5 research outputs found
Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments
This paper presents a multifunctional interdisciplinary framework that makes
four scientific contributions towards the development of personalized ambient
assisted living, with a specific focus to address the different and dynamic
needs of the diverse aging population in the future of smart living
environments. First, it presents a probabilistic reasoning-based mathematical
approach to model all possible forms of user interactions for any activity
arising from the user diversity of multiple users in such environments. Second,
it presents a system that uses this approach with a machine learning method to
model individual user profiles and user-specific user interactions for
detecting the dynamic indoor location of each specific user. Third, to address
the need to develop highly accurate indoor localization systems for increased
trust, reliance, and seamless user acceptance, the framework introduces a novel
methodology where two boosting approaches Gradient Boosting and the AdaBoost
algorithm are integrated and used on a decision tree-based learning model to
perform indoor localization. Fourth, the framework introduces two novel
functionalities to provide semantic context to indoor localization in terms of
detecting each user's floor-specific location as well as tracking whether a
specific user was located inside or outside a given spatial region in a
multi-floor-based indoor setting. These novel functionalities of the proposed
framework were tested on a dataset of localization-related Big Data collected
from 18 different users who navigated in 3 buildings consisting of 5 floors and
254 indoor spatial regions. The results show that this approach of indoor
localization for personalized AAL that models each specific user always
achieves higher accuracy as compared to the traditional approach of modeling an
average user
Real-time localisation system for GPS denied open areas using smart street furniture
Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestriansā locations is required. However, estimating pedestriansā locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems.
Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities.
In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 usersā devices.
Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved
An INS/WiFi Indoor Localization System Based on the Weighted Least Squares
For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m
Dynamic spatial segmentation strategy based magnetic field indoor positioning system
In this day and age, it is imperative for anyone who relies on a mobile device to
track and navigate themselves using the Global Positioning System (GPS). Such
satellite-based positioning works as intended when in the outdoors, or when the
device is able to have unobstructed communication with GPS satellites.
Nevertheless, at the same time, GPS signal fades away in indoor environments due
to the effects of multi-path components and obstructed line-of-sight to the
satellite. Therefore, numerous indoor localisation applications have emerged in
the market, geared towards finding a practical solution to satisfy the need for
accuracy and efficiency.
The case of Indoor Positioning System (IPS) is promoted by recent smart devices,
which have evolved into a multimedia device with various sensors and optimised
connectivity. By sensing the deviceās surroundings and inferring its context,
current IPS technology has proven its ability to provide stable and reliable indoor
localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based
IPS). To make a trade-off between accuracy and cost, considerable attention from
many researchers has been paid to the range of infrastructure-free technologies,
particularly exploiting the earthās magnetic field (EMF).
EMF is a promising signal type that features ubiquitous availability, location
specificity and long-term stability. When considering the practicality of this
typical signal in IPS, such a system only consists of mobile device and the EMF
signal. To fully comprehend the conventional EMF-based IPS reported in the
literature, a preliminary experimental study on indoor EMF characteristics was
carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance
sources was lowered to a minimum. In response to this finding, a new concept of
spatial segmentation is devised in this research based on magnetic anomaly (MA).
Therefore, this study focuses on developing innovative techniques based on spatial
segmentation strategy and machine learning algorithms for effective indoor
localisation using EMF.
In this thesis, four closely correlated components in the proposed system are
included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic
intensity-based spatial segmentation; (iii) weighted NaĆÆve Bayes classification
(WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm.
Kriging interpolation-based fingerprinting map reconstructs the original observed
EMF positioning database in the calibration phase by interpolating predicted
points. The magnetic intensity-based spatial segmentation component then
investigates the variation tendency of ambient EMF signals in the new database to
analyse the distribution of magnetic disturbance sources, and accordingly,
segmenting the test site. Then, WNBC blends the exclusive characteristics of
indoor EMF into original NaĆÆve Bayes Classification (NBC) to enable a more
accurate and efficient segmentation approach. It is well known that the best IPS
implementation often exerts the use of multiple positing sources in order to
maximise accuracy. The fused features-based kNN component used in the
positioning phase finally learns the various parameters collected in the calibration
phase, continuously improving the positioning accuracy of the system.
The proposed system was evaluated on multiple indoor sites with diverse layouts.
The results show that it outperforms state-of-the-art approaches and demonstrate
an average accuracy between 1-2 meters achieved in typical sites by the best
methods proposed in this thesis across most of the experimental environments. It
can be believed that such an accurate approach will enable the future of
infrastructureāfree IPS technologies
Visual-Inertial first responder localisation in large-scale indoor training environments.
Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise.
This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder.
This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises