MSc Internet of Things with Data Analytics

• Mobile & Wireless Networks: Mobile and wireless networking technologies are ubiquitous nowadays. A key issue facing enterprises today is how to harness the potential of mobile and wireless technologies to enhance the operational effectiveness of the business and illuminate potential opportunities. This unit reviews the broad spectrum of relevant technologies (e.g., WiFi, ad hoc networks, cellular networks, etc.), examines their operating principles/protocols and applicable standards, and the use of these networks in different contexts and scenarios. The unit also explores these networks' main challenges and how they fit into the latest networking paradigms.

• Wireless, Sensor & Actuator Networks: You will explore the operating principles, standards, design, and configuration of WSANs. Future and emerging paradigms – such as Mobile Crowdsensing Systems, and technologies, like Wireless Power Transfer in ad-hoc networks, will also be addressed.

• Security and Privacy in IoT: Smart objects, IoT applications, and their enabling platforms are often vulnerable to security attacks and changing operating and context conditions that can compromise the security of some of their components (e.g., local sensors, network components, application-level components). They also generate, use, and inter-relate massive amounts of personal data in ways that can potentially breach legal and privacy requirements. This unit will develop your ability to understand and produce security architectures for IoT-related networks and systems.

• Research Methods & Professional Issues: Research requires a structured and disciplined approach at all stages. We will help you to develop critical research skills in many areas, from project proposals and planning to critical analysis of research findings, academic writing, and dissemination. Ensuring you give due consideration to professional standards and ethical issues in research.

• Individual Masters Project: You will develop an understanding of the characteristics and implications inherent in the solution of a complex, real-world problem within the context of a substantial, independently-conducted piece of work.

Option units (choose one semester 1)

• Cloud Computing: Cloud computing provides a secure infrastructure for systems integration, data processing, manipulation, and storage. The concept of virtualization will be discussed, including servers, storage, and networks, as well as the cloud structure, services, and deployment models. Locally-based networks, IT resources/services, and their migration to the cloud are elaborated, and strategies for migration to the cloud will be explored.

• Neuronal Analysis: This unit focuses on studying and applying data analysis techniques in neuroscience. Such algorithms are either adapted or specifically designed for analyzing neuronal activity in physiological and pathological brain states. The mathematical modeling of essential neuronal functions over the last century inspired the development of sophisticated parallel data processors that imitate biological neurons and networks to a certain degree. A successful example is the widely used family of deep learning-based approaches. In parallel, methods from, e.g., computational statistics, dynamical systems, partial differential equations, and, more recently, machine learning, provided feedback to the neuroscience field and valuable insights into the understanding of the nervous system. This unit discusses state-of-the-art analytical tools for identifying normal and altered behavior of neuronal activity at multiple spatiotemporal scales, their applications, and current limitations.

• Search & Optimisation: Search & optimization techniques are employed in many areas, including medicine, defense, transportation, aerospace, and finance. Whether the goal is to improve the performance of a new drug, aircraft, traffic signal controllers, or investment decisions, researchers and practitioners can employ stochastic optimization algorithms to design optimal, diverse, and pertinent solutions to many real-world problems.

Option units (choose one semester 2)

• Data Processing and Analytics: The unit aims to develop student knowledge and skills in the evolving areas of Data Modelling, Data Analytics, and Big Data Analytics. Students develop an understanding of data design, implementation, and use of data-driven systems. Moreover, they learn how to model data and big data, discover knowledge within the data and deal with the dimensionality of the data. Overall, they develop a critical understanding of the methodologies and techniques to cope with Data and Big Data, i.e., data of high volume, high velocity, and wide variety, utilizing the appropriate platform (e.g., Hadoop, Storm, Spark, MongoDB).

• Artificial Intelligence: This unit aims to introduce you to the principles and techniques employed within the greater field and sub-fields of Artificial Intelligence (AI) and the skills and knowledge required to use AI techniques to solve real-world and synthetic problems. We will approach AI from a computer-science perspective, focusing on the challenges faced within the field, nature-inspired algorithms, and their applications to complex real-world problems.

• Computer Vision: The field of Computer Vision has been developing rapidly over the last decade and has become the cornerstone of many recent innovations across several domains and application areas, including state-of-the-art image classification (e.g., for use in search engines), object detection (e.g., in autonomous vehicles), semantic segmentation (e.g., in the identification of tumors in x-ray images), biometrics (e.g., face verification) or automatic annotation of medical images, to name a few. There is a shortage of skilled individuals with a deep understanding of the underlying principles and able to apply them to world problems confidently. This unit focuses on several Computer Vision techniques, from traditional approaches established over many decades of research to the latest state-of-the-art developments, together with appropriate tools. The team entails a strong empirical element where Computer Vision models will be built and evaluated.