Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Optimizing Deployment and Maintenance of Indoor Localization Systems

Pervasive computing envisions the achievement of seamless and distraction-free support for tasks by means of context-aware applications. Context can be defined as the information which can be used to characterize the situation of an entity such as persons or objects which are relevant for the behaviour of an application. A context-aware application is one which can adapt its functionality based on changes in the context of the user or entity. Location is an important piece of context because a lot of information can be inferred about the situation of an entity just by knowing where it is. This makes location very useful for many context-aware applications. In outdoor scenarios, the Global Positioning System (GPS) is used for acquiring location information. However, GPS signals are relatively weak and do not penetrate buildings well, rendering them less than suitable for location estimation in indoor environments. However, people spend most of their time in indoor locations and therefore it is necessary to have location systems which would work in these scenarios. In the last two decades, there has been a lot of research into and development of indoor localization systems. A wide range of technologies have been applied in the development of these systems ranging from vision-based systems, sound-based systems as well as Radio Frequency (RF) signal based systems. In a typical indoor localization system deployment, an indoor environment is setup with different signal sources and then the distribution of the signals in the environment is recorded in a process known as calibration. The distribution of signals, also known as a radio map, is then later employed to estimate location of users by matching their signal observations to the radio map. However, not all the different signal technologies and approaches provide the right balance of accuracy, precision and cost to be suitable for most real world deployment scenarios. Of the different RF signal technologies, WLAN and Bluetooth based indoor localization systems are the most common due to the ubiquity of the signal deployments for communication purposes, and the accessibility of compatible mobile computing devices to the users of the system. Many of the indoor localization systems have been developed under laboratory conditions or only with small-scale controlled indoor areas taken into account. This poses a challenge when transposing these systems to real-world indoor environments which can be rather large and dynamic, thereby significantly raising the cost, effort and practicality of the deployment. Furthermore, due to the fact that indoor environments are rarely static, changes in the environment such as moving of furniture or changes in the building layout could adversely impact the performance of the localization system deployment. The system would then need to be recalibrated to the new environmental conditions in order to achieve and maintain optimal localization performance in the indoor environment. If this happens regularly, it can significantly increase the cost and effort for maintenance of the indoor localization system over time. In order to address these issues, this dissertation develops methods for more efficient deployment and maintenance of the indoor localization systems. A localization system deployment consists of three main phases; setup and calibration, localization and maintenance. The main contributions of this dissertation are proposed optimizations to the different stages of the localization system deployment lifecycle. First, the focus is on optimizing setup and calibration of fingerprinting-based indoor localization systems. A new method for dense and efficient calibration of the indoor environmental areas is proposed, with minimal effort and consequently reduced cost. During calibration, the signal distribution in the indoor environment is distorted by the presence of the person doing the calibration. This leads to a radio map which is not a very accurate representation of the environment. Therefore a model for WLAN signal attenuation by the human body is proposed in this dissertation. The model captures the pattern of change to the signal due the presence of the human body in the signal path. By applying the model, we can compensate for the attenuation caused by the person and thereby generate a more accurate map of the signal distribution in the environment. A more precise signal distribution leads to better precision during location estimation. Secondly, some optimizations to the localization phase are presented. The dense fingerprints of the environment created during the setup phase are used for generating location estimates by matching the captured signal distribution with the pre-recorded distribution in the environment. However, the location estimates can be further refined given additional context information. This approach makes use of sensor fusion and ambient intelligence in order to improve the accuracy of the location estimates. The ambient intelligence can be gotten from smart environments such as smart homes or offices, which trigger events that can be applied to location estimation. These optimizations are especially useful for indoor tracking applications where continuous location estimation and accurate high frequency location updates are critical. Lastly, two methods for autonomous recalibration of localization systems are presented as optimizations to the maintenance phase of the deployment. One approach is based on using the localization system infrastructure to monitor the signal characteristic distribution in the environment. The results from the monitoring are used by the system to recalibrate the signal distribution map as needed. The second approach evaluates the Received Signal Strength Indicator (RSSI) of the signals as measured by the devices using the localization system. An algorithm for detecting signal displacements and changes in the distribution is proposed, as well as an approach for subsequently applying the measurements to update the radio map. By constantly self-evaluating and recalibrating the system, it is possible to maintain the system over time by limiting the degradation of the localization performance. It is demonstrated that the proposed approach achieves results comparable to those obtained by manual calibration of the system. The above optimizations to the different stages of the localization deployment lifecycle serve to reduce the effort and cost of running the system while increasing the accuracy and reliability. These optimizations can be applied individually or together depending on the scenario and the localization system considered

MapSense: Mitigating Inconsistent WiFi Signals using Signal Patterns and Pathway Map for Indoor Positioning

The indoor positioning technology plays a significant role in the scenarios of the Internet of Things (IoT) which require indoor location context. In this paper, the WiFi signals under modern enterprise WiFi infrastructure, signal patterns between coexisting access points (APs) and signals’ correlation with indoor pathway map are investigated to address the problem of inconsistent WiFi signal observations. The sibling signal patterns (SSP) are defined for the first time and processed to generate Beacon APs which have higher confidence in positioning. The spatial signal patterns are used to bring the estimated location into a limited area through signal coverage constraint (SCC). A positioning scheme using SSP and SCC is proposed and shows improved positioning accuracy. The proposed scheme is fully designed, implemented and evaluated in a real-world environment, revealing its effectiveness and efficiency

Environment-adaptive RF Sensing with Transferable ANN Features

Radio frequency (RF) sensing arises as a promising option for enabling the internet of things (IoT) applications that transform our life into a world of smart homes, smart cities, and smart industries. The innovation of IoT reveals the benefits of RF sensing across cost, pervasiveness, unobtrusiveness, and privacy. However, challenges like interference and multipath are underway in realizing those promises. Furthermore, crucial studies demonstrate the trade-offs in accuracy, accessibility, power consumption, and many other factors for undertaking RF sensing. This dissertation presents a set of studies, including RF channel model characterization, the design of a novel RF sensing system for indoor localization, and the environmental impact of RF exposure in such systems. The first part covers a use case of measurement-based RF channel modeling in a challenging environment. The second part introduces an environment-adaptive RF sensing system for indoor localization that consists of 1) a dynamic phase calibration de-noising method, and 2) The implementation of a localization system that utilizes an artificial neural network (ANN) with transferable features. Lastly, a collaboration work that explores the potential impact of RF radiation and how RF exposure could affect human health

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Sensor-fusion for smartphone location tracking using hybrid multimodal deep neural networks

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, MM-Loc, relying on zero hand-engineered features, but learning automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures can estimate the location independently. But for better accuracy, a multimodal neural network that fuses the features of early modality-specific representations is a better proposition. Our proposed MM-Loc system is tested on cross-modality samples characterised by different sampling rate and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition

Crowdsourcing based Room Localization on Smartphones

People spend approximately 90% of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or smartphone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Designing a room localization system that is practically useful in real-world environments is challenging. First, due to the complex multi-path propagation problem, Wi-Fi signals obtained by smartphones are dynamic and noisy. Such noise obscures the unique relationship between Wi-Fi signals and individual rooms. Second, existing room localization methods require labor-intensive manual annotation of individual rooms. The process is time-consuming and expensive, which is a key limitation of existing room localization applications. Third, knowledge of indoor floorplans is often required by room localization applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor. In addition, the overhead of room localization, e.g., energy consumption, imposed on personal smartphones must be low. To tackle those challenges, this thesis proposed a set of techniques: (1) an accurate temporal n-gram augmented Bayesian room positioning method that leverages the ordered sequence information of access points and users' daily motion pattern among rooms; (2) an automatic room fingerprinting approach that identifies in-room occupancy ``hotspot(s)" using density of Wi-Fi signals, and then learns the inter-zone correlation -- thereby distinguishing different rooms; (3) an automatic floorplan construction method that determines the geometries of individual rooms, as well as room adjacency information, and then constructs an indoor floorplan through hallway layout learning and force directed dilation; and (4) an energy-efficient trip detection framework that consists of two modes: the deliberation mode learns cell-id patterns using GPS/Wi-Fi based localization methods, and the intuition mode only uses cell-ids and learned patterns for trip detection