Generation of an Indoor 2D Map and Track Encryption Based on Mobile Crowdsourcing

  • Authors

    • Tianyang Cao
    2018-09-07
    https://doi.org/10.14419/ijet.v7i3.19.16982
  • mobile crowdsourcing, feature recognition, track integration, unlinear digital filter, multibit adaptive quantization

  • Abstract

    The widespread application of mobile crowdsourcing modes provides new ideas for generating indoor maps. By collecting and analyzing the trajectory datas of users properly, we can obtain the location information of indoor paths.  Unfortunately, currently studies usually rely heavily on a satellite location, which restricts their indoor application. In this paper, a simple and  practical method of generating indoor maps on Andriod platform is presented, and this method  is able to correct deviation duly. User's datas collected by several bulit-in sensors are preprocessed utilizing Gaussian filter, after which we adopt feature recognition to confirming one's  walking track based on multiple experiment datas. In order to integrate tracks generated by different persons, we then propose a new data structure based on a transition probability that can be updated online to store track information. In addition, we minimize possible deviations by testing the signal power launched by four Bluetooth base stations.  Discrete tracks are finally integrated into a complete indoor map using a graph_based model. We then propose a novel encryption scheme exploiting chaos in a nonlinear digital filter, where secure key generation methods are discussed in detail. The secure key scheme includes: 1)channel measurement 2)a decorrelation transform 3)multibit adaptive quantization and encoding. Experiments are conducted in   rectangle fields of 8m*8m, 44m*44m, respectively, and the results show our method can attain a maximum error of 5.94%.

     

     

     

  • References

    1. [1] K. Liu, G. Motta, and T. M, “Navigation services for indoor and outdoor user mobility: An overview,†in 2016 9th International Conference on Service Science (ICSS), Chongqing, 2016, pp. 74-81, pp. 74–81, 2016.

      [2] X. Hai, X. Li, and C. Pan, “Compatibility study between CDR and aeronautical radio navigation service ils/vor,†in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–5, 2017.

      [3] D. V. G. K. I. Dyuldina, S. I. Snopko, K. J. Shakhgeldyan, and V. V. Kryukov, “Indoor navigation service based on Wi-Fi positioning,,†in 2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC), pp. 68–71, 2017.

      [4] K. Liu, G. Motta, and T. Ma, “Navigation services for indoor and out- door user mobility: An overview,†in 2016 9th International Conference on Service Science (ICSS), pp. 74–81, 2016.

      [5] M. H. E. W. T. Sadhu, A. B. Albu and B. Wyvill, “Obstacle Detection for Image-Guided Surface Water Navigation,†in 2016 13th Conference on Computer and Robot Vision (CRV), Victoria, BC, pp. 45–52, 2016.

      [6] T. A. Teo and C. Yu, “Three-dimensional positioning using ALOS/Prism triple linear-array satellite images,†in 2017 IEEE 2nd International Con- ference on Signal and Image Processing (ICSIP), Singapore, pp. 232– 236, 2017.

      [7] M. E. Gorbunov, “Three-dimensional satellite refractive tomography of the atmosphere: Numerical simulation,†Radio Science, vol. 31, pp. 95– 104, Jan.-Feb 1996.

      [8] S. Spira, M. Schneider, T. Welker, J. Mller, and M. A. Hein, “Com- pact three-dimensional four-way vectorial steering module for ka-band multiple feeds-per-beam satellite payload applications,†in 2016 IEEE MTT-S International Microwave Symposium (IMS), San Francisco, CA, pp. 1–4, 2016.

      [9] X. Du, K. Yang, and D. Zhou, “Mapsense: Mitigating Inconsistent WiFi Signals using Signal Patterns and Pathway Map for Indoor Positioning,†IEEE Internet of Things Journal, vol. 99, no. 99, pp. 1–1, 2018.

      [10] K. Nguyen-Huu, K. Lee, and S. W. Lee, “An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided,†in 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, pp. 1–8, 2017.

      [11] S. Bhattacharjee, N. Ghosh, V. K. Shah, and S. K. Das, “QnQ: A reputation model to secure mobile crowdsourcing applications from incentive losses,†in 2017 IEEE Conference on Communications and Network Security (CNS), Las Vegas, NV, pp. 1–9, 2017.

      [12] Z. Chi, Y. Wang, Y. Huang, and X. Tong, “The Novel Location privacy- preserving CKD for Mobile Crowdsourcing Systems,†IEEE Access, vol. 6, pp. 5678–5687, 2017

      [13] Y. Miao, X. L. J. Ma, Z. L. X. Li, and H. Li, “Practical Attribute-Based Multi-keyword search scheme in Mobile crowdsourcing,†IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1–1, 2017.

      [14] J. Rivas, R. Wunderlich, and S. J. Heinen, “An integrated acceleration sensor for traffic condition detection,†in Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, Beijing, pp. 127–132, 2012

      [15] L. Shan, C. Yang, W. Xu, and M. Zhang, “Heterogeneous acceler- ation for CNN training with many integrated core,†in 2017 IEEE International Conference on Signal Processing, Communications and Computing(ICSPCC), Xiamen, pp. 1–6, 2017.

      [16] R. Pich, S. Chivapreecha, and J. Prabnasak, “A new key generator for data encryption using chaos in digital filter,†in 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, pp. 87–92, 2017.

      [17] Y. Umezawa, M. Dobashi, H. Kamata, and T. Endo, “Chaos signal generator by iir digital filters including nonlinear functions and its appli- cation,†in Proceedings KES ’98. 1998 Second International Conference on, Adelaide, SA, pp. 169–175, 1998.

      [18] N. Patwari, J. Croft, S. Jana, and S. K. Kasera, “High-rate uncorrelated bit extraction for shared secret key generation from channel measure- ments,†IEEE Transactions on Mobile Computing, vol. 9, pp. 17–30, Jan 2017.

      [19] S. H. K. H. et.al, “Evaluation of denoising performance indices for noisy partial discharge signal based on DWT technique,†in 2017 IEEE 15th Student Conference on Research and Development (SCOReD), pp. 392– 397, 2017.

      [20] A. Limmanee and W. Henkel, “Secure physical-layer key generation protocol and key encoding in wireless communications,†in 2010 IEEE Globecom Workshops, Miami, FL, pp. 94–98, 2010

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  • How to Cite

    Cao, T. (2018). Generation of an Indoor 2D Map and Track Encryption Based on Mobile Crowdsourcing. International Journal of Engineering & Technology, 7(3.19), 4-19. https://doi.org/10.14419/ijet.v7i3.19.16982

    Received date: 2018-08-06

    Accepted date: 2018-08-06

    Published date: 2018-09-07