III: Small: Indoor Spatial Query Evaluation and Trajectory Tracking with Bayesian Filtering Techniques

* Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No.1618669.

* Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

 

·       Award number: 1618669

·       Duration: September 1, 2016 to August 31, 2020 (Estimated)

·       Award amount: $499,995.00

·       Award title: Indoor Spatial Query Evaluation and Trajectory Tracking with Bayesian Filtering Techniques

·       PI and co-PI: Wei-Shinn Ku and Xiao Qin

·       Students: Wenlu Wang

                  Ting Shen

                  Jingjing Li

                  Bo Hui

·       Collaborators: Hua Lu (Aalborg University, Denmark)

                         Kun-Ta Chuang (National Cheng Kung University, Taiwan, ROC)

                         Asif I. Baba (Tuskegee University, AL, USA)

                         Min-Te Sun (National Central University, Taiwan, ROC)

                         Kazuya Sakai (Tokyo Metropolitan University, Japan)

·       Project Goals:

Users have more and more demand for launching spatial queries for finding friends or points of interest in indoor spaces. However, existing spatial query evaluation techniques for outdoor environments (either based on Euclidean distance or network distance) cannot be applied in indoor spaces because these techniques assume that user locations can be acquired from GPS signals or cellular positioning, but the assumption does not hold in covered indoor spaces. We study various indoor spatial data management challenges in this project.

·       Research Challenges:

Existing spatial query evaluation techniques for outdoor environments (either based on Euclidean distance or network distance) cannot be applied in indoor spaces because these techniques assume that user locations can be acquired from GPS signals or cellular positioning, but the assumption does not hold in covered indoor spaces. Furthermore, indoor spaces are usually modeled differently from outdoor spaces. In indoor environments, user movements are enabled or constrained by entities and topologies such as doors, walls, and hallways.

·       Current Results:

Six peer-reviewed journal papers and seven peer-reviewed conference papers have been published based on results of this project.

·       Publications and Presentations:

1.    Wenlu Wang, Zhitao Gong, Ji Zhang, Hua Lu, and Wei-Shinn Ku, “On Location Privacy in Fingerprinting-based Indoor Positioning System: An Encryption Approach,” in Proceedings of the 27^th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), Chicago, IL, USA, 2019.

2.    Jingjing Li, Wenlu Wang, Wei-Shinn Ku, Yingtao Tian, and Haixun Wang, “SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension,” in Proceedings of the 27^th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), Chicago, IL, USA, 2019.

3.    Ji Zhang, Wenlu Wang, Xunfei Jiang, Wei-Shinn Ku, and Hua Lu, “An MBR-Oriented Approach for Efficient Skyline Query Processing,” in Proceedings of the 35^th IEEE International Conference on Data Engineering (ICDE), Macau, China, 2019.

4.    Wenlu Wang, Ji Zhang, Min-Te Sun, and Wei-Shinn Ku, “A Scalable Spatial Skyline Evaluation System Utilizing Parallel Independent Region Groups,” The International Journal on Very Large Data Bases (VLDBJ), Vol. 28, No. 1, pp. 73-98, 2019.

5.    Ji Zhang, Ting Shen, Wenlu Wang, Xunfei Jiang, Wei-Shinn Ku, Min-Te Sun, and Yao-Yi Chiang, “A VLOS Compliance Solution to Ground/Aerial Parcel Delivery Problem,” in Proceedings of the 20^th IEEE International Conference on Mobile Data Management (MDM), Hong Kong, 2019.

6.    Ji Zhang, Po-Wei Harn, Wei-Shinn Ku, Min-Te Sun, Xiao Qin, Hua Lu, and Xunfei Jiang, “An Overlapping Voronoi Diagram-based System for Multi-Criteria Optimal Location Queries,” GeoInformatica, Vol. 23, Issue 1, pp. 105-161, 2019.

7.    Kazuya Sakai, Min-Te Sun, Wei-Shinn Ku, Hua Lu, and Ten H. Lai, “Data Verification in Integrated RFID Systems,” IEEE Systems Journal, Vol. 13, Issue 2, pp. 1969-1980, 2019.

8.    Ting Shen, Haiquan Chen, and Wei-Shinn Ku, “Time-aware Location Sequence Recommendation for Cold-start Mobile Users,” in Proceedings of the 26^th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), Seattle, WA, USA, 2018.

9.    Wenlu Wang and Wei-Shinn Ku, “Recommendation-based Smart Indoor Navigation”, in Proceedings of the 2^nd ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), Pittsburgh, PA, USA, 2017. doi:10.1145/3054977.3057288

10. Shan-Yun Teng, Wei-Shinn Ku, and Kun-Ta Chuang, “Toward Mining Stop-by Behaviors in Indoor Space,” ACM Transactions on Spatial Algorithms and Systems (TSAS), Vol. 3, Issue 2, 2017. doi:10.1145/3106736

11. Shan-Yun Teng, Wei-Shinn Ku, and Kun-Ta Chuang, “Toward Mining User Movement Behaviors in Indoor Environments,” ACM SIGSPATIAL Special, Vol. 9, Issue 2, pp. 19-27, 2017. doi:10.1145/3151123.3151127

12. Wenlu Wang, Ji Zhang, Min-Te Sun, and Wei-Shinn Ku, “Efficient Parallel Spatial Skyline Evaluation Using MapReduce,” in Proceedings of the 20^th International Conference on Extending Database Technology (EDBT), Venice, Italy, 2017. doi:10.5441/002/edbt.2017.38, project poster

13. Wenlu Wang and Wei-Shinn Ku, “Dynamic Indoor Navigation with Bayesian Filters,” ACM SIGSPATIAL Special, Vol. 8, Issue 3, pp. 9-10, 2016. doi:10.1145/3100243.3100249

·       Data: GeoNames

·       Software Downloads: https://github.com/DataScienceLab18/IndoorToolKit

·       Broader Impacts:

The research results of this project will improve the performance of numerous high value-added indoor applications and hence benefit the economy of our country. In addition, the ability to be able to locate people in indoor spaces will improve emergency response. The project will promote teaching, learning, and training by exposing both undergraduate and graduate students to mathematical and technological underpinnings in the field of spatial data management.

·       Educational Material:

The project will promote teaching, learning, and training by exposing both undergraduate and graduate students to mathematical and technological underpinnings in the field of spatial data management.

·       Awards: ACM SIGSPATIAL 1^st Student Research Competition, 3^rd place

·       Point of Contact: Dr. Wei-Shinn Ku (weishinn@auburn.edu)

·       Date of Last Update: 9/3/2019