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Shimosaka Research Group

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Datasets

Datasets

SCS-UT-2014-dataset: Location-Based Game Result for Wireless Indoor Localization Deployment

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This is a dataset that is collected on a location-based game for deploying Wireless Indoor Localization. Our main aim of the experiment is the evaluation of effectiveness of controlling game elements with the iterative data-driven feedback. This dataset includes not only training data for localization but also the game data such as how to control users and how users behaved. This experiments was conducted on six floors with about 60 meters by 70 meters in the University of Tokyo. We recruited 18 students as participants five weeks. The more details of the experiment are in our paper [1]. This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at:
http://opendatacommons.org/licenses/odbl/1.0/.
Please feel free to send any questions or comments to us scs-ut-2014-dataset@ics.t.u-tokyo.ac.jp.

References

[1] Ryoma Kawajiri, Masamichi Shimosaka, Hisashi Kashima. Steered Crowdsensing: Incentive Design towards Quality-Oriented Place-Centric Crowdsensing. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014) pp. 691–701, 2014.

Related Project: http://www.miubiq.cs.titech.ac.jp/steered-crowdsensing/

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Recent News

  • Device-Free Multi-Person Indoor Localization Using the Change of ToF 2023/03/03
  • Presenting our paper on Device-Free Multi-Person Indoor Localization Using the Change of ToF at PerCom2023 2023/02/28
  • Our paper on Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring has been published in IMWUT. 2023/01/24
  • Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022 2022/06/09
  • Presenting our paper on Efficient Indoor Localization Model Construction by Sequential Recommendation of Data Gathering Position based on Bayesian Optimization at IPIN2021 2021/11/29
  • Adaptive incremental beacon placement optimization for crowd density monitoring applications 2021/11/01
  • Presenting 2 papers at ACM SIGSPATIAL 2021 2021/11/01
  • Fine-grained Urban Dynamics Prediction using Large-Scale Mobile Phone Location Data 2021/10/05
  • Robustifying Wi-Fi localization by Between-Location data augmentation 2021/09/28
  • Our paper on robustifying Wi-Fi localization by “Between-Location” data augmentation has been published in IEEE Sensors Journal 2021/09/17

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