Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

Mobility increases localizability: A survey on wireless indoor localization using inertial sensors

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p

Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around $30$\% to $55$\% compared with three baselines from the existing literature

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Machine Learning Approaches for WiFi Round Trip Time Indoor Positioning Systems

Indoor positioning systems based on WiFi Round-Trip Time (RTT) measurements, as per the IEEE 802.11mc standard, have demonstrated sub-metre level accuracy using trilateration under ideal indoor conditions. However, the efficacy of WiFi RTT positioning in complex, non-line-of-sight (NLOS) environments remains an open research question. Therefore, this thesis addresses the challenge by proposing novel machine learning algorithms and validating their performance through extensive empirical experiments in real-world testbeds. Recent literature has shown improvements in WiFi fingerprinting systems utilising deep learning methods, achieving sub-metre accuracy. However, it was observed that simpler neural networks can sometimes outperform complex ones in certain environments. Moreover, our comprehensive survey of public WiFi datasets has identified several limitations, all of which pose challenges to accessing or accurately using these datasets over time. To provide a comprehensive analysis of WiFi RTT for indoor positioning, we investigate its properties in several real-world indoor environments on heterogeneous smartphones. We present three publicly available datasets collected on large-scale real-world scenarios, containing both RTT and received signal strength (RSS) signal measures. Using the proposed datasets, we achieved a baseline accuracy below 0.7 metres. WiFi RTT has shown promising sub-metre level accuracy under a clear line-of-sight path to the user. However, typical workplace environments often cause wireless signals to reflect, attenuate, and diffract. Identifying the NLOS condition of WiFi Access Points (APs) is thus crucial for indoor positioning systems. To this end, we propose a novel feature selection algorithm for NLOS identification of WiFi APs. Utilising RSS and RTT as inputs, our algorithm employs multi-scale selection and machine learning-based weighting methods to identify the most optimal feature sets, achieving an accuracy of up to 98% in NLOS detection of APs. Different WiFi technologies and algorithms for indoor positioning have strengths and weaknesses that vary by location. Thus, we propose an algorithm to dynamically switch to the most effective positioning model for an unknown location using a machine learning-based weighted model selection algorithm. We evaluated our algorithm across different complex real-world indoor scenarios, demonstrating an improvement of up to 1.8 metres compared to the standard WiFi fingerprinting technique.<br/

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Understanding mobile network quality and infrastructure with user-side measurements

Measurement collection is a primary step towards analyzing and optimizing performance of a telecommunication service. With an Mobile Broadband (MBB) network, the measurement process has not only to track the network’s Quality of Service (QoS) features but also to asses a user’s perspective about its service performance. The later requirement leads to “user-side measurements” which assist in discovery of performance issues that makes a user of a service unsatisfied and finally switch to another network. User-side measurements also serve as first-hand survey of the problem domain. In this thesis, we exhibit the potential in the measurements collected at network edge by considering two well-known approaches namely crowdsourced and distributed testbed-based measurements. Primary focus is on exploiting crowdsourced measurements while dealing with the challenges associated with it. These challenges consist of differences in sampling densities at different parts of the region, skewed and non-uniform measurement layouts, inaccuracy in sampling locations, differences in RSS readings due to device-diversity and other non-ideal measurement sampling characteristics. In presence of heterogeneous characteristics of the user-side measurements we propose how to accurately detect mobile coverage holes, to devise sample selection process so to generate a reliable radio map with reduced sample cost, and to identify cellular infrastructure at places where the information is not public. Finally, the thesis unveils potential of a distributed measurement test-bed in retrieving performance features from domains including user’s context, service content and network features, and understanding impact from these features upon the MBB service at the application layer. By taking web-browsing as a case study, it further presents an objective web-browsing Quality of Experience (QoE) model

On the efficient design of scalable indoor positioning systems based on Wi-Fi fingerprinting

This thesis investigates the design and implementation of Wi-Fi fingerprinting based Indoor Positioning Systems (IPS), with a focus on enhancing their efficiency, scalability, and accuracy. Wi-Fi fingerprinting, particularly utilising Received Signal Strength Indicator (RSSI) data, offers a cost-effective and non-intrusive method for indoor positioning. Despite its advantages, existing systems encounter challenges such as high computational complexity, the need for frequent manual updates, and difficulties in managing large datasets. The research commences by evaluating various position estimation algorithms, including k-Nearest Neighbour (k-NN) and its weighted variant (Wk-NN), identifying the correlation distance function as a highly effective approach when combined with exponential data representation. This combination was found to balance accuracy with computational simplicity, making it a viable option for efficient IPS. To address scalability and reliability, the thesis introduces a cloud-based Indoor Positioning System (CB-IPS) framework that leverages cloud computing, edge computing, and cache technologies. This framework significantly enhances the management of large fingerprint databases, optimises computational resources, and supports real-time processing, thereby improving the overall performance of the IPS. Furthermore, the research addresses the complexity of database management by implementing data preprocessing techniques, dimensionality reduction through Principal Component Analysis (PCA), and auto-update mechanisms. These strategies effectively reduce computational load and storage requirements, thereby ensuring that the system remains scalable and efficient. The findings demonstrate that the proposed optimisations can substantially enhance the performance of Wi-Fi fingerprinting-based IPS, making them more competitive with state-of-the-art systems. The research contributes to the advancement of indoor positioning technologies, offering practical solutions that address current limitations while laying the foundation for future innovations. This thesis concludes by outlining potential directions for future research, including the integration of advanced machine learning techniques, and further optimisation of real-time implementations. These efforts are essential for fully realising the potential of Wi-Fi fingerprinting-based indoor positioning systems across various real-world applications

Location estimation and collective inference in indoor spaces using smartphones

In the last decade, indoor localization-based smart, innovative services have become very popular in public spaces (retail spaces, malls, museums, and warehouses). We have state-of-art RSSI techniques to more accurate CSI techniques to infer indoor location. Since the past year, the pandemic has raised an important challenge of determining if a pair of individuals are ``social-distancing,'' separated by more than 6ft. Most solutions have used `presence'-if one device can hear another--- which is a poor proxy for distance since devices can be heard well beyond 6 ft social distancing radius and across aisles and walls. Here we ask the key question: what needs to be added to our current indoor localization solutions to deploy them towards scenarios like reliable contact tracing solutions easily. And we identified three main limitations---deployability, accuracy, and privacy. Location solutions need to deploy on ubiquitous devices like smartphones. They should be accurate under different environmental conditions. The solutions need to respect a person's privacy settings. Our main contributions are twofold -a new statistical feature for localization, Packet Reception Probability (PRP) which correlates with distance and is different from other physical measures of distance like CSI or RSSI. PRP can easily deploy on smartphones (unlike CSI) and is more accurate than RSSI. Second, we develop a crowd tool to audit the level of location surveillance in space which is the first step towards achieving privacy. Specifically, we first solve a location estimation problem with the help of infrastructure devices (mainly Bluetooth Low Energy or BLE devices). BLE has turned out to be a key contact tracing technology during the pandemic. We have identified three fundamental limitations with BLE RSSI---biased RSSI Estimates due to packet loss, mean RSSI de-correlated with distance due to high packet loss in BLE, and well-known multipath effects. We built the new localization feature, Packet Reception Probability (PRP), to solve the packet loss problem in RSSI. PRP measures the probability that a receiver successfully receives packets from the transmitter. We have shown through empirical experiments that PRP encodes distance. We also incorporated a new stack-based model of multipath in our framework. We have evaluated B-PRP in two real-world public places, an academic library setting and a real-world retail store. PRP gives significantly lower errors than RSSI. Fusion of PRP and RSSI further improves the overall localization accuracy over PRP. Next, we solved a peer-to-peer distance estimation problem that uses minimal infrastructure. Most apps like aarogya setu, bluetrace have solved peer-to-peer distances through the presence of Bluetooth Low-Energy (BLE) signals. Apps that rely on pairwise measurements like RSSI suffer from latent factors like device relative positioning on the human body, the orientation of the people carrying the devices, and the environmental multipath effect. We have proposed two solutions in this work---using known distances and collaboration to solve distances more robustly. First, if we have a few infrastructure devices installed at known locations in an environment, we can make more measurements with the devices. We can also use the known distances between the devices to constrain the unknown distances in a triangle inequality framework. Second, in an outdoor environment where we cannot install infrastructure devices, we can collaborate between people to jointly constrain many unknown distances. Finally, we solve a collaborative tracking estimation problem where people audit the properties of localization infrastructure. While people want services, they do not want to be surveilled. Further, people using an indoor location system do not know the current surveillance level. The granularity of the location information that the system collects about people depends on the nature of the infrastructure. Our system, the CrowdEstimator, provides a tool to people to harness their collective power and collect traces for inferring the level of surveillance. We further propose the insight that surveillance is not a single number, instead of a spatial map. We introduce active learning algorithms to infer all parts of the spatial map with uniform accuracy. Auditing the location infrastructure is the first step towards achieving the bigger goal of declarative privacy, where a person can specify their comfortable level of surveillance