Reaping the benefits of the Internet of Things (IoT) system is contingent upon developing IoT-specific security and privacy solutions. Conventional security and authentication solutions often fail to meet IoT requirements due to the computationally limited and portable nature of IoT objects. Privacy in IoT is a major issue especially in the light of current attacks on Facebook and Uber. Research efforts in both the academic and the industrial fields have been focused on providing security and privacy solutions that are specific to IoT systems. These solutions include systems to manage keys, systems to handle routing protocols, systems that handle data transmission, access control for devices, and authentication of devices. One of these solutions is Blockchain, a trust-less peer-to-peer network of devices with an immutable data storage that does not require a trusted party to maintain and validate data entries in it. This emerging technology solves the problem of centralization in systems and has the potential to end the corporations control over our personal information. This unique characteristic makes blockchain an excellent candidate to handle data communication and storage between IoT devices without the need of oracle nodes to monitor and validate each data transaction. The peer-to-peer network of IoT devices validates data entries before being added to the blockchain database. However, accurate authentication of each IoT device using simple methods is another challenging problem. In this dissertation, a complete novel system is proposed to authenticate, verify, and secure devices in IoT systems. The proposed system consists of a blockchain framework to collect, monitor, and analyze data in IoT systems. The blockchain based system exploits a method, called Sharding, in which devices are grouped into smaller subsets to provide a scalable system. In addition to solving the scalability problem in blockchain, the proposed system is secured against the 51% attack in which a malicious node tries to gain control over the majority of devices in a single shard in order to disrupt the validation process of data entries. The proposed system dynamically changes the assignment of devices to shards to significantly decrease the possibility of performing 51% attacks. The second part of the novel system presented in this work handles IoT device authentication. The authentication framework uses device-specific information, called fingerprints, along with a transfer learning tool to authenticate objects in the IoT. The framework tracks the effect of changes in the physical environment on fingerprints and uses unique IoT environmental effects features to detect both cyber and cyber-physical emulation attacks. The proposed environmental effects estimation framework showed an improvement in the detection rate of attackers without increasing the false positives rate. The proposed framework is also shown to be able to detect cyber-physical attackers that are capable of replicating the fingerprints of target objects which conventional methods are unable to detect. In addition, a transfer learning approach is proposed to allow the use of objects with different types and features in the environmental effects estimation process. The transfer learning approach was also implemented in cognitive radio networks to prevent primary users emulation attacks that exist in these networks. Lastly, this dissertation investigated the challenge of preserving privacy of data stored in the proposed blockchain-IoT system. The approach presented continuously analyzes the data collected anonymously from IoT devices to insure that a malicious entity will not be able to use these anonymous datasets to uniquely identify individual users. The dissertation led to the following key results. First, the proposed blockchain based framework that uses sharding was able to provide a decentralized, scalable, and secured platform to handle data exchange between IoT devices. The security of the system against 51% attacks was simulated and showed significant improvements compared to typical blockchain implementations. Second, the authentication framework of IoT devices is shown to yield to a 40% improvement in the detection of cyber emulation attacks and is able to detect cyber-physical emulation attacks that conventional methods cannot detect. The key results also show that the proposed framework improves the authentication accuracy while the transfer learning approach yields up to 70% additional performance gains. Third, the transfer learning approach to combine knowledge about features from multiple device types was also implemented in cognitive radio networks and showed performance gains with an average of 3.4% for only 10% relevant information between the past knowledge and the current environment signals. / Doctor of Philosophy / The Internet of things (IoT) system is anticipated to reach billions of devices by the year 2020. With this massive increase in the number of devices, conventional security and authentication solutions will face many challenges from computational limits to privacy and security challenges. Research on solving the challenges of IoT systems is focused on providing lightweight solutions to be implemented on these low energy IoT devices. However these solutions are often prone to different types of attacks. The goal of this dissertation is to present a complete custom solution to secure IoT devices and systems. The system presented to solve IoT challenges consists of three main components. The first component focuses on solving scalability and centralization challenges that current IoT systems suffer from. To accomplish this a combination of distributed system, called blocchain, and a method to increase scalability, called Sharding, were used to provide both scalability and decentralization while maintaining high levels of security. The second component of the proposed solution consists of a novel framework to authenticate the identity of each IoT device. To provide an authentication solution that is both simple and effective, the framework proposed used a combination of features that are easy to collect, called fingerprints. These features were used to model the environment surrounding each IoT device to validate its identity. The solution uses a method called transfer learning to allow the framework to run on different types of devices. The proposed frameworks were able to provide a solution that is scalable, simple, and secured to handle data exchange between IoT devices. The simulation presented showed significant improvements compared to typical blockchain implementations. In addition, the frameworks proposed were able to detect attackers that have the resources to replicate all the device specific features. The proposed authentication framework is the first framework to be able to detect such an advanced attacker. The transfer learning tool added to the authentication framework showed performance gains of up to 70%.

Yes / The rapid proliferation of internet-connected devices in public and private spaces offers humanity numerous conveniences, including many safety benefits. However, unlocking the full potential of the Internet of Things (IoT) would require the assurance that IoT devices and applications do not pose any safety hazards to the stakeholders. While numerous efforts have been made to address security-related challenges in the IoT environment, safety issues have yet to receive similar attention. The safety attribute of IoT systems has been one of the system’s vital non-functional properties and a remarkable attribute of its dependability. IoT systems are susceptible to safety breaches due to a variety of factors, such as hardware failures, misconfigurations, conflicting interactions of devices, human error, and deliberate attacks. Maintaining safety requirements is challenging due to the complexity, autonomy, and heterogeneity of the IoT environment. This article explores safety challenges across the IoT architecture and some application domains and highlights the importance of safety attributes, requirements, and mechanisms in IoT design. By analysing these issues, we can protect people from hazards that could negatively impact their health, safety, and the environment. / The full text will be available at the end of the publisher's embargo: 11th Feb 2025

Internet of things (IoT) is envisioned as a promising paradigm to interconnect enormous wireless devices. However, the success of IoT is challenged by the difficulty of access management of the massive amount of sporadic and unpredictable user traffics. This thesis focuses on the contention-based random access in massive cellular IoT systems and introduces two novel frameworks to provide enhanced scalability, real-time quality of service management, and resource efficiency. First, a local communication based congestion control framework is introduced to distribute the random access attempts evenly over time under bursty traffic. Second, a multi-agent reinforcement learning based preamble selection framework is designed to increase the access capacity under a fixed number of preambles. Combining the two mechanisms provides superior performance under various 3GPP-specified machine type communication evaluation scenarios in terms of achieving much lower access latency and fewer access failures. / Master of Science / In the age of internet of things (IoT), massive amount of devices are expected to be connected to the wireless networks in a sporadic and unpredictable manner. The wireless connection is usually established by contention-based random access, a four-step handshaking process initiated by a device through sending a randomly selected preamble sequence to the base station. While different preambles are orthogonal, preamble collision happens when two or more devices send the same preamble to a base station simultaneously, and a device experiences access failure if the transmitted preamble cannot be successfully received and decoded. A failed device needs to wait for another random access opportunity to restart the aforementioned process and hence the access delay and resource consumption are increased. The random access control in massive IoT systems is challenged by the increased access intensity, which results in higher collision probability. In this work, we aim to provide better scalability, real-time quality of service management, and resource efficiency in random access control for such systems. Towards this end, we introduce 1) a local communication based congestion control framework by enabling a device to cooperate with neighboring devices and 2) a multi-agent reinforcement learning (MARL) based preamble selection framework by leveraging the ability of MARL in forming the decision-making policy through the collected experience. The introduced frameworks are evaluated under the 3GPP-specified scenarios and shown to outperform the existing standard solutions in terms of achieving lower access delays with fewer access failures.

The Internet of Things (IoT) is composed of billions of sensors and actuators that have varying tasks aimed at making industry, healthcare, and home life more efficient. These sensors and actuators are mainly low-powered and resource-constrained embedded devices with little room for implementing IP security in addition to their main function. With the fact that more of these devices are using IPv6 addressing, we seek to adapt a moving-target defense measure called Moving Target IPv6 Defense for use with embedded devices in order to add an additional layer of security. This adaptation, which we call Micro-Moving Target IPv6 Defense, operates within IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) which is used in IEEE 802.15.4 wireless networks in order to establish IPv6 communications. The purpose of this defense is to obfuscate the communications between a sensor and a server in order to thwart a potential attacker from performing eavesdropping, denial-of-service, or man-in-the-middle attacks. We present our work in establishing this security mechanism and analyze the required control overhead on the wireless network. / Master of Science

Advances in speech recognition and voice synthesis software now allow "smart agents" (e.g., voice-controlled devices like Amazon's Alexa and Google Home) to interact naturally with humans. The machines have a skills repertoire with which they can "communicate" and form relationships with consumers – managing aspects of their daily lives and providing advice on various issues including purchases. This dissertation develops three essays that examine the role played by the smart agent's voice (rational vs. emotional) in such relationships. The social cognition and persuasion literature on interpersonal communication serves as a comparison backdrop. In Essay 1, I investigate how identical purchase recommendations delivered in a rational or an emotional voice elicit different consumer responses, when the voice is ascribed to a human versus a smart agent. I argue that consumers distinctively categorize smart agents and humans, which, in turn, leads them to have different expectations when interacting with them. In Essay 2, I focus on how a smart agent's vocal tone (rational vs. emotional) influences consumer compliance with the agent's recommendation as well as the role of trust as a mediator of the underlying process. I find that the level of intimacy in the relationship between the smart agent and the human user moderates whether the voice effect on persuasion operates through trust that is cognitively or affectively rooted. In Essay 3, I examine the proposition that consumers may anthropomorphize a smart agent both mindfully (consciously) and mindlessly (non-consciously), depending on the agent's voice. In addition to using extant measures of the degree to which anthropomorphism is explicit (conscious), I develop an auditory analog of the implicit association test (IAT) that assesses implicit (non-conscious) anthropomorphism. In additional experiments, I further assess the robustness of the auditory IAT test and demonstrated a dissociation between the measures of the explicit and implicit subconstructs of anthropomorphism. Taken together, these essays contribute to our understanding of the factors driving consumer relationships with smart agents in the rapidly evolving IoT world. / Doctor of Philosophy / Advances in artificial intelligence technologies are creating "smart devices," i.e., machines that can "understand" how people talk and respond meaningfully to such communication in their own voices. Thus, familiar voice-controlled devices like Amazon's Alexa and Google Home are now increasingly able to "communicate" and form relationships with consumers – managing aspects of their daily lives and providing advice on various issues including purchases. However, little is known about how a smart agent's vocal tones (rational vs. emotional) may influence how consumers perceive and relate to the smart agent. My primary goal in this research is to contribute to our understanding of the role played by the smart agent's voice (rational vs. emotional) in such relationships. Specifically, in Essay 1, I investigate how identical purchase recommendations delivered in a rational or an emotional voice elicit different consumer responses, when the voice is ascribed to a human versus a smart agent. I argue that consumers perceive smart agents and humans as belonging to distinct categories, which leads them to have different expectations when interacting with them. In Essay 2, I focus on how a smart agent's vocal tone (rational vs. emotional) influences consumer compliance with the agent's recommendation as well as the role of trust as a mediator of the underlying process. The level of intimacy in the relationship between the smart agent and the human user influences whether the voice effect on persuasion is driven by trust that is rooted in cognition (knowledge, competence) or affect (caring, warmth). In Essay 3, I examine whether consumers imbue humanlike qualities (anthropomorphize) a smart agent both mindfully (consciously) and mindlessly (non-consciously) based on the agent's voice. In addition to using available measures of conscious anthropomorphism, I develop an auditory analog of the implicit association test (IAT) to assesses implicit (non-conscious) anthropomorphism. In additional experiments, I assess the robustness of the auditory IAT test and the relationship between measures of mindful and mindless anthropomorphism. Taken together, the research reported in these three essays contributes to our understanding of the factors driving consumer relationships with smart agents in the rapidly evolving IoT (Internet of Things) world.

Smart built environments (SBE) include fundamentally different and enhanced capabilities compared to the traditional built environments. Traditional built environments consist of basic building elements and plain physical objects. These objects offer primitive interactions, basic use cases and direct affordances. As a result, the traditional architectural process is completely focused on two dimensions of design, i.e., the physical environment based on context and functional requirements based on the users. Whereas, SBEs have a third dimension, computational and communication capabilities embedded with physical objects enabling enhanced affordance and multi-modal interaction with the surrounding environment. As a result of the added capability, there is a significant change in activity pattern/spatial use pattern in an SBE. So, the traditional architectural design process needs to be modified to meet the unique requirements of SBE design. The aim of this thesis is to modify the traditional architectural design process by introducing SBE requirements. Secondly, this thesis explores a reference implementation of immersive technology based SBE design framework. The traditional architectural design tools are not always enough to represent, visualize or model the vast amount of data and digital components of SBE. SBE empowered with IoT needs a combination of the virtual and real world to assist in the design, evaluation and interaction process. A detailed discussion explored the required capabilities for facilitating an MR-based SBE design approach. An immersive technology is particularly helpful for SBE design because SBEs offer novel interaction scenarios and complex affordance which can be tested using immersive techniques. / Master of Science / Smart built environments (SBE) are fundamentally different from our everyday built environments. SBEs have enhanced capabilities compared to the traditional built environments because computational and communication capabilities are embedded with everyday objects in case of SBEs. An wall or a table is no longer just a simple object rather an interactive component that can process information and communicate with people or other devices. The introduction of these smart capabilities in physical environment leads to change in user's everyday activity pattern. So the spatial design approach also needs to be reflect these changes. As a result, the traditional architectural design process needs to be modified for designing SBEs. The aim of this thesis is to introduce a modified SBE design process based on the traditional architectural design process. Secondly, this thesis explores an immersive technology (e.g.- mixed reality, virtual reality etc.) based SBE design framework. The traditional architectural design tools mostly provide two dimensional representations like sketches or renderings. But two dimensional drawings are not always enough to represent, visualize or model the vast amount of data and digital components associated with SBE. The SBE design process needs enhanced capabilities to represent the interdependency of connected devices and interaction scenarios with people. Immersive technology can be introduced to address this problem, to test the proposed SBE in a virtual/mixed reality environment and to test the proposed 'smartness' of the objects. This thesis explores the potentials of this type of immersive technology based SBE design approach.

In today’s world, IoT systems are more and more overwhelming. All electronic devices are becoming connected. From lamps and refrigerators in smart homes, smoke detectors and cameras in monitoring systems, to scales and thermometers in healthcare systems, until phones, cars and watches in smart cities. All these connected devices generate a huge amount of data collected from the environment. To take advantage of these data, a processing phase is needed in order to extract useful information, allowing the best management of the system. Since most objects in IoT systems are resource limited, the processing step, usually performed by an artificial intelligence model, is offloaded to a more powerful machine such as the cloud server in order to benefit from its high storage and processing capacities. However, the cloud server is geographically remote from the connected device, which leads to a long communication delay and harms the effectiveness of the system. Moreover, due to the incredibly increasing number of IoT devices and therefore offloading operations, the load on the network has increased significantly. In order to benefit from the advantages of cloud based AIoT systems, we seek to minimize its shortcomings. In this thesis, we design a distributed architecture that allows combining these three domains while reducing latency and bandwidth consumption as well as the IoT device’s energy and resource consumption. Experiments conducted on different cloud based AIoT systems showed that the designed architecture is capable of reducing up to 80% of the transmitted data. / En el mundo actual, los sistemas de IoT (Internet de las cosas) son cada vez más abrumadores. Todos los dispositivos electrónicos se están conectando entre sí. Desde lámparas y refrigeradores en hogares inteligentes, detectores de humo y cámaras para sistemas de monitoreo, hasta básculas y termómetros para sistemas de atención médica, pasando por teléfonos, automóviles y relojes en ciudades inteligentes. Todos estos dispositivos conectados generan una enorme cantidad de datos recopilados del entorno. Para aprovechar estos datos, es necesario un proceso de análisis para extraer información útil que permita una gestión óptima del sistema. Dado que la mayoría de los objetos en los sistemas de IoT tienen recursos limitados, la etapa de procesamiento, generalmente realizada por un modelo de inteligencia artificial, se traslada a una máquina más potente, como el servidor en la nube, para beneficiarse de su alta capacidad de almacenamiento y procesamiento. Sin embargo, el servidor en la nube está geográficamente alejado del dispositivo conectado, lo que conduce a una larga demora en la comunicación y perjudica la eficacia del sistema. Además, debido al increíble aumento en el número de dispositivos de IoT y, por lo tanto, de las operaciones de transferencia de datos, la carga en la red ha aumentado significativamente. Con el fin de aprovechar las ventajas de los sistemas de AIoT (Inteligencia Artificial en el IoT) basados en la nube, buscamos minimizar sus desventajas. En esta tesis, hemos diseñado una arquitectura distribuida que permite combinar estos tres dominios al tiempo que reduce la latencia y el consumo de ancho de banda, así como el consumo de energía y recursos del dispositivo IoT. Los experimentos realizados en diferentes sistemas de AIoT basados en la nube mostraron que la arquitectura diseñada es capaz de reducir hasta un 80% de los datos transmitidos.

Smart buildings are one of the most important emerging applications of Internet of Things (IoT). The astronomical growth in IoT devices, data generated from these devices and ubiquitous connectivity have given rise to a new computing paradigm, referred to as "Edge computing", which argues for data analysis to be performed at the "edge" of the IoT infrastructure, near the data source. The development of efficient Edge computing systems must be based on advanced understanding of performance benefits that Edge computing can offer. The goal of this work is to develop this understanding by examining the end-to-end latency and throughput performance characteristics of Smart building streaming IoT applications when deployed at the resource-constrained infrastructure Edge and to compare it against the performance that can be achieved by utilizing Cloud's data-center resources. This work also presents a real-time streaming application to detect and localize the footstep impacts generated by a building's occupant while walking. We characterize this application's performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck. / Master of Science / Among the various emerging applications of Internet of Things (IoT) are Smart buildings, that allow us to monitor and manipulate various operating parameters of a building by instrumenting it with sensor and actuator devices (Things). These devices operate continuously and generate unbounded streams of data that needs to be processed at low latency. This data, until recently, has been processed by the IoT applications deployed in the Cloud at the cost of high network latency of accessing Cloud’s resources. However, the increasing availability of IoT devices, ubiquitous connectivity, and exponential growth in the volume of IoT data has given rise to a new computing paradigm, referred to as “Edge computing”. Edge computing argues that IoT data should be analyzed near its source (at the network’s Edge) in order to eliminate high latency of accessing Cloud for data processing. In order to develop efficient Edge computing systems, an in-depth understanding of the trade-offs involved in Edge and Cloud computing paradigms is required. In this work, we seek to understand these trade-offs and the potential benefits of Edge computing. We examine end to-end latency and throughput performance characteristics of Smart building streaming IoT applications by deploying them at the resource-constrained Edge and compare it against the performance that can be achieved by Cloud deployment. We also present a real-time streaming application to detect and localize the footstep impacts generated by a building’s occupant while walking. We characterize this application’s performance for Edge and Cloud computing and utilize a hybrid scheme that (1) offers maximum of around 60% and 65% reduced latency compared to Edge and Cloud respectively for similar throughput performance and (2) enables processing of higher ingestion rates by eliminating network bottleneck.

No / Abstract: Internet of Things (IoT) aims to enable the interconnection of a large number of smart devices (things) using a combination of networks and computing technologies. But an influx of interconnected things makes a greater demand on the underlying communication networks and affects the quality of service (QoS). This paper investigates into the QoS of delay sensitive things and the corresponding traffic they generate over the network. Things such as security alarms, cameras, etc, generate delay sensitive information that must be communicated in a real time. Such things have heterogeneous features with limited buffer capacity, storage and processing power. Thus the most commonly used Best Effort service model cannot be an attractive mechanism to treat delay sensitive traffic. This paper proposes a cost-effective analytical model for a finite capacity queueing system with pre-emptive resume service priority and push-out buffer management scheme. Based on the analytical model various simulation results are generated in order to analyse the mean queue length and the blocking probability of high and low priority traffic for system with various capacities.

Yes / Welcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies.