Internet of Things - MSc drafted

Program Overview

Typically 5-10 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Research Project takes place in the final stage of the course separately.

Modules;

IoT Networks & Security

IoT describes the interconnectivity of uniquely identifiable devices embedded in the environment through existing internet protocols and infrastructure. It is an evolution of the Internet which is set to massively impact all aspects of our daily lives, from driverless cars to smart healthcare. There are more connective devices than people and by 2020, it is estimated that there will be 50 billion IoT devices. This disruptive technology offers a multitude of sensing and actuation opportunities, however, also creates new challenges in security, privacy and data governance. This module provides a critical understanding of IoT architecture, storage and communication; and the ensuing computing challenges of managing big data in a secure way.

Big Data & Infrastructure

Big Data is the term for a collection of datasets so large and complex that they become difficult to process using traditional database tools. The challenges posed by big data include capture, curation, storage, search, scaling, sharing, transfer, analysis, and visualisation. Database systems for big data support numerous data storage strategies within their specific class. These classes of databases may be oriented towards handling specific types of data or may operate generically. These storage strategies, and related schemas, are more dynamic than traditional database systems, offering the potential for increased flexibility, scalability and customisation. In this context cloud computing has provided a new type of dynamically scalable platform on which to store and process data. Cloud capabilities are highly available, highly durable, and can dynamically scale to meet the storage and processing demands of the application.

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.

Pervasive Computing

Technology is now being used transparently and seamlessly within every part of our daily lives. This is driven by advances in computing technology, creating devices that are progressively smaller, more powerful and increasingly connected. This in turn has led to a growing trend of embedding computational capability into everyday objects. We are now witnessing an era, where almost any device, from clothing, to appliances, homes, cars, and the human body, is imbedded with a microprocessor that connects the device to an infinite network of other devices. With such a technology rich paradigm we are now witnessing, for the first time, pervasive computing solutions with the ability to provide support within our homes, the community and in the workplace. This module provides a critical awareness of the emerging hardware and software components within pervasive computing and demonstrates its use across a range of application domains. The module examines the issues and challenges concerned with wireless networks, resource restricted computing, protocols and algorithms. The module has a strong practical element and provides the opportunity for students to develop applications for wireless sensing devices, context aware solutions and allows them to systematically test these solutions through development of evaluation frameworks.

Statistical Modelling & Data Mining

With huge amounts of data being stored within databases and data warehouses, automated data analytics and mining techniques are increasingly becoming essential components of any information system. With this comes the potential to automate the extraction of interesting knowledge, through descriptive and predictive models. These techniques span both contemporary statistics and data mining and can provide valuable information to inform business strategy, identify purchase patterns, and so forth. This module first provides a systematic understanding of probability and statistics, including topics such as, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals and linear regression. It then considers the main ideas of linear and generalised linear statistical modelling which can be applied in data mining and the exploration of data. Finally, students will develop in-depth understanding of Data Mining principles and techniques and will apply these to data from within various domains.

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 Project

The aim of the project is to allow the student to demonstrate their ability in undertaking an independent research project for developing theoretical perspectives, addressing research questions using data, or analysing and developing real-world solutions. They will be expected to utilise appropriate methodologies and demonstrate the skills gained earlier in the course when implementing the project.

As part of the project development activity, they will be required to extract and demonstrate knowledge from the literature in an analytic manner and develop ideas and appropriate hardware and software implementations. This may involve the development of a hardware sensor component or may access existing hardware to develop new/ novel software processing or data analytics. This will typically be followed by a structured analysis of needs for a realistic application or actual organisation and identification and application of tools/techniques required to deliver a well-formed solution. Through the project, the student will develop capabilities to analyse cases studies related to IoT / Artificial Intelligence / Advanced Computer Science and its application in a range of domains including transport, environment, health and commerce. The project may further create improvement plans and recommendations for future implementation based on the tools/technologies experienced during the programme of study.

In summary, the Masters Project represents a piece of work performed by the student under suitable staff supervision which draws both from the practical and creative nature of a problem-solving project and the traditional, scholarly exposition of an area of study. The content of the work must be original and contain a critical appraisal of the subject area.

Attendance

Typically 5-10 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Research Project takes place in the final stage of the course seperately.

Start dates

Teaching, Learning and Assessment

Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.

The course is assessed by 100% coursework.

Academic profile

Academic staff in the School of Computing are qualified to teach in higher education with most of them holding at least a Postgraduate Certificate in Higher Education Practice. The majority of academic staff in the School (83%) are accredited fellows of the Higher Education Academy (HEA). Within the School of Computing courses are taught by staff who are Professors (20%), Readers/Senior Lecturers (32%) and Lecturers (48%).

The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.

Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (20%) or Lecturers (55%).

We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) by Advanced HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.

The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise.  The precise staffing for a course will depend on the department(s) involved and the availability and management of staff.  This is subject to change annually and is confirmed in the timetable issued at the start of the course.

Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.

Figures correct for academic year 2021-2022.

Program Outline

Careers & opportunities

In this section

1. Career options
2. Work placement / study abroad

Career options

The Internet of Things has become one of the most discussed technology trends of recent years, mainly due to the expected impact that it will have and, as a result, how it will change the way people live, work and travel. As the expectations of how IoT will redefine an organisation’s operations grow, so too are the expectations to have knowledgeable and skilled staff in the areas of computing, engineering and data science in addition to having an appreciation for business processes and market potential. Taking all of this into consideration, graduates from the course will be well placed to progress into a wide variety of careers, across a range of industrial settings within the sector across the key verticals of Smart Cities, Industrial IoT, Connected Health and Smart Homes. We have active Industry engagement and links with vibrant technology sector in Northern Ireland. Graduates from the course also have opportunity to embarke on further research at the Ph.D. level.

Work placement / study abroad

The course doesn’t require placement experience.

There are opportunities in the course for you to participate in research and industry related projects in the IoT domain through our two Innovation centres BTIIC and CHIC.

BTIIC is the BT Ireland Innovation Centre (BTIIC) in collaboration with Ulster University and BT. The centre aims to invent new ways of using data analytics, artificial intelligence and the IoT, through two work streams of Intelligent System and IoT.

CHIC is the Connected Health Innovation Centre is funded by Invest NI to support business led research in the area of connected health, with focus on data analytics and IoT. The centre currently has over 30 national and international member companies with both technical expertise and clinical experience.