Mobile networks and internet of things infrastructures to characterize smart human mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Machine Learning Techniques for Device-Free Indoor Person Tracking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Employing multi-modal sensors for personalised smart home health monitoring.

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limits of health monitoring using simple IoT sensors, and establishing deployable solutions for new rich sensing technologies. The FITsense system collects data from FitHomes and generates behavioural insights for health monitoring. To allow the system to expand to arbitrary home layouts, sensing applications must be delivered while relying on sparse "ground truth" data. An enhanced data representation was tested for improving activity recognition performance by encoding observed temporal dependencies. Experiments showed an improvement in activity recognition accuracy over baseline data representations with standard classifiers. Channel State Information (CSI) was chosen as our rich sensing technology for its ambient nature and potential deployability. We developed a novel Python toolkit, called CSIKit, to handle various CSI software implementations, including automatic detection for off-the-shelf CSI formats. Previous researchers proposed a method to address AGC effects on COTS CSI hardware, which we tested and found to improve correlation with a baseline without AGC. This implementation was included in the public release of CSIKit. Two sensing applications were delivered using CSIKit to demonstrate its functionality. Our statistical approach to motion detection with CSI data showed a 32% increase in accuracy over an infrared sensor-based solution using data from 2 unique environments. We also demonstrated the first CSI activity recognition application on a Raspberry Pi 4, which achieved an accuracy of 92% with 11 activity classes. An application was then trained to support movement detection using data from all COTS CSI hardware. This was combined with our signal divider implementation to compare CSI wireless and sensing performance characteristics. The IWL5300 exhibited the most consistent wireless performance, while the ESP32 was found to produce viable CSI data for sensing applications. This establishes the ESP32 as a low-cost high-value hardware solution for CSI sensing. To complete this work, an in-home study was performed using real-world sensor data. An ESP32-based CSI sensor was developed to be integrated into our IoT network. This sensor was tested in a FitHome environment to identify how the data from our existing simple sensors could aid sensor development. We performed an experiment to demonstrate that annotations for CSI data could be gathered with infrared motion sensors. Results showed that our new CSI sensor collected real-world data of similar utility to that collected manually in a controlled environment

Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality