Yes / In recent times, the proliferation of Internet of Things (IoT) technology has brought a significant shift in the digital transformation of various industries. The enabling technologies have accelerated this adoption. The possibilities unlocked by IoT have been unprecedented, leading to the emergence of smart applications that have been integrated into national infrastructure. However, the popularity of IoT technology has also attracted the attention of adversaries, who have leveraged the inherent limitations of IoT devices to launch sophisticated attacks, including Multi-Stage attacks (MSAs) such as IoT botnet attacks. These attacks have caused significant losses in revenue across industries, amounting to billions of dollars. To address this challenge, this paper proposes a system for IoT botnet detection that comprises two phases. The first phase aims to identify IoT botnet traffic, the input to this phase is the IoT traffic, which is subjected to feature selection and classification model training to distinguish malicious traffic from normal traffic. The second phase analyses the malicious traffic from stage one to identify different botnet attack campaigns. The second stage employs an alert correlation approach that combines the Latent Semantic Analysis (LSA) unsupervised learning and graph theory based techniques. The proposed system was evaluated using a publicly available real IoT traffic dataset and yielded promising results, with a True Positive Rate (TPR) of over 99% and a False Positive Rate (FPR) of 0%. / Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Grant RSPD2024R953

<p> In computer vision, accurately detecting objects of varying sizes is essential for various applications, such as autonomous vehicle navigation and medical imaging diagnostics. Addressing the variance in object sizes presents a significant challenge requiring advanced computational solutions for reliable object recognition and processing. This research introduces a size-adaptive Convolutional Neural Network (CNN) framework to enhance detection performance across different object sizes. By dynamically adjusting the CNN’s configuration based on the observed distribution of object sizes, the framework employs statistical analysis and algorithmic decision-making to improve detection capabilities. Further innovation is presented through the Parameterized-Swish activation function. Distinguished by its dynamic parameters, this function is designed to better adapt to varying input patterns. It exceeds the performance of traditional activation functions by enabling faster model convergence and increasing detection accuracy, showcasing the effectiveness of adaptive activation functions in enhancing object detection systems. The implementation of this model has led to notable performance improvements: a 11.4% increase in mean Average Precision (mAP) and a 40.63% increase in frames per second (FPS) for small objects, demonstrating enhanced detection speed and accuracy. The model has achieved a 48.42% reduction in training time for medium-sized objects while still improving mAP, indicating significant efficiency gains without compromising precision. Large objects have seen a 16.9% reduction in training time and a 76.04% increase in inference speed, showcasing the model’s ability to expedite processing times substantially. Collectively, these advancements contribute to a more than 12% increase in detection efficiency and accuracy across various scenarios, highlighting the model’s robustness and adaptability in addressing the critical challenge of size variance in object detection. </p>

Purpose: This study aims to identify how OEM can design value propositions when addressing cybersecurity challenges. Currently there are no studies found that pinpoint the value that can be created regarding cybersecurity. Therefore, the purpose of this master thesis is to bridge cybersecurity and value proposition into a roadmap OEM can use to organize the activities required for mitigating cyberthreats, and thereby create value. Method: An abductive approach has been utilized in this thesis. The analysis was based on 15 interviews with industry experts and employees at the thesis company. Secondary data was gathered through a thorough literature review. To derive findings from the data collection, a thematic analysis was conducted. Findings: The findings resulted in 3 clusters, cybersecurity challenges, mitigation strategies and value proposition. From this, the value proposition for secure IoT devices framework was developed. The framework has three elements which is derived from the thematical clustering’s. Cybersecurity challenges, Value proposition design and core value dimensions.   Theoretical contributions: We believe our thesis have three theoretical contributions. Firstly, it contributes to the literature on crafting value propositions for IoT products. Secondly, the report adds to the growing literature regarding V2X. Lastly, the thesis presents the fusion of the two first contributions, where value proposition and V2X works in continuum, thereby contributing to business and commercialisation aspect of V2X. Practical contributions: The practical contribution for the thesis is the framework which can be used as a managerial guide in designing value propositions for IoT devices. The framework brings together different strategies to address cybersecurity challenges, and the importance of collaborative value creation. The practical contributions also include the placement of cybersecurity within the kano model, which is important to keep in mind when creating value. Limitations and future research: The first limitation is that the data collection was mainly conducted with industry professionals specializing in cybersecurity, though not specifically within the automotive sector. This may have introduced some bias in the findings. Another limitation is that majority of end users don’t have general knowledge regarding cybersecurity, which led to the decision to not pursue interviews directly with end users. Consequently, there are no mitigation activities based on end user’s input. However, anticipating that awareness and perceptions on cybersecurity will intensify in the future, this presents an opportunity for future research. / Syfte: Denna studie syftar till att identifiera hur OEMs kan utforma värdeerbjudanden genom att adressera diverse cybersäkerhetsutmaningar. För närvarande finns det inga studier som undersöker det värde som kan skapas gällande cybersäkerhet. Syftet med denna uppsats är därför att integrera cybersäkerhet och värdeerbjudande i en färdplan som OEMs kan använda för att organisera de aktiviteter som krävs för att motverka cyberhot och därigenom skapa värde. Metod: I denna rapport har en abduktiv ansats använts. Analysen baserades på 15 intervjuer med branschexperter och anställda på exjobb-företaget. Sekundärdata samlades in genom en noggrann litteraturöversikt. För att analysera resultat från datainsamlingen genomfördes en tematisk analys som resulterade i tre huvudteman; Cybersäkerhetsutmaningar, förebyggande strategier och värdeerbjudande. Resultat: Studien resulterade i flera viktiga aspekter att ta i beaktning vid konstruerandet av värdeerbjudanden för säkra IoT-enheter. Utifrån våra resultat konstruerades ett ramverk som ämnas användas av OEMs vid utformning av värdeerbjudanden. Ramverket består av tre element som härstammar från de tematiska klustren. Cybersäkerhetsutmaningar, Värdeerbjudande design och kärnvärden. Teoretiska bidrag: Vi anser att vår studie har tre teoretiska bidrag. För det första bidrar den till litteraturen för att utforma värdeerbjudanden för IoT-enheter. För det andra bidrar rapporten till den växande litteraturen inom V2X. Slutligen presenterar studien fusionen av de två första bidragen, där värdeförslag och V2X fungerar i kontinuitet och därigenom bidrar till affärs- och kommersialiseringssidan av V2X. Praktiska bidrag: Det praktiska bidraget för studien är ramverket som kan användas som en ledningsguide vid utformningen av värdeerbjudanden för V2X och övriga IoT-enheter. Ramverket sammanför olika strategier för att hantera cybersäkerhetsutmaningar och betydelsen av samarbete vid värdeskapande. De praktiska bidragen inkluderar också placeringen av cybersäkerhet inom Kano-modellen, vilket är viktigt att ha i åtanke när värde ska skapas för IoT produkter. Begränsningar och vidare forskning: Det finns två huvudsakliga begränsningar i vår studie. För det första så utfördes datainsamlingen huvudsakligen med branschexperter som specialiserat sig på cybersäkerhet, även om inte specifikt inom V2X säkerhet. Detta kan ha introducerat viss partiskhet i resultaten. En annan begränsning är att majoriteten av slutanvändare saknar allmän kunskap om cybersäkerhet, vilket ledde till beslutet att inte genomföra intervjuer direkt med slutanvändare. Följaktligen finns det inga förebyggande aktiviteter baserade på slutanvändares input. Däremot, med tanke på att medvetenheten och uppfattningarna om cybersäkerhet förväntas öka i framtiden, utgör detta en möjlighet för framtida forskning. Nyckelord: Innovation; Värdeerbjudande; Cybersäkerhet, Internet of Things, V2X

A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS. We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions. / Ph. D. / A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. We have developed a distributed trust protocol called Adaptive IoT Trust for distributed IoT applications, a centralized trust protocol called IoT-TaaS for centralized IoT applications with cloud access, and a hierarchical trust management protocol called IoT-HiTrust for hybrid IoT applications. We have verified that desirable properties, including solution quality, accuracy, convergence, resiliency, and scalability have been achieved. Furthermore, we validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions.

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. / In the US and many other countries around the world, the daily peak load experienced is frequently much higher than the daily average load. This low load factor causes inefficient use of generation and transmission resources. Besides inefficient use, the peak load also increases system stress conditions resulting from inadequate generation, transmission line outages or transformer failures. This can create supply-limit conditions which may induce cascaded failures and large area blackouts. To avoid system stress conditions due to increasing demand and to use power system resources more efficiently, demand response (DR) serves as an effective tool to reduce peak demand through demand-side load curtailment. This dissertation focuses on DR applications in buildings. In the United States, buildings consume over 40% of the country’s total energy use. These includes both commercial and residential buildings. Most of the commercial buildings are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for demand response, which can be implemented through use of building energy management/building automation software. But, building automation software is not yet very popular in small and medium-sized buildings due to lack of low-cost and easy-to-use software solutions. A DR program offered by a utility can be price-based or incentive-based. Price-based DR programs employ dynamic pricing structure to encourage customers to reduce consumption to save bills, whereas incentive-based programs focus on customer commitment to the utility for providing requested load curtailment during peak load situations, in return for monthly or yearly monetary incentives. As most of the peak load reduction potential comes from incentive-based DR programs, this dissertation focuses on an incentive-based DR program. A customer can conveniently participate in such a program by using automated DR algorithms within an energy management software that can control building loads without customer intervention. Providing load curtailment may interfere with customer comfort, and therefore these algorithms must learn customer comfort preferences and consider them while making load shedding decisions. In this dissertation, a software solution is developed for demand response implementation in buildings, which includes contribution to a secure software platform that enables monitoring and control of loads, and automated learning-based algorithms that can learn customer comfort ranges for building loads and use this learning to make load curtailment decisions in an incentive-based DR program, while ensuring customer comfort.

Yes / The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap toward Industry 4.0 is complex and multifaceted, as manufacturers seek to transition toward new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals.

Yes / Disaster management aims to mitigate the potential damage from the disasters, ensure immediate and suitable assistance to the victims, and attain effective and rapid recovery. These objectives require a planned and effective rescue operation post such disasters. Different types of information about the impact of the disaster are, hence, required for planning an effective and immediate relief operation. The IoT technology available today is quite mature and has the potential to be very useful in disaster situations. This paper analyzes the requirements for planning rescue operation for such natural disasters and proposes an IoT based solution to cater the identified requirements. The proposed solution is further validated using the task-technology fit (TTF) approach for analyzing the significance of the adoption of IoT technology for disaster management. Results from the exploratory study established the core dimensions of the task requirements and the TTF constructs. Results from the confirmatory factor analysis using PLS path modelling, further, suggest that both task requirements and IoT technology have significant impact on the IoT TTF in the disaster management scenario. This paper makes significant contributions in the development of appropriate constructs for modeling TTF for IoT Technology in the context of disaster management.

Yes / The wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities. Design/methodology/approach: Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence. Findings: Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure. Practical implications: This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects. Originality/value: A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges.

In recent years, the remarkable development in ubiquitous communication networks and smart portable devices spawned a wide variety of real-time applications that require timely information updates (e.g., autonomous vehicular systems, industrial automation systems, and live streaming services). These real-time applications all have one thing in common: they desire their knowledge of the information source to be as fresh as possible. In order to measure the freshness of information, a new metric, called the Age-of-Information (AoI) is proposed. AoI is defined as the time elapsed since the generation time of the freshest delivered update. This metric is influenced by both the inter-arrival time and the delay of the updates. As a result of these dependencies, the AoI metric exhibits distinct characteristics compared to traditional delay and throughput metrics. In this dissertation, our goal is to optimize AoI under various real-time network applications. Firstly, we investigate a fundamental problem of how exactly various scheduling policies impact AoI performance. Though there is a large body of work studying the AoI performance under different scheduling policies, the use of the update-size information and its combinations with other information (such as arrival-time information and service preemption) to reduce AoI has still not been explored yet. Secondly, as a recently introduced measure of freshness, the relationship between AoI and other performance metrics remains largely ambiguous. We analyze the tradeoffs between AoI and additional performance metrics, including service performance and update cost, within real-world applications. This dissertation is organized into three parts. In the first part, we realize that scheduling policies leveraging the update-size information can substantially reduce the delay, one of the key components of AoI. However, it remains largely unknown how exactly scheduling policies (especially those making use of update-size information) impact the AoI performance. To this end, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. In the second part, we analyze the tradeoffs between AoI and other performance metrics in real-world systems. Specifically, we focus on the following two important tradeoffs. (i) The tradeoff between service performance and AoI that arises in the data-driven real-time applications (e.g., Google Maps and stock trading applications). In these applications, the computing resource is often shared for processing both updates from information sources and queries from end users. Hence there is a natural tradeoff between service performance (e.g., response time to queries) and AoI (i.e., the freshness of data in response to user queries). To address this tradeoff, we begin by introducing a simple single-server two-queue model that captures the coupled scheduling between updates and queries. Subsequently, we design threshold-based scheduling policies to prioritize either updates or queries. Finally, we conduct a rigorous analysis of the performance of these threshold-based scheduling policies. (ii) The tradeoff between update cost and AoI that appear in the crowdsensing-based applications (e.g., Google Waze and GasBuddy). On the one hand, users are not satisfied if the responses to their requests are stale; on the other side, there is a cost for the applications to update their information regarding certain points of interest since they typically need to make monetary payments to incentivize users. To capture this tradeoff, we first formulate an optimization problem with the objective of minimizing the sum of the staleness cost (which is a function of the AoI) and the update cost, then we obtain a closed-form optimal threshold-based policy by reformulating the problem as a Markov decision process (MDP). In the third part, we study the minimization of data freshness and transmission costs (e.g., energy cost) under an (arbitrary) time-varying wireless channel without and with machine learning (ML) advice. We consider a discrete-time system where a resource-constrained source transmits time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed cost, while not transmitting results in a staleness cost measured by the AoI. The source needs to balance the tradeoff between these transmission and staleness costs. To tackle this challenge, we develop a robust online algorithm aimed at minimizing the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they tend to be overly conservative and may perform poorly on average in typical scenarios. In contrast, ML algorithms, which leverage historical data and prediction models, generally perform well on average but lack worst-case performance guarantees. To harness the advantages of both approaches, we design a learning-augmented online algorithm that achieves two key properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: providing a worst-case performance guarantee even when ML predictions are inaccurate. / Doctor of Philosophy / In recent years, the rapid growth of communication networks and smart devices has spurred the emergence of real-time applications like autonomous vehicles and industrial automation systems. These applications share a common need for timely information. The freshness of information can be measured using a new metric called Age-of-Information (AoI). This dissertation aims to optimize AoI across various real-time network applications, organized into three parts. In the first part, we explore how scheduling policies (particularly those considering update size) impact the AoI performance. Through a systematic and comparative study in single-server queues, we provide useful guidelines for the design of AoI-efficient scheduling policies. The second part explores the tradeoff between update cost and AoI in crowdsensing applications like Google Waze and GasBuddy, where users demand fresh responses to their requests; however, updating information incurs update costs for applications. We aim to minimize the sum of staleness cost (a function of AoI) and update cost. By reformulating the problem as a Markov decision process (MDP), we design a simple threshold-based policy and prove its optimality. In the third part, we study the minimization of data freshness and transmission costs (e.g., energy cost) under a time-varying wireless channel. We first develop a robust online algorithm that achieves a competitive ratio of 3, ensuring a worst-case performance guarantee. Furthermore, when advice is available, e.g., predictions from machine learning (ML) models, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: guaranteeing worst-case performance even with inaccurate ML prediction. While this dissertation marks a significant advancement in AoI research, numerous open problems remain. For instance, our learning-augmented online algorithm treats ML predictions as external inputs. Exploring the co-design and training of ML and online algorithms to improve performance could yield interesting insights. Additionally, while AoI typically assesses update importance based solely on timestamps, the content of updates also holds significance. Incorporating considerations of both age and semantics of information is imperative in future research.

Yes / The rise of artificial intelligence in parallel with the fusion of the physical and digital worlds is sustained by the development and progressive adoption of cyber-physical systems (CPSs) and the Internet of Things (IoT). Cooperative and autonomous CPSs have been shown to have significant economic and societal potential in numerous domains, where human lives and the environment are at stake. To unlock the full potential of such systems, it is necessary to improve stakeholders' confidence in such systems, by providing safety assurances. Due to the open and adaptive nature of such systems, special attention was invested in the runtime assurance, based on the real-time monitoring of the system behaviour. IoT-enabled multi-agent systems have been widely used for different types of monitoring applications. In this paper, we discuss the opportunities for applying IoT-based solutions for the cooperative CPSs safety assurance through an illustrative example. Future research directions have been drawn based on the identification of the current challenges.