No description available.

No description available.

<p>The advent of small-scale, distributed generators of energy has resulted in the problem of integrating them in the conventional electric power system, which is characterized by large-scale, centralized energy generators. MicroGrids have emerged as a promising solution to the integration problem and have duly received increasing research attention. Microgrids are semi-autonomous collections of controllable microsources and loads, which present themselves to the utility grid as single, controlled entities. In order to achieve the semi-autonomous and controlled nature of microgrids, especially,overcoming the challenge of balancing demand and power generation, an intelligent energy management scheme is required.</p> <p>Developing an energy management scheme is an interesting and challenging task, which provides the potential to exploit ideas from a plethora of fields like Artificial Intelligence and Machine Learning, Constrained Optimization, etc. However, testing energy management strategies on a microgrid would pose a multitude of problems,the most important of them being the unreliability and inconvenience of testing an energy management strategy, which is not optimal, on a functional microgrid. Errors in a test strategy might cause power outages and damage installed devices. Hence it is necessary to test energy management strategies on simulated microgrids.</p> <p>This thesis presents a Software Testbed of MicroGrids, specifically designed to suit the purposes of development of energy management strategies. The testbed consists of two components: Simulation Framework and Analysis Tool. The modular simulation framework enables simulation of a microgrid with microsources and loads,whose configurations can be specified by the user. The analysis tool enables visual analysis of data generated using simulations, which would enable the improvement of not only the management strategy and prediction techniques, but also the computer models used in the simulation framework. A demonstration of the software testbed's simulation and analysis capabilities is presented and possible directions for future research are suggested.</p> / Master of Science (MSc)

Managing mobile ad hoc networks (MANETs) presents new challenges due to the need for a distributed management mechanism that can efficiently adapt to the dynamic nature of these networks. In particular, provisioning and management of Quality of Service (QoS) in such networks remains a challenging task. Previous works in this field have focused largely on the monitoring and data collection aspects of network management; literature on the provisioning of devices and protocol support for MANET configuration is scarce. One approach for QoS provisioning and management in the Internet that has met with considerable interest in the networking community is that of Policy-Based Network Management (PBNM). However, its application has been so far limited mainly to fixed high-bandwidth networks. In this research, we apply the PBNM concept, for the first time, for managing QoS in ad hoc networks. We formulate a framework to understand the various crucial components that should comprise an ad hoc network management system. We propose a taxonomy of policy architectures to classify the various feasible architectures into distinct categories. Based on our assessment using the taxonomy, we identify architectures that seem promising for managing ad hoc networks. We propose a solution suite to address the different challenges in deploying policy-based management in MANETs. These solutions include k-hop clustering, Dynamic Service Redundancy (DynaSeR), inter-domain policy negotiation, and automated service discovery. We propose extensions to the standard Common Open Policy Service (COPS) protocol and suggest methods for cross-layer interaction to implement our solutions. Our methodology focuses on both a prototype implementation and experimental analysis using wired and wireless testbed networks, and modeling and performance evaluation using simulation. The whole exercise of conducting experiments provided valuable insight into the challenges of operating in an actual ad hoc network environment; implementation and testing facilitated assessment of the feasibility of our proposed schemes. Simulation allowed us to evaluate our solutions for different cluster sizes, network densities, and node mobility. The scalability of our solutions was tested with networks of up to 100 nodes. In general, average service availability for the PBNM system improved as the cluster size increased, with decreased COPS connection overhead (the tradeoff is increased unpredictability, longer response time, and resource requirements at intermediate nodes to support larger clusters). We were also able to determine that, for a given cluster size, our proposed delegation scheme resulted in a 10 to 25% improvement in service availability. Using our proposed time-based heuristic, savings on the order of 50 to 400% were obtained in the service discovery overhead for larger cluster sizes. We also validated some of the simulation results against proof-of-concept experiments conducted using the testbed. We presented a working illustration of our PBNM system prototype by demonstrating its application for managing QoS for multimedia and real-time mission critical applications in a multi-domain ad hoc network. The policy-based approach is a promising one for the management of MANETs, but it requires the flexibility to adapt to a constantly changing environment. Through experimental studies and simulation, we were able to determine that using our proposed solution suite and through the addition of a set of extensions to the COPS protocol, we can achieve our objective of a self-organizing, robust, and efficient PBNM system for managing MANETs. / Ph. D.

A wireless Mobile Ad Hoc Network (MANET) comprises of a number of mobile nodes that uses multi-hop routing to provide network connectivity. MANETs require self-organizing capabilities as there are no centralized points (base stations, access points etc), and each mobile node functions as router and/or hosts. The wireless topology in MANET can change rapidly with mobility of nodes in unpredictable ways or remain static for long periods of time. MANETs have applications in neighborhood area networks (NANs), impromptu communication among groups of people, disaster management and dynamic military systems. As progress in MANET continues, there is an increase in demand with regard to supporting content-rich video streaming in such networks. This is due to the fact that real-time video is far more substantive than simple data communication. This work involves implementing a Genetic Algorithm (GA) based multipath routing methodologies in a proactive routing protocol (Optimized Link State Routing Protocol) to send/forward/receive multimedia streams on experimental testbed. We study the problem of multipath video routing in wireless ad hoc networks by following an application-centric cross-layer approach. A full implementation of GA-based routing and real-time video conferencing application (server and client) written in C++ is presented. The robustness of our routing scheme was tested through experiments using five computer nodes. The performance of the routing protocol for video, as well as issues such as applicability and scalability in practice are addressed. / Master of Science

Spectrum sharing is possible, but lacks R & D support for practical solutions that satisfy both the incumbent and secondary or opportunistic users. The author found a lack of an openly available framework supporting experimental research on the performance of a Spectrum Access System (SAS) and propose to build an open-source Software Defined Radio (SDR) based framework. This framework will test different dynamic spectrum scenarios in a wireless testbed. This thesis presents our Spectrum Access System prototype, discusses the design choices and trade-offs and provides a proof of concept implementation. We show that an Internet-accessible CORNET test bed provides the ideal platform for developing and testing the SAS functionality and its building blocks and offerss the hardware and software as a community resource for research and education. This design provides the necessary interfaces for researchers to develop and test their SAS-related modules, waveforms and scenarios. / Master of Science / In this information age, the number of wireless devices is growing faster than the infrastructure required to make wireless communication possible. This creates a possibility of not having enough radio spectrum to keep up with this growing demand. To alleviate this issue, there is a need to research and find more ways of efficiently utilizing the current spectrum resources available. Dynamic spectrum allocation is one way forward to archiving this goal. Frequency channels are assigned to devices based on prevailing conditions like device location and availability of channels that would cause low interference to other devices. Spectrum utilization is based on time, frequency and space with devices having the ability to hop to the best channel available. In this thesis, an open-source Spectrum Access System (SAS) was created as a platform through which dynamic spectrum allocation research can be done. The SAS is centralized management system that logs information about the prevailing spectrum usage, and in turn uses this information to dynamically allocate spectrum to devices and networks. This thesis shows how it was implemented, its current performance, and the steps that different researchers can take to add their own functionalities.

Future generations of unmanned spacecraft, aircraft, ground, and submersible vehicles will require precise relative navigation capabilities to accomplish missions such as formation operations and autonomous rendezvous and docking. The development of relative navigation sensing and control techniques is quite challenging, in part because of the difficulty of accurately simulating the physical relative navigation problems in which the control systems are designed to operate. A hardware testbed that can simulate the complex relative motion of many different relative navigation problems is being developed. This testbed simulates near-planar relative motion by using software to prescribe the motion of an unmanned ground vehicle and provides the attached sensor packages with realistic relative motion. This testbed is designed to operate over a wide variety of conditions in both indoor and outdoor environments, at short and long ranges, and its modular design allows it to easily test many different sensing and control technologies. / Master of Science

The integration of Artificial Intelligence (AI) into water supply systems (WSSs) has revolutionized real-time monitoring, automated operational control, and predictive decision-making analytics. However, AI also introduces security vulnerabilities, such as data poisoning. In this context, data poisoning could involve the malicious manipulation of critical data, including water quality parameters, flow rates, and chemical composition levels. The consequences of such threats are significant, potentially jeopardizing public safety and health due to decisions being made based on poisoned data. This thesis aims to exploit these vulnerabilities in data-driven applications within WSSs. Proposing Water Generative Adversarial Networks, H2OGAN, a time-series GAN-based model designed to synthesize water data. H2OGAN produces water data based on the characteristics within the expected constraints of water data cardinality. This generative model serves multiple purposes, including data augmentation, anomaly detection, risk assessment, cost-effectiveness, predictive model optimization, and understanding complex patterns within water systems. Experiments are conducted in AI and Cyber for Water and Agriculture (ACWA) Lab, a cyber-physical water testbed that generates datasets replicating both operational and adversarial scenarios in WSSs. Identifying adversarial scenarios is particularly importance due to their potential to compromise water security. The datasets consist of 10 physical incidents, including normal conditions, sensor anomalies, and malicious attacks. A recurrent neural network (RNN) model, i.e., gated recurrent unit (GRU), is used to classify and capture the temporal dynamics those events. Subsequently, experiments with real-world data from Alexandria Renew Enterprises (AlexRenew), a wastewater treatment plant in Alexandria, Virginia, are conducted to assess the effectiveness of H2OGAN in real-world applications. / Master of Science / Today, a significant portion of the global population struggles with access to essential services: 25% lack clean water, 50% lack sanitation services, and 30% lack hygiene facilities. In response, AI is being leveraged to tackle these deficiencies within water supply systems. Investments in AI are expected to reach an estimated $6.3 billion by 2030, with potential savings of 20% to 30% in operational expenditures by optimizing chemical usage in water treatment. The flexibility and efficiency of AI applications have fueled optimism about their potential to revolutionize water management. As the era of Industry 4.0 progresses, the role of AI in transforming critical infrastructures, including water supply systems, becomes increasingly vital. However, this technological integration brings with it heightened vulnerabilities. The water sector, recognized as one of the 16 critical infrastructures by the Cybersecurity and Infrastructure Security Agency (CISA), has seen a notable increase in cyberattack incidents. These attacks underscore the urgent need for sophisticated AI-driven security solutions to protect these essential systems against potential compromises that could pose significant public health risks. Addressing these challenges, this thesis introduces H2OGAN, a time-series GAN-based model developed to generate and analyze realistic water data within the expected constraints of water parameter characteristics. H2OGAN supports various functions including data augmentation, anomaly detection, risk assessment, and predictive model optimization, thereby enhancing the security and efficiency of water supply systems. Extensive testing is conducted in ACWA Lab, a cyber-physical testbed that replicates both operational and adversarial scenarios. These experiments utilize a RNN model, specifically a GRU, to classify and analyze the dynamics of various scenarios including normal operations, sensor anomalies, and malicious attacks. Further real-world validation is carried out at AlexRenew, a wastewater treatment facility in Alexandria, Virginia, confirming the effectiveness of H2OGAN in practical applications. This research not only advances the understanding of AI in water management but also emphasizes the critical need for robust security measures to protect against the evolving landscape of cyber threats.

In many industrial and scientific applications, accurate sensor measurements are crucial. Instruments such as nitrate sensors are vulnerable to environmental conditions, calibration drift, high maintenance costs, and degrading. Researchers have turned to advanced computational methods, including mathematical modeling, statistical analysis, and machine learning, to overcome these limitations. Deep learning techniques have shown promise in outperforming traditional methods in many applications by achieving higher accuracy, but they are often criticized as 'black-box' models due to their lack of transparency. This thesis presents a framework for deep learning-based soft sensors that can quantify the robustness of soft sensors by estimating predictive uncertainty and evaluating performance across various scenarios. The framework facilitates comparisons between hard and soft sensors. To validate the framework, I conduct experiments using data generated by AI and Cyber for Water and Ag (ACWA), a cyber-physical system water-controlled environment testbed. Afterwards, the framework is tested on real-world environment data from Alexandria Renew Enterprise (AlexRenew), establishing its applicability and effectiveness in practical settings. / Master of Science / Sensors are essential in various industrial systems and offer numerous advantages. Essential to measurement science and technology, it allows reliable high-resolution low-cost measurement and impacts areas such as environmental monitoring, medical applications and security. The importance of sensors extends to Internet of Things (IoT) and large-scale data analytics fields. In these areas, sensors are vital to the generation of data that is used in industries such as health care, transportation and surveillance. Big Data analytics processes this data for a variety of purposes, including health management and disease prediction, demonstrating the growing importance of sensors in data-driven decision making. In many industrial and scientific applications, precision and trustworthiness in measurements are crucial for informed decision-making and maintaining high-quality processes. Instruments such as nitrate sensors are particularly susceptible to environmental conditions, calibration drift, high maintenance costs, and a tendency to become less reliable over time due to aging. The lifespan of these instruments can be as short as two weeks, posing significant challenges. To overcome these limitations, researchers have turned to advanced computational methods, including mathematical modeling, statistical analysis, and machine learning. Traditional methods have had some success, but they often struggle to fully capture the complex dynamics of natural environments. This has led to increased interest in more sophisticated approaches, such as deep learning techniques. Deep learning-based soft sensors have shown promise in outperforming traditional methods in many applications by achieving higher accuracy. However, they are often criticized as "black-box" models due to their lack of transparency. This raises questions about their reliability and trustworthiness, making it critical to assess these aspects. This thesis presents a comprehensive framework for deep learning-based soft sensors. The framework will quantify the robustness of soft sensors by estimating predictive uncertainty and evaluating performance across a range of contextual scenarios, such as weather conditions, flood events, and water parameters. These evaluations will help define the trustworthiness of the soft sensor and facilitate comparisons between hard and soft sensors. To validate the framework, we will conduct experiments using data generated by ACWA, a cyber-physical system water-controlled environment testbed we developed. This will provide a controlled environment to test and refine our framework. Subsequently, we will test the framework on real-world environment data from AlexRenew. This will further establish its applicability and effectiveness in practical settings, providing a robust and reliable tool for sensor data analysis and prediction. Ultimately, this work aims to contribute to the broader field of sensor technology, enhancing our ability to make informed decisions based on reliable and accurate sensor data.

As applications for shape memory polymers (SMPs) become more advanced, it is necessary to have the ability to monitor both the actuation and thermal properties of structures made of such materials. In this paper, a method of using three stereo pairs of webcams and a single thermal camera is studied for the purposes of both tracking three dimensional motion of shape memory polymers, as well as the temperature of points of interest within the SMP structure. The method used includes a stereo camera calibration with integrated local minimum tracking algorithms to locate points of interest on the material and measure their temperature through interpolation techniques. The importance of the proposed method is that it allows a means to cost effectively monitor the surface temperature of a shape memory polymer structure without having to place intrusive sensors on the samples, which would limit the performance of the shape memory effect. The ability to monitor the surface temperatures of a SMP structure allows for more complex configurations to be created while increasing the performance and durability of the material. Additionally, as compared to the previous version, both the functionalities of the testbed and the user interface have been significantly improved.