H
Heidi Kuusniemi
Researcher at Finnish Geodetic Institute
Publications - 90
Citations - 2673
Heidi Kuusniemi is an academic researcher from Finnish Geodetic Institute. The author has contributed to research in topics: GNSS applications & Global Positioning System. The author has an hindex of 25, co-authored 83 publications receiving 2228 citations. Previous affiliations of Heidi Kuusniemi include Tampere University of Technology.
Papers
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Journal ArticleDOI
Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey
Liang Chen,Sarang Thombre,Kimmo Järvinen,Elena Simona Lohan,Anette Alen-Savikko,Helena Leppäkoski,M. Zahidul H. Bhuiyan,Shakila Bu-Pasha,Giorgia Ferrara,Salomon Honkala,Jenna Lindqvist,Laura Ruotsalainen,Päivi Korpisaari,Heidi Kuusniemi +13 more
TL;DR: This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.
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A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS
TL;DR: The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations, and the reliability of the positioning solution was found to increase with increasing precision of the MDI data.
Journal ArticleDOI
Human Behavior Cognition Using Smartphone Sensors
Ling Pei,Robert Guinness,Ruizhi Chen,Jingbin Liu,Heidi Kuusniemi,Yuwei Chen,Liang Chen,Jyrki Kaistinen +7 more
TL;DR: Preliminary tests indicate that the LoMoCo (Location-Motion-Context) model, which combines the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior, has successfully achieved the Activity-Level Descriptors level.
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Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning
TL;DR: An indoor navigation solution by combining physical motion recognition with wireless positioning with results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study.
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Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints
TL;DR: A Bayesian fusion (BF) method is proposed to combine the statistical information from the RSSI measurements and the prior information from a motion model to achieve horizontal positioning accuracy in a Bluetooth network for indoor positioning.