Indoor Location Fingerprint Dataset for Multi-Floor Environments

Citation Author(s):
Ikhmal Syahidi Bin
Idi Amir
Multimedia University
Muhammad Zarul Ikhmal Bin
Sapian
Multimedia University
Yin Hoe
Ng
Multimedia University
Chee Keong
Tan
Monash University Malaysia
Submitted by:
Yin Hoe Ng
Last updated:
Thu, 06/16/2022 - 05:20
DOI:
10.21227/mabd-w688
Research Article Link:
A Fast Indoor Positioning Using a Knowledge-Distilled Convolutional Neural Network (KD-CNN)
License:
Creative Commons Attribution
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Categories:
Artificial Intelligence
Communications
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Abstract 

An indoor positioning testbed was set up to collect location fingerprint dataset for multi-floor environments and an extensive fingerprint measurement campaign was carried out at the second and third floors of Wing B of the Faculty of Engineering (FOE), Multimedia University, on its campus in Cyberjaya, Malaysia. The total area of the evaluation site is approximately around 1112 m2.

Instructions: 

The fingerprint database has 20 attributes, which consists of the measured received signal strength indicators (RSSIs) collected from 16 Bluetooth Low Energy (BLE) beacons, the corresponding floor number and coordinates (x, y, z). To be more specific, z is the relative height of the reference point from the second floor.

Funding Agency: 
Ministry of Higher Education Malaysia

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