Internet of Things
MSc

About this course

About

This intensive one-year specialist master’s course on Internet of Things is aimed at highly-motivated graduates with a good honours degree in computing, engineering or a related discipline. While the course has a particular focus on the employment needs of the local economy, the skills and abilities developed are easily transferred to a more global stage.

The Internet of Things is an exciting and exponentially growing area both within industry and academic. It sits at the intersection between Computing Science, Engineering and Data Science. The proposed MSc in Internet of Things will, therefore, prepare students for both an industrial career with skills in computing, networks, sensor technologies and data analytics in addition to providing a relevant platform to embark on research studies. These types of skills are in high demand within the sector across the key verticals of Smart Cities, Industrial IoT, Connected Health and Smart Homes.

Attendance

The full-time provision offers two points of entry in each academic year: September and January. For the September intake, the degree will normally be completed in three semesters across a single academic year. For the January intake, the degree will normally be completed in three semesters but across two academic years.

Start dates

• September 2019
• January 2020

Teaching, Learning and Assessment

Teaching is delivered through a combination of lectures, directed tutorials, seminars and practical sessions. Support is also provided for project preparation and implementation.

The course is assessed by coursework.

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  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:

  • the relevant generic national Qualification Descriptor
  • the applicable Subject Benchmark Statement
  • the requirements of any professional, regulatory, statutory and accrediting bodies.

  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

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.

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  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.

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

Big Data & Infrastructure

Year: 1

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.

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.

Statistical Modelling & Data Mining

Year: 1

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.

Masters Project

Year: 1

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 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.

Digital Signal Processing

Year: 1

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: 1

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.

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

Applicants must:

(a) have gained

(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 is expected to have a significant impact on industry with predictions of its success and growth constantly rising. It is at the same time the most anticipated and least understood initiative within IT departments. Figures at the start of 2018 suggest that nearly 70% of organisations have developed plans to embrace IoT in their organisation within the next year. As the expectations of how IoT will redefine an organisation’s operations grow so too are the expectations to have appropriately 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 MSc in Internet of Things will be well placed to progress into a wide variety of careers, across a range of industrial settings and application domains.

There are also opportunities for graduates from the MSc Internet of Things to embark on further research by enrolling for PhD study affiliated with the research centres within the School of Computing and the School of Engineering. Computing related PhD studies can be perused in the areas of Pervasive Computing and Artificial Intelligence within the School of Computing whilst sensor technology, networking and RTOS research can be undertaken within the School of Computing.

Work placement / study abroad

N/A

Apply

Start dates

• September 2019
• January 2020

Fees and funding

Fees (total cost)

Northern Ireland & EU: £4,395

Scholarships, awards and prizes

N/A

Additional mandatory costs

None

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.

Contact

Admissions contact for entry requirements:
Helen Gibson
T: +44 (0)28 9036 6069
E: h.gibson@ulster.ac.uk

Centralised Admissions staff:
T: +44 (0)28 9036 6305
E: admissionsjn@ulster.ac.uk

For course specific enquiries:
Dr Shuai Zhang
T: +44 28 90366367
E: s.zhang@ulster.ac.uk

For more information visit

Faculty of Computing, Engineering and the Built Environment

School of Computing

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