Task-oriented communication for edge intelligence enabled connected robotics systems

Traditional digital communication systems are built on the principle of source-channel separation, guided by rate-distortion theory and channel coding. This reconstruction-oriented communication paradigm served as a cornerstone through multiple generations of communication technologies. However, with the rise of machine-to-machine communications and human-to machine interactions, task-specific representations are often more compact and more efficient than full-scale reconstructions, and End-to-End (E2E) trained communication systems have demonstrated superior task performance over traditional communications. This thesis explores task-oriented communication as a paradigm shift from traditional reconstruction-oriented transmission, focusing on optimizing data exchange for machine-driven decision-making rather than full data fidelity. We develop a Task-Oriented Source-Channel Coding (TSCC) framework designed for edge-enabled autonomous driving. By integrating deep learning-based Joint Source-Channel Coding (JSCC) with an end-to-end autonomous driving agent, TSCC minimizes communication overhead while maintaining high inference accuracy, ensuring robustness against noisy channels. Our results demonstrate a 98.36% reduction in communication bandwidth while maintaining driving performance under low Signal-to-Noise Ratio (SNR) conditions. To enhance compatibility with existing digital communication infrastructures, we propose Aligned Task- and Reconstruction-Oriented Communication (ATROC), which bridges task-oriented communication with traditional reconstruction-oriented paradigms. By leveraging an information reshaper and variational information bottleneck (VIB) theory, ATROC improves AI-driven inference on edge servers while ensuring seamless integration with digital communication standards. Experimental results validate that ATROC reduces 99.19% of the communication load while preserving autonomous driving efficiency. Recognizing the need for a holistic approach, we introduce a task-oriented co-design of communication, computing, and control framework tailored for edge-enabled industrial Cyber-Physical Systems (CPS). This framework jointly optimizes data transmission, computational efficiency, and control decisions, and integrates task-oriented JSCC with Delay-aware Trajectory-guided Control Prediction (DTCP) to reduce E2E delay. Experimental results in autonomous driving simulations demonstrate that our co-design approach significantly improves driving performance under high latency scenarios

Leveraging machine learning and computer vision for advanced UAV communications

In the rapidly developing field of wireless communication, there is a growing demand for technologies that can provide flexible deployment, extended coverage, and enhanced performance in next-generation networks. Traditional networks often struggle with high mobility and environmental blockages, highlighting the need for innovative solutions like Unmanned Aerial Vehicles based (UAV-based) dynamic base stations. UAVs offer a promising solution by functioning as dynamic base stations in 5G and 6G networks, with the potential to address these challenges and improve communication reliability and efficiency. However, the integration of UAVs into wireless communication presents significant challenges. Ensuring reliable communication in high-mobility environments, optimizing beam management techniques, predicting blockages in real time, and managing the latency inherent in UAV-assisted networks all require innovative solutions. These challenges are combined by the need to balance power consumption and processing capacity, especially when performing complex tasks such as on-device machine learning and computer vision-based beamforming. The first study of this dissertation focuses on the challenge of beam management in milimeter wave (mmWave) 5G and beyond networks, where speedy environmental changes in highmobility scenarios degrade signal quality. Previous studies have highlighted the limitations of traditional beamforming approaches, especially in their ability to adjust to dynamic environments. To enhance this, a novel technique is proposed that integrates computer vision (CV) with ensemble learning, employing the "you look only once" (YOLO-v5) for precise UAV detection and positioning. By stacking two neural networks to refine a meta-learner, this method achieves approximately 90% top-1 accuracy in K-beam predictions, significantly enhancing the signal-to-noise ratio and improving network performance in high-mobility scenarios. The second study focuses on the problem of proactive blockage prediction and management in mmWave communications, where maintaining line-of-sight connectivity is necessary. Previous studies have stated that traditional reactive handover methods often result in service disruptions due to unexpected blockages. Computer vision techniques used previously resulted to a 40% improvement in user connectivity by predicting and managing blockages. Extending this concept, the study addresses proactive blockage prediction and management in mmWave communications, employing UAVs not only as base stations but also as proactive agents in handover processes. By leveraging CV to detect potential blockages and monitor user movement, the system facilitates proactive handovers to maintain line-of-sight connectivity. This approach, evaluated using a publicly available dataset and incorporating advanced antenna modeling techniques, has demonstrated a 20% enhancement in network performance. The third study reveals a new approach that utilizes vision-aided machine learning for efficiently and precisely predicting the optimum beam orientations for UAVs using mmWave and terahertz (THz) technologies. Previous research has shown that, while utilizing visual data from UAVs can increase beam prediction accuracy, there are still issues in reducing beam training overhead and managing real-time mobility. Employing data from UAV cameras, the proposed method achieves approximately 90% accuracy in predicting the best beam direction for the top-1, and nearly 100% for the top-3. Performing these computations directly on the UAV (on-device inference) reduces communication delays by 15% and lowers the cost of communication by 50% in terms of power consumption in comparison with ground-based processing, greatly increasing the efficiency of real-time UAV communication. Collectively, these studies underline the potential of using UAVs to improve wireless communication providing innovative solutions for network expansion, precise beam management, and proactive blockage prediction. This thesis emphasizes UAVs as a cornerstone technology for advancing future wireless communication, setting the stage for more reliable, efficient, and comprehensive communication systems

SAMFusion3D: Self-adaptive multi-modality fusion for 3D object detection in autonomous driving

Autonomous vehicles rely on a diverse array of sensors to achieve comprehensive visual perception of their surroundings. Consequently, the integration of multimodal data, aimed at harnessing the complete spectrum of features from each sensor’s Bird’s Eye View (BEV) information, has emerged as a pivotal area of interest for numerous researchers. Currently, the research community is dedicated to enhancing the accuracy of detection models. However, given that the visual perception systems of autonomous vehicles are typically compact to medium-sized mobile platforms, computational complexity and efficiency are paramount. As the surrounding environment of an autonomous vehicle can fluctuate rapidly at times, maintaining a static sampling rate in such varied contexts results in suboptimal computational efficiency. Furthermore, as each modality’s features are processed through Vision Transformers, particularly in the self-attention mechanism where the attention values for features are computed, it has been observed that adhering to the conventional pipeline approach results in elevated computational complexity and diminished efficiency. For the self-adaptive sampling mechanism, we adeptly extract depth information from camera features by utilizing point cloud data. Then, the fusion rate, which functions as a regulatory factor, dynamically adjusts the size of the effective sampling intervals, significantly impacting the computational load of the feature integration process. We also adopted the structure of the iTransformer that masterfully inverts the dimensions of the embedding. Our experiments conducted on the nuScenes dataset prove that our model can perform with reduced computational complexity while maintaining results comparable to those of the baseline model

Overcoming critical interface design challenges for Automated Vehicle-cyclist interaction

Cyclists are vulnerable road users who must share the road with motorised vehicles [66]. They rely on social interactions with drivers to resolve space-sharing conflicts safely and without ambiguity [82]. The advent of automated vehicles (AVs) will remove these social interactions, compromising the safety of cyclists [97]. AV-cyclist interfaces are promising solutions; these devices facilitate clear communication by allowing AVs to communicate explicit signals [37]. For example, displaying the AV’s intentions via LED lights on the AV or augmented reality glasses worn by cyclists [67]. However, AV-cyclist interfaces must overcome four key design challenges to be usable on real roads: acceptability to match the needs and requirements of cyclists [41, 49]; versatility to operate across various traffic scenarios, such as intersections or roundabouts [12]; cultural inclusivity between countries with different cultural norms and traffic infrastructure [108], and scalablity for many-to-many AV-cyclist interaction [123]. This thesis describes 10 studies conducted to overcome these challenges. These established requirements for AV-cyclist interfaces through observations and eye-tracking studies conducted in real traffic. The requirements were used to design interfaces through participatory design and test them in outdoor and simulator-based user studies. Findings for acceptability showed that interfaces should be placed on the surrounding environment or the AV itself to avoid compelling cyclists to carry devices on every trip. However, optional wearable devices can be used for added support. Results for versatility showed that AV-cyclist interfaces must be viewable from anywhere around the vehicle, and the AV’s intentions should be communicated in a simple, binary manner (i.e., AV-yielding or not yielding) to work consistently between traffic scenarios. Investigating cultural inclusivity showed that interfaces were needed to facilitate interaction regardless of the cultural setting. Cyclists accustomed to riding in mixed traffic found interface messages on AV intentions sufficient. However, those accustomed to greater segregation from vehicles needed to verify these messages with AV driving behaviours. For scalability, results showed that incorporating additional wearable devices was useful to avoid ambiguity in multi-cyclist situations and centralise information from multiple AVs. Multi-AV information should be communicated using visual and auditory signals without diverting cyclist attention from the road ahead, e.g. by integrating visual displays into the environment. The thesis contributes novel design guidelines for each design challenge. This is critical for supporting the large scale, global deployment of AVs and ensuring safe cycling experiences

Ideology, epistemic injustice, and ignorance: an analysis of the trans panic

This thesis is a philosophical analysis of the trans panic (the ongoing moral panic about trans people in the UK) which utilises the philosophical tools of ideology and epistemic injustice. Drawing on the work of Sally Haslanger, I define ideologies as epistemically defective networks of social meanings that function to uphold oppression. I describe how dominant ideologies cause hermeneutical injustice by failing to provide sufficient hermeneutical resources. I argue that an ideology I name ‘cissexist ideology’ functions to uphold the oppression of trans people and consists of social meanings about the supposedly binary nature of sex and gender, and stereotypes about trans people. I argue that notable features of the trans panic, such as its persistence and resistance to efforts to tackle it, and its wide scope, can be explained by a feedback loop between ideology and epistemic injustice. Although I don’t attribute ignorance with a substantial causal role in the trans panic, I also trace several ways that ideology leads to ignorance, including in the context of the trans panic. I continue my analysis of the trans panic by providing accounts of two phenomena that are occurring within it. The first is a type of epistemic injustice that I term ‘hermeneutical sabotage’. Hermeneutical sabotage occurs when dominantly situated knowers actively worsen the available hermeneutical resources for understanding the experiences of a marginalised group. They do this by distorting hermeneutical resources necessary for understanding marginalised groups’ experiences, and introducing new, prejudiced hermeneutical resources. I explain how this is taking place within the trans panic and how hermeneutical sabotage is used as a tactic to further the aims of harmful political movements. I also give an account of the phenomenon I name ‘ideological true beliefs’: true beliefs about the world which are made true by ideological social construction and function to uphold an ideology. Ideological true beliefs are often expressed as claims and used to provide faulty evidence for false ideological claims. Even though they are constructed by ideology, it is not in the interest of activists to deny ideological true beliefs because they reflect the reality of the unjust world that activists must acknowledge. Activists therefore need other strategies to tackle them. Finally, I turn to the question of how to tackle the trans panic. I argue that consciousness raising offers a method for generating warranted ideology critique. I then outline some tactics that activists can use to tackle the trans panic and explain how these intervene in the cissexist ideology/epistemic injustice feedback loop and tackle the phenomena I describe. Ultimately, I argue it will take a multiplicity of tactics to tackle the trans panic

Applications of model checking in the context of cyber security for digital twins

Cyber security attacks on Industrial Control Systems (ICSs) are increasingly sophisticated, targeting their ability to manage critical processes and posing risks to national infrastructure. Addressing this threat requires innovative methods to ensure the secure design and operation of ICS. Digital Twins (DTs) have emerged as a promising tool for enhancing the efficiency and cyber security of the systems they represent; however, their effectiveness depends on reliable intrusion detection methods and secure integration within existing industrial control environments. Securely deploying a DT to an ICS requires careful consideration of existing architecture and the potential security risks of incorporating the DT itself. Formal methods, in particular model checking, are an effective tool for analysing system design and detecting cyber security vulnerabilities. We present two complementary applications of model checking techniques to support the deployment of DTs in ICS environments. We first develop a specification-based intrusion detection approach utilising the SPIN model checker and deploy it into a DT environment for a hydroelectric dam testbed. We explain the process we followed to develop Promela models from PLC code to detect inconsistencies between received data and specified system behaviours. Our evaluation shows that the models achieved performance on a par with machine learning approaches while maintaining explainability and delivering metrics of 99.99% precision, 99.05% recall, a 99.52% F1-score, and 99.05% accuracy. We then address the expanded attack surface that can result from integrating DTs into ICSs. We explore this issue by developing a series of Alloy models that consider the dataflow between a DT and its underlying asset. The developed models incorporate novel modelling of an attacker’s action space to represent how threat actors can move through a network. Using our approach, we model our hydroelectric testbed DT to identify security vulnerabilities in our design and develop an improved network design to mitigate them. Our approach successfully identified security vulnerabilities within the DT-ICS integration and informed network design improvements to reduce the attack surface significantly. Our evaluation confirms that model checking techniques enhance both intrusion detection and security assessment, offering a structured and explainable alternative to machine learning methods. We discuss the merits and drawbacks of each of our approaches and discuss methods of expanding and improving them to support DT development

Empirical investigation of selection and evolution processes by causal molecular inference

Abstract not currently available

Accountability and issue politics: a case of shale gas extraction in a Chinese province

Abstract not currently available

AI-driven operations for flexible and secure microgrids and multi-energy microgrids

Abstract not currently available

Green public transportation at the crossroads: technological choice dilemmas for bus operators from shareholders’ and other stakeholders’ perspectives in achieving business sustainability

Abstract not currently available