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 pedestrians’ locations is required. However, estimating pedestrians’ 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 users’ 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

Characterization of the Log-Normal Model for Received Signal Strength

Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model

On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)

Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE\u27s embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm

BlueSeer: AI-Driven Environment Detection via BLE Scans

IoT devices rely on environment detection to trigger specific actions, e.g., for headphones to adapt noise cancellation to the surroundings. While phones feature many sensors, from GNSS to cameras, small wearables must rely on the few energy-efficient components they already incorporate. In this paper, we demonstrate that a Bluetooth radio is the only component required to accurately classify environments and present BlueSeer, an environment-detection system that solely relies on received BLE packets and an embedded neural network. BlueSeer achieves an accuracy of up to 84% differentiating between 7 environments on resource-constrained devices, and requires only ~12 ms for inference on a 64 MHz microcontroller-unit

On the Accuracy of Wireless Capsule Endoscope RF and Visual Localization

Wireless capsule endoscope (WCE) is becoming one of the most patient-friendly inspection device which provides visual investigation of entire gastrointestinal (GI) tract, while the other traditional (wired) endoscopic devices are usually designed for colon inspection. Locating abnormalities tract such as tumors, polyps and bleedings with wire-connected endoscope in GI tract is simple as long as we could measure the length of the wires inside human body. When WCE is applied, however, this becomes a critical challenge of examination since there is no wires connected to WCE while physicians need to find the exact locations of WCE to identify the position of abnormalities. To locate the WCE accurately, methods have come up in last decade including time of arrival (TOA) based methods, received signal strength (RSS) based methods, phase difference of arrival (PDOA) based methods, electromagnetic methods and video-based tracking methods, etc.. In this thesis, time of arrival (TOA), phase difference of arrival (PDOA) and video based localization methods are proposed and their performance are analyzed. We first propose an novel video-based tracking technique based on maximum mutual information. With this technique, we can tell the displacement and rotation between consecutive frames. Then in TOA chapter, the Cramer-Rao lower bound (CRLB) of TOA ranging inside homogeneous tissue is calculated first then three TOA ranging methods are proposed and compared with the CRLB which is used as the performance guideline. After that, PDOA based ranging technique is applied exploiting phase difference of two signals. Since the phase difference is taken into consideration, the ranging ambiguity is eliminated. We also evaluate the performance of the proposed PDOA ranging method. Finally, these ranging methods are evaluated in non-homogeneous tissues, the results of which are also compared to that in homogeneous tissue to analyze the impact of non-homogeneity

Self-organizing Fast Routing Protocols for Underwater Acoustic Communications Networks

To address this problem, in this thesis we propose a cross-layer proactive routing initialization mechanism that does not require additional measurements and, at the same time, is energy efficient. Two routing protocols are proposed: Self-Organized Fast Routing Protocol for Radial Underwater Networks (SOFRP) for radial topology and Self-organized Proactive Routing Protocol for Non-uniformly Deployed Underwater Networks (SPRINT) for a randomly deployed network. SOFRP is based on the algorithm to recreate a radial topology with a gateway node, such that packets always use the shortest possible path from source to sink, thus minimizing consumed energy. Collisions are avoided as much as possible during the path initialization. The algorithm is suitable for 2D or 3D areas, and automatically adapts to a varying number of nodes. In SPRINT the routing path to the gateway is formed on the basis of the distance, measured by the signal strength received. The data sending node prefers to choose the neighbor node which is closest to it. It is designed to achieve high data throughput and low energy consumption of the nodes. There is a tradeoff between the throughput and the energy consumption: more distance needs more transmission energy, and more relay nodes (hops) to the destination node affects the throughput. Each hop increases the packet delay and decreases the throughput. Hence, energy consumption requires nearest nodes to be chosen as forwarding node whereas the throughput requires farthest node to be selected to minimize the number of hops. Fecha de lectura de Tesis Doctoral: 11 mayo 2020Underwater Wireless Sensor Networks (UWSNs) constitute an emerging technology for marine surveillance, natural disaster alert and environmental monitoring. Unlike terrestrial Wireless Sensor Networks (WSNs), electromagnetic waves cannot propagate more than few meters in water (high absorption rate). However, acoustic waves can travel long distances in underwater. Therefore, acoustic waves are preferred for underwater communications, but they travel very slow compare to EM waves (typical speed in water is 1500 m/s against 2x10^8 m/s for EM waves). This physical effect makes a high propagation delay and cannot be avoided, but the end-to-end packet delay it can be reduced. Routing delay is one of the major factors in end-to-end packet delay. In reactive routing protocols, when a packet arrives to a node, the node takes some time to select the node to which the data packet would be forwarded. We may reduce the routing delay for time-critical applications by using proactive routing protocols. Other two critical issues in UWSNs are determining the position of the nodes and time synchronization. Wireless sensor nodes need to determine the position of the surrounding nodes to select the next node in the path to reach the sink node. A Global Navigation Satellite System (GNSS) cannot be used because of the very short underwater range of the GNSS signal. Timestamping to estimate the distance is possible but the limited mobility of the UWSN nodes and variation in the propagation speed of the acoustic waves make the time synchronization a challenging task. For these reasons, terrestrial WSN protocols cannot be readily used for underwater acoustic networks

ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed