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A futuristic connected world, where we increasingly interact with smart objects, on-body, in buildings, in cities and in distant, harsher environments, was once science fiction. This is now a reality: parts of buildings can now interact with each other, smart vehicles can be autonomously controlled and humans can interact with all these using smart phones and wearables.

This innovative Internet of Things (IoT) MSc programme will help you adapt to become one of the highly skilled and in-demand engineers who are able to fully exploit the potential that these technologies offer.

The Internet of Things (IoT) focuses on a vision of more connected, different, things (or digital devices) than in previous visions of the Internet. More ‘things’ are part of the physical world that connect to form smart environments. Humans are constantly increasing the frequency and range of ‘things’ (sensors, tags, cards, phones, actuator, wearables) they interact with in the world. Machine-to-machine interaction will allow more physical things to interact with other things without human intervention for scalability.

The MSc in IoT is designed to meet the demand for a new kind of IT specialist and skills, those who can:

  •    engineer new interactive products – things;
  •    acquire, fuse and process the data they collect from things;
  •    interact with, and interconnect these things as part of larger, more diverse, systems.

The School of Electronic Engineering and Computer Science draws on its strengths of highly rated R&D centres of excellence in core subject areas comprising Networks, Cognitive Science, Antennas together with interdisciplinary centres such as the Centre for Intelligent Sensing (CIS) and the Centre for Digital Music (C4DM). The MSc IoT is organised along 3 pathways: Data pathway, Engineering pathway, and the Intelligent Sensing pathway to enable students to focus on these different aspects of the course.

Structure

MSc Internet of Things is currently available for one year full-time study, two years part-time study, or two years full time with the second year spent in industry.

Semester 1 - 4 modules

  • Introduction to IOT
  • Enabling Communication Technologies for IOT
  • Applied Statistics

Plus one from the following:

  • Machine Learning
  • Big Data Processing
  • Data Mining

Semester 2 - 4 modules

  • Mobile Services
  • Data Mining
  • Security and Authenticaton
  • Data Analytics

One from the following:

  • The Semantic Web
  • Digital Media and Social Networks
  • Cloud Computing

Semester 3

  • Project (mandatory; must pass)

 Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.

Part-time Study

Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.

We regret that, due to complex timetabling constraints, we are not able to guarantee that lectures and labs for part time students will be limited to two days per week, neither do we currently support any evening classes. If you have specific enquiries about the timetabling of part time courses, please contact the MSc Administrator.

Please check the School website for further module information.

Learning and teaching

As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.

Teaching for modules includes a combination of lectures, seminars and a virtual learning environment. Each module provides 36 hours of contact time, supported by labs and directed further study.

Assessment

Students are assessed by a combination of coursework and exams. A few modules are assessed by coursework only. If a module is assessed by means of coursework alone, this is usually in the form of a research project or dissertation, and the tutor project supervisor offers guidance and support in the researching and writing of this piece of assessment.

Dissertation

You will also be assessed on a supervised 10,000- to 15,000-word dissertation.

Fees

Tuition fees for Home and EU students

2019/20 Academic Year

Full time £9,900

Part time £5,175

Tuition fees for International students

2019/20 Academic Year

Full time £21,250

Part time £10,625

Queen Mary bursaries and scholarships

We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.

Find out more about QMUL bursaries and scholarships.

Alternative sources of funding

Download our Postgraduate Funding Guide for detailed information about postgraduate funding options for Home/EU students.

Read more about alternative sources of funding for Home/EU studentsand for Overseas students.

Graduate employment

Queen Mary's Computer Science postgraduates go on to work in a wide variety of careers, mostly within IT and information services. The broad range of skills gained through programmes in this School has enabled postgraduates to move into careers such as:

  • Technical Analyst, Credit Suisse
  • Interactive Systems Developer, Sky
  • Software Developer, Accenture
  • Analyst Technical Associate, Bank of America Merrill Lynch
  • IT Contractor, FDM
  • Computer Analyst, ITRS Group
  • IT Developer, Qube Global Software
  • Team Manager, Bromley-by-Bow Centre
  • Computer Programmer, Rightmove
  • Computer Consultant, Mac Experts Ltd
  • Graduate Engineer, Ministry of Defence

Visit the MSc Internet of Things page on the Queen Mary University of London website for more details!

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