Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique challenges pertaining to environmental quality and food production, which can negate the effectiveness of the aforementioned boons. As such, there is an emphasis on mitigating these negative effects through the construction of smart and connected communities (S&CC), which integrate both artificial intelligence (AI) and the Internet of Things (IoT). This coupling of intelligent technologies also poses interesting system design challenges pertaining to the fusion of the diverse, heterogeneous datasets available to IoT environments, and the ability to learn multiple S&CC problem sets concurrently. Attention-based Transformer networks are of particular interest given their success across diverse fields of natural language processing (NLP), computer vision, time-series regression, and multi-modal data fusion in recent years. This begs the question whether Transformers can be further diversified to leverage fusions of IoT data sources for heterogeneous multi-task learning in S&CC trade spaces. This is a fundamental question that this thesis seeks to answer. Indeed, the key contribution of this thesis is the design and application of Transformer networks for developing AI systems in emerging smart cities. This is executed within a collaborative U.S.-Indonesia effort between Virginia Tech, the University of Colorado Boulder, the Universitas Gadjah Mada, and the Institut Teknologi Bandung with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia. Specifically, a proof-of-concept AI nerve-center is proposed using a backbone of pure-encoder Transformer architectures to learn a diverse set of tasks such as multivariate time-series regression, visual plant disease classification, and image-time-series fusion. To facilitate the data fusion tasks, an effective algorithm is also proposed to synthesize heterogeneous feature sets, such as multivariate time-series and time-correlated images. Moreover, a hyperparameter tuning framework is also proposed to standardize and automate model training regimes. Extensive experimentation shows that the proposed Transformer-based systems can handle various input data types via custom sequence embedding techniques, and are naturally suited to learning a diverse set of tasks. Further, the results also show that multi-task learners increase both memory and computational efficiency while maintaining comparable performance to both single-task variants, and non-Transformer baselines. This demonstrates the flexibility of Transformer networks to learn from a fusion of IoT data sources, their applicability in S&CC trade spaces, and their further potential for deployment on edge computing devices. / Master of Science / Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique environmental and food cultivation challenges. Hence, there is a focus on reducing these negative effects through building smart and connected communities (S&CC). The term connected is derived from the integration of small, low-cost devices which gather information from the surrounding environment, called the Internet of Things (IoT). Likewise, smart is a term derived from the integration of artificial intelligence (AI), which is used to make informed decisions based on IoT-collected information. This coupling of intelligent technologies also poses its own unique challenges pertaining to the blending of IoT data with highly diverse characteristics. Of specific interest is the design of AI models that can not only learn from a fusion of this diverse information, but also learn to perform multiple tasks in parallel. Attention-based networks are a relatively new category of AI which learn to focus on, or attend to, the most important portions of an arbitrary data sequence. Transformers are AI models which are designed using attention as their backbone, and have been employed to much success in many fields in recent years. This success begs the question whether Transformers can be further extended to put the smart in S&CC. The overarching goal of this thesis is to design and implement a Transformer-based AI system for emerging smart cities. In particular, this is accomplished within a U.S.-Indonesia collaborative effort with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia.

The design and development of technology applications has to deal with many variables. Reference is obviously made to established hardware and software support, particularly with regard to the choice of appropriate operating systems, development model, environment and programming language. With the growth of networked and web-exposed systems, we are increasingly dealing with IoT (Internet-of-Things) systems: complex applications consisting of a network of often heterogeneous elements to be managed like an orchestra, using existing elements and creating new ones. Among the many fields affected by this phenomenon, two in particular are considered here: industry and medical, key sectors of modern society. Given the inherently parallel nature of such networks and the fact that it is commonly necessary to manage them via the Web, the most prevalent de facto model employs an architecture relying on a paradigm based on data flows, representing the entire system as a kind of assembly line in which each entity acquires input data and returns an output in a perfectly asynchronous manner. This thesis highlights some notable limitations of this approach and proposes an evolution that resolves some key issues. This has been done not only on a purely theoretical level, but with actual implementations currently operational and thus demonstrated in the field. Rather than proposing an abstract formalisation of a new solution, the basic principles of a whole new architecture are presented here instead, going into more detail on some key features and with experimental and practical feedback implemented as a full blown software platform. A first contribution is the definition of the principles of a new programming architecture, disseminated with some published articles and a speech in an international congress. A second contribution concerns a lightweight data synchronisation strategy, which is particularly useful for components that need to continue working during offline periods. A third contribution concerns a method of storing a symmetric encryption key combined with a peculiar retrieval and verification technique: this has resulted in an international patent, already registered. A fourth contribution concerns a new data classification model, which is particularly effective for processing information asynchronously. Issues related to possible integrations with artificial intelligence systems have also been addressed, for which a number of papers are being written, introduced by a presentation that has just been published.

The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings. / Ph. D.

Cyber-physical systems (CPSs) are large-scale systems that seamlessly integrate physical and human elements via a cyber layer that enables connectivity, sensing, and data processing. Key examples of CPSs include smart power systems, smart transportation systems, and the Internet of Things (IoT). This wide-scale cyber-physical interconnection introduces various operational benefits and promises to transform cities, infrastructure, and networked systems into more efficient, interactive, and interconnected smart systems. However, this ubiquitous connectivity leaves CPSs vulnerable to menacing security threats as evidenced by the recent discovery of the Stuxnet worm and the Mirai malware, as well as the latest reported security breaches in a number of CPS application domains such as the power grid and the IoT. Addressing these culminating security challenges requires a holistic analysis of CPS security which necessitates: 1) Determining the effects of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, 2) Analyzing the multi-agent interactions -- among humans and automated systems -- that occur within CPSs and which have direct effects on the security state of the system, and 3) Recognizing the role that humans and their decision making processes play in the security of CPSs. Based on these three tenets, the central goal of this dissertation is to enhance the security of CPSs with human actors by developing fool-proof defense strategies founded on novel theoretical frameworks which integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models. Towards realizing this overarching goal, this dissertation presents a number of key contributions targeting two prominent CPS application domains: the smart electric grid and drone systems. In smart grids, first, a novel analytical framework is developed which generalizes the analysis of a wide set of security attacks targeting the state estimator of the power grid, including observability and data injection attacks. This framework provides a unified basis for solving a broad set of known smart grid security problems. Indeed, the developed tools allow a precise characterization of optimal observability and data injection attack strategies which can target the grid as well as the derivation of optimal defense strategies to thwart these attacks. For instance, the results show that the proposed framework provides an effective and tractable approach for the identification of the sparsest stealthy attacks as well as the minimum sets of measurements to defend for protecting the system. Second, a novel game-theoretic framework is developed to derive optimal defense strategies to thwart stealthy data injection attacks on the smart grid, launched by multiple adversaries, while accounting for the limited resources of the adversaries and the system operator. The analytical results show the existence of a diminishing effect of aggregated multiple attacks which can be leveraged to successfully secure the system; a novel result which leads to more efficiently and effectively protecting the system. Third, a novel analytical framework is developed to enhance the resilience of the smart grid against blackout-inducing cyber attacks by leveraging distributed storage capacity to meet the grid's critical load during emergency events. In this respect, the results demonstrate that the potential subjectivity of storage units' owners plays a key role in shaping their energy storage and trading strategies. As such, financial incentives must be carefully designed, while accounting for this subjectivity, in order to provide effective incentives for storage owners to commit the needed portions of their storage capacity for possible emergency events. Next, the security of time-critical drone-based CPSs is studied. In this regard, a stochastic network interdiction game is developed which addresses pertinent security problems in two prominent time-critical drone systems: drone delivery and anti-drone systems. Using the developed network interdiction framework, the optimal path selection policies for evading attacks and minimizing mission completion times, as well as the optimal interdiction strategies for effectively intercepting the paths of the drones, are analytically characterized. Using advanced notions from Nobel-prize winning prospect theory, the developed framework characterizes the direct impacts of humans' bounded rationality on their chosen strategies and the achieved mission completion times. For instance, the results show that this bounded rationality can lead to mission completion times that significantly surpass the desired target times. Such deviations from the desired target times can lead to detrimental consequences primarily in drone delivery systems used for the carriage of emergency medical products. Finally, a generic security model for CPSs with human actors is proposed to study the diffusion of threats across the cyber and physical realms. This proposed framework can capture several application domains and allows a precise characterization of optimal defense strategies to protect the critical physical components of the system from threats emanating from the cyber layer. The developed framework accounts for the presence of attackers that can have varying skill levels. The results show that considering such differing skills leads to defense strategies which can better protect the system. In a nutshell, this dissertation presents new theoretical foundations for the security of large-scale CPSs, that tightly integrate cyber, physical, and human elements, thus paving the way towards the wide-scale adoption of CPSs in tomorrow's smart cities and critical infrastructure. / Ph. D. / Enhancing the efficiency, sustainability, and resilience of cities, infrastructure, and industrial systems is contingent on their transformation into more interactive and interconnected smart systems. This has led to the emergence of what is known as cyber-physical systems (CPSs). CPSs are widescale distributed and interconnected systems integrating physical components and humans via a cyber layer that enables sensing, connectivity, and data processing. Some of the most prominent examples of CPSs include the smart electric grid, smart cities, intelligent transportation systems, and the Internet of Things. The seamless interconnectivity between the various elements of a CPS introduces a wealth of operational benefits. However, this wide-scale interconnectivity and ubiquitous integration of cyber technologies render CPSs vulnerable to a range of security threats as manifested by recently reported security breaches in a number of CPS application domains. Addressing these culminating security challenges requires the development and implementation of fool-proof defense strategies grounded in solid theoretical foundations. To this end, the central goal of this dissertation is to enhance the security of CPSs by advancing novel analytical frameworks which tightly integrate the cyber, physical, and human elements of a CPS. The developed frameworks and tools enable the derivation of holistic defense strategies by: a) Characterizing the security interdependence between the various elements of a CPS, b) Quantifying the consequences of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, c) Modeling the multi-agent interactions in CPSs, involving humans and automated systems, which have a direct effect on the security state of the system, and d) Capturing the role that human perceptions and decision making processes play in the security of CPSs. The developed tools and performed analyses integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models and introduce key contributions to a number of CPS application domains such as the smart electric grid and drone systems. The introduced results enable strengthening the security of CPSs, thereby paving the way for their wide-scale adoption in smart cities and critical infrastructure.

Internet of Things (IoT) is the idea that every device around us is connected and these devices continually collect and communicate data for analysis at a large scale in order to enable better end user experience, resource utilization and device performance. Therefore, data is central to the concept of IoT and the amount being collected is growing at an unprecedented rate. Current networking systems and hardware are not fully equipped to handle influx of data at this scale which is a serious problem because it can lead to erroneous interpretation of the data resulting in low resource utilization and bad end user experience defeating the purpose of IoT. This thesis aims at improving data transportation for IoT. In IoT systems, devices are connected to one or more cloud services over the internet via an access link. The cloud processes the data sent by the devices and sends back appropriate instructions. Hence, the performance of the two ends of the network ie the access networks and datacenter network, directly impacts the performance of IoT. The first portion of the our research targets improvement of the access networks by improving access link (router) design. Among the important design aspects of routers is the size of their output buffer queue. %Selecting an appropriate size of this buffer is crucial because it impacts two key metrics of an IoT system: 1) access link utilization and 2) latency. We have developed a probabilistic model to calculate the size of the output buffer that ensures high link utilization and low latency for packets. We have eliminated limiting assumptions of prior art that do not hold true for IoT. Our results show that for TCP only traffic, buffer size calculated by the state of the art schemes results in at least 60% higher queuing delay compared to our scheme while achieving almost similar access link utilization, loss-rate, and goodput. For UDP only traffic, our scheme achieves at least 91% link utilization with very low queuing delays and aggregate goodput that is approx. 90% of link capacity. Finally, for mixed traffic scenarios our scheme achieves higher link utilization than TCP only and UDP only scenarios as well as low delays, low loss-rates and aggregate goodput that is approx 94% of link capacity. The second portion of the thesis focuses on datacenter networks. Applications that control IoT devices reside here. Performance of these applications is affected by the choice of TCP used for data communication between Virtual Machines (VM). However, cloud users have little to no knowledge about the network between the VMs and hence, lack a systematic method to select a TCP variant. We have focused on characterizing TCP Cubic, Reno, Vegas and DCTCP from the perspective of cloud tenants while treating the network as a black box. We have conducted experiments on the transport layer and the application layer. The observations from our transport layer experiments show TCP Vegas outperforms the other variants in terms of throughput, RTT, and stability. Application layer experiments show that Vegas has the worst response time while all other variants perform similarly. The results also show that different inter-request delay distributions have no effect on the throughput, RTT, or response time. / Master of Science / Internet of Things (IoT) is the idea that every electronic device around us, like watches, thermostats and even refrigerators, is connected to one another and these devices continually collect and communicate data. This data is analyzed at a large scale in order to enable better user experience and improve the utilization and performance of the devices. Therefore, data is central to the concept of IoT and because of the unprecedented increase in the number of connected devices, the amount being collected is growing at an unprecedented rate. Current computer networks over which the data is transported, are not fully equipped to handle influx of data at this scale. This is a serious problem because it can lead to erroneous analysis of the data, resulting in low device utilization and bad user experience, hence, defeating the purpose of IoT. This thesis aims at improving data transportation for IoT by improving different components involved in computer networks. In IoT systems, devices are connected to cloud computing services over the internet through a router. The router acts a gateway to send data to and receive data from the cloud services. The cloud services act as the brain of IoT i.e. they process the data sent by the devices and send back appropriate instructions for the devices to perform. Hence, the performance of the two ends of the network i.e. routers in the access networks and cloud services in datacenter network, directly impacts the performance of IoT. The first portion of our research targets the design of routers. Among the important design aspects of routers is their size of their output buffer queue which holds the data packets to be sent out. We have developed a novel probabilistic model to calculate the size of the output buffer that ensures that the link utilization stays high and the latency of the IoT devices stays low, ensuring good performance. Results show that that our scheme outperforms state-of-the-art schemes for TCP only traffic and shows very favorable results for UDP only and mixed traffic scenarios. The second portion of the thesis focuses on improving application service performance in datacenter networks. Applications that control IoT devices reside in the cloud and their performance is directly affected by the protocol chosen to send data between different machines. However, cloud users have almost no knowledge about the configuration of the network between the machines allotted to them in the cloud. Hence, they lack a systematic method to select a protocol variant that is suitable for their application. We have focused on characterizing different protocols: TCP Cubic, Reno, Vegas and DCTCP from the perspective of cloud tenants while treating the network as a black-box (unknown). We have provided in depth analysis and insights into the throughput and latency behaviors which should help the cloud tenants make a more informed choice of TCP congestion control.

No / The Internet of Things (IoT) has brought significant advancements in various domains, providing innovative and efficient solutions. However, ensuring the safe design and operation of IoT devices is crucial, as the consequences of component failure can range from system downtime to dangerous operating states. Several methods have been proposed to evaluate the failure behaviours of IoT-based systems, including Fault Tree Analysis (FTA), a methodology adopted from other safetycritical domains. This study integrated FTA and Bayesian Network (BN) models to assess IoT system reliability based on components’ reliability data and other statistical information. The integrated model achieved efficient predictive failure analysis, considering combinations of 12 basic events to quantify the overall system’s reliability. The model also enables criticality analysis, ranking basic events based on their contributions to system failure and providing a guide for design modification in order to enhance IoT safety. By comparing failure data in FTA and criticality indices obtained using the BN model, the proposed integration offers a probabilistic estimation of IoT system failure and a viable safety guide for designing IoT systems.

In recent years, Internet-of-Things technology (IoT) has been the subject of research in a diverse field of applications, given its essential role in transitioning society towards a more interconnected paradigm of conducting manufacturing, logistics, services, and business, what is also known as Industry 4.0. Consistent with this line of research, this project addresses the application of IoT in distribution packaging as a way to better understand supply chain conditions. Specifically, this work presents an operational concept for a system that implements IoT technology in the pallets that are used to move products along supply chains and serve as a vehicle to gain insight into the conditions experienced by products and unit loads. The development of this operational concept leverages a systems engineering framework to discover user needs, and stakeholders, and apply model-based systems engineering to create system models that capture expected system behavior and the outputs necessary to create value for the user. A semi structured interview was conducted with eleven companies in order to discover user needs related to their packaging during distribution processes in their supply chain. A system operational concept was developed through use cases, concept of operations, and formal modeling using Cameo System Modeling Software. A review of sensor and communication technologies is presented, as well as a description of the challenges and future research opportunities for the proposed operational concept in distribution packaging. The application of systems engineering framework, and model-based systems engineering to the distribution packaging domain brings clarity to problem formulation in order to lay-out solid value propositions for the adoption of IoT technologies, and to ensure successful realization of systems that achieve customer satisfaction. This work offers three main contributions. First, it provides an identification and description of the needs that industrial companies have in relation to their product and packaging performance during distribution operations. Secondly, it shows how a systems-based approach, leveraging on model-based systems engineering can be employed to conceptualize systems that use innovative technologies like IoT in the domain of distribution packaging. Third, it provides an overview of open research challenges and practical considerations for the implementation of IoT technology in the field of distribution packaging. / Master of Science / In 2007, The World Bank published a study which states that "eighty percent of US trade is carried on pallets" (Raballand and Aldaz-Carroll, 2007). Furthermore, in the year 2015, a report estimated that there would be 2.6 billion pallets circulating in the United States by the year 2017 (Freedonia Group, 2015). Pallets are ubiquitous and a key component of distribution operations in supply chains, as they transport goods, and are the main interface that connects material handling equipment and packaged products (White and Hamner, 2005). Based on that distinctive characteristic, this study contends that pallet can be used as a window to gain insight into the realities of what is experienced by products and packaging during distribution. This can be done by using sensors imbedded in pallets to capture data of interest about the physical conditions in the supply chains, which opens the potential for more customized and optimized packaging design, supported by more reliable and representative information. This idea is particularly relevant, as established protocols for packaging testing are limited in their capacity to accurately simulate the real-world conditions that occur in the supply chain. This has resulted in suboptimal packaging design (Rouillard, 2008) that decreases the efficiency of logistics operations. This study found that industrial companies are most concerned with avoiding damage that their products can suffer during transportation as a result of temperature, relative humidity, shock, and vibration. Thus, it is necessary to gather data about these distribution parameters for product shipments. Using a model-based system engineering approach, an operational concept is proposed to show what is needed from a system to be able to track these parameters. Furthermore, a review of current available technology for IoT is presented, as well as an examination of the challenges posed to the realization of the proposed operational concept, including factors like cybersecurity, and energy resources constraints. This work offers three main contributions. First, it provides an identification and description of the needs that industrial companies have in relation to their product and packaging performance during distribution operations. Secondly, it shows how a systems-based approach, leveraging on model-based systems engineering can be employed to conceptualize systems that use innovative technologies like IoT in the domain of distribution packaging. Third, it provides an overview of open research challenges and practical considerations for the implementation of IoT technology in the field of distribution packaging.

Asthma, a chronic respiratory disease affecting millions of people worldwide, can vary in severity depending on individual triggers such as Carbon Dioxide, Particulate Matter, dust mites, tobacco smoke, and indoor household activities such as cooking, cleaning, use of heating, and window opening, which can have a negative impact on indoor air quality (IAQ) and exacerbate asthma symptoms. Investigating the relationship between IAQ and asthma severity, a case study was conducted on five asthmatic participants from Bradford, UK. IAQ was measured using IoT indoor air quality monitoring devices. Indoor activities were recorded using a daily household activities questionnaire, and asthma severity was assessed using the Asthma Control Questionnaire (ACQ). Machine learning prediction models were used to analyse various IAQ parameters, such as particulate matter, carbon dioxide, and humidity levels, to identify the most significant predictors of asthma severity with IAQ. The study aimed to develop targeted interventions to improve IAQ and reduce the burden of asthma. Results showed that higher asthma severity scores were associated with increased indoor activity and higher levels of indoor air pollution. Some interventions were implemented to improve ventilation hours, significantly improving IAQ and reducing asthma symptoms, particularly those with more severe asthma. The findings indicate that interventions targeting IAQ, and indoor activities can effectively reduce asthma severity, with up to a 60% reduction in symptoms for asthma patients.

La visión de la "Internet de las Cosas", hace énfasis en la integración entre elementos del mundo real y los Sistemas de Información. Gracias a tecnologías de Identificación Automática (Auto-ID) cómo RFID, los sistemas pueden percibir objetos del mundo físico. Cuando éstos participan de manera activa en los procesos de negocio, se evita el uso de los seres humanos como transportadores de información. Por tanto, el número de errores se reduce y la eficiencia de los procesos aumenta. Aunque actualmente ya es posible el desarrollo de estos sistemas, la heterogeneidad tecnológica en Auto-ID y los requisitos cambiantes de los procesos de negocio dificultan su construcción, mantenimiento y evolución. Por lo tanto, es necesaria la definición de soluciones que afronten la construcción de estos sistemas mediante métodos sólidos de desarrollo para garantizar la calidad final del producto. Partiendo de las bases de la Ingeniería Dirigida por Modelos (MDE), esta tesis presenta un proceso de desarrollo para la construcción de este tipo de sistemas. Este proceso cubre desde la especificación del sistema hasta su implementación, centrándose en los requisitos particulares del enlace entre los mundos físico y virtual. Para la especificación de los sistemas se ha definido un Lenguaje de modelado adaptado a los requisitos de la "Internet de las Cosas". A partir de esta especificación se puede obtener una solución software de manera sistemática. Como validación de la propuesta, ésta se ha aplicado en la práctica con usuarios finales. Pese a que el proceso de desarrollo no ofrece una automatización completa, las guías ofrecidas y la formalización de los conceptos implicados ha demostrado ser útil a la hora de elevar el nivel de abstracción en el desarrollo, evitando el esfuerzo de enfrentarse a detalles tecnológicos. / Giner Blasco, P. (2010). Automating the development of Physical Mobile Workflows. A Model Driven Engineering approach [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8272

The overall energy consumption of portable devices has been projected to triple over the next decade, growing to match the total power generated by the European Union and Canada by 2025. The rise of the internet-of-things (IoT) and ubiquitous and embedded computing has resulted in an exponential increase in such devices, wherein projections estimate that 50 billion smart devices will be connected and online by 2020. In order to alleviate the associated stresses placed on power generation and distribution networks, a holistic approach must be taken to conserve energy usage in electronic devices from the component to the circuit level. An effective approach to reduce power dissipation has been a continual reduction in operating voltage, thereby quadratically down-scaling active power dissipation. However, as state-of-the-art silicon (Si) complimentary metal-oxide-semiconductor (CMOS) field-effect transistors (FETs) enter sub-threshold operation in the ultra-low supply voltage regime, their drive current is noticeable degraded. Therefore, new energy-efficient MOSFETs and circuit architectures must be introduced. In this work, tunnel FETs (TFETs), which operate leveraging quantum mechanical tunneling, are investigated. A comprehensive investigation detailing electronic materials, to novel TFET device designs, to memory and logic digital circuits based upon those TFETs is provided in this work. Combined, these advances offer a computing platform that could save considerable energy and reduce power consumption in next-generation, ultra-low voltage applications. / Ph. D. / The overall energy consumption of portable devices has been projected to triple over the next decade, growing to match the total power generated by the European Union and Canada by 2025. The rise of the internet-of-things (IoT) and ubiquitous and embedded computing has resulted in an exponential increase in such devices, wherein projections estimate that 50 billion “smart” devices will be connected and “online” by 2020. In order to alleviate the associated stresses placed on power generation and distribution networks, a holistic approach must be taken to conserve energy usage in electronic devices from the component to the circuit level. An effective approach to reduce power dissipation has been a continual reduction in operating voltage, thereby quadratically down-scaling active power dissipation. However, as state-of-the-art silicon (Si) complimentary metal-oxide-semiconductor (CMOS) field-effect transistors (FETs) enter sub-threshold operation in the ultra-low supply voltage regime, their drive current is noticeable degraded. Therefore, new energy-efficient MOSFETs and circuit architectures must be introduced. In this work, tunnel FETs (TFETs), which operate leveraging quantum mechanical tunneling, are investigated. A comprehensive investigation detailing electronic materials, to novel TFET device designs, to memory and logic digital circuits based upon those TFETs is provided in this work. Combined, these advances offer a computing platform that could save considerable energy and reduce power consumption in next-generation, ultra-low voltage applications.