LIPS: Learning Based Indoor Positioning System using mobile phone-based sensors

Abstract

Abstract-In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a realworld environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.

NNT: Nearest Neighbour Trapezoid Algorithm for IoT WLAN Smart Indoor Localization Leveraging RSSI Height Estimation

“…Considering the amount of time for the surveyor to interact with the developed APP, our developed android platform sampling APP is simpler and user friendly than in [30], requiring only the reference point name, the interval at which we sample the RSSI. On scan initialization for a predefined time stamp, it records the interval, the RSSI value followed by MAC address of the source transceiver AP.…”

A Deep Learning Approach to Position Estimation from Channel Impulse Responses

“…There is much work that uses the RSS [ 20 , 21 , 22 , 23 , 24 ], the ToA [ 25 , 26 , 27 ], or their combinations [ 28 ]. Some use machine learning (ML) based schemes such as neural networks with a single hidden layer [ 21 , 26 , 28 ], variants of neural networks (i.e., deep belief networks [ 22 , 29 ], deep neural networks [ 30 , 31 ], fuzzy neural networks [ 32 ], artificial synaptic networks [ 25 ]), Gaussian regression [ 33 ], support vector machines (SVM) [ 27 ], random decision forest [ 34 ], or combinations of them [ 20 ]. Iqbal et al [ 35 ] monitor patients using CNNs to correlate RSS measurement in a clinical environment.…”

Machine Learning-Based Real-Time Indoor Landmark Localization

“…Thus, collecting samples from numerous survey points will become a demanding process, which makes the system unsuitable to large environments. In [10], authors validated the performance of different individual machine learning approaches for indoor positioning systems. However, they rather compare the results without any deep analysis of the performance difference.…”