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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures.

Tesfay Gidey Hailu^1,2, Xiansheng Guo², Haonan Si²

¹Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

Accurate indoor positioning systems (IPSs) are crucial for the Internet of Things. This study analyzes wireless technologies, challenges, and future strategies like machine learning to improve indoor localization accuracy.

Keywords:

IoT comparative analysis data fusion ensemble learning feature engineering indoor positioning system design transfer learning

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Area of Science:

Computer Science
Electrical Engineering
Geographic Information Science

Background:

The Internet of Things (IoT) era necessitates precise positioning services, with location-based services (LBSs) relying on user location data.
Global Positioning System (GPS) excels in outdoor localization but struggles with indoor environments due to complexity.
Indoor positioning systems (IPSs) face unique challenges not addressed by outdoor standards.

Purpose of the Study:

To examine measurement techniques and technological solutions for indoor positioning systems (IPSs).
To conduct a comparative analysis of wireless technologies for IPSs.
To identify challenges in IPS deployment and propose future research strategies.

Main Methods:

Comparative analysis of various wireless technologies for IPSs.
Evaluation of current IPSs using multidimensional matrices and established metrics.
Examination of challenges hindering IPS deployment and standardization.

Main Results:

Identified limitations of current IPSs and the lack of a universal standard compared to GPS.
Highlighted diverse architectural and design considerations for IPSs.
Assessed various evaluation metrics relevant to indoor localization performance.

Conclusions:

Advanced methodologies such as transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning can significantly enhance IPS accuracy and reliability.
Addressing deployment challenges is crucial for widespread adoption of IPSs.
Future research should focus on developing robust and dependable LBSs for indoor environments.