Overview
Creating the next generation of high-quality practitioners for the IoT industry.
Important notice – campus change
For operational readiness and simplicity, the vast majority of Academic Year 21/22 Semester Two teaching will continue on our Jordanstown campus, enabling firm and precise timetabling to be available for staff and students.
As phases of the new building become available, all staff and students will have the option to transfer teaching and research at a point that is practical and suitable for all, or if they prefer, stay in Jordanstown.
Precise timetabling will be provided to all students at the beginning of Semester Two as usual.
Find out about the campus transition
Summary
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 is a specialist programme that prepares you for an industrial career with skills in Computing Science, Engineering and Data Analytics. 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.
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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
-
September 2021
-
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.
Belfast campus
A globally recognised hub of creativity, innovation and entrepreneurship.
Important notice – campus change
For operational readiness and simplicity, the vast majority of Academic Year 21/22 Semester Two teaching will continue on our Jordanstown campus, enabling firm and precise timetabling to be available for staff and students.
As phases of the new building become available, all staff and students will have the option to transfer teaching and research at a point that is practical and suitable for all, or if they prefer, stay in Jordanstown.
Precise timetabling will be provided to all students at the beginning of Semester Two as usual.
Find out about the campus transition
Accommodation
High quality apartment living in Belfast city centre adjacent to the university campus.
Find out more - information about accommodation
Student Wellbeing
At Student Wellbeing we provide many services to help students through their time at Ulster University.
Find out more - information about student wellbeing
Address
Ulster University
York Street
Belfast
County Antrim
BT15 1ED
T: 028 7012 3456
Modules
Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
Year one
IoT Networks & Security
Year: 1
Status: C
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
Pervasive Computing
Year: 1
Status: C
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.
Year two
Big Data & Infrastructure
Year: 2
Status: C
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
Year: 2
Status: C
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.
Digital Signal Processing
Year: 2
Status: C
This module enables the student to gain deep understanding and enable them to design, apply, and evaluate digital signal processing techniques as related to IoT.
Embedded Systems & Sensors
Year: 2
Status: C
This module enables the student to understand, design, apply, and critically evaluate embedded systems and their applications as enabling technology for the IoT.
Year three
Masters Project
Year: 3
Status: C
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.
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.
Careers & opportunities
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.
We also have strong links with IAESTE (International Association for the Exchange of Students for Technical Experience). It provides students industry placement opportunities from six weeks to 1 year in one of 80 countries linked with the Association.
Apply
Start dates
-
September 2021
-
January 2022
Fees and funding
Fees (total cost)
Important notice - fees information
Fees illustrated are based on 21/22 entry and are subject to an annual increase.
Correct at the time of publishing. Terms and conditions apply.
Additional mandatory costs are highlighted where they are known in advance. There are other costs associated with university study.
Northern Ireland, Great Britain and EU Fees
£6,270.00
International Fees
£14,910.00
Part-time postgraduate fees
The fees indicated are for full-time study.
The price of your overall programme will be determined by the number of modules that you initiate in the relevant academic year.
Additional mandatory costs
Tuition fees and costs associated with accommodation, travel (including car parking charges), and normal living are a part of university life.
Where a course has additional mandatory expenses we make every effort to highlight them. These may include residential visits, field trips, materials (e.g. art, design, engineering) inoculations, security checks, computer equipment, uniforms, professional memberships etc.
We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. Computer suites and free wifi is also available on each of the campuses.
There will be some additional costs to being a student which cannot be itemised and these will be different for each student. You may choose to purchase your own textbooks and course materials or prefer your own computer and software. Printing and binding may also be required. There are additional fees for graduation ceremonies, examination resits and library fines. Additional costs vary from course to course.
Students choosing a period of paid work placement or study abroad as part of their course should be aware that there may be additional travel and living costs as well as tuition fees.
Please contact the course team for more information.
Disclaimer
- The University endeavours to deliver courses and programmes of study in accordance with the description set out in this prospectus. The University’s prospectus is produced at the earliest possible date in order to provide maximum assistance to individuals considering applying for a course of study offered by the University. The University makes every effort to ensure that the information contained in the prospectus is accurate but it is possible that some changes will occur between the date of printing and the start of the academic year to which it relates. Please note that the University’s website is the most up-to-date source of information regarding courses and facilities and we strongly recommend that you always visit the website before making any commitments.
- Although reasonable steps are taken to provide the programmes and services described, the University cannot guarantee the provision of any course or facility and the University may make variations to the contents or methods of delivery of courses, discontinue, merge or combine courses and introduce new courses if such action is reasonably considered to be necessary by the University. Such circumstances include (but are not limited to) industrial action, lack of demand, departure of key staff, changes in legislation or government policy including changes, if any, resulting from the UK departing the European Union, withdrawal or reduction of funding or other circumstances beyond the University’s reasonable control.
- If the University discontinues any courses, it will use its best endeavours to provide a suitable alternative course. In addition, courses may change during the course of study and in such circumstances the University will normally undertake a consultation process prior to any such changes being introduced and seek to ensure that no student is unreasonably prejudiced as a consequence of any such change.
- The University does not accept responsibility (other than through the negligence of the University, its staff or agents), for the consequences of any modification or cancellation of any course, or part of a course, offered by the University but will take into consideration the effects on individual students and seek to minimise the impact of such effects where reasonably practicable.
- The University cannot accept any liability for disruption to its provision of educational or other services caused by circumstances beyond its control, but the University will take all reasonable steps to minimise the resultant disruption to such services.
