As global location systems offer only restricted availability, they are not suitable for a world- wide tracking application without extensions. This thesis contains a goods-tracking solution, which can be considered globally working in contrast to formerly developed technologies. For the creation of an innovative approach, an evaluation of the previous efforts has to be made. As a result of this assessment, a newly developed solution is presented in this thesis that uses the Global Positioning System (GPS) in connection with the database correlation method involving Global System for Mobile Communications (GSM) fingerprints. The database entries are generated automatically by measuring numerous GSM parameters such as Cell Identity and signal strength involving handsets of several different providers and the real reference position obtained via a high sensitivity GPS receiver.

Conventional Wi-Fi location estimation techniques using radio fingerprinting typically require a lengthy initial site survey. It is suggested that the lengthy site survey is a barrier to adoption of the radio fingerprinting technique. This research investigated two methods for reducing or eliminating the site survey and instead build the radio map on-the-fly. The first approach utilized a deterministic algorithm to predict the user's location near each access point and subsequently construct a radio map of the entire area. This deterministic algorithm performed only fairly and only under limited conditions, rendering it unsuitable for most typical real-world deployments. Subsequently, a probabilistic algorithm was developed, derived from a robotic mapping technique called simultaneous localization and mapping. The standard robotic algorithm was augmented with a modified particle filter, modified motion and sensor models, and techniques for hardware-agnostic radio measurements (utilizing radio gradients and ranked radio maps). This algorithm performed favorably when compared to a standard implementation of the radio fingerprinting technique, but without needing an initial site survey. The algorithm was also reasonably robust even when the number of available access points were decreased.

abstract: In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered. In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived. In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived. A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

A tremendous amount of information is being shared every day on social media sites such as Facebook, Twitter or Google+. However, only a small portion of users provide their location information, which can be helpful in targeted advertising and many other services. Current methods in location estimation using social relationships consider social friendship as a simple binary relationship. However, social closeness between users and structure of friends have strong implications on geographic distances. In the first task, we introduce new measures to evaluate the social closeness between users and structure of friends. Then we propose models that use them for location estimation. Compared with the models which take the friend relation as a binary feature, social closeness can help identify which friend of a user is more important and friend structure can help to determine significance level of locations, thus improving the accuracy of the location estimation models. A confidence iteration method is further introduced to improve estimation accuracy and overcome the problem of scarce location information. We evaluate our methods on two different datasets, Twitter and Gowalla. The results show that our model can improve the estimation accuracy by 5% - 20% compared with state-of-the-art friend-based models. In the second task, we also propose a Local Event Discovery and Summarization (LEDS) framework to detect local events from Twitter. Many existing algorithms for event detection focus on larger-scale events and are not sensitive to smaller-scale local events. Most of the local events detected by these methods are major events like important sports, shows, or big natural disasters. In this work, we propose the LEDS framework to detect both bigger and smaller events. LEDS contains three key steps: 1) Detecting possible event related terms by monitoring abnormal distribution in different locations and times; 2) Clustering tweets based on their key terms, time, and location distribution; and 3) Extracting descriptions include time, location, and key sentences of local events from clusters. The model is evaluated on a real-world Twitter dataset with more than 60 million tweets. The analysis of Twitter data can help to predict or explain many real-world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In the third task, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results.

No description available.

The Internet Based real-time GPS synchronized wide-area Frequency Monitoring Network (FNET) is an extremely low cost and quickly deployable wide-area frequency measurement system with high dynamic accuracy which consists of Frequency Disturbance Recorder (FDR) distributed to more than 100 places around North America and an Information Management System situated at Virginia Tech. Since its first FDR deployment in 2003, the FNET system has been proved to be able to reliably receive phasor data accurately measured at and instantaneously sent via the Internet from different locations of interest, and efficiently run the analyzing program to detect and record significant system disturbances and subsequently estimate the location of disturbance center, namely the event location, in the electric grid based on the information gathered. The excellent performance of the FNET system so far has made power grid situation awareness and monitoring based on distribution level frequency measurements a reality, and thus advances our understanding of power system dynamics to a higher level and in a broader dimensionality. Chapter 1 and Chapter 2 of this dissertation briefly introduce the genesis and the architecture of the FNET system, followed by a summary of its concrete implementations. Chapter 3 and Chapter 4 outline FNET frequency estimation algorithm and phase angle estimation algorithm, including their attributes and the new methodologies to enhance them. In Chapter 5, the report discusses the algorithms developed at FNET to detect the frequency disturbance and estimate the disturbance location by the triangulation procedure using real-time frequency data and geographic topology of the FNET units in the power grid where the disturbance occurs. Then, the dissertation proceeds to introduce the FNET angle-based power system oscillation detection and present some research about Matrix Pencil Modal Analysis of FNET phase angle oscillation data in the following two chapters. Lastly, the content of this report is summarized and the future work envisioned in Chapter 8. / Ph. D.

Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling.

Effective automated tracking and locating of the thousands of materials on construction sites improves material distribution and project performance and thus has a significant positive impact on construction productivity. Many locating technologies and data sources have therefore been developed, and the deployment of a cost-effective, scalable, and easy-to-implement materials location sensing system at actual construction sites has very recently become both technically and economically feasible. However, considerable opportunity still exists to improve the accuracy, precision, and robustness of such systems. The quest for fundamental methods that can take advantage of the relative strengths of each individual technology and data source motivated this research, which has led to the development of new data fusion methods for improving materials location estimation. In this study a data fusion model is used to generate an integrated solution for the automated identification, location estimation, and relocation detection of construction materials. The developed model is a modified functional data fusion model. Particular attention is paid to noisy environments where low-cost RFID tags are attached to all materials, which are sometimes moved repeatedly around the site. A portion of the work focuses partly on relocation detection because it is closely coupled with location estimation and because it can be used to detect the multi-handling of materials, which is a key indicator of inefficiency. This research has successfully addressed the challenges of fusing data from multiple sources of information in a very noisy and dynamic environment. The results indicate potential for the proposed model to improve location estimation and movement detection as well as to automate the calculation of the incidence of multi-handling.

This thesis concerns the development of optimisation algorithms to determine the relative co-location, (localisation), of a number of freely-flying 'Smart Dust mote' sensor platform elements using a non-deterministic data-set derived from the duplex wireless transmissions between elements. Smart dust motes are miniaturised, microprocessor based, electronic sensor platforms, frequently used for a wide range of remote environmental monitoring applications; including specific climate synoptic observation research and more general meteorology. For the application proposed in this thesis a cluster of the notional smart dust motes are configured to imitate discrete 'Radio Drop Sonde' elements of the wireless enabled monitoring system in use by meteorological research organisations worldwide. This cluster is modelled in software in order to establish the relative positions during the 'flight' ; the normal mode of deployment for the Drop Sonde is by ejection from an aeroplane into an upper-air zone of interest, such as a storm cloud. Therefore the underlying research question is, how to track a number of these independent, duplex wireless linked, free-flying monitoring devices in 3-dimensions and time (to give the monitored data complete spatio-temporal validity). This represents a significant practical challenge, the solution applied in this thesis was to generate 3-dimensional geometries using the only 'real-time' data available; the Radio Signal Strength Indicator (RSSI) data is generated through the 'normal' duplex wireless communications between motes. Individual RSSI values can be considered as a 'representation of the distance magnitude' between wireless devices; when collated into a spatio-temporal data-set it 'encodes' the relative, co-locational, 3-dimensional geometry of all devices in the cluster. The reconstruction, (or decoding), of the 3-dimensional geometries encoded in the spatio-temporal data-set is a complex problem that is addressed through the application of various algorithms. These include, Random Search, and optimisation algorithms, such as the Stochastic Hill-climber, and various forms of Evolutionary Algorithm. It was found that the performance of the geometric reconstruction could be improved through identification of salient aspects of the modelled environment, the result was heuristic operators. In general these led to a decrease in the time taken to reach a convergent solution or a reduction in the number of candidate search space solutions that must be considered. The software model written for this thesis has been implemented to generalise the fundamental characteristics of an optimisation algorithm and to incorporate them into a generic software framework; this then provides the common code to all model algorithms used.

Passive source localization is the estimation of the positions of the sources or emitters given the sensor data. In this thesis, some of the well known methods for passive source localization are investigated and compared in a stationary emitter sensor framework. These algorithms are discussed in detail in two and three dimensions for both single and multiple target cases. Passive source localization methods can be divided into two groups as two-step algorithms and single-step algorithms. Angle-of-Arrival (AOA) based Maximum Likelihood (ML) and Least Squares (LS) source localization algorithms, Time- Difference-of-Arrival (TDOA) based ML and LS methods, AOA-TDOA based hybrid ML methods are presented as conventional two step techniques. Direct Position Determination (DPD) method is a well known technique within the single step approaches. In thesis, a number of variants of DPD technique with better computational complexity (the proposed methods do not need eigen-decomposition in the grid search) are presented. These are the Direct Localization (DL) with Multiple Signal Classification (MUSIC), DL with Deterministic ML (DML) and DL with Stochastic ML (SML) methods. The evaluation of these algorithms is done by considering the Cramer Rao Lower Bound (CRLB). Some of the CRLB expressions given in two dimensions in the literature are presented for threedimensions. Extensive simulations are done and the effects of different parameters on the performances of the methods are investigated. It is shown that the performance of the single step algorithms is good even at low SNR. DL with MUSIC algorithm performs as good as the DPD while it has significant savings in computational complexity. AOA, TDOA and hybrid algorithms are compared in different scenarios. It is shown that the improvement achieved by single-step techniques may be acceptable when the system cost and complexity are ignored. The localization algorithms are compared for the multiple target case as well. The effect of sensor deployments on the location performance is investigated.