The University of Connecticut

Ubiquitous and Urban Computing (U2C) Lab

Journal Publications


Preprints


  1. S. He and K. G. Shin, "Distribution Prediction for Reconfiguring Urban Dockless E-Scooter Sharing Systems", IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear.
  2. S. He and K. G. Shin, "Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Coordination", IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear.
  3. J. Luo, R. Bai, S. He and K. G. Shin, "Pervasive Pose Estimation for Fall Detection", IACM Transactions on Computing for Healthcare (HEALTH), to appear.
  4. C. Xiang, Y. Zhou, H. Dai, Y. Qu, S. He, C. Chen and P. Yang, "​Reusing Delivery Drones for Urban Crowdsensing", IEEE Transactions on Mobile Computing (TMC), to appear.


2022


  1. S. He and K. G. Shin, "Information Fusion for (Re)Configuring Bike Station Networks with Crowdsourcing", IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 34, no. 2, pp. 736--752, Feb. 2022. [pdf].
  2. X. Yang, S. He, B. Wang, and M. Tabatabaie, "Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions: A Wi-Fi-based Mobility Case Study", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2022), vol. 5, no. 187, pp 1–24, Dec. 2021. [pdf].


2021


  1. N. Liu, T. He, S. He and Q. Niu, "Indoor Localization with Adaptive Signal Sequence Representations", IEEE Transactions on Vehicular Technology (TVT), vol. 70, no. 11, pp. 11678-11694, Nov. 2021. [pdf].
  2. H. Huang, X. Yang, and S. He, "Multi-Head Spatio-Temporal Attention Mechanism For Urban Anomaly Event Prediction", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2021), vol. 5, no. 104, pp 3:1–3:21, Sept. 2021. [pdf].
  3. C. Xiang, S. He, K. G. Shin, Y. Qu and P. Yang, "Incentivizing Platform--User Interactions for Crowdsensing", IEEE Internet of Things Journal (IoT-J), vol. 8, no. 10, pp. 8314--8327, May 2021. [pdf].


2020


  1. Q. Niu, T. He, N. Liu, S. He, X. Luo and F. Zhou, "MAIL: Multi-Scale Attention-Guided Indoor Localization Using Geomagnetic Sequences", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2020), vol. 4, no. 2, pp. 54:1-54:23, June 2020. [pdf].


2019


  1. Q. Niu, N. Liu, J. Huang, Y. Luo, S. He, T. He, S.-H. Chan and X. Luo, “DeepNavi: A Deep Signal-Fusion Framework for Accurate and Applicable Indoor Navigation”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2019), vol. 3, No. 3, pp. 99:1-99:24, September 2019, [pdf].
  2. S. He and K. G. Shin, "Spatio-Temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 4, pp. 39:1--39:28, July 2019. (IF: 3.19). [pdf].
  3. S. He, S.-H. Chan, L. Yu and N. Liu, "Maxlifd: Joint Maximum Likelihood Localization Fusing Fingerprints and Mutual Distances," IEEE Transactions on Mobile Computing (TMC), vol. 18, no. 3, pp. 602 - 617, March 1, 2019. (IF: 4.098), [pdf].
  4. K.-H. Chow, S. He*, J. Tan and S.-H. Chan, "Efficient Locality Classification for Indoor Fingerprint-based Systems," IEEE Transactions on Mobile Computing (TMC), vol. 18, no. 2, pp. 290-304, February 1, 2019. (IF: 4.098), [pdf].
  5. H. Wu, Z. Mo, J. Tan, S. He and S.-H. Chan, "Efficient Indoor Localization Based on Geomagnetism," ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 4, pp. 42:1--42:25, 2019. (IF: 2.313), [pdf].
  6. Q. Niu, M. Li, S. He, C. Gao, S.-H. Chan and X. Luo, "Resource-efficient and Automated Image-based Indoor Localization," ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 2, pp. 19:1 - 19:31, February, 2019. (IF: 2.313), [pdf].


2018


  1. S. He, S.-H. Chan, L. Yu and N. Liu, "SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization," IEEE Transactions on Mobile Computing (TMC), vol. 17, no. 5, pp. 1176-1189, May 1, 2018. ​(IF: 4.098), [pdf].
  2. S. He and K. G. Shin, "Geomagnetism for Smartphone-based Indoor Localization: Challenges, Advances, and Comparison," ACM Computing Surveys (CSUR), vol. 50, no. 6, pp. 1-37, December 2017/January 2018. (IF: 6.748), [pdf].


2017


  1. S. He, W. Lin and S.-H. Chan, ​"Indoor Localization and Automatic Fingerprint Update with Altered AP Signals," IEEE Transactions on Mobile Computing (TMC), vol. 16, no. 7, pp. 1897-1910, July 1 2017. (IF: 4.098), [pdf].
  2. S. He and S.-H. Chan, ​"INTRI: Contour-based Trilateration for Indoor Fingerprint-based Localization," IEEE Transactions on Mobile Computing (TMC), vol. 16, no. 6, pp. 1676-1690, June 1 2017. (IF: 4.098), [pdf].
  3. S. He, T. Hu and S.-H. Chan, "Towards Practical Deployment of Fingerprint-based Indoor Localization," IEEE Pervasive Computing Magazine, vol. 16, no. 2, pp. 76-83, April - June 2017. (IF: 3.022)​, [pdf].


2016


  1. S. He, B. Ji and S.-H. Chan, ''Chameleon: Survey-free Updating of Fingerprint Database for Indoor Localization," IEEE Pervasive Computing Magazine, Vol. 15, pp. 66-75, October - December 2016. (IF: 3.022), [pdf].
  2. S. He and S.-H. Chan, "Tilejunction: Mitigating Signal Noise for Fingerprint-based Indoor Localization,'' IEEE Transactions on Mobile Computing (TMC), Vol. 15, No. 6, pp. 1554-1568, June 2016. (IF: 4.098), [pdf].
  3. S. He and S.-H. Chan, "Wi-Fi Fingerprint-based Indoor Positioning: Recent Advances and Comparisons,'' IEEE Communications Surveys and Tutorials, Vol. 18, pp. 466-490, First quarter 2016. (IF: 20.23), [pdf] (Highly-cited Paper!).

Conference Publications


2022


  1. C. Xiang, Y. Li, Y. Zhou, S. He, Y. Qu, Z. Li, L. Gong, and C. Chen, "A Comparative Approach to Resurrecting the Market of MOD Vehicular Crowdsensing", in Proceedings of IEEE Conference on Computer Communications (INFOCOM 2022), to appear, Acceptance Rate: 19.9%, (225 out of 1129).


2021


  1. B. Guo, S. Wang, Y. Ding, G. Wang, S. He, D. Zhang and T. He, "Concurrent Order Dispatch for Instant Delivery with Time-Constrained Actor-Critic Reinforcement Learning", in Proceedings of 42nd IEEE Real-Time Systems Symposium (RTSS 2021), pp. 176-187, (Outstanding Paper Award & Best Paper Candidate), [pdf].
  2. M. Tabatabaie, S. He and X. Yang, "Reinforced Feature Extraction and Multi-Resolution Learning for Driver Mobility Fingerprint Identification", in Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2021 (SIGSPATIAL 2021), pp. 69--80, November 2-5, 2021. Acceptance Rate: 22.4% (34 out of 152), [pdf].


2020


  1. X. Yang, S. He and H. Huang, "Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction", in Proceedings of IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS 2020), (Delhi NCR, India), pp. 640--648, December 10--13, 2020, [pdf].
  2. X. Yang and S. He, "Towards Dynamic Urban Bike Usage Prediction for Station Network Reconfiguration", in Proceedings of the 9th ACM SIGKDD International Workshop on Urban Computing (UrbComp), (San Diego, CA), August 24, 2020, [pdf].
  3. S. He and K. G. Shin, "Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems" (oral presentation/full paper), in Proceedings of The World Wide Web Conference (WWW 2020), pp. 88--98, April 20-24, 2020, Acceptance Rate: 19.2%, (217 out of 1129), [pdf].
  4. S. He and K. G. Shin, "Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration" (oral presentation/full paper), in Proceedings of The World Wide Web Conference (WWW 2020), pp. 133--143, April 20-24, 2020. Acceptance Rate: 19.2%, (217 out of 1129), [pdf].


2019


  1. T. He, Q. Niu, S. He and N. Liu, “Indoor Localization with Spatial and Temporal Representations of Signal Sequences”, in Proceedings of IEEE Global Communications Conference: Wireless Communications (Globecom 2019 WC), (Waikoloa, USA), pp. 1--7, December, 2019, [pdf].
  2. S. He and K. G. Shin, "Crowd-Flow Graph Construction and Identification with Spatio-Temporal Signal Feature Fusion," in Proceedings of The 38th Annual IEEE International Conference on Computer Communications (INFOCOM 2019), (Paris, France), pp. 757--765, April 29 - May 2, 2019, Acceptance Rate: 19.7%, (288 out of 1464), [pdf].
  3. S. He and K. G. Shin, "Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination" (short paper), in Proceedings of The World Wide Web Conference (WWW 2019), (San Francisco, CA), pp. 2806--2813, May 13-17, 2019, Acceptance Rate: 19.9%, (72 out of 361), [pdf].


2018


  1. X. Xie, K. G. Shin, H. Yousefi and S. He, "Wireless CSI-Based Head Tracking in The Driver Seat," in Proceedings of ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2018), (Heraklion/Crete, Greece), pp. 112--125, December 4-7, 2018, Acceptance Rate: 17.3%, (32 out of 185), [pdf].
  2. S. He and K. G. Shin, "(Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization," in Proceedings of the Nineteenth International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2018), (Los Angeles, CA), pp. 1--10, June 25-28, 2018, Acceptance Rate: 16.9%, (30 out of 178), [pdf].
  3. Q. Niu, Y. Nie, S. He, N. Liu and X. Luo, "RecNet: A Convolutional Network for Efficient Radiomap Reconstruction," in Proceedings of IEEE International Conference on Communications - Selected Areas in Communications Symposium - Smart Cities Track (ICC'18 SAC-SC), (Kansas City, MO), pp. 1--7, May 20-24, 2018, [pdf].
  4. S. He and K. G. Shin, "Steering Crowdsourced Signal Map Construction via Bayesian Compressive Sensing," in Proceedings of The 37th Annual IEEE International Conference on Computer Communications (INFOCOM 2018), (Honolulu, Hawaii), April 15-19, 2018, Acceptance Rate: 19.2%, (302 out of 1606), [pdf].


2017


  1. H. Wu, S. He and S.-H. Chan, "A Graphical Model Approach for Efficient Geomagnetism-Pedometer Indoor Localization," in Proceedings of The 14th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2017), (Orlando, FL), pp. 371--379, 22-25 October 2017, [pdf].
  2. S. He and S.-H. Chan, "Towards Crowdsourced Signal Map Construction via Implicit Interaction of IoT Devices," in Proceedings of Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2017), (San Diego, CA), pp. 145--153, 12-14 June, 2017, Acceptance Rate: 26.5% (45 out of 170), [pdf].
  3. H. Wu, S. He and S.-H. Chan, "Efficient Sequence Matching and Path Construction for Geomagnetic Indoor Localization," in Proceedings of The International Conference on Embedded Wireless Systems and Networks (EWSN 2017), (Uppsala, Sweden), pp. 156--167, 20-22 February 2017, Acceptance Rate: 36.7% (18 out of 49), [pdf].

2016


  1. B. Yang, S. He and S.-H. Chan, "Updating Wireless Signal Map with Bayesian Compressive Sensing," in Proceedings of ​The 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM'16), (Malta), pp. 310--317, 13-17 November 2016, Acceptance Rate: 27% (36 out of 133), [pdf].
  2. S. He, J. Tan and S.-H. Chan, "Towards Area Classification for Large-scale Fingerprint-based System,'' in Proceedings of The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), (Heidelberg, Germany), 12-16 September 2016, Acceptance Rate: 23.9% (115 out of 481), [pdf].

2015


  1. S. He, T. Hu and S.-H. Chan, "Contour-based Trilateration for Indoor Fingerprinting Localization," in Proceedings of The 13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), (Seoul, South Korea), pp. 225-238, 1-4 November, 2015, Acceptance Rate: 20.5% (27 out of 132), [pdf].
  2. S. He, S.-H. Chan, L. Yu and N. Liu, "Calibration-free Fusion of Step Counter and Wireless Fingerprints for Indoor Localization," in Proceedings of The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), (Osaka, Japan), pp. 897-908, 7-11 September 2015, Acceptance Rate: 23.6% (93 out of 394), [pdf].
  3. S. He, S.-H. Chan, L. Yu and N. Liu, "Fusing Noisy Fingerprints with Distance Bounds for Indoor Localization,'' in Proceedings of The 34th Annual IEEE International Conference on Computer Communications (INFOCOM 2015), (Hong Kong, P.R. China), pp. 2506-2514, 26 April - 1 May 2015, Acceptance Rate: 19.2% (316 out of 1640), [pdf].


2014


  1. S. He and S.-H. Chan, "Sectjunction: Wi-Fi Indoor Localization Based on Junction of Signal Sectors," in Proceedings of IEEE International Conference on Communications - Mobile and Wireless Networking Symposium (ICC'14 MWN), (Sydney, Australia), pp. 2611-2616, 10-14 June 2014, [pdf].