https://doi.org/10.1007/11902140_62
Ren, H., et al.: Mtrajrec: map-constrained trajectory recovery via seq2seq multi-task learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1410–1419 (2021)
Sun, H., Yang, C., Deng, L., Zhou, F., Huang, F., Zheng, K.: Periodicmove: shift-aware human mobility recovery with graph neural network. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 1734–1743 (2021)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Tong, C., Chen, H., Xuan, Q., Yang, X.: A framework for bus trajectory extraction and missing data recovery for data sampled from the internet. Sensors 17(2), 342 (2017)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS 2017, pp. 6000–6010. Curran Associates Inc., Red Hook (2017)
Wang, J., Wu, N., Lu, X., Zhao, W.X., Feng, K.: Deep trajectory recovery with fine-grained calibration using Kalman filter. IEEE Trans. Knowl. Data Eng. 33(3), 921–934 (2019)
Wang, S., Cao, Z., Zhang, Y., Huang, W., Jiang, J.: A temporal and spatial data redundancy processing algorithm for RFID surveillance data. Wirel. Commun. Mob. Comput. 2020 (2020)
Wheeb, A.H.: Performance analysis of VOIP in wireless networks. Int. J. Comput. Netw. Wirel. Commun. (IJCNWC) 7(4), 1–5 (2017)
Wu, H., Chen, Z., Sun, W., Zheng, B., Wang, W.: Modeling trajectories with recurrent neural networks. In: IJCAI (2017)
**, D., Zhuang, F., Liu, Y., Gu, J., **ong, H., He, Q.: Modelling of bi-directional spatio-temporal dependence and users’ dynamic preferences for missing poi check-in identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5458–5465 (2019)
**a, T., et al.: AttnMove: history enhanced trajectory recovery via attentional network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4494–4502 (2021)
**e, L., Yin, Y., Vasilakos, A.V., Lu, S.: Managing RFID data: challenges, opportunities and solutions. IEEE Commun. Surv. Tutor. 16(3), 1294–1311 (2014)
Yang, C., Sun, M., Zhao, W.X., Liu, Z., Chang, E.Y.: A neural network approach to jointly modeling social networks and mobile trajectories. ACM Trans. Inf. Syst. (TOIS) 35(4), 1–28 (2017)
Zhao, J., Xu, J., Zhou, R., Zhao, P., Zhu, F.: On prediction of user destination by sub-trajectory understanding: a deep learning based approach. In: the 27th ACM International Conference (2018)
Zhao, Z., Ng, W.: A model-based approach for RFID data stream cleansing. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 862–871 (2012)
Zheng, S., Yue, Y., Hobbs, J.: Generating long-term trajectories using deep hierarchical networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Acknowledgments
This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040300.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cao, Z., Wang, S., Sun, D., Zhang, Y., Feng, Y., Jiang, S. (2022). ITAR: A Method for Indoor RFID Trajectory Automatic Recovery. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-24386-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24385-1
Online ISBN: 978-3-031-24386-8
eBook Packages: Computer ScienceComputer Science (R0)