The ability to pair sensors with increasing amounts of local compute and machine learning promises to be a powerful tool in fields such as healthcare, environmental monitoring, autonomous vehicles, robotics, earth observation and more. However, a common challenge in these applications is that systems often operate in environments where power, weight, size and communication options are restricted. Unfortunately, this severely limits the capabilities of current systems, which often discard large amounts of data before its true value is known. This project aims to enhance the performance of such sensing systems by leveraging very large memories. We focus on extracting more precise and nuanced insights from data on edge sensor devices that are constrained by power and communication, but not by memory. Ultimately, our objective is to develop memory-centric systems capable of extracting larger volumes of valuable information from sensor data in a timely and energy-efficient manner.

Large-scale wireless networks are expected to become prevalent in various Internet-of-Things (IoT) applications involving environment sensing and monitoring, communications, and computing. It is a fundamental task of many networks to deduce the network topology, both during the establishment of the network and periodically as the network state evolves. The availability of network topology and performance information is crucial for the operation and management of large wireless systems comprising low-power devices that are required to provide low-latency, high-reliability services. For example, state-of-the-art smart meter networks require this information to carry out routing and resource scheduling tasks, and the estimation of the number of devices in a network is useful for finding out how many sensors are still active or for detecting failures of some subnetworks. Inferring topology information even possess great importance in matters of national security in which one may have to learn the structure of a target network passively from external observables, such as the spectral activity of devices, without having access to the network devices and protocols. Many network characteristics can be inferred by observing end-to-end data, which often takes the form of packet probes. The general field of study concentrating on such techniques is known as "network tomography". Over the past twenty years, this field has been developed to include the inference of link loss statistics (loss tomography), internal queuing delays (delay tomography), and structural characteristics (topology tomography). Much of the work to date has focused on the formulation of optimal and efficient estimation methods that are primarily geared toward computer networks that exhibit certain constraints on their topologies. Some more recent studies of network tomography have considered wireless systems. However, investigations have largely been limited by the lack of available statistical models that incorporate spatial and physical characteristics inherent to wireless networks. For example, spatial (wireless) networks exhibit distinctive features (e.g., transitivity, clustering), which have not been fully exploited in topology inference tasks. This project is concerned with developing improved active methods (topology discovery) and passive techniques (topology inference) of obtaining the topology of a wireless communications network or a portion thereof. The underlying hypothesis is that probabilistic knowledge of structural properties of wireless networks can be used as prior information to improve network inference tasks, particularly topology tomography, in practical systems. The project will begin with fundamental research into the correct modelling and statistical characterisation of wireless networks designed for particular applications, such as smart meter infrastructure and tactical systems. The results of this research will be exploited to develop new topology tomography algorithms that are optimised for use in the chosen applications. The technical contributions of the project will be accompanied and supported by a number of activities aimed at delivering impact through dissemination and technology transfer. The project is supported by three hands-on partners (Toshiba, Moogsoft, and HMGCC), each of which is at the leading edge of its respective field.

Most electrical equipment requires a power supply which usually incorporates a magnetic transformer to provide safety isolation and to step up or step down the input voltage. Piezoelectric transformers (PTs) offer an exciting alternative to conventional transformers particularly in applications requiring high power density, low electromagnetic interference and high temperature operation. Their widespread adoption is hindered, however, by the need for power supply designers to possess knowledge and training in both materials science and power electronics, combined expertise that is rarely found in industry or even academia. This lacking knowledge base represents a real impediment for power supply manufacturers who may wish to adopt PT technology and consequently PTs have only seen marginal market penetration. The project addresses these issues by producing a multi-physics design framework which provides abstraction from the fundamental science and therefore allows the design engineer to focus on the overall system design. The framework converts a high-level power supply specification into a PT power supply solution through a series of circuit and materials based transformations. An optimisation process (using evolutionary computing and finite element analysis) produces a fully characterised final design. The output of this process includes a circuit design and a "recipe" for the piezoelectric transformer, including materials and construction details presented in a format suitable for manufacture. The framework will be encapsulated in a user-friendly software design tool and validated against real-world power supply applications suggested by the project's industrial partners thereby ensuring the relevance of the research. The research, which will transcend the traditional barriers between electrical engineering and materials science, has an investigatory team with expertise in both areas. As well as developing a framework, the research will develop novel piezoelectric materials particularly suited to high temperature operation, finding promise in a number of application areas including aerospace, oil/gas exploration, electric vehicles and for remote monitoring in harsh environments. Additionally, the need for environmentally damaging lead-based PTs will be diminished through the development of new materials which comply with Restriction on Hazardous Substances 2016. The research programme will culminate in an open workshop where industry and academic researchers can learn about PT power supplies and evaluate the design tool for themselves. To ensure that the research remains industrially relevant we have partnered with several leading companies who will provide expertise and commercial drive and in return they will receive proof-of-concept power supplies ready for commercialisation.

The complexity of wireless communication networks has grown considerably in recent years. This has been driven in part by academic research that has started to define the information theoretic boundaries and advantages of certain complex networking topologies and protocols. On the other hand, the demands from consumers and industry have pushed wireless networks towards more sophisticated architectures and solutions, primarily in order to ensure a broad range of services can be delivered using a common infrastructure. This is particularly true of 4/5G technologies, which many believe should support all things for all people, including voice, data, public safety, distributed sensing and monitoring, etc. However, similar beliefs and trends can be found in other sectors, such as smart grid networks and even satellite networks. It is important that engineers understand the global properties of complex networks, and how these properties arise from local structure. Such information can be fed into models and optimisation routines so that practical networks can be designed to perform as well as possible. A common approach to tackling complex problems is to exploit randomness and statistical properties of the underlying system. Probabilistic approaches to network modelling are not without their difficulties, and some of the main problems that researchers have struggled with over the years arise from the fact that networks are finite entities with physical boundaries. Recent research by the investigators has focused on the effects that boundaries have on connectivity when networks are embedded in some finite spatial domain. Analytic expressions for the overall connection probability have been obtained. These formulae quantify the intuitive phenomenon that nodes near the boundary are more likely to disconnect, and thus they explain how the network outage probability behaves at high node densities. This work has been extended considerably to explore notions of resilience (k-connectivity), the effects of node directivity, diversity and power scaling laws, complicated geometric bounding domains (both convex and non-convex), and even the interplay between higher layer trust protocols and the physical network set-up and spatial domain. In this project, the probabilistic formalism alluded to above will be exploited further to study several key concepts that influence the structure of spatially embedded networks. The following four topics will be treated: - continuum models of spatially embedded networks, including the investigation of spectral and centrality properties of random networks; - mobility models in spatially embedded networks, including random waypoint and Levy flight processes; - trust models in spatially embedded networks, including trust dynamics and protocol design; - temporal models of spatially embedded networks, including dynamical node and link (edge) models. The work will take a mathematical approach, but will always maintain a focus on practical implications and designs.

The proposal will develop one of the three UK energy materials hubs, which will carry out cutting edge research in close collaboration with industry in the development of materials up to demonstrator level (pre-commercial) devices. The hub will also have a major role in networking, training, educating in energy materials and devices across UK groups and industry, and will link-up and compliment existing energy related networks and groups to benefit the UK. The "JUICED" Hub [Joint University-Industry Consortium for Energy (Materials) and Devices Hub] will focus its research on nano-enabled energy materials (ceramic materials on a scale of a billionth of a meter wide). Energy materials will be made and developed in applications, such as high performance batteries and similar energy storage devices for automotive, grid or consumer device applications, low cost materials for electrolysers (which use electrical energy to split water into oxygen and hydrogen fuel), fuel cells [devices which take chemical energy and can (sometimes) reversibly convert it to electrical energy]. Other energy materials of interest are materials which can scavenge low grade heat or energy and convert it into electrical energy or materials which can help store, transfer or regulate thermal energy. The novelty in the hub's approach is that it will be able to considerably accelerate the development of new sustainable materials ; (i) Use high throughput synthesis (making a large number of samples quickly in parallel or in series) and in many cases, computational methods (use of computers to simulate and understand and predict materials properties) and appropriate (rapid) screening of materials properties, which will identify lead materials in each application area (ii) Laboratory-scale synthesis of the highest performing samples from above and testing to identify materials for larger scale syntheses (iii) pilot scale syntheses and tests on samples on pre-commercial demonstrator devices, (in collaboration with industry or end users with a strong emphasis on replacing precious or unsustainable metals such as Pt, Ir, Ru, Pb, etc.). How the research aligns with the Industrial Strategy Challenge Fund objectives; The proposed energy hub aligns well to the Industrial Strategy Challenge Fund objectives as follows; the interactions with the industrial consortium in the hub will work with UK industry and accelerate discoveries of new advanced functional materials which will increase UK businesses' investment in R&D and improved R&D capability and capacity. The research in the hub, which covers aspects of materials, testing and characterisation as well as scale-up will lead to an increase multi- and interdisciplinary research around the challenge area of "clean and flexible energy", particularly in the design, development and manufacture of energy storage devices (batteries or similar devices) for the electrification of vehicles to support the business opportunities presented by the low carbon economy and tackle air pollution (e.g. new sustainable catalysts for oxygen evolution and reduction which can also be used in next generation batteries). Other areas that the hub covers that are which are linked to the Industrial Strategy Challenge Fund include "Manufacturing and Materials of the Future" (develop new, affordable, materials for advanced manufacturing sectors). Some of these materials are important components in devices which have applications also in Satellites and space technologies. The JUICED hub includes a number of scale-up and demonstrator activities and therefore this will lead to increased business-academic engagement on innovation activities relating to the same aforementioned challenge areas. The JUICED energy hub will include a number of larger and smaller companies and it will reach out to even more potential companies in the UK (SMEs and larger companies) with its workshops which will publicise capabilities.