The Internet of Things (IoT) is a fast-spreading technology that enables new types of services in several domains, such as transportation, health, and building automation. To exploit the potential of the IoT effectively, several challenges have to be tackled including the following ones. First, the proposed IoT visions provide a fragmented picture, leading to a lack of consensus about IoT systems and their constituents. A second set of challenges concerns the environment of IoT systems that is often dynamic and uncertain, e.g. devices can appear and be discovered at runtime as well as become suddenly unavailable. Additionally, the in- volvement of human users complicates the scene as people’s activities are not always predictable . The majority of existing approaches to en- gineer IoT systems rely on predefined processes to achieve users’ goals. Consequently, such systems have significant shortcomings in coping with dynamic and uncertain environments. To piece together the fragmented picture of IoT systems, we sys- tematically identified their characteristics by analyzing and synthesizing existing taxonomies. To address the challenges related to the IoT envir- onment and the involvement of human users, we used the concept of Emergent Configurations (ECs) to engineer IoT systems. An EC consists of a dynamic set of devices that cooperate temporarily to achieve a user goal. To realize this vision, we proposed novel approaches that enable users to achieve their goals by supporting the automated formation, en- actment, and self-adaptation of IoT systems. / <p>Note: The papers are not included in the fulltext online.</p><p>Paper I in dissertation as manuscript.</p>

Yes / Advancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems. / Jaguar Land Rover funded research “Intelligent Personalised Powertrain Healthcare” 2016-2019

Yes / The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system. / Supported in part by Singapore Ministry of Education Academic Research Fund Tier 1 (R-263-000- D63-114). / Research Development Fund Publication Prize Award winner, July 2020.

Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last thirty years the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company and software tools have been developed by PTC Inc. to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo Cyber-Physical Lab, an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.

Yes / The Internet of Things (IoT) has improved our lives through various applications such as home automation, smart city monitoring, environmental monitoring, intelligent farming, and a host of others. IoT is increasingly being used for environmental monitoring to prevent fire incidents and other environmental hazards. However, for IoT systems to function effectively in preventing fire incidents, they must operate in a safe, reliable, and dependable manner. The intelligent sensors and devices that constitute the system are prone to different types of failures, which can lead to unsafe or dangerous conditions. Failure of a fire prevention system can pose significant risks to Health, Safety, and the Environment (HSE). To address these concerns, it is essential to understand how component failures can contribute to the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to qualitatively analyse an intelligent fire detection system in an industrial setting. The analysis outlines the ways in which the system can fail and the necessary prevention mechanism to guard against undesired system failure. / The full-text of this article will be released for public view at the end of the publisher embargo on 27 Apr 2025.

Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. Since its inception, it has captured the attention of the research community and is now an area of active research. By its definition, AoI measures the elapsed time period between the present time and the generation time of the information. AoI is fundamentally different from traditional metrics such as delay or latency as the latter only considers the transit time for a packet to traverse the network. Among the state-of-the-art in the literature, we identify two limitations that deserve further investigation. First, many existing efforts on AoI have been limited to information-theoretic exploration by considering extremely simple models and unrealistic assumptions, which are far from real-world communication systems. Second, among most existing work on scheduling algorithms to optimize AoI, there is a lack of research on guaranteeing AoI deadlines. The goal of this dissertation is to address these two limitations in the state-of-the-art. First, we design schedulers to minimize AoI under more practical settings, including varying sampling periods, varying sample sizes, cellular transmission models, dynamic channel conditions, etc. Second, we design schedulers to guarantee hard or soft AoI deadlines for each information source. More important, inspired by our results from guaranteeing AoI deadlines, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems. This dissertation is organized into three parts. In the first part, we study two problems on AoI minimization under general settings. (i) We consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and a cellular-based transmission model. We develop a near-optimal low-complexity scheduler---code-named Juventas---to minimize AoI. (ii) We study the AoI minimization problem under a 5G network with dynamic channels. To meet the stringent real-time requirement for 5G, we develop a GPU-based near-optimal algorithm---code-named Kronos---and implement it on commercial off-the-shelf (COTS) GPUs. In the second part, we investigate three problems on guaranteeing AoI deadlines. (i) We study the problem to guarantee a hard AoI deadline for information from each source. We present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any hard deadline vector when the system load is under ln 2. (ii) For soft AoI deadlines, i.e., occasional violations can be tolerated, we present a novel procedure called Unstable Tolerant Scheduler (UTS). UTS hinges upon the notions of Almost Uniform Schedulers (AUSs) and step-down rate vectors. We show that UTS has strong performance guarantees under different settings. (iii) We investigate a 5G scheduling problem to minimize the proportion of time when the AoI exceeds a soft deadline. We derive a property called uniform fairness and use it as a guideline to develop a 5G scheduler---Aequitas. To meet the real-time requirement in 5G, we implement Aequitas on a COTS GPU. In the third part, we present Eywa---a general design framework that can be applied to construct high-performance schedulers for AoI-related optimization and decision problems. The design of Eywa is inspired by the notions of AUS schedulers and step-down rate vectors when we develop UTS in the second part. To validate the efficacy of the proposed Eywa framework, we apply it to solve a number of problems, such as minimizing the sum of AoIs, minimizing bandwidth requirement under AoI constraints, and determining the existence of feasible schedulers to satisfy AoI constraints. We find that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms, or provide new/general results that are not available in the literature. / Doctor of Philosophy / Age of Information (AoI) is a performance metric that can be used to measure the freshness of information. It measures the elapsed time period between the present time and the generation time of the information. Through a literature review, we have identified two limitations: (i) many existing efforts on AoI have employed extremely simple models and unrealistic assumptions, and (ii) most existing work focuses on optimizing AoI, while overlooking AoI deadline requirements in some applications. The goal of this dissertation is to address these two limitations. For the first limitation, we study the problem to minimize the average AoI in general and practical settings, such as dynamic channels and 5G NR networks. For the second limitation, we design schedulers to guarantee hard or soft AoI deadlines for information from each source. Finally, we develop a general design framework that can be applied to construct high-performance schedulers for AoI-related problems.

As the use of low-power and low-resource embedded devices continues to increase dramatically with the introduction of new Internet of Things (IoT) devices, security techniques are necessary which are compatible with these devices. This research advances the knowledge in the area of cybersecurity for the IoT through the exploration of a moving target defense to apply for limiting the time attackers may conduct reconnaissance on embedded systems while considering the challenges presented from IoT devices such as resource and performance constraints. We introduce the design and optimizations for µMT6D, a Micro-Moving Target IPv6 Defense, including a description of the modes of operation and use of lightweight hash algorithms. Through simulations and experiments µMT6D is shown to be viable for use on low power and low resource embedded devices in terms of footprint, power consumption, and energy consumption increases in comparison to the given security benefits. Finally, this provides information on other future considerations and possible avenues of further experimentation and research. / Doctor of Philosophy / This research aims to advance knowledge in the area of cybersecurity for the Internet of Things through the exploration and validation of a moving target defense to apply for limiting the time attackers may conduct reconnaissance on low powered embedded system devices considering the challenges presented from IoT devices such as resource and performance constraints. When an attack is carried out against a network, reconnaissance is utilized to identify the target machine or device. Limiting the time for reconnaissance, therefore has a direct impact on the ability of an adversary to carry out an attack. Many of the security techniques utilized today do not fit the IoT constraints. Research in this area is just beginning and security is often not considered. Sensors collecting and sending information can be compromised both through the network and access to the physical devices. How can these devices securely send information? How can these devices withstand attacks aiming to stop their functionality or to gain information? There are many aspects which need to be investigated to understand security vulnerabilities and potential defenses. As our technologies evolve our security defenses need to evolve as well. My research aims to further the understanding of the security of the IoT devices which have quickly become pervasive in our society. This research will expand the knowledge of the ability to safe guard connected devices from cyber-attacks and provide insight into the space and performance requirements of a technique previously only used on large scale systems. By designing, implementing experimental prototypes, and conducting simulations and experiments this research assesses the viable use of a Micro Moving Target IPv6 Defense (µMT6D).

Balancing indoor comfort and energy consumption is crucial to building energy efficiency. Occupancy information is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy. / Master of Science / The motivation for energy conservation and sustainable development is rapidly increasing, and building energy consumption is a significant part of overall energy use. In order to make buildings more energy efficient, it is necessary to obtain information on the occupancy level of rooms in the building. Environmental sensors are used to measure factors such as humidity and sound to determine occupancy information. However, the relationship between sensor readings and occupancy levels is complex, making it necessary to use machine learning algorithms to establish a connection. As a subfield of machine learning, deep learning is capable of processing complex data. This research aims to utilize advanced deep learning algorithms to estimate building occupancy levels based on environmental sensor data.

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D.

Yes / The Internet of Things (IoT) paradigm has continued to provide valuable services across various domains. However, guaranteeing the safety assurance of the IoT system is increasingly becoming a concern. While the growing complexity of IoT design has brought additional safety requirements, developing safe systems remains a critical design objective. In earlier studies, a limited number of approaches have been proposed to evaluate the safety requirements of IoT systems through the generation of static safety artefacts based on manual processes. This paper proposes a model-based approach to the safety analysis of the IoT system. The proposed framework explores the expressiveness of UML/SysML graphical modelling languages to develop a dynamic fault tree (DFT) as an analysis artefact of the IoT system. The framework was validated using a hypothetical IoT-enabled Smart Fire Detection and Prevention System (SFDS). The novel framework can capture dynamic failure behaviour, often ignored in most model-based approaches. This effort complements the inherent limitations of existing manual static failure analysis of the IoT systems and, consequently, facilitates a viable safety analysis that increases public assurance in the IoT systems. / The full text of this accepted manuscript will be available at the end of the publisher's embargo: 11th Feb 2025