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Internet of Things

Summary

The Internet of Things (IoT) 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 a discipline, it covers elements of Computing Science, Engineering, and Data Science.

The MSc in Internet of Things aims to train computing and engineering professionals to follow a career where they can apply leading-edge computing, engineering, sensor technology, networks and data science skills across a range of application domains. The course content has been informed by internationally leading research being conducted by the School of Computing and the School of Engineering. The delivery of the course is supported by a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities.

About

This intensive two-years plus 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.

Teaching and learning 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.

Entry 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.

Duration

Attendance

The part-time provision offers two points of entry in each academic year: September and January. For both points of entry, the degree will normally be completed in six semesters across three academic years.

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.

Careers or further progression

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 Ph.D. 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.

Further enquiries

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 Jose Santos
T: +44 (0)28 9036 6585
E: ja.santos@ulster.ac.uk

Subjects taught

Year one

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

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.

Year two

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.

Digital Signal Processing
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
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
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.

Application date

Application is through the University's online application system.

Enrolment and start dates

Start Dates:September 2019 & January 2020

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