ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Wireless sensor networks
- Creators: Tsakalis, Konstantinos
Description
Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
ContributorsLee, Jongmin (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
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.
ContributorsZhang, Xue (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2016