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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
380 Views
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.

Funding Agency: 
Ministry of Higher Education Malaysia

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