The Connected Everything II (CEII) Network Plus will deliver a network of networks which will accelerate multi-disciplinary collaboration, foster new collaborations between industry and academia and tackle emerging challenges which will underpin the UK academic community's research in support of people, technologies, products and systems for digital manufacturing. Through a range of activities, including feasibility studies, networking, and thematic research, CEII will bring together new teams within a multidisciplinary community to explore new ideas, demonstrate novel technologies in the context of digital manufacturing, and accelerate impact of research into industry. The Network is inspired by the context of the four tenets of Industry 4.0: Interoperability; Information Transparency; Cognitive and Physical Assistance; and Decentralised Decisions and Actions. It will enable the multidisciplinary community to consider cross-cutting themes, some of which will emerge during the lifetime of the Network, but others - Creativity; Data-rich sociotechnical systems; and Regulation - which have been co-created by the industrial and academic members of the Network management team. The CEII Network Plus aligns with a National and International priority of Digital Manufacturing, as highlighted in the Made Smarter report. It will contribute to the delivery of a Connected Nation, through consideration of how advances in digital technologies which connect objects and data in future, distributed manufacturing systems. It will contribute to a Productive Nation, through development of demonstrator projects that take concepts from other domains and apply them to Digital Manufacturing, as well as a range of activities which will support the development of future leaders.

The IoT represents a convergence of ubiquitous computing and communication technologies, with emerging uses that actuate in the real world. No longer do ubiquitous computing systems simply sense and respond digitally, now they physically interact with the world, ultimately becoming embodied and autonomous. At the same time, the game is changing from one of privacy, where it is often (contestably) cited that "users don't care", to one of user safety, where users (along with regulators, governments, and other stakeholders) certainly do care. Likewise, industry needs to become aware that this shift also changes the legal basis under which companies need to operate, from one of disparate and often weakly enforced privacy laws, to one of product liability. The current widely adopted approach in which cloud services underpin IoT devices has already raised major privacy issues. Importantly in an actuated future, untrammelled communications implicating a plethora of heterogeneous online services in their normal operation also brings with it resilience challenges. We must ensure the integrity of actuating systems, which will require greater local autonomy alongside increased situated accountability to users. This problem applies in many areas: industrial control, autonomous vehicles, and smart cities and buildings, including the intimate and shared context of the home. This research seeks to address the challenge in the context of the home, where the network infrastructure protection is minimal, providing little or no isolation between attached devices and the traffic they carry. Scant attention has been paid by the research community to home network security, and its acceptability and usability, from the viewpoint of ordinary citizens. This research is also deeply rooted in pragmatism and recognises the 'real world, real time' conditions that attach to the IoT: - that the cyber security solutions currently being defined for IoT systems will not deal with legacy issues and will never achieve 100% adoption; - that extant businesses limit the period of time for which they will provide software and security updates (if they even remain in business); - that cyber security is an arms race and threats will continue to emerge in future; - and that the public will never become network security experts.

Optical networks underpin the global digital communications infrastructure, and their development has simultaneously stimulated the growth in demand for data, and responded to this demand by unlocking the capacity of fibre-optic channels. The work within the UNLOC programme grant proved successful in understanding the fundamental limits in point-to-point nonlinear fibre channel capacity. However, the next-generation digital infrastructure needs more than raw capacity - it requires channel and flexible resource and capacity provision in combination with low latency, simplified and modular network architectures with maximum data throughput, and network resilience combined with overall network security. How to build such an intelligent and flexible network is a major problem of global importance. To cope with increasingly dynamic variations of delay-sensitive demands within the network and to enable the Internet of Skills, current optical networks overprovision capacity, resulting in both over- engineering and unutilised capacity. A key challenge is, therefore, to understand how to intelligently utilise the finite optical network resources to dynamically maximise performance, while also increasing robustness to future unknown requirements. The aim of TRANSNET is to address this challenge by creating an adaptive intelligent optical network that is able to dynamically provide capacity where and when it is needed - the backbone of the next-generation digital infrastructure. Our vision and ambition is to introduce intelligence into all levels of optical communication, cloud and data centre infrastructure and to develop optical transceivers that are optimally able to dynamically respond to varying application requirements of capacity, reach and delay. We envisage that machine learning (ML) will become ubiquitous in future optical networks, at all levels of design and operation, from digital coding, equalisation and impairment mitigation, through to monitoring, fault prediction and identification, and signal restoration, traffic pattern prediction and resource planning. TRANSNET will focus on the application of machine techniques to develop a new family of optical transceiver technologies, tailored to the needs of a new generation of self-x (x = configuring, monitoring, planning, learning, repairing and optimising) network architectures, capable of taking account of physical channel properties and high-level applications while optimising the use of resources. We will apply ML techniques to bring together the physical layer and the network; the nonlinearity of the fibres brings about a particularly complex challenge in the network context as it creates an interdependence between the signal quality of all transmitted wavelength channels. When optimising over tens of possible modulation formats, for hundreds of independent channels, over thousands of kilometres, a brute force optimisation becomes unfeasible. Particular challenges are the heterogeneity of large scale networks and the computational complexity of optimising network topology and resource allocation, as well as dynamical and data-driven management, monitoring and control of future networks, which requires a new way of thinking and tailored methodology. We propose to reduce the complexity of network design to allow self-learned network intelligence and adaptation through a combination of machine learning and probabilistic techniques. This will lead to the creation of computationally efficient approaches to deal with the complexity of the emerging nonlinear systems with memory and noise, for networks that operate dynamically on different time- and length-scales. This is a fundamentally new approach to optical network design and optimisation, requiring a cross-disciplinary approach to advance machine learning and heuristic algorithm design based on the understanding of nonlinear physics, signal processing and optical networking.