Wi-Fi Indoor Positioning Fingerprint Health Analysis for a Large Scale Deployment

Autor: KS Yeo, A Ting, Seh Chun Ng, N Anas, David Chieng
Rok vydání: 2018
Předmět:
Zdroj: International Journal on Advanced Science, Engineering and Information Technology. 8:1411
ISSN: 2460-6952
2088-5334
DOI: 10.18517/ijaseit.8.4-2.6837
Popis: Indoor positioning systems (IPS) have witnessed continuous improvements over the years. However, large scale commercial deployments remain elusive due to various factors such as high deployment cost and/or lacked of market drivers. Among the state of the art indoor positioning approaches, the Wi-Fi fingerprinting technique in particular, is gaining a lot of attention due its ease of deployment. This is largely due to widespread deployment of WiFi infrastructure and its availability in all existing mobile devices. Although WiFi fingerprinting approach is relatively low cost and fast to deploy, the accuracy of the system tends to deteriorate over time due to WiFi access points (APs) being removed and shifted. In this paper, we carried out a study on such deterioration, which we refer to as fingerprint health analysis on a 2 million square feet shopping mall in South of Kuala Lumpur, Malaysia. We focus our study on APs removal using the actual data collected from the premise. The study reveals the following findings: 1) Based on per location pin analysis, ~50% of APs belong to the mall operator which is a preferred group of APs for fingerprinting. For some location however, the number of operator-managed APs are too few for fingerprinting positioning approach. 2) To maintain mean error distance of ~5 meter, up to 80% of the APs can be removed using the selected positioning algorithms at some locations. At some other locations however, the accuracy will exceed 5m upon >20% of APs being removed. 3) On average, around 40% - 60% of the APs can be removed in random manner in order to maintain the accuracy of ~5m.
Databáze: OpenAIRE
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