QSI aims at training a world-class cohort of doctoral researchers (DRs) capable of taking the next essential steps in the highly demanding area of cybersecurity. We aim to build strong lasting links between strategically selected industry and academic partners, in different disciplines, via the development of novel technologies for practical applications in data security. In parallel, we will also combine, via a collaborative long-term interdisciplinary approach, expertise in all relevant communities to address key fundamental problems in secure communications in the quantum era, and the important applications therein. The planned training network will provide research and training opportunities to a new generation of DRs, who, in the long-run, shall address the Grand Challenge of providing "Quantum-Safe Internet", i.e., a communication infrastructure that is secure against not only classical attacks but also those enabled by quantum technologies. Today's Internet security heavily relies on computational complexity assumptions, and as such is seriously threatened by advancements in quantum computing technologies. Indeed, we have recently witnessed a wave of key developments in this direction by a number of IT giants, e.g., Google, IBM, Microsoft, and Intel. This particularly jeopardizes applications that require long-term security. The number of such applications is continuously growing as more and more of our private information is stored and communicated in a digital way, e.g., electronic health records, which are now required by European legislation to remain secure for a long time. This requires us to urgently develop and implement new solutions, as we plan to do in this Doctoral Network (DN).

Unlike previous generations of mobile networks, the beyond 5G (B5G) network is envisioned to support edge intelligence, which is to provide both communication and computing capabilities to the proximity of end users. Wireless edge intelligence is particularly important to those crucial use cases of B5G, including smart cities, autonomous driving, wireless healthcare, virtual reality (VR) and augmented reality (AR) gaming, where mobile networks are expected to be equipped with intelligent capabilities for prediction and shaping experiences to individuals. Federated learning (FL) is a key enabling technology for wireless edge intelligence, by performing the model training in a decentralized manner and keeping the data where it is generated. However, a straightforward adaption of FL from computer networks to wireless systems can suffer performance degradation in spectral and implementation efficiency, because of the complex wireless environment with heterogeneous resources and a massive number of devices. The aim of this project is to develop a novel scalable hybrid architecture for wireless FL by efficiently utilising the physical layer dynamics of the mobile communication environments and exploiting sophisticated service-aware and resource-aware collaborative edge learning. The novelty of the project is the development of this novel edge learning architecture, where the fundamental limits of the learning architecture is characterised by advanced mathematical tools, such as graph theory and stochastic learning. In addition, an algorithmic framework for quantifying challenging design trade-offs in the presence of practical constraints by applying sophisticated tools such as compressed sensing and machine learning.

The rapid growth of the rich variety of connected devices, from sensors, to cars, to wearables, to smart buildings, is placing a varied and highly complex set of bandwidth, latency, priority, reliability, power, roaming, and cost requirements on how these devices connect and on how information is moved around. Efficient communications remains a very difficult challenge for our digital world, and understanding how to design devices and systems that make good trade-offs between these different requirements requires skills from several disciplines. MANGI will underpin the critical mass and expertise in Bristol's Smart Internet and Devices Laboratory (SIDL) enabling the creation of a Next Generation Internet, with career development of our senior and most talented postdoctoral researchers forming a core part of our activity. Bristol's SIDL brings together the Smart Internet Lab (SIL) in Electrical & Electronic Engineering and the Centre for Device Thermography and Reliability (CDTR) in Physics at the University of Bristol, and has a world-leading track record, spanning the complete digital communication engine from novel wide bandgap semiconductor RF/optical devices to state-of-the-art high performance network architecture design and operation, on the pathway to enabling the Next Generation Internet. New devices and materials are critically needed as key enablers for the necessary transition from the current to the Next Generation Internet which needs to be energy efficient and provide highly flexible connectivity across optical-wireless domains. Using pump-priming projects to retain and develop our outstanding postdoctoral researchers, revolutionary interdisciplinary approaches will be developed in order to adopt high risk strategies focused on grand challenges aimed at enabling the Next Generation Internet. This approach taken is not possible with standard mode funding. Advances in component technologies, to provide higher speed/linearity, higher power devices, more compact device and packaging design, alongside use of new materials will have transformative impact upon network operation. The flexibility of the platform will be a corner stone of MANGI, allowing our most senior postdoctoral researchers to develop and drive their own research ideas, with interdisciplinary mentoring by senior members of SIDL and industry. This will help remove blockages in current technology and overcome the current internet infrastructure challenges. Standard research paths are not able to support independent development and innovation at physical and network layer functionalities, protocols, and services, while at the same time supporting the increasing bandwidth demands of changing and diverse applications, largely because of current limitations in semiconductor device and packaging technology and a lack of co-design of the multitude of constituent parts.

'Autonomous systems' are machines with some form of decision-making ability, which allows them to act independently from a human controller. This kind of technology is already all around us, from traction control systems in cars, to the helpful assistant in mobile phones and computers (Siri, Alexa, Cortana). Some of these systems have more autonomy than others, meaning that some are very predictable and will only react in the way they are initially set up, whereas others have more freedom and can learn and react in ways that go beyond their initial setup. This can make them more useful, but also less predictable. Some autonomous systems have the potential to change what they do, and we call this 'evolving functionality'. This means that a system designed to do a certain task in a certain way, may 'evolve' over time to either do the same task a different way, or to do a different task. All without a human controller telling it what to do. These kinds of systems are being developed because they are potentially very useful, with a wide range of possible applications ranging from minimal down-time manufacturing through to emergency response and robotic surgery. The ability to evolve in functionality offers the potential for autonomous systems to move from conducting well defined tasks in predictable situations, to undertaking complex tasks in changing real-world environments. However, systems that can evolve in function lead to legitimate concerns about safety, responsibility and trust. We learn to trust technology because it is reliable, and when a technology is not reliable, we discard it because it cannot be trusted to function properly. But it may be difficult to learn to trust technology whose function is changing. We might also ask important questions about how functional evolutions are monitored, tested and regulated for safety in appropriate ways. For example, just because a robot with the ability to adapt to handle different shaped objects passes safety testing in a warehouse does not mean that it will necessarily be safe if it is used to do a similar task in a surgical setting. It is also unclear who, if anyone, bears the responsibility for the outcome of functional evolution - whether positive or negative. This research seeks to explore and address these issues, by asking how we can, or should, place trust in autonomous systems with evolving functionality. Our approach is to use three evolving technologies - swarm systems, soft robotics and unmanned air vehicles - which operate in fundamentally different ways, to allow our findings to be used across a wide range of different application areas. We will study these systems in real time to explore both how these systems are developed and how features can be built into the design process to increase trustworthiness, termed Design-for-Trustworthiness. This will support the development of autonomous systems with the ability to adapt, evolve and improve, but with the reassurance that these systems have been developed with methods that ensure they are safe, reliable, and trustworthy.