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

Machine Learning-based Real-Time Indoor Landmark Localization

Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning

Travel Mode Identification with Smartphone Sensors

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller\u27s transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals\u27 GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest Android and iOS platforms with multimodality sensors. It takes smartphone sensor data as the input, and aims to identify six travel modes: walking, jogging, bicycling, driving a car, riding a bus, taking a subway. The methods and algorithms presented in our work are guided by two key design principles. First, careful consideration of smartphones\u27 limited computing resources and batteries should be taken. Second, careful balancing of the following dimensions (i) user-adaptability, (ii) energy efficiency, and (iii) computation speed. There are three key challenges in travel mode identification with smartphone sensors, stemming from the three steps in a typical mobile mining procedure. They are (C1) data capturing and preprocessing, (C2) feature engineering, and (C3) model training and adaptation. This thesis is our response to the challenges above. To address the first challenge (C1), in Chapter 4 we develop a smartphone app that collects a multitude of smartphone sensor measurement data, and showcase a comprehensive set of de-noising techniques. To tackle challenge (C2), in Chapter 5 we design feature extraction methods that carefully balance prediction accuracy, computation time, and battery consumption. And to answer challenge (C3), in Chapters 6,7 and 8 we design different learning models to accommodate different situations in model training. A hierarchical model with dynamic sensor selection is designed to address the energy consumption issue. We propose a personalized model that adapts to each traveller\u27s specific travel behavior using limited labeled data. We also propose an online model for the purpose of addressing the model updating problem with large scaled data. In addressing the challenges and proposing solutions, this thesis provides an comprehensive study and gives a systematic solution for travel mode detection with smartphone sensors