Disruption resilient manufacturing is becoming increasingly important, with the current COVID-19 pandemic bringing this to the fore. Whilst COVID-19 was a natural disaster, the increasing digitisation of supply chains and manufacturing processes means further widespread challenges with respect to malicious activity and cyber attacks that can cause significant disruption. Whilst the news suggests many of these take place on digital platforms or within financial or health institutions, there is growing evidence that cyber-physical systems, such as manufacturing, are becoming more regularly targeted and therefore subject to disruption. For instance, a recent Cisco (2017) report found that 28% of manufacturers across 13 countries suffered cyber-attacks that resulted in revenue loss, with this set to increase as digitisation of the manufacturing industry increases. Therefore, it is crucial to identify methods of both securing against and reconfiguring if needed the point of production within the supply network should a string within the supply network become compromised. This research focuses specifically on additive manufacturing supply chains as part of a responsive manufacturing system, to address the significant security challenges within manufacturing supply chains to ensure greater levels of supply chain resilience for both UK and global manufacturing. In particular, this would address the call from Additive Manufacturing UKs (2017) UK National Strategy Report for AM, where they highlighted a critical challenge is to address security related challenges in AM production, with the importance of this increasing if production is to be distributed and responsive to emergent changes within the system, such as an adversary infiltrating elements of the supply chain. To support such rapid reconfiguration of the manufacturing system across the supply network, our vision is to create a practicable methodology for manufacturing systems that can detect a threat and reconfigure themselves rapidly in the presence of an adversary. The work packages developed as part of this research further address the critical challenges outlined above and underpin our vision through the development of 'double lock' system, of physical hash on the product and digital hash on component files secured against a distributed ledger technology, that can be scaled across and tailored to different SC configurations, allowing manufacturing to be responsive to disruption and enable greater resilience and agility in UK manufacturing SCs. This proposal also considers both the current state of the art in academic research, and the fundamental needs and applied research from industry. This research is transformative as it meets the twin hurdle of academic rigour and industrial relevance. To create tools and techniques for resilient additive manufacturing this research will address the following challenges: - How to develop effective techniques to detect disruption; - How to effectively and accurately analyse the disruption; and - How to respond to disruption through reconfigured manufacture.

We propose the development of a secure platform to enable remote working on untrusted (BYOD) user devices. We will show how an untrusted (BYOD) device can be used as an ultra-thin client in combination with a trusted intermediary (mobile wiifi like) device to provide secure access to company IT systems. Our solution uses an innovative combination of TrustZone security functionality and remote / virtual desktop software in a trusted device with an ultra-thin client app running on the untrusted user device. This combination ensures that no company data is stored on the untrusted device and that access to the company network cannot be gained from the untrusted device. Many previous attempts to provide trusted BYOD for sensitive workers have foundered due to their reliance on special hardware or feature integrations in the untrusted device itself, which severely limits choice and value of the BYOD aspect. This project solves that problem whilst bringing more reliable security. The consortium comprises Thales UK Research and Technology, Trustonic Ltd and HW Communications Ltd.

National Air Traffic Services (NATS) has forecast that UK air traffic will increase by 45% by 2015. This is expected to have a major environmental impact at, and in the vicinity of airports. While there are a number of European wide initiatives to address the technical and operational impacts associated with such growth, our programme is aimed at mitigating the environmental impacts in this sensitive area. It is a key component of a larger, medium term, collaborative programme of applied research in the Air Traffic Management (ATM) domain. It is derived from the Aerospace Innovation and Growth Team (AeIGT), the objective of which is to define and implement a National Aerospace Technology Strategy. Outputs from the proposed ATM programme will contribute to the development of technologies and operational practices to deliver measurable environmental benefits.

The 5G-and-Beyond cellular networks promise UAVs with ultra-reliable low-latency control, ubiquitous coverage, and seamless swarm connectivity under complex and highly flexible multi-UAV behaviours in three-dimension (3D), which will unlock the full potential of UAVs. This so-called cellular-connected UAVs (C-UAVs) system creates a radically different and rapidly evolving networking and control environment compared to conventional terrestrial networks: 1) The UAV-ground BS/user channels enjoy fewer channel variations due to their dominant line-of-sight (LOS) characteristics, which imposes severe air-ground interference to the coexisting BSs/users in the uplink/downlink. 2) Operating in existing cellular networks designed mainly for dominate downlink traffic (e.g., video), the UAVs with high data rate requirement in uplink payload uploading, and ultra-reliable low-latency communication (URLLC) requirement in downlink command and control communication can hardly be satisfied. 3) Maintaining seamless connectivity for mission-centric UAV swarms with 3D high mobility is essential for UAV cooperation but extremely challenging. 4) Controlling a swarm of UAVs to accomplish complex tasks with limited human supervision under the connectivity constraints is of capital importance but challenging. The above challenges can hardly be solved via conventional model-driven approaches, which are limited to performance evaluation or optimisation at one time instant in an offline or semi-offline manner, relying on given ideal probabilistic channel models without time correlation. Meanwhile, the future cellular networks in 5G-and-Beyond moves towards an open, programmable, and virtualised architecture with unprecedented data availability. Both facts mandate a fundamental change in the way we model, design, control, and optimise the C-UAVs system, from reactive/incident driven decoupled networking and control operation to proactive/ data-driven joint network and control design. This project has the ambitious vision to develop artificial intelligence (AI)-powered C-UAVs system with full network automation and conditional control automation, that allow for joint design and optimization of the network operation and the UAVs control in real-time with minimum human supervision and the target of mission completion under the long-term quality of service (QoS) guarantees. The project will engage with the end-users to exploit the C-UAVs applications in surveillance and emergency services in urban areas. Our results on network automation and control automation will directly benefit the telecom manufacturers (e.g., Ericsson AB, Toshiba Europe, AccelerComm), and broader UAV industries (e.g., Airborne Robotics, Thales, Northrop Grumman) internationally with foreseeable industrial impact. The NGMN and CommNet will facilitate the dissemination of the research outcomes nationally and internationally. The development, implementation, and testing of our proposed solutions serve as a platform towards the commercialisation of our research outcomes, putting the UK at the forefront of the "connected aerial vehicles" revolution.

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