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Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics

Published: 10 October 2005 Publication History

Abstract

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positioning accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems, instability in positioning accuracy due to changing environmental dynamics, and the need for manual offline calibration during site survey. To address these two challenges, three environmental factors (people, doors, and humidity) that can interfere with radio signals and cause positioning inaccuracy are identified. Then, we have proposed a sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics. The proposed adaptation method performs online calibration to build multiple context-aware radio maps under various environmental conditions. Experiments were performed on the sensor-assisted adaptation method. The experimental results show that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changing environmental dynamics.

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      cover image ACM Conferences
      MSWiM '05: Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
      October 2005
      372 pages
      ISBN:1595931880
      DOI:10.1145/1089444
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 10 October 2005

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      Author Tags

      1. adaptive system
      2. indoor location system
      3. performance evaluation
      4. sensors

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      MSWiM '05 Paper Acceptance Rate 48 of 165 submissions, 29%;
      Overall Acceptance Rate 398 of 1,577 submissions, 25%

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      Cited By

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      • (2023)Data Assimilation for Agent-Based ModelsMathematics10.3390/math1120429611:20(4296)Online publication date: 15-Oct-2023
      • (2023)Fingerprint localization using data from different radio environmentsIEICE Communications Express10.23919/comex.2023XBT000512:10(564-567)Online publication date: Oct-2023
      • (2023)Learning to Locate: Adaptive Fingerprint-Based Localization With Few-Shot Relation Learning in Dynamic Indoor EnvironmentsIEEE Transactions on Wireless Communications10.1109/TWC.2022.323285822:8(5253-5264)Online publication date: Aug-2023
      • (2023)Enabling Temporal Variation Resilience for ML-Based Indoor LocalizationMachine Learning for Indoor Localization and Navigation10.1007/978-3-031-26712-3_16(379-421)Online publication date: 19-Mar-2023
      • (2021)A Small World Graph Approach for an Efficient Indoor Positioning SystemSensors10.3390/s2115501321:15(5013)Online publication date: 23-Jul-2021
      • (2021)An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2019.291795551:5(2926-2936)Online publication date: May-2021
      • (2021)Radio Map Crowdsourcing Update Method Using Sparse Representation and Low Rank Matrix Recovery for WLAN Indoor Positioning SystemIEEE Wireless Communications Letters10.1109/LWC.2021.306153910:6(1188-1191)Online publication date: Jun-2021
      • (2021)A Bisection Reinforcement Learning Approach to 3-D Indoor LocalizationIEEE Internet of Things Journal10.1109/JIOT.2020.30412048:8(6519-6535)Online publication date: 15-Apr-2021
      • (2021)Bus OD matrix reconstruction based on clustering Wi-Fi probe dataTransportmetrica B: Transport Dynamics10.1080/21680566.2021.195638810:1(864-879)Online publication date: 23-Jul-2021
      • (2021)A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machineSignal Processing10.1016/j.sigpro.2020.107915181(107915)Online publication date: Apr-2021
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