Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

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

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

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio