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Distributed Sensor Networks (DSNs) face a myriad of technical challenges. This dissertation examines two important DSN challenges.
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One problem is converting ``primitive" sensor data into actionable products and insights. For example, a DSN for power quality (PQ) might gather primitive data in the form of raw voltage waveforms and produce actionable insights in the form of the ability to predict when PQ events are going to occur by observing cyclical data. For another example, a DSN for infrasound might gather primitive data in the form of microphone counts and produce actionable insight in the form of determining what, when, and where the signal came from. To make progress towards this problem, DSNs typically implement one or more of the following strategies: detecting signals in the primitive data (deciding if something is there), classification of signals from primitive data (deciding what is there), and localization of signals (when and from where did the signals come). Further, DSNs make progress towards this problem by forming relationships between primitive data by finding correlations between spatial attributes, temporal attributes, and by associating metadata with primitive data to provide contextual information not collected by the DSN. These strategies can be employed recursively. As an example, the result of aggregating typed primitive data provides a new higher level of typed data which contains more context than the data from which is was derived from. This new typed data can itself be aggregated into new, higher level types and also participate in relationships.
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A second important challenge is managing data volume. Most DSNs produce large amounts of (increasingly multimodal) primitive data, of which only a tiny fraction (the signals) is actually interesting and useful. The DSN can utilize one of two strategies: keep all of the information and primitive data forever, or employ some sort of strategy for systematically discarding (hopefully uninteresting and not useful) data. As sensor networks scale in size, the first strategy becomes unfeasible. Therefore, DSNs must find and implement a strategy for managing large amounts of sensor data. The difficult part is finding an effective and efficient strategy deciding what data is interesting and must be kept and what data to discard.
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This dissertation investigates the design, implementation, and evaluation of the Laha framework, which provides new insight into both of these problems. First, the Laha framework provides a multi-leveled representation for structuring and processing DSN data. The structure and processing at each level is designed with the explicit goal of turning low-level data into actionable insights. Second, each level in the framework implements a ``time-to-live" (TTL) strategy for data within the level. This strategy states that data must either ``progress" upwards through the levels towards more abstract, useful representations within a fixed time window, or be discarded and lost forever. The TTL strategy is useful because when implemented, it allows DSN designers to calculate upper bounds on data storage at each level of the framework and supports graceful degradation of DSN performance.
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There are several smaller, but still important problems that exist within the context of these two larger problems. Examples of the smaller problems that Laha hopes to overcome in transit to the larger goals include optimization of triggering, detection, and classification, building a model of sensing field topology, optimizing sensor energy use, optimizing bandwidth, and providing predictive analytics for DSNs.
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Laha provides four contributions to the area of DSNs. First, the Laha design, a novel abstract distributed sensor network that provides useful properties relating to data management. Second, an evaluation of the Laha abstract framework through the deployment of two Laha-compliant reference implementations, validated data collection, and several experiments that are used to either confirm or deny the benefits touted by Laha. Third, two Laha-compliant reference implementations, OPQ and Lokahi, which can be used to form DSNs for the collection of distributed power quality signals and the distributed collection of infrasound signals. Fourth, a set of implications for modern distributed sensor networks as a result of the evaluation of Laha.
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The major claim of this dissertation is that the Laha Framework provides a generally useful representation for real-time high-volume DSNs that address several major issues that modern DSNs face.
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}
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}
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@phdthesis{csdl2-20-02,
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author = {Sergey Negrashov},
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title = {Design, Implementation, and Evaluation of Napali: A novel distributed sensor network for improved power quality monitoring},
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school = {University of Hawaii, Department of Information and Computer Sciences},
Today's big data world heavily relies upon providing precise, timely, and actionable intelligence, while being burdened by the ever increasing need for data cleaning and preprocessing.
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While in the case of ingesting large quantity of unstructured data this problem is unavoidable, when it comes to sensor networks built for a specific purpose, such as anomaly detection, some of that computation can be moved to the edge of the network.
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This thesis concerns the special case of sensor networks tailored for monitoring the power grid for anomalous behavior.
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These networks monitor power delivery infrastructure with the intent of finding deviations from the nominal steady state, across multiple geographical locations.
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Aforementioned deviations, known as power quality anomalies, may originate, and be localized to the location of the sensor, or may affect a sizable portion of the power grid.
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The difficulty of evaluating the extent of a power quality anomaly stems directly from their short temporal and variable geographical impact.
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I present a novel distributed power quality monitoring system called Napali which relies on extracted metrics from individual meters and their temporal locality in order to intelligently detect anomalies and extract raw data within temporal window and geographical areas of interest.
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The claims of this thesis are that Napali outperforms existing power quality monitoring gridwide event detection methods in resource utilization and sensitivity.
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Furthermore, Napali residential monitoring is capable of power grid monitoring without deployment on the high voltage transmission lines.
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Final claim of this thesis is that Napali capability of extracting portions of the events which did not pass the critical thresholds used in other detection methods allows for better localization of power quality disturbances.
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Napali claim validation was performed through deployment at the University of Hawaii.
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Fifteen OPQ Box devices, designed specifically to operate with Napali were located in various locations on campus.
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Data collected from these monitors was compared with smart meters already deployed across the University.
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Additionally, Napali was compared with standard methods of power quality event detection running along side the Napali systems.
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Napali methodology outperformed the standard methods of power quality monitoring in resource consumption, event quality and sensitivity.
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Additionally, I was able to validate that residential utility monitoring is capable of event detection and localization without monitoring higher levels of the power grid hierarchy.
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Finally, as a demonstration of Napali capabilities, I showed how data collected by my framework can be used to partition the power delivery infrastructure without prior knowledge of the power grid topology.
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RadGrad is a curriculum initiative implemented via a web-based application that combines features of social networks, degree planners, and serious games. RadGrad redefines the traditional meaning of “progress” and “success” in the undergraduate computer science degree program, with the ultimate goal of improving student engagement, diversity, and retention. In this paper, we relate RadGrad to other curriculum initiatives, overview its key functionality, present results from an evaluation conducted during its first year of deployment, and discuss our lessons learned and future directions.
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</font></blockquote>
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<p>
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</p>
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<p><aname="csdl2-20-01"></a>
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Anthony J. Christe.
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<em>LAHA: A framework for adaptive optimization of distributed
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sensor frameworks</em>.
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PhD thesis, University of Hawaii, Department of Information and
Distributed Sensor Networks (DSNs) face a myriad of technical challenges. This dissertation examines two important DSN challenges.
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+
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+
One problem is converting “primitive" sensor data into actionable products and insights. For example, a DSN for power quality (PQ) might gather primitive data in the form of raw voltage waveforms and produce actionable insights in the form of the ability to predict when PQ events are going to occur by observing cyclical data. For another example, a DSN for infrasound might gather primitive data in the form of microphone counts and produce actionable insight in the form of determining what, when, and where the signal came from. To make progress towards this problem, DSNs typically implement one or more of the following strategies: detecting signals in the primitive data (deciding if something is there), classification of signals from primitive data (deciding what is there), and localization of signals (when and from where did the signals come). Further, DSNs make progress towards this problem by forming relationships between primitive data by finding correlations between spatial attributes, temporal attributes, and by associating metadata with primitive data to provide contextual information not collected by the DSN. These strategies can be employed recursively. As an example, the result of aggregating typed primitive data provides a new higher level of typed data which contains more context than the data from which is was derived from. This new typed data can itself be aggregated into new, higher level types and also participate in relationships.
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A second important challenge is managing data volume. Most DSNs produce large amounts of (increasingly multimodal) primitive data, of which only a tiny fraction (the signals) is actually interesting and useful. The DSN can utilize one of two strategies: keep all of the information and primitive data forever, or employ some sort of strategy for systematically discarding (hopefully uninteresting and not useful) data. As sensor networks scale in size, the first strategy becomes unfeasible. Therefore, DSNs must find and implement a strategy for managing large amounts of sensor data. The difficult part is finding an effective and efficient strategy deciding what data is interesting and must be kept and what data to discard.
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+
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This dissertation investigates the design, implementation, and evaluation of the Laha framework, which provides new insight into both of these problems. First, the Laha framework provides a multi-leveled representation for structuring and processing DSN data. The structure and processing at each level is designed with the explicit goal of turning low-level data into actionable insights. Second, each level in the framework implements a “time-to-live" (TTL) strategy for data within the level. This strategy states that data must either “progress" upwards through the levels towards more abstract, useful representations within a fixed time window, or be discarded and lost forever. The TTL strategy is useful because when implemented, it allows DSN designers to calculate upper bounds on data storage at each level of the framework and supports graceful degradation of DSN performance.
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+
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There are several smaller, but still important problems that exist within the context of these two larger problems. Examples of the smaller problems that Laha hopes to overcome in transit to the larger goals include optimization of triggering, detection, and classification, building a model of sensing field topology, optimizing sensor energy use, optimizing bandwidth, and providing predictive analytics for DSNs.
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+
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+
Laha provides four contributions to the area of DSNs. First, the Laha design, a novel abstract distributed sensor network that provides useful properties relating to data management. Second, an evaluation of the Laha abstract framework through the deployment of two Laha-compliant reference implementations, validated data collection, and several experiments that are used to either confirm or deny the benefits touted by Laha. Third, two Laha-compliant reference implementations, OPQ and Lokahi, which can be used to form DSNs for the collection of distributed power quality signals and the distributed collection of infrasound signals. Fourth, a set of implications for modern distributed sensor networks as a result of the evaluation of Laha.
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+
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The major claim of this dissertation is that the Laha Framework provides a generally useful representation for real-time high-volume DSNs that address several major issues that modern DSNs face.
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</font></blockquote>
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<p>
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</p>
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<p><aname="csdl2-20-02"></a>
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Sergey Negrashov.
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<em>Design, Implementation, and Evaluation of Napali: A novel
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+
distributed sensor network for improved power quality monitoring</em>.
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+
PhD thesis, University of Hawaii, Department of Information and
Today's big data world heavily relies upon providing precise, timely, and actionable intelligence, while being burdened by the ever increasing need for data cleaning and preprocessing.
65
+
While in the case of ingesting large quantity of unstructured data this problem is unavoidable, when it comes to sensor networks built for a specific purpose, such as anomaly detection, some of that computation can be moved to the edge of the network.
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+
This thesis concerns the special case of sensor networks tailored for monitoring the power grid for anomalous behavior.
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+
These networks monitor power delivery infrastructure with the intent of finding deviations from the nominal steady state, across multiple geographical locations.
68
+
Aforementioned deviations, known as power quality anomalies, may originate, and be localized to the location of the sensor, or may affect a sizable portion of the power grid.
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+
The difficulty of evaluating the extent of a power quality anomaly stems directly from their short temporal and variable geographical impact.
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+
I present a novel distributed power quality monitoring system called Napali which relies on extracted metrics from individual meters and their temporal locality in order to intelligently detect anomalies and extract raw data within temporal window and geographical areas of interest.
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+
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+
The claims of this thesis are that Napali outperforms existing power quality monitoring gridwide event detection methods in resource utilization and sensitivity.
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+
Furthermore, Napali residential monitoring is capable of power grid monitoring without deployment on the high voltage transmission lines.
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+
Final claim of this thesis is that Napali capability of extracting portions of the events which did not pass the critical thresholds used in other detection methods allows for better localization of power quality disturbances.
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+
Napali claim validation was performed through deployment at the University of Hawaii.
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+
Fifteen OPQ Box devices, designed specifically to operate with Napali were located in various locations on campus.
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+
Data collected from these monitors was compared with smart meters already deployed across the University.
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+
Additionally, Napali was compared with standard methods of power quality event detection running along side the Napali systems.
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+
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Napali methodology outperformed the standard methods of power quality monitoring in resource consumption, event quality and sensitivity.
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Additionally, I was able to validate that residential utility monitoring is capable of event detection and localization without monitoring higher levels of the power grid hierarchy.
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Finally, as a demonstration of Napali capabilities, I showed how data collected by my framework can be used to partition the power delivery infrastructure without prior knowledge of the power grid topology.
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