Matching Items (17)

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Statistically Based Registration in Sensor Networks

Description

In recent years, networked systems have become prevalent in communications, computing, sensing, and many other areas. In a network composed of spatially distributed agents, network-wide synchronization of information about the physical environment and the network configuration must be maintained using

In recent years, networked systems have become prevalent in communications, computing, sensing, and many other areas. In a network composed of spatially distributed agents, network-wide synchronization of information about the physical environment and the network configuration must be maintained using measurements collected locally by the agents. Registration is a process for connecting the coordinate frames of multiple sets of data. This poses numerous challenges, particularly due to availability of direct communication only between neighboring agents in the network. These are exacerbated by uncertainty in the measurements and also by imperfect communication links. This research explored statistically based registration in a sensor network. The approach developed exploits measurements of offsets formed as differences of state values between pairs of agents that share a link in the network graph. It takes into account that the true offsets around any closed cycle in the network graph must sum to zero.

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2014-05

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Estimation Theory on Random Graphs for Offset Detection in Sensor Networks

Description

A distributed sensor network (DSN) is a set of spatially scattered intelligent sensors designed to obtain data across an environment. DSNs are becoming a standard architecture for collecting data over a large area. We need registration of nodal data across

A distributed sensor network (DSN) is a set of spatially scattered intelligent sensors designed to obtain data across an environment. DSNs are becoming a standard architecture for collecting data over a large area. We need registration of nodal data across the network in order to properly exploit having multiple sensors. One major problem worth investigating is ensuring the integrity of the data received, such as time synchronization. Consider a group of match filter sensors. Each sensor is collecting the same data, and comparing the data collected to a known signal. In an ideal world, each sensor would be able to collect the data without offsets or noise in the system. Two models can be followed from this. First, each sensor could make a decision on its own, and then the decisions could be collected at a ``fusion center'' which could then decide if the signal is present or not. The fusion center can then decide if the signal is present or not based on the number true-or-false decisions that each sensor has made. Alternatively, each sensor could relay the data that it collects to the fusion center, and it could then make a decision based on all of the data that it then receives. Since the fusion center would have more information to base its decision on in the latter case--as opposed to the former case where it only receives a true or false from each sensor--one would expect the latter model to perform better. In fact, this would be the gold standard for detection across a DSN. However, there is random noise in the world that causes corruption of data collection, especially among sensors in a DSN. Each sensor does not collect the data in the exact same way or with the same precision. We classify these imperfections in data collections as offsets, specifically the offset present in the data collected by one sensor with respect to the rest of the sensors in the network. Therefore, reconsider the two models for a DSN described above. We can naively implement either of these models for data collection. Alternatively, we can attempt to estimate the offsets between the sensors and compensate. One could see how it would be expected that estimating the offsets within the DSN would provide better overall results than not finding estimators. This thesis will be structured as follows. First, there will be an extensive investigation into detection theory and the impact that different types of offsets have on sensor networks. Following the theory, an algorithm for estimating the data offsets will be proposed correct for the offsets. Next, we will look at Monte Carlo simulation results to see the impact on sensor performance of data offsets in comparison to a sensor network without offsets present. The algorithm is then implemented, and further experiments will demonstrate sensor performance with offset detection.

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Date Created
2016-05

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Operating Systems for Underwater Wireless Sensor Networks

Description

As the need for data concerning the health of the world's oceans increases, it becomes necessary to develop large, networked communication systems underwater. This research involves the development of an embedded operating system that is suited for optically-linked underwater wireless

As the need for data concerning the health of the world's oceans increases, it becomes necessary to develop large, networked communication systems underwater. This research involves the development of an embedded operating system that is suited for optically-linked underwater wireless sensor networks (WSNs). Optical WSNs are unique in that large sums of data may be received relatively infrequently, and so an operating system for each node must be very responsive. Additionally, the volatile nature of the underwater environment means that the operating system must be accurate, while still maintaining a low profile on a relatively small microprocessor core. The first part of this research concerns the actual implementation of the operating system's task scheduler and additional libraries to maintain synchronization, and the second part involves testing the operating system for responsiveness to interrupts and overall performance.

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2016-05

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Security and privacy in heterogeneous wireless and mobile networks: challenges and solutions

Description

The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions

The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.

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Date Created
2013

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Underwater optical sensorbot for in situ pH monitoring

Description

Continuous underwater observation is a challenging engineering task that could be accomplished by development and deployment of a sensor array that can survive harsh underwater conditions. One approach to this challenge is a swarm of micro underwater robots, known as

Continuous underwater observation is a challenging engineering task that could be accomplished by development and deployment of a sensor array that can survive harsh underwater conditions. One approach to this challenge is a swarm of micro underwater robots, known as Sensorbots, that are equipped with biogeochemical sensors that can relay information among themselves in real-time. This innovative method for underwater exploration can contribute to a more comprehensive understanding of the ocean by not limiting sampling to a single point and time. In this thesis, Sensorbot Beta, a low-cost fully enclosed Sensorbot prototype for bench-top characterization and short-term field testing, is presented in a modular format that provides flexibility and the potential for rapid design. Sensorbot Beta is designed around a microcontroller driven platform comprised of commercial off-the-shelf components for all hardware to reduce cost and development time. The primary sensor incorporated into Sensorbot Beta is an in situ fluorescent pH sensor. Design considerations have been made for easy adoption of other fluorescent or phosphorescent sensors, such as dissolved oxygen or temperature. Optical components are designed in a format that enables additional sensors. A real-time data acquisition system, utilizing Bluetooth, allows for characterization of the sensor in bench top experiments. The Sensorbot Beta demonstrates rapid calibration and future work will include deployment for large scale experiments in a lake or ocean.

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Date Created
2012

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Distributed inference using bounded transmissions

Description

Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are

Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.

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Date Created
2013

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Distributed inference over multiple-access channels with wireless sensor networks

Description

Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects

Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using the asymptotic variance of the estimator. The loss in performance due to varying assumptions on the limited amounts of channel information at the sensors is quantified. With multiple antennas at the FC, a distributed detection problem is also considered, where the error exponent is used to evaluate performance. It is shown that for zero-mean channels between the sensors and the FC when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the FC. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the FC is shown to be no more than a factor of 8/π for Rayleigh fading channels between the sensors and the FC, independent of the number of antennas at the FC, or correlation among noise samples across sensors. In a second class of problems, sensor observations are transmitted to the FC using constant-modulus phase modulation over Gaussian multiple-access-channels. The phase modulation scheme allows for constant transmit power and estimation of moments other than the mean with a single transmission from the sensors. Estimators are developed for the mean, variance and signal-to-noise ratio (SNR) of the sensor observations. The performance of these estimators is studied for different distributions of the observations. It is proved that the estimator of the mean is asymptotically efficient if and only if the distribution of the sensor observations is Gaussian.

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Date Created
2010

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A computational framework for wearable accelerometer based activity and gesture recognition

Description

Advances in the area of ubiquitous, pervasive and wearable computing have resulted in the development of low band-width, data rich environmental and body sensor networks, providing a reliable and non-intrusive methodology for capturing activity data from humans and the environments

Advances in the area of ubiquitous, pervasive and wearable computing have resulted in the development of low band-width, data rich environmental and body sensor networks, providing a reliable and non-intrusive methodology for capturing activity data from humans and the environments they inhabit. Assistive technologies that promote independent living amongst elderly and individuals with cognitive impairment are a major motivating factor for sensor-based activity recognition systems. However, the process of discerning relevant activity information from these sensor streams such as accelerometers is a non-trivial task and is an on-going research area. The difficulty stems from factors such as spatio-temporal variations in movement patterns induced by different individuals and contexts, sparse occurrence of relevant activity gestures in a continuous stream of irrelevant movements and the lack of real-world data for training learning algorithms. This work addresses these challenges in the context of wearable accelerometer-based simple activity and gesture recognition. The proposed computational framework utilizes discriminative classifiers for learning the spatio-temporal variations in movement patterns and demonstrates its effectiveness through a real-time simple activity recognition system and short duration, non- repetitive activity gesture recognition. Furthermore, it proposes adaptive discriminative threshold models trained only on relevant activity gestures for filtering irrelevant movement patterns in a continuous stream. These models are integrated into a gesture spotting network for detecting activity gestures involved in complex activities of daily living. The framework addresses the lack of real world data for training, by using auxiliary, yet related data samples for training in a transfer learning setting. Finally the problem of predicting activity tasks involved in the execution of a complex activity of daily living is described and a solution based on hierarchical Markov models is discussed and evaluated.

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Date Created
2010

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Autonomous structural materials with controlled toughening and healing

Description

The field of Structural Health Monitoring (SHM) has grown significantly over the past few years due to safety and performance enhancing benefits as well as potential life saving capabilities offered by technology. Current advances in SHM systems have lead to

The field of Structural Health Monitoring (SHM) has grown significantly over the past few years due to safety and performance enhancing benefits as well as potential life saving capabilities offered by technology. Current advances in SHM systems have lead to a variety of techniques capable of identifying damage. However, few strategies exist for using this information to quickly react to environmental or material conditions needed to repair or protect the system. Rather, current systems simply relay this information to a central processor or human operator who then decides on a course of action, such as altering the mission or scheduling a repair operation. Biological systems exhibit many advanced sensory and healing traits that can be applied to the design of material systems. For instance, bones are the major structural component in vertebrates; however, unlike modern structural materials, bones have many properties that make it effective for arresting the development and propagation of cracks and subsequent healing of the damaged region. Mimicking biological materials, an autonomous material system was developed that uses Shape Memory Polymers (SMPs) with an embedded fiber optic network. This thesis researches a novel system that uses SMPs and employs an optical fiber network as both a damage detection sensor and a network to deliver stimulus to the damage site, initiating active toughening and healing algorithms. In the presence of damage, the fiber optic fractures, which allowed a high power laser diode to deposit a controlled level of thermal energy at the damage site, locally reducing the modulus and blunting the crack tip. The shape memory polymer not only provided a sharp glass transition, but also allowed for the application of an programmed global pre-strain, which under thermal loads induced the shape memory effect to close the crack and adequately heal the polymer to its designed operational conditions recovering full strength. It will be shown that the material can be significantly toughened and that control algorithms combined with the shape memory properties can further increase the toughening and healing effect. The entire system will be able to effectively sense damage, defend its propagation by actively toughening, and subsequently heal the structure, autonomously in a real time operational environment.

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Created

Date Created
2010

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A cross-layer power analysis and profiling of wireless video sensor node platform applications

Description

Wireless video sensor networks has been examined and evaluated for wide range

of applications comprising of video surveillance, video tracking, computer vision, remote

live video and control. The reason behind importance of sensor nodes is its ease

of implementation, ability to operate in

Wireless video sensor networks has been examined and evaluated for wide range

of applications comprising of video surveillance, video tracking, computer vision, remote

live video and control. The reason behind importance of sensor nodes is its ease

of implementation, ability to operate in adverse environments, easy to troubleshoot,

repair and the high performance level. The biggest challenges with the architectural

design of wireless video sensor networks are power consumption, node failure,

throughput, durability and scalability. The whole project here is to create a gateway

node to integrate between "Internet of things" framework and wireless sensor network.

Our Flexi-Wireless Video Sensor Node Platform (WVSNP) is a low cost, low

power and compatible with traditional sensor network where the main focus was on

maximizing throughput or minimizing node deployment. My task here in this project

was to address the challenges of video power consumption for wireless video sensor

nodes. While addressing the challenges, I performed analysis of predicting the nodes

durability when it is battery operated and to choose appropriate design parameters.

I created a small optimized image to boot up Wandboard DUAL/QUAD board, capture

videos in small/big chunks from the board. The power analysis was performed

for only capturing scenarios, playback of reference videos and, live capturing and realtime

playing of videos on WVSNP player. Each sensor node in sensor network are

battery operated and runs without human intervention. Thus to predict nodes durability,

for dierent video size and format, I have collected power consumption results

and based on this I have provided some recommendation of HW/SW architecture.

i

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Created

Date Created
2014