The Internet of Things is expected to have a significant impact on industry with predictions of its success and growth constantly rising.
The MSc Internet of Things Applied Research is a specialist programme that prepares you for a career with skills in Computing Science, Engineering and Data Analytics with a full appreciation for the research / innovation process and how this could be transferred into business should be developed. The course covers leading-edge knowledge of Sensor technology, Networks, Security, Pervasive Computing, Big Data and Data Mining in IoT domain. The course is accredited (initial) by BCS, The Chartered Institute for IT, for Partial CITP (Chartered IT Professional) and Partial CEng (Chartered Engineer).
The delivery of the course is supported by multi-million pound infrastructure of a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities. The course content has been informed by internationally leading research being conducted by the School of Computing and the School of Engineering and by our strong industry partnerships, most notably with BT through the jointly established £28.6 million BT Ireland Innovation Centre.
The Internet of Things is an exciting and exponentially growing area both within industry and academic. The skills trained from the course are in high demand within the sector across the key verticals of Smart Cities, Industrial IoT, Connected Health and Smart Homes. The course also provides a platform to embark on further research studies.
The MSc award consists of six compulsory taught modules (totaling 120 credits) and a substantial piece of independent Masters Applied Research Project (120 credits).
IoT Networks & Security
IOT has emerged as a significant technology that can be used for automation and empowerment. The module covers the life cycle of IoT security mechanisms, including the design, development, management and, most importantly, how they are sustained. The module provides an understanding of the IoT architecture, protocols and security considerations; and the ensuing computing challenges of managing big data in a secure way.
The focus of this module is to provide an opportunity for students to gain an in-depth understanding of pervasive computing and to apply this understanding to a range of application domains through working with wireless sensor networks. The module surveys emerging hardware and software components associated with Pervasive Computing Systems, examining the technical and societal issues concerned with a pervasive infrastructure, wireless networks, protocols and emergent algorithms. In doing so a number of examples of innovative systems and applications are reviewed. The module includes a strong practical element where students will be asked to develop services providing support for wearable and smart home context-aware solutions.
Big Data & Infrastructure
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources. The core concepts of distributed computing will be examined in the context of Hadoop. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
Statistical Modelling & Data Mining
This module first provides a systematic understanding of probability and statistics. It then provides an in-depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as Python, R and Weka. Online tools, such as Blackboard will be used to facilitate blended learning approach. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real-world contexts.
Embedded Systems & Sensors
An embedded system is an electronic or computer system which performs dedicated control and data access functions in electronics-based systems and applications. Embedded systems play a crucial role in modern communications, automotive systems, consumer electronics and medical devices and will provide the foundation for the next generation of smart connected IoT devices and the digital enterprise. This module covers the most important aspects of embedded systems and will provide a successful student with theoretical and practical knowledge on the feasibility, reliability, and security of electronic systems, especially those important for existing and future IoT applications.
Digital Signal Processing
The rapid growth of computer processing especially in embedded systems and, more particularly, with digital signals makes it essential that studies specialising in IoT should acquire a knowledge of digital signal processing methods. Digital signal processing concerns all aspects of the acquisition to processing life-cycle of real world signals. The emergence of low cost and pervasive systems in the form of the IoT provides new opportunities for the embedding of DSP technology. This module will introduce students to the concept of sampling real world signals that are often initially present as analogue quantities. Students will gain a fundamental understanding of issues associated with the digitisation of these signals and this will form the necessary foundation for advanced understanding of complex DSP systems. Students will appreciate the properties of signals in both the time and frequency domain and will build upon this appreciation to understand and develop algorithms for the conditioning, processing and analysis of a range of digital signals. Included will be the in-depth investigation of techniques to filter digital signals. This topic will be approached from both a design and an implementation perspective. The module will provide numerous opportunities for students to apply DSP techniques to real world examples.
Masters Applied Research Project
Masters Applied Research Project provides the opportunity for you to demonstrate independence and originality, to plan and organise a large project and to implement this over a sustained period of time. The project will follow a user centred design approach and requires you to identify, define, develop, evaluate and implement a novel solution in a selected application area. This will require you to put into practice the techniques you have been taught throughout the course. With this in mind, the project must be tightly constrained to be achievable, whilst sufficiently open enough for you to demonstrate your ability to engage in innovative approaches towards a contribution to research knowledge.
The applied research project offers an opportunity to deepen your knowledge and develop your skills regarding an area in which you have a special interest. Focusing on this specialised area, the project should investigate all aspects of the user centred design and development process from project definition and ideation through to development, evaluation and implementation. In doing so, a full appreciation for the research / innovation process and how this could be transferred into business should be developed. This will allow you to investigate the pathways to impact from an academic, commercial and social aspect.
The project will undertake an applied piece of research which is novel yet realistic and which builds upon skills and knowledge developed throughout the course. The project will be motivated through a critical evaluation of existing literature and theory and the identification and justification of novel research questions. The project tests the inventiveness, the critical capacities, the project management and the in-depth knowledge and problem-solving skills of the student.
The project must involve design, implementation, experimentation and critical analysis of results, benchmarked against other approaches drawn from the literature. This should follow the user centred design process.