Abstract
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.
Highlights
The goal of indoor localization is to estimate the location of a person or object inside a building, where the intensity of the GPS signal is too low to be detected
We present a generative algorithm for indoor localization using Wi-Fi fingerprinting which exploits the temporal autocorrelation present in the Wi-Fi signal
Similar results were obtained for the data received from different WAPs. In light of these results, we propose to use have compared our method (HMM) to simulate Wi-Fi data instead of a Gaussian probability distribution function, because it preserves the autocorrelation present in the time series of Received Signal Strength Indicator (RSSI) Wi-Fi samples received from a WAP, and Kullback–Leibler divergence between the real and simulated data is lower than when using a Gaussian fit
Summary
The goal of indoor localization is to estimate the location of a person or object inside a building, where the intensity of the GPS signal is too low to be detected. Indoor localization is an enabling technology for providing services relying on the user’s location information. Some of these services are replicates of GPS-based services, such as route planning, whereas others are new, such us assistive technologies in the realms of Ambient Assisted. User localization was highlighted as a key concept for AAL in [2], in order to provide e-health solutions based on IoT to improve elderly people’s quality of life. The propagation model provides the RSSI of the Wi-Fi signal for every point in the environment. In [30] the authors presented a Wi-Fi signal propagation model based on the radiosity method. An ad hoc method was used to replicate the temporal autocorrelation present in the signal
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