MSc Internet of Things draft

Program Overview

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This course has been designed to give you advanced theoretical and specialist practical knowledge of progressive and emerging topics in Internet of Things to design and engineer the next generation of connected devices and systems.

You’ll learn to apply key concepts in Internet of Things such as hardware/software co-design; programming and interfacing of embedded processors; IoT hardware components; and Networking, and how to acquire, fuse and analyse data collected from sensors, using AI and data science-driven approaches.

You will also plan, manage and deliver significant Internet of Things-based projects, resulting in a high-quality research output through a dissertation. You’ll investigate and develop Internet of Things projects within the confines of the ethical, social and professional contexts of the next generation of smart devices and networked systems.

From this course, you’ll come away with the professional skills needed for a senior career in the IT industry.

This course is also available as a two-year masters with an

industrial placement year

. The embedded industrial placement offers a unique opportunity to apply the skills learnt on real-life projects.

Program Outline

Knowledge

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of emerging technologies in Internet of Things and the next generation of connected devices.

Knowledge and understanding of contemporary tools and technologies to produce solutions relevant to the domain of Internet of Things to meet a set of agreed requirements.

Thinking skills

Demonstrate innovative independent thinking, critical investigation, and analytical skills in the real-world problems related to Internet of Things.

Subject-based practical skills

Design, analyse, synthesise and evaluate hardware and software components of Internet of Things used in the diverse fields of medical, environmental monitoring and industrial domains.

Implementation of appropriate wired and wireless networks for connected devices and systems.

Hands on-experience in sensor system design using state-of-the art embedded programming languages.

Skills for life and work (general skills)

Structure and communicate ideas effectively, both orally and in writing.

Work professionally as an individual and as a team player to develop creative solutions to problems.

In addition, the industrial placement will provide opportunities to apply key technical knowledge and skills learn in the taught modules, enhance their communication and interpersonal skills and improve their employment potential.

You will develop a thorough critical understanding through our specialist technical laboratories and gain valuable hands-on experience in a wide range of emerging technologies in hardware and software components of Internet of Things.

Our dedicated and specialist staff will support you throughout the course and you will be assigned a personal tutor, a course tutor and module tutors to oversee your progress. During the completion of the dissertation on a cutting-edge IoT topic of your choice, you will obtain an excellent experience for a future career in IoT.

When not attending timetabled lectures you will be expected to continue learning independently through self-study.

Each semester you will spend around 600 hours of timetabled learning and teaching activities. These may be lectures, workshops, seminars and individual and group tutorials. Contact hours may vary depending on each module.

The approximate percentages for this course are: scheduled teaching: 48 hours; workshops/practicals: 72 hours; guided independent study: 480 hours, per semester.

Your individualised timetable is normally available to students within 72 hours of enrolment. Whilst we make every effort to ensure timetables are as student-friendly as possible, scheduled teaching can take place on any day of the week between 9am and 5pm. Timetables for part-time students will depend on the modules selected.

The learning outcomes of the programme are assessed through laboratory session portfolios, group and individual coursework, research dissertation, presentations, reports and in-class time constraint assessments.

Coursework can take a variety of forms, including laboratory work, data analysis and oral presentations. The Research Dissertation is assessed on a final written report, a poster presentation and a practical component.

You will receive detailed feedback on all assessment outlining strengths and areas of improvement. The approximate percentages for this course are more than 90% coursework, and less than 10% time constraint assessments. The module specifications specify the mode of assessment for each module.