Full bibliography

In this paper, we develop an indoor positioning system using smartphones. An indoor positioning system plays a vital role in indoor spaces such as home, office, university, airport, and hospital buildings by locating and tracking persons, devices, and assets. Our indoor positioning system is applicable in any indoor spaces which has smart devices such as smartphones, tablets, smartwatches, and robots with a Wi-Fi connection. We used Wi-Fi-based fingerprinting technique t o build o ur indoor positioning system because a Wi-Fi-based system can leverage existing Wi-Fi infrastructure and hence, it is cost effective. A major challenge in implementing a Wi-Fi-based fingerprinting technique is the missed access points (APs) problem. In this paper, we address this critical challenge by proposing a localization procedure called ‘cosine similarity + k-means clustering'. In this localization procedure, we leverage k-means clustering algorithm in identifying the wrong location estimates produced by the cosine similarity measure because of missed APs problem. To evaluate the effectiveness of our proposed localization procedure, we collected data from three different scenarios, specifically, home, office, a nd university f or creating signal m ap a nd performing localization tests. Additionally, we tested both stationary and walk data. Our experimental results prove that our ‘cosine similarity + k-means clustering’ localization procedure is effective in mitigating the detrimental impact of missed APs, and consequently, it significantly improves localization accuracy.