+publisher:"Syracuse University" +contributor:("Pramod k. Varshney")

Syracuse University

1. Iyengar, Satish Giridhar. Decision-Making with Heterogeneous Sensors - A Copula Based Approach.

Degree: PhD, Electrical Engineering and Computer Science, 2011, Syracuse University

URL: https://surface.syr.edu/eecs_etd/310

► Statistical decision making has wide ranging applications, from communications and signal processing to econometrics and finance. In contrast to the classical one source-one receiver… (more)

▼ Statistical decision making has wide ranging applications, from communications and signal processing to econometrics and finance. In contrast to the classical one source-one receiver paradigm, several applications have been identified in the recent past that require acquiring data from multiple sources or sensors. Information from the multiple sensors are transmitted to a remotely located receiver known as the fusion center which makes a global decision. Past work has largely focused on fusion of information from homogeneous sensors. This dissertation extends the formulation to the case when the local sensors may possess disparate sensing modalities. Both the theoretical and practical aspects of multimodal signal processing are considered. The first and foremost challenge is to 'adequately' model the joint statistics of such heterogeneous sensors. We propose the use of copula theory for this purpose. Copula models are general descriptors of dependence. They provide a way to characterize the nonlinear functional relationships between the multiple modalities, which are otherwise difficult to formalize. The important problem of selecting the `best' copula function from a given set of valid copula densities is addressed, especially in the context of binary hypothesis testing problems. Both, the training-testing paradigm, where a training set is assumed to be available for learning the copula models prior to system deployment, as well as generalized likelihood ratio test (GLRT) based fusion rule for the online selection and estimation of copula parameters are considered. The developed theory is corroborated with extensive computer simulations as well as results on real-world data. Sensor observations (or features extracted thereof) are most often quantized before their transmission to the fusion center for bandwidth and power conservation. A detection scheme is proposed for this problem assuming unifom scalar quantizers at each sensor. The designed rule is applicable for both binary and multibit local sensor decisions. An alternative suboptimal but computationally efficient fusion rule is also designed which involves injecting a deliberate disturbance to the local sensor decisions before fusion. The rule is based on Widrow's statistical theory of quantization. Addition of controlled noise helps to 'linearize' the higly nonlinear quantization process thus resulting in computational savings. It is shown that although the introduction of external noise does cause a reduction in the received signal to noise ratio, the proposed approach can be highly accurate when the input signals have bandlimited characteristic functions, and the number of quantization levels is large. The problem of quantifying neural synchrony using copula functions is also investigated. It has been widely accepted that multiple simultaneously recorded electroencephalographic signals exhibit nonlinear and non-Gaussian statistics. While the existing and popular measures such as correlation coefficient, corr-entropy coefficient,… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Biometrics; Detection Theory; Hypothesis Testing; Multimodal; Multisensor Fusion; Neural Synchrony; Electrical and Computer Engineering

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APA (6^th Edition):

Iyengar, S. G. (2011). Decision-Making with Heterogeneous Sensors - A Copula Based Approach. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/310

Chicago Manual of Style (16^th Edition):

Iyengar, Satish Giridhar. “Decision-Making with Heterogeneous Sensors - A Copula Based Approach.” 2011. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/310.

MLA Handbook (7^th Edition):

Iyengar, Satish Giridhar. “Decision-Making with Heterogeneous Sensors - A Copula Based Approach.” 2011. Web. 15 Dec 2019.

Vancouver:

Iyengar SG. Decision-Making with Heterogeneous Sensors - A Copula Based Approach. [Internet] [Doctoral dissertation]. Syracuse University; 2011. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/310.

Council of Science Editors:

Iyengar SG. Decision-Making with Heterogeneous Sensors - A Copula Based Approach. [Doctoral Dissertation]. Syracuse University; 2011. Available from: https://surface.syr.edu/eecs_etd/310

2. He, Hao. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.

Degree: PhD, Electrical Engineering and Computer Science, 2015, Syracuse University

URL: https://surface.syr.edu/etd/390

► Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related… (more)

▼ Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related to inference using heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data. In centralized detection, where sensor observations are available at the fusion center (FC), we study copula-based fusion. We design detection algorithms based on sample-wise copula selection and mixture of copulas model in different scenarios of the true dependence. The proposed approaches are theoretically justified and perform well when applied to fuse acoustic and seismic sensor data for personnel detection. Besides traditional sensors, the access to the massive amount of social media data provides a unique opportunity for extracting information about unfolding events. We further study how sensor networks and social media complement each other in facilitating the data-to-decision making process. We propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock/flu data for prediction, where the stock/flu data serves as a surrogate for sensor data. In energy constrained networks, local observations are compressed before they are transmitted to the FC. In these cases, conditional dependence and heterogeneity complicate the system design particularly. We consider the classification of discrete random signals in Wireless Sensor Networks (WSNs), where, for communication efficiency, only local decisions are transmitted. We derive the necessary conditions for the optimal decision rules at the sensors and the FC by introducing a "hidden" random variable. An iterative algorithm is designed to search for the optimal decision rules. Its convergence and asymptotical optimality are also proved. The performance of the proposed scheme is illustrated for the distributed Automatic Modulation Classification (AMC) problem. Censoring is another communication efficient strategy, in which sensors transmit only "informative" observations to the FC, and censor those deemed "uninformative". We design the detectors that take into account the spatial dependence among observations. Fusion rules for censored data are proposed with continuous and discrete local messages, respectively. Their computationally efficient counterparts based on the key idea of injecting controlled noise at the FC before fusion are also investigated. In this thesis, with heterogeneous and dependent sensor observations, we consider not only inference in parallel frameworks but also the problem of collaborative inference where collaboration exists among local sensors. Each sensor forms coalition with other sensors and shares information within the coalition, to maximize its inference performance. The collaboration strategy… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Copula Theory; Dependent observations; Detection; Estimation; Sensor Fusion; Engineering

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APA (6^th Edition):

He, H. (2015). Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/390

Chicago Manual of Style (16^th Edition):

He, Hao. “Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.” 2015. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/390.

MLA Handbook (7^th Edition):

He, Hao. “Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence.” 2015. Web. 15 Dec 2019.

Vancouver:

He H. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. [Internet] [Doctoral dissertation]. Syracuse University; 2015. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/390.

Council of Science Editors:

He H. Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence. [Doctoral Dissertation]. Syracuse University; 2015. Available from: https://surface.syr.edu/etd/390

Syracuse University

3. Cao, Nianxia. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/563

► Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring… (more)

▼ Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring and control. The classical WSNs are composed of large number of densely deployed sensors, where sensors are battery-powered devices with limited signal processing capabilities. In the crowdsourcing based WSNs, users who carry devices with built-in sensors are recruited as sensors. In both WSNs, the sensors send their observations regarding the target to a central node called the fusion center for final inference. With limited resources, such as limited communication bandwidth among the WSNs and limited sensor battery power, it is important to investigate algorithms which consider the trade-off between system performance and energy cost in the WSNs. The goal of this thesis is to study the sensor management problems in resource limited WSNs while performing target localization or tracking tasks. Most research on sensor management problems in classical WSNs assumes that the number of sensors to be selected is given a priori, which is often not true in practice. Moreover, sensor network design usually involves consideration of multiple conflicting objectives, such as maximization of the lifetime of the network or the inference performance, while minimizing the cost of resources such as energy, communication or deployment costs. Thus, in this thesis, we formulate the sensor management problem in a classical resource limited WSN as a multi-objective optimization problem (MOP), whose goal is to find a set of sensor selection strategies which re- veal the trade-off between the target tracking performance and the number of selected sensors to perform the task. In this part of the thesis, we propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same as the Fisher information (FI) based sensor selection scheme, and gives estimation performance similar to the mutual information (MI) based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the MOP gives a set of sensor selection strategies that reveal different trade-offs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Crowdsourcing has been applied to sensing applications recently where users carrying devices with built-in sensors are allowed or even encouraged to contribute toward the inference tasks. Crowdsourcing based WSNs provide cost effectiveness since a dedicated sensing infrastructure is no longer needed for different inference tasks, also, such architectures allow ubiquitous coverage. Most sensing applications and systems assume voluntary participation of users. However, users consume… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Localization; Sensor management; Tracking; Wireless sensor networks; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cao, N. (2016). SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/563

Chicago Manual of Style (16^th Edition):

Cao, Nianxia. “SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/563.

MLA Handbook (7^th Edition):

Cao, Nianxia. “SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS.” 2016. Web. 15 Dec 2019.

Vancouver:

Cao N. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/563.

Council of Science Editors:

Cao N. SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/563

Syracuse University

4. Nadendla, Venkata Sriram Siddhardh. On the Design and Analysis of Secure Inference Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/590

► Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that… (more)

▼ Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that a global inference is made regarding the phenomenon-of-interest (PoI). In this dissertation, we address two types of statistical inference, namely binary-hypothesis testing and scalar parameter estimation in parallel-topology inference networks. We address three different types of security threats in parallel-topology inference networks, namely Eavesdropping (Data-Confidentiality), Byzantine (Data-Integrity) or Jamming (Data-Availability) attacks. In an attempt to alleviate information leakage to the eavesdropper, we present optimal/near-optimal binary quantizers under two different frameworks, namely differential secrecy where the difference in performances between the FC and Eve is maximized, and constrained secrecy where FC’s performance is maximized in the presence of tolerable secrecy constraints. We also propose near-optimal transmit diversity mechanisms at the sensing agents in detection networks in the presence of tolerable secrecy constraints. In the context of distributed inference networks with M-ary quantized sensing data, we propose a novel Byzantine attack model and find optimal attack strategies that minimize KL Divergence at the FC in the presence of both ideal and non-ideal channels. Furthermore, we also propose a novel deviation-based reputation scheme to detect Byzantine nodes in a distributed inference network. Finally, we investigate optimal jamming attacks in detection networks where the jammer distributes its power across the sensing and the communication channels. We also model the interaction between the jammer and a centralized detection network as a complete information zero-sum game. We find closed-form expressions for pure-strategy Nash equilibria and show that both the players converge to these equilibria in a repeated game. Finally, we show that the jammer finds no incentive to employ pure-strategy equilibria, and causes greater impact on the network performance by employing mixed strategies. Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Byzantine Attack; Eavesdropping; Inference Networks; Jamming; Security; Engineering

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APA (6^th Edition):

Nadendla, V. S. S. (2016). On the Design and Analysis of Secure Inference Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/590

Chicago Manual of Style (16^th Edition):

Nadendla, Venkata Sriram Siddhardh. “On the Design and Analysis of Secure Inference Networks.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/590.

MLA Handbook (7^th Edition):

Nadendla, Venkata Sriram Siddhardh. “On the Design and Analysis of Secure Inference Networks.” 2016. Web. 15 Dec 2019.

Vancouver:

Nadendla VSS. On the Design and Analysis of Secure Inference Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/590.

Council of Science Editors:

Nadendla VSS. On the Design and Analysis of Secure Inference Networks. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/590

5. El Bardan, Raghed. Resource Allocation for Interference Management in Wireless Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/663

► Interference in wireless networks is a major problem that impacts system performance quite substantially. Combined with the fact that the spectrum is limited and… (more)

Subjects/Keywords: Coding theory; Cognitive Radio Networks; Game theory; Interference; Matching theory; Resource allocation; Engineering

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APA (6^th Edition):

El Bardan, R. (2016). Resource Allocation for Interference Management in Wireless Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/663

Chicago Manual of Style (16^th Edition):

El Bardan, Raghed. “Resource Allocation for Interference Management in Wireless Networks.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/663.

MLA Handbook (7^th Edition):

El Bardan, Raghed. “Resource Allocation for Interference Management in Wireless Networks.” 2016. Web. 15 Dec 2019.

Vancouver:

El Bardan R. Resource Allocation for Interference Management in Wireless Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/663.

Council of Science Editors:

El Bardan R. Resource Allocation for Interference Management in Wireless Networks. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/663

Syracuse University

6. Kailkhura, Bhavya. Distributed Inference and Learning with Byzantine Data.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/629

► We are living in an increasingly networked world with sensing networks of varying shapes and sizes: the network often comprises of several tiny devices… (more)

▼ We are living in an increasingly networked world with sensing networks of varying shapes and sizes: the network often comprises of several tiny devices (or nodes) communicating with each other via different topologies. To make the problem even more complicated, the nodes in the network can be unreliable due to a variety of reasons: noise, faults and attacks, thus, providing corrupted data. Although the area of statistical inference has been an active area of research in the past, distributed learning and inference in a networked setup with potentially unreliable components has only gained attention recently. The emergence of big and dirty data era demands new distributed learning and inference solutions to tackle the problem of inference with corrupted data. Distributed inference networks (DINs) consist of a group of networked entities which acquire observations regarding a phenomenon of interest (POI), collaborate with other entities in the network by sharing their inference via different topologies to make a global inference. The central goal of this thesis is to analyze the effect of corrupted (or falsified) data on the inference performance of DINs and design robust strategies to ensure reliable overall performance for several practical network architectures. Specifically, the inference (or learning) process can be that of detection or estimation or classification, and the topology of the system can be parallel, hierarchical or fully decentralized (peer to peer). Note that, the corrupted data model may seem similar to the scenario where local decisions are transmitted over a Binary Symmetric Channel (BSC) with a certain cross over probability, however, there are fundamental differences. Over the last three decades, research community has extensively studied the impact of transmission channels or faults on the distributed detection system and related problems due to its importance in several applications. However, corrupted (Byzantine) data models considered in this thesis, are philosophically different from the BSC or the faulty sensor cases. Byzantines are intentional and intelligent, therefore, they can optimize over the data corruption parameters. Thus, in contrast to channel aware detection, both the FC and the Byzantines can optimize their utility by choosing their actions based on the knowledge of their opponent’s behavior. Study of these practically motivated scenarios in the presence of Byzantines is of utmost importance, and is missing from the channel aware detection and fault tolerant detection literature. This thesis advances the distributed inference literature by providing fundamental limits of distributed inference with Byzantine data and provides optimal counter-measures (using the insights provided by these fundamental limits) from a network designer’s perspective. Note that, the analysis of problems related… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Byzantines; Corrupted Data; Distributed Inference; Distributed Learning; Engineering

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APA (6^th Edition):

Kailkhura, B. (2016). Distributed Inference and Learning with Byzantine Data. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/629

Chicago Manual of Style (16^th Edition):

Kailkhura, Bhavya. “Distributed Inference and Learning with Byzantine Data.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/629.

MLA Handbook (7^th Edition):

Kailkhura, Bhavya. “Distributed Inference and Learning with Byzantine Data.” 2016. Web. 15 Dec 2019.

Vancouver:

Kailkhura B. Distributed Inference and Learning with Byzantine Data. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/629.

Council of Science Editors:

Kailkhura B. Distributed Inference and Learning with Byzantine Data. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/629

Syracuse University

7. Li, Qunwei. On Classification in Human-driven and Data-driven Systems.

Degree: PhD, Electrical Engineering and Computer Science, 2018, Syracuse University

URL: https://surface.syr.edu/etd/991

► Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of… (more)

▼ Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of machine learning techniques. Despite the significant efforts devoted to training and feature selection in classification systems, misclassifications do occur and their effects can be critical in various applications. The central goal of this thesis is to analyze classification problems in human-driven and data-driven systems, with potentially unreliable components and design effective strategies to ensure reliable and effective classification algorithms in such systems. The components/agents in the system can be machines and/or humans. The system components can be unreliable due to a variety of reasons such as faulty machines, security attacks causing machines to send falsified information, unskilled human workers sending imperfect information, or human workers providing random responses. This thesis first quantifies the effect of such unreliable agents on the classification performance of the systems and then designs schemes that mitigate misclassifications and their effects by adapting the behavior of the classifier on samples from machines and/or humans and ensure an effective and reliable overall classification. In the first part of this thesis, we study the case when only humans are present in the systems, and consider crowdsourcing systems. Human workers in crowdsourcing systems observe the data and respond individually by providing label related information to a fusion center in a distributed manner. In such systems, we consider the presence of unskilled human workers where they have a reject option so that they may choose not to provide information regarding the label of the data. To maximize the classification performance at the fusion center, an optimal aggregation rule is proposed to fuse the human workers' responses in a weighted majority voting manner. Next, the presence of unreliable human workers, referred to as spammers, is considered. Spammers are human workers that provide random guesses regarding the data label information to the fusion center in crowdsourcing systems. The effect of spammers on the overall classification performance is characterized when the spammers can strategically respond to maximize their reward in reward-based crowdsourcing systems. For such systems, an optimal aggregation rule is proposed by adapting the classifier based on the responses from the workers. The next line of human-driven classification is considered in the context of social networks. The classification problem is studied to classify a human whether he/she is influential or not in propagating information in social networks. Since the knowledge of social network structures is not always available, the influential agent classification problem without knowing the social network structure is studied. A multi-task low rank linear influence model is proposed to exploit the relationships between different… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Engineering

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APA (6^th Edition):

Li, Q. (2018). On Classification in Human-driven and Data-driven Systems. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/991

Chicago Manual of Style (16^th Edition):

Li, Qunwei. “On Classification in Human-driven and Data-driven Systems.” 2018. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/991.

MLA Handbook (7^th Edition):

Li, Qunwei. “On Classification in Human-driven and Data-driven Systems.” 2018. Web. 15 Dec 2019.

Vancouver:

Li Q. On Classification in Human-driven and Data-driven Systems. [Internet] [Doctoral dissertation]. Syracuse University; 2018. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/991.

Council of Science Editors:

Li Q. On Classification in Human-driven and Data-driven Systems. [Doctoral Dissertation]. Syracuse University; 2018. Available from: https://surface.syr.edu/etd/991

Syracuse University

8. ZHENG, YUJIAO. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2014, Syracuse University

URL: https://surface.syr.edu/etd/71

► Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate… (more)

▼ Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate several issues related to distributed inference in sensor networks, emphasizing parameter estimation and target tracking with resource-constrainted networks. To reduce the transmissions between sensors and the fusion center thereby saving bandwidth and energy consumption in sensor networks, a novel methodology, where each local sensor performs a censoring procedure based on the normalized innovation square (NIS), is proposed for the sequential Bayesian estimation problem in this dissertation. In this methodology, each sensor sends only the informative measurements and the fusion center fuses both missing measurements and received ones to yield more accurate inference. The new methodology is derived for both linear and nonlinear dynamic systems, and both scalar and vector measurements. The relationship between the censoring rule based on NIS and the one based on Kullback-Leibler (KL) divergence is investigated. A probabilistic transmission model over multiple access channels (MACs) is investigated. With this model, a relationship between the sensor management and compressive sensing problems is established, based on which, the sensor management problem becomes a constrained optimization problem, where the goal is to determine the optimal values of probabilities that each sensor should transmit with such that the determinant of the Fisher information matrix (FIM) at any given time step is maximized. The performance of the proposed compressive sensing based sensor management methodology in terms of accuracy of inference is investigated. For the Bayesian parameter estimation problem, a framework is proposed where quantized observations from local sensors are not directly fused at the fusion center, instead, an additive noise is injected independently to each quantized observation. The injected noise performs as a low-pass filter in the characteristic function (CF) domain, and therefore, is capable of recoverving the original analog data if certain conditions are satisfied. The optimal estimator based on the new framework is derived, so is the performance bound in terms of Fisher information. Moreover, a sub-optimal estimator, namely, linear minimum mean square error estimator (LMMSE) is derived, due to the fact that the proposed framework theoretically justifies the additive noise modeling of the quantization process. The bit allocation problem based on the framework is also investigated. A source localization problem in a large-scale sensor network is explored. The maximum-likelihood (ML) estimator based on the quantized data from local sensors and its performance bound in terms of Cramér-Rao lower bound (CRLB) are derived. Since the number of sensors is large, the law of large numbers (LLN) is utilized to obtain a closed-form version of the performance bound, which clearly… Advisors/Committee Members: Pramod k. Varshney, Ruixin Niu.

Subjects/Keywords: Bayesian estimation; Kalman filtering; Particle filtering; Sensor networks; Target tracking; Engineering

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APA (6^th Edition):

ZHENG, Y. (2014). Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/71

Chicago Manual of Style (16^th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/71.

MLA Handbook (7^th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Web. 15 Dec 2019.

Vancouver:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2014. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/71.

Council of Science Editors:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Doctoral Dissertation]. Syracuse University; 2014. Available from: https://surface.syr.edu/etd/71

Syracuse University

9. Liu, Sijia. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

URL: https://surface.syr.edu/etd/441

► Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate… (more)

▼ Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in a sensor network. These sensor nodes can sense, measure, and gather information from the environment and, based on some local processing, they transmit the sensed data to a fusion center that is responsible for making the global inference. Sensor networks are often tasked to perform parameter estimation; example applications include battlefield surveillance, medical monitoring, and navigation. However, under limited resources, such as limited communication bandwidth and sensor battery power, it is important to design an energy-efficient estimation architecture. The goal of this thesis is to provide a fundamental understanding and characterization of the optimal tradeoffs between estimation accuracy and resource usage in sensor networks. In the thesis, two basic issues of resource management are studied, sensor selection/scheduling and sensor collaboration for distributed estimation, where the former refers to finding the best subset of sensors to activate for data acquisition in order to minimize the estimation error subject to a constraint on the number of activations, and the latter refers to seeking the optimal inter-sensor communication topology and energy allocation scheme for distributed estimation systems. Most research on resource management so far has been based on several key assumptions, a) independence of observation, b) strict resource constraints, and c) absence of inter-sensor communication, which lend analytical tractability to the problem but are often found lacking in practice. This thesis introduces novel techniques to relax these assumptions and provide new insights into addressing resource management problems. The thesis analyzes how noise correlation affects solutions of sensor selection problems, and proposes both a convex relaxation approach and a greedy algorithm to find these solutions. Compared to the existing sensor selection approaches that are limited to the case of uncorrelated noise or weakly correlated noise, the methodology proposed in this thesis is valid for any arbitrary noise correlation regime. Moreover, this thesis shows a correspondence between active sensors and the nonzero columns of an estimator gain matrix. Based on this association, a sparsity-promoting optimization framework is established, where the desire to reduce the number of selected sensors is characterized by a sparsity-promoting penalty term in the objective function. Instead of placing a hard constraint on sensor activations, the promotion of sparsity leads to trade-offs between estimation performance and the number of selected sensors. To account for the individual power constraint of each sensor, a novel sparsity-promoting penalty function is presented to avoid scenarios in which the same sensors are successively selected. For solving the proposed optimization problem, we employ the… Advisors/Committee Members: Pramod K. Varshney, Makan Fardad.

Subjects/Keywords: convex optimization; distributed estimation; resource management; sparsity; wireless sensor networks; Engineering

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APA (6^th Edition):

Liu, S. (2016). Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/441

Chicago Manual of Style (16^th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/441.

MLA Handbook (7^th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Web. 15 Dec 2019.

Vancouver:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/441.

Council of Science Editors:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/441

10. Subramanian, Arun. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.

Degree: PhD, Electrical Engineering and Computer Science, 2014, Syracuse University

URL: https://surface.syr.edu/etd/192

► The detection of spatially dependent heavy-tailed signals is considered in this dissertation. While the central limit theorem, and its implication of asymptotic normality of… (more)

▼ The detection of spatially dependent heavy-tailed signals is considered in this dissertation. While the central limit theorem, and its implication of asymptotic normality of interacting random processes, is generally useful for the theoretical characterization of a wide variety of natural and man-made signals, sensor data from many different applications, in fact, are characterized by non-Gaussian distributions. A common characteristic observed in non-Gaussian data is the presence of heavy-tails or fat tails. For such data, the probability density function (p.d.f.) of extreme values decay at a slower-than-exponential rate, implying that extreme events occur with greater probability. When these events are observed simultaneously by several sensors, their observations are also spatially dependent. In this dissertation, we develop the theory of detection for such data, obtained through heterogeneous sensors. In order to validate our theoretical results and proposed algorithms, we collect and analyze the behavior of indoor footstep data using a linear array of seismic sensors. We characterize the inter-sensor dependence using copula theory. Copulas are parametric functions which bind univariate p.d.f. s, to generate a valid joint p.d.f. We model the heavy-tailed data using the class of alpha-stable distributions. We consider a two-sided test in the Neyman-Pearson framework and present an asymptotic analysis of the generalized likelihood test (GLRT). Both, nested and non-nested models are considered in the analysis. We also use a likelihood maximization-based copula selection scheme as an integral part of the detection process. Since many types of copula functions are available in the literature, selecting the appropriate copula becomes an important component of the detection problem. The performance of the proposed scheme is evaluated numerically on simulated data, as well as using indoor seismic data. With appropriately selected models, our results demonstrate that a high probability of detection can be achieved for false alarm probabilities of the order of 10^-4. These results, using dependent alpha-stable signals, are presented for a two-sensor case. We identify the computational challenges associated with dependent alpha-stable modeling and propose alternative schemes to extend the detector design to a multisensor (multivariate) setting. We use a hierarchical tree based approach, called vines, to model the multivariate copulas, i.e., model the spatial dependence between multiple sensors. The performance of the proposed detectors under the vine-based scheme are evaluated on the indoor footstep data, and significant improvement is observed when compared against the case when only two sensors are deployed. Some open research issues are identified and discussed. Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Detection; Heavy-tailed signals; Hypothesis testing; Inference; Information fusion; Statistical dependence; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Subramanian, A. (2014). Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/192

Chicago Manual of Style (16^th Edition):

Subramanian, Arun. “Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.” 2014. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/192.

MLA Handbook (7^th Edition):

Subramanian, Arun. “Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data.” 2014. Web. 15 Dec 2019.

Vancouver:

Subramanian A. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. [Internet] [Doctoral dissertation]. Syracuse University; 2014. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/192.

Council of Science Editors:

Subramanian A. Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data. [Doctoral Dissertation]. Syracuse University; 2014. Available from: https://surface.syr.edu/etd/192

11. Peng, Renbin. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.

Degree: PhD, Electrical Engineering and Computer Science, 2011, Syracuse University

URL: https://surface.syr.edu/eecs_etd/311

► This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks… (more)

▼ This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound. Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Computer Vision; Human Visual System; Image Processing; Medical Imaging; Performance Limits; Stochastic Resonance Noise; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Peng, R. (2011). Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/311

Chicago Manual of Style (16^th Edition):

Peng, Renbin. “Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.” 2011. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/311.

MLA Handbook (7^th Edition):

Peng, Renbin. “Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits.” 2011. Web. 15 Dec 2019.

Vancouver:

Peng R. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. [Internet] [Doctoral dissertation]. Syracuse University; 2011. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/311.

Council of Science Editors:

Peng R. Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits. [Doctoral Dissertation]. Syracuse University; 2011. Available from: https://surface.syr.edu/eecs_etd/311

12. Kar, Swarnendu. Collaborative Estimation in Distributed Sensor Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2013, Syracuse University

URL: https://surface.syr.edu/eecs_etd/333

► Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication… (more)

▼ Networks of smart ultra-portable devices are already indispensable in our lives, augmenting our senses and connecting our lives through real time processing and communication of sensory (e.g., audio, video, location) inputs. Though usually hidden from the user's sight, the engineering of these devices involves fierce tradeoffs between energy availability (battery sizes impact portability) and signal processing / communication capability (which impacts the "smartness" of the devices). The goal of this dissertation is to provide a fundamental understanding and characterization of these tradeoffs in the context of a sensor network, where the goal is to estimate a common signal by coordinating a multitude of battery-powered sensor nodes. Most of the research so far has been based on two key assumptions – "distributed processing" and "temporal independence" – that lend analytical tractability to the problem but otherwise are often found lacking in practice. This dissertation introduces novel techniques to relax these assumptions – leading to vastly efficient energy usage in typical networks (up to 20% savings) and new insights on the quality of inference. For example, the phenomenon of sensor drift is ubiquitous in applications such as air-quality monitoring, oceanography and bridge monitoring, where calibration is often difficult and costly. This dissertation provides an analytical framework linking the state of calibration to the overall uncertainty of the inferred parameters. In distributed estimation, sensor nodes locally process their observed data and send the resulting messages to a sink, which combines the received messages to produce a final estimate of the unknown parameter. In this dissertation, this problem is generalized and called "collaborative estimation", where some sensors can potentially have access to the observations from neighboring sensors and use that information to enhance the quality of their messages sent to the sink, while using the same (or lower) energy resources. This is motivated by the fact that inter-sensor communication may be possible if sensors are geographically close. As demonstrated in this dissertation, collaborative estimation is particularly effective in "energy-skewed" and "information-skewed" networks, where some nodes may have larger batteries than others and similarly some nodes may be more informative (less noisy) compared to others. Since the node with the largest battery is not necessarily also the most informative, the proposed inter-sensor collaboration provides a natural framework to route the relevant information from low-energy-high-quality nodes to high-energy-low-quality nodes in a manner that enhances the overall power-distortion tradeoff. This dissertation also analyzes how time-correlated measurement noise affects the uncertainties of inferred parameters. Imperfections such as baseline drift in sensors result in a time-correlated additive component in the measurement noise. Though some models of drift have been reported in the literature… Advisors/Committee Members: Pramod K. Varshney.

Subjects/Keywords: Baseline Drift; Estimaton Theory; Spatial Collaboration; Spatial Whitening; Library and Information Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kar, S. (2013). Collaborative Estimation in Distributed Sensor Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/eecs_etd/333

Chicago Manual of Style (16^th Edition):

Kar, Swarnendu. “Collaborative Estimation in Distributed Sensor Networks.” 2013. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/eecs_etd/333.

MLA Handbook (7^th Edition):

Kar, Swarnendu. “Collaborative Estimation in Distributed Sensor Networks.” 2013. Web. 15 Dec 2019.

Vancouver:

Kar S. Collaborative Estimation in Distributed Sensor Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2013. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/eecs_etd/333.

Council of Science Editors:

Kar S. Collaborative Estimation in Distributed Sensor Networks. [Doctoral Dissertation]. Syracuse University; 2013. Available from: https://surface.syr.edu/eecs_etd/333

13. Vempaty, Aditya. Reliable Inference from Unreliable Agents.

Degree: PhD, Electrical Engineering and Computer Science, 2015, Syracuse University

URL: https://surface.syr.edu/etd/332

► Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed… (more)

▼ Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system's perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents L and the sum rate R tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the … Advisors/Committee Members: Pramod K. Varshney, Lav R. Varshney.

Subjects/Keywords: Detection and Estimation; Distributed Inferece; Human-Machine Systems; Reliable Systems; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vempaty, A. (2015). Reliable Inference from Unreliable Agents. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/332

Chicago Manual of Style (16^th Edition):

Vempaty, Aditya. “Reliable Inference from Unreliable Agents.” 2015. Doctoral Dissertation, Syracuse University. Accessed December 15, 2019. https://surface.syr.edu/etd/332.

MLA Handbook (7^th Edition):

Vempaty, Aditya. “Reliable Inference from Unreliable Agents.” 2015. Web. 15 Dec 2019.

Vancouver:

Vempaty A. Reliable Inference from Unreliable Agents. [Internet] [Doctoral dissertation]. Syracuse University; 2015. [cited 2019 Dec 15]. Available from: https://surface.syr.edu/etd/332.

Council of Science Editors:

Vempaty A. Reliable Inference from Unreliable Agents. [Doctoral Dissertation]. Syracuse University; 2015. Available from: https://surface.syr.edu/etd/332

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