About this course
About
Modules
The MSc award consists of six compulsory taught modules (totaling 120 credits) and an independent Masters Project (60 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.
Pervasive Computing
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 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
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September 2021
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January 2022
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.
Content
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
Attendance and Independent Study
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules (more usually 20) and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
Calculation of the Final Award
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Academic profile
We have a highly experienced and energetic course team, in terms of both teaching and research. All members of the course team are research active and will be included in the forthcoming REF2021 submission. The course team have been grantholders of multi-million pound research projects, they have produced world-leading and internationally excellent research outputs in the area of IoT and have demonstrated research impact from their endeavours. We also fully embrace the importance of innovative teaching and assessment methods and are all Fellows of the Higher Education Academy in the UK.
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 (18%) or Lecturers (57%).
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) - 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 2019-2020.
Standard entry conditions
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
Entry Requirements
You need:
(a)
(i) a second class lower division honours degree or better, in the subject areas of computing, engineering or cognate area from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing, engineering or related discipline
and
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent).
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme
English Language Requirements
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
Exemptions and transferability
The entry requirements facilitate accreditation of prior learning.
