<p>Event monitoring has been employed in many applications such as network monitoring, active databases etc.; however, there is only an insignificant amount work done on parameterized event monitoring, a feature that is necessary in any real application. The aim of this work is to investigate solutions for parameterized event composition that is scalable and efficient; these solutions are refined from existing event monitoring algorithms. An algorithm for parameterized event composition is proposed and analysis on algorithmic time complexity is performed. In addition to this, experiments on the prototype Solicitor, a software component in DeeDS, along with simulated input of events are conducted in order to validate the theoretical model and the hypothesis that were made. The experiments support the theoretical model and suggest that it is possible to build an efficient and scalable parameterized event composition that is useful in real applications.</p>

Event monitoring has been employed in many applications such as network monitoring, active databases etc.; however, there is only an insignificant amount work done on parameterized event monitoring, a feature that is necessary in any real application. The aim of this work is to investigate solutions for parameterized event composition that is scalable and efficient; these solutions are refined from existing event monitoring algorithms. An algorithm for parameterized event composition is proposed and analysis on algorithmic time complexity is performed. In addition to this, experiments on the prototype Solicitor, a software component in DeeDS, along with simulated input of events are conducted in order to validate the theoretical model and the hypothesis that were made. The experiments support the theoretical model and suggest that it is possible to build an efficient and scalable parameterized event composition that is useful in real applications.

<p>An event monitor is a part of a real-time system that can be used to</p><p>check if the system follows the specifications posed on its behavior. This dissertation covers an approach to event monitoring where such specifications (represented by time constraints) are represented by graphs.</p><p>Not much work has previously been done on designing and implementing constraint graph-based event monitors. In this work, we focus on presenting an extensible design for such an event monitor. We also evaluate different data structure types (linked lists, dynamic arrays, and static arrays) that can be used for representing the constraint graphs internally. This is done by creating an event monitor implementation, and conducting a number of benchmarks where the time used by the monitor is measured.</p><p>The result is presented in the form of a design specification and a summary of the benchmark results. Dynamic arrays are found to be the generally most efficient, but advantages and disadvantages of all the data structure types are discussed.</p>

An event monitor is a part of a real-time system that can be used to check if the system follows the specifications posed on its behavior. This dissertation covers an approach to event monitoring where such specifications (represented by time constraints) are represented by graphs. Not much work has previously been done on designing and implementing constraint graph-based event monitors. In this work, we focus on presenting an extensible design for such an event monitor. We also evaluate different data structure types (linked lists, dynamic arrays, and static arrays) that can be used for representing the constraint graphs internally. This is done by creating an event monitor implementation, and conducting a number of benchmarks where the time used by the monitor is measured. The result is presented in the form of a design specification and a summary of the benchmark results. Dynamic arrays are found to be the generally most efficient, but advantages and disadvantages of all the data structure types are discussed.

<p>Events play an important role in many computer systems, from small reactive embedded applications to large distributed systems. Many applications react to events generated by a graphical user interface or by external sensors that monitor the system environment, and other systems use events for communication and synchronisation between independent subsystems. In some applications, however, individual event occurrences are not the main point of concern. Instead, the system should respond to certain event patterns, such as "the start button being pushed, followed by a temperature alarm within two seconds". One way to specify such event patterns is by means of an event algebra with operators for combining the simple events of a system into specifications of complex patterns.</p><p>This thesis presents an event algebra with two important characteristics. First, it complies with a number of algebraic laws, which shows that the algebra operators behave as expected. Second, any pattern represented by an expression in this algebra can be efficiently detected with bounded resources in terms of memory and time, which is particularly important when event pattern detection is used in embedded systems, where resource efficiency and predictability are crucial.</p><p>In addition to the formal algebra semantics and an efficient detection algorithm, the thesis describes how event pattern detection can be used in real-time systems without support from the underlying operating system, and presents schedulability theory for such systems. It also describes how the event algebra can be combined with a component model for embedded system, to support high level design of systems that react to event patterns.</p>

<p>To gain confidence in that a dynamic real-time system behaves correctly, we test it. Automated verification & validation can be used to conduct testing of such systems in an effective and economic way.</p><p>An event monitor can be used as a part of a test oracle to monitor the system that is being tested. The test oracle could use the data (i.e., the streams of events) derived from the tested system, to determine if an executed test case gave a positive or negative result. To do this, the test oracle compares the streams of events received from the event monitor with the event expressions derived from the formal specification, and decides if the executed test case has responded positive or negative. Any deviations between observed behaviour and accepted behaviour should be reported by the test oracle as a negative result. If the executed test case gave a negative result, the monitor part should signal this to the reporter part of the test oracle.</p><p>This work aims to investigate how the event expressions can be derived from the formal specification, and in particular, how the event specification language Solicitor can be used to represent these event expressions.</p><p>We also discuss the need for parameterized event types in Solicitor, and any other event specification languages used in event monitoring. We also show that support for parameterized event types is a significant requirement for such languages.</p>

Events play an important role in many computer systems, from small reactive embedded applications to large distributed systems. Many applications react to events generated by a graphical user interface or by external sensors that monitor the system environment, and other systems use events for communication and synchronisation between independent subsystems. In some applications, however, individual event occurrences are not the main point of concern. Instead, the system should respond to certain event patterns, such as "the start button being pushed, followed by a temperature alarm within two seconds". One way to specify such event patterns is by means of an event algebra with operators for combining the simple events of a system into specifications of complex patterns. This thesis presents an event algebra with two important characteristics. First, it complies with a number of algebraic laws, which shows that the algebra operators behave as expected. Second, any pattern represented by an expression in this algebra can be efficiently detected with bounded resources in terms of memory and time, which is particularly important when event pattern detection is used in embedded systems, where resource efficiency and predictability are crucial. In addition to the formal algebra semantics and an efficient detection algorithm, the thesis describes how event pattern detection can be used in real-time systems without support from the underlying operating system, and presents schedulability theory for such systems. It also describes how the event algebra can be combined with a component model for embedded system, to support high level design of systems that react to event patterns.

To gain confidence in that a dynamic real-time system behaves correctly, we test it. Automated verification &amp; validation can be used to conduct testing of such systems in an effective and economic way. An event monitor can be used as a part of a test oracle to monitor the system that is being tested. The test oracle could use the data (i.e., the streams of events) derived from the tested system, to determine if an executed test case gave a positive or negative result. To do this, the test oracle compares the streams of events received from the event monitor with the event expressions derived from the formal specification, and decides if the executed test case has responded positive or negative. Any deviations between observed behaviour and accepted behaviour should be reported by the test oracle as a negative result. If the executed test case gave a negative result, the monitor part should signal this to the reporter part of the test oracle. This work aims to investigate how the event expressions can be derived from the formal specification, and in particular, how the event specification language Solicitor can be used to represent these event expressions. We also discuss the need for parameterized event types in Solicitor, and any other event specification languages used in event monitoring. We also show that support for parameterized event types is a significant requirement for such languages.

Understanding the behavior of pedestrians in road transportation is critical to maintain a safe en- vironment. Accidents on road transportation are one of the most common causes of death today. As autonomous vehicles start to become a standard in our society, safety on road transportation becomes increasingly important. Road transportation is a complex system with a lot of dierent factors. Identifying risky behaviors and preventing accidents from occurring requires better under- standing of the behaviors of the dierent persons involved. In this thesis the activities and behavior of a pedestrian is analyzed. Using sensor data from phones, eight dierent events of a pedestrian are classied using machine learning algorithms. Features extracted from phone sensors that can be used to model dierent pedestrian activities are identied. Current state of the art literature is researched to nd relevant machine learning algorithms for a classication model. Two models are implemented using two dierent machine learning algorithms: Articial Neural Network and Hid- den Markov Model. Two dierent experiments are conducted where phone sensor data is collected and classied using the models, achieving a classication accuracy of up to 93%.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.