The qualitative step forward that Complexity Science has experienced in the last years is directly related to an increase of computation capacity, enabling the possibility of running large scale simulations and handling large amounts of (empirical) data: the so called Big Data paradigm. It is fundamental to come along with new methods and insights to deal, store and extract information from large amounts of data. These datasets naturally come in two different types. First, from the time evolution of some financial indicator or the irregular motion of turbulent fluids to the waveform signal of speech, complex systems produce incredibly complicated univariate/multivariate time series, whose hidden structure should be processed and analysed using fast and novel approaches. Second, the intertwined architecture of the interaction patterns of complex systems is naturally represented and modeled in terms of graphs -a paradigmatic of this approach being the brain, modeled by single units (neurons) connected by edges that model synaptic connections. These distributed processing systems usually lay at the edge between order and randomness (the so-called complex network paradigm) and come in different flavours (undirected/directed, static/temporal, monolayer/multilayer). Each of these two families of datasets have its own mathematical corpus that deals with the description and characterisation of these data, namely signal processing and network science. The working hypothesis of this project is that information encoded or hidden in a data set can be retrieved by mapping such data set into an alternative mathematical representation, where the extraction of information may be eventually simpler. As such, we aim to explore what new information can be extracted by mapping time series into graphs and therefore using network science to characterise signals and their underlying dynamics: in short, to make graph-theoretical time series analysis. We are also interested in the dual problem, namely extracting time series from graphs and therefore using the tools of time series analysis and signal processing to describe, compare and classify networks of many kinds: a signal processing of graphs. We will consider specific methods (visibility algorithms, Markov chain theory, fluctuation analysis) and will be able to define and validate new graph-theoretical measures to describe signals and new signal-theoretic measures to describe graphs, as well as to build a mathematically sound and solid theory to relate these two approaches. Ultimately, the results of our research will be implemented in a software whose input is a time series/complex network and whose output is a set of key features which describe the object under study from several angles (both the signal processing and graph theoretic angle). These features will then feed automatic classifiers for pattern recognition and data analytics.

Building privacy, trust and security into the evolving digital ecosystem is broadly recognized as a key societal challenge. Regulatory activities in the US, Europe and Japan are complemented by industry initiatives that seek to rebalance "the crisis in trust" occasioned by widespread personal data harvesting. All parties agree that key to this challenge are increased accountability and control. Accountability not only seeks to strengthen compliance but also make the emerging ecosystem more transparent to consumers, while control seeks to empower consumers and provide them with the means of actively exercising choice. This proposal will develop the underlying technology infrastructure required to deliver both accountability and control. Although personal data management is generally considered an intensely personal matter, it is also inherently social: it is impractical to withdraw from all online activity simply to protect one's privacy. The success of the modern Internet and the "free" services it supports largely rests on the ability for advertisers and analytics providers to make money with the result that approaches that remove or diminish advertising revenues have been doomed to failure. The many motivations and uses for systems enabling personal management of personal data point to a need for tools enabling individuals to take more explicit control over the collection and usage of their data and the information inferred from their online activities, while addressing the challenges of HDI. Working with partner organisations we have refined our vision of just such a tool, a Databox, an on-demand personal data aggregation and query point, control over which rests directly with the user. The Databox vision is of an open-source personal networked device augmented by cloud-hosted services that collates, curates, and mediates access to our personal data. The Databox will enable and, in some cases, may even host third party applications and services that process personal data. The Databox will form the heart of an individual's personal data processing ecosystem, providing a platform for managing secure access to these data and enabling authorised third parties to provide the owner with authenticated services while roaming outside the home environment.

Building privacy, trust and security into the evolving digital ecosystem is broadly recognized as a key societal challenge. Regulatory activities in the US, Europe and Japan are complemented by industry initiatives that seek to rebalance "the crisis in trust" occasioned by widespread personal data harvesting. All parties agree that key to this challenge are increased accountability and control. Accountability not only seeks to strengthen compliance but also make the emerging ecosystem more transparent to consumers, while control seeks to empower consumers and provide them with the means of actively exercising choice. This proposal will develop the underlying technology infrastructure required to deliver both accountability and control. Although personal data management is generally considered an intensely personal matter, it is also inherently social: it is impractical to withdraw from all online activity simply to protect one's privacy. The success of the modern Internet and the "free" services it supports largely rests on the ability for advertisers and analytics providers to make money with the result that approaches that remove or diminish advertising revenues have been doomed to failure. The many motivations and uses for systems enabling personal management of personal data point to a need for tools enabling individuals to take more explicit control over the collection and usage of their data and the information inferred from their online activities, while addressing the challenges of HDI. Working with partner organisations we have refined our vision of just such a tool, a Databox, an on-demand personal data aggregation and query point, control over which rests directly with the user. The Databox vision is of an open-source personal networked device augmented by cloud-hosted services that collates, curates, and mediates access to our personal data. The Databox will enable and, in some cases, may even host third party applications and services that process personal data. The Databox will form the heart of an individual's personal data processing ecosystem, providing a platform for managing secure access to these data and enabling authorised third parties to provide the owner with authenticated services while roaming outside the home environment.

We are living through a revolution, as electronic communications become ever more ubiquitous in our daily lives. The use of mobile and smart phone technology is becoming increasingly universal, with applications beyond voice communications including access to social and business data, entertainment through live and more immersive video streaming and distributed processing and storage of information through high performance data centres and the cloud. All of this needs to be achieved with high levels of reliability, flexibility and at low cost, and solutions need to integrate developments in theoretical algorithms, optimization of software and ongoing advances in hardware performance. These trends will continue to shape our future. By 2020 it is predicted that the number of network-connected devices will reach 1000 times the world's population: there will be 7 trillion connected devices for 7 billion people. This will result in 1.3 zettabytes of global internet traffic by 2016 (with over 80% of this being due to video), requiring a 27% increase in energy consumption by telecommunications networks. The UK's excellence in communications has been a focal point for inward investment for many years - already this sector has a value of £82Bn a year to the UK economy (~5.7% GDP). However this strength is threatened by an age imbalance in the workforce and a shortage of highly skilled researchers. Our CDT will bridge this skills gap, by training the next generation of researchers, who can ensure that the UK remains at the heart of the worldwide communications industry, providing a much needed growth dividend for our economy. It will be guided by the commercial imperatives from our industry partners, and motivated by application drivers in future cities, transport, e-health, homeland security and entertainment. The expansion of the UK internet business is fuelled by innovative product development in optical transport mechanisms, wireless enabled technologies and efficient data representations. It is thus essential that communications practitioners of the future have an overall system perspective, bridging the gaps between hardware and software, wireless and wired communications, and application drivers and network constraints. While communications technology is the enabler, it is humans that are the producers, consumers and beneficiaries in terms of its broader applications. Our programme will thus focus on the challenges within and the interactions between the key domains of People, Power and Performance. Over three cohorts, the new CDT will build on Bristol's core expertise in Efficient Systems and Enabling Technologies to engineer novel solutions, offering enhanced performance, lower cost and reduced environmental impact. We will train our students in the mathematical fundamentals which underpin modern communication systems and deliver both human and technological solutions for the communication systems landscape of the future. In summary, Future Communications 2 will produce a new type of PhD graduate: one who is intellectually leading, creative, mathematically rigorous and who understands the commercial implications of his or her work - people who are the future technical leaders in the sector.