Embedded Systems and Internet of Things MSc

Program Overview

Through a blend of compulsory and optional modules, students gain specialized technical knowledge and practical skills in tools, techniques, networking, sensors, security, and computer programming.

Program Outline

Taught by world-leading academics and researchers, the program focuses on the fast-changing area of embedded digital systems for communication and control.

Objectives:

• Equip graduates with a deep understanding of the design and analysis of embedded and M2M systems for the Internet of Things (IoT).
• Provide specialized technical knowledge and skills necessary for success in this dynamic industry.
• Develop practical skills through a unique blend of modules that complement each other:
• Tools, techniques, and design of ES-IoT and subsystems.
• Scientific and engineering principles and practices of Computing Science and Electronic Engineering.
• Networking and communication systems.
• Sensors and security.
• Computer programming.

Outline: The ES-IoT MSc curriculum comprises both compulsory and optional modules, offering students a comprehensive and flexible learning experience.

Compulsory Modules:

• Real Time Embedded Systems (20 credits):
• Introduction to real-time embedded systems.
• Real-time operating systems and scheduling algorithms.
• Interrupts, device drivers, and memory management.
• Real-time programming techniques and tools.
• Case studies of real-world real-time embedded systems.
• Reconfigurable Hardware Design (20 credits):
• Introduction to reconfigurable hardware platforms (FPGAs).
• Design methodologies for reconfigurable hardware.
• High-level synthesis (HLS) tools and techniques.
• Implementation of digital signal processing (DSP) algorithms on FPGAs.
• Case studies of reconfigurable hardware applications.
• M2M Technology Internet of Things (20 credits):
• Introduction to the Internet of Things (IoT) and M2M technology.
• IoT architectures, protocols, and standards.
• IoT security considerations and privacy implications.
• Sensor networks and data acquisition in the IoT.
• Case studies of M2M and IoT applications.
• Individual Project (60 credits):
• Conduct original research in a chosen area related to ES-IoT.
• Apply design, analysis, and implementation skills gained throughout the program.
• Develop critical thinking, problem-solving, and research communication abilities.
• Wired and Wireless Communication Networks and Security (20 credits):
• Introduction to wired and wireless communication networks.
• Network architectures, protocols, and standards.
• Communication network security principles and technologies.
• Wireless sensor networks and network management in the IoT.
• Case studies of communication network technologies and applications.
• Machine Learning for Engineering Applications (20 credits):
• Introduction to machine learning algorithms in engineering applications.
• Machine learning implementations for data acquisition, analysis, and control.
• Design and application of machine learning-based solutions for complex engineering problems.
• Ethical considerations and responsible use of machine learning in engineering.
• Research Skills and Development for Engineers (20 credits):
• Develop advanced research methodologies specific to engineering disciplines.
• Enhance critical analysis, evaluation, and research communication ability.
• Gain skills in project planning, literature review, and research ethics.

Optional Modules (Subject to availability):

• Wireless Sensor Networks (10 credits):
• Cloud Computing and Big Data Analytics (10 credits):
• Advanced Topics in Embedded Systems Design (10 credits):

Assessment: Students will be assessed through various methods throughout the ES-IoT MSc program, depending on individual modules. Primary assessment methods may include:

• Written examinations: Testing theoretical understanding and knowledge gained in lectures and coursework.
• Written exercises: Assessing problem-solving skills and applying theoretical concepts to specific tasks or scenarios.
• Oral examinations: Evaluating presentation and explanation skills in a one-on-one or group setting.
• Oral presentations: Assessing research project findings or communicating technical information effectively to a given audience.
The specific criteria used in each assessment vary based on the module's learning outcomes and objectives.

Teaching:

Teaching Methods:

• Lectures: Delivering core theoretical concepts and fundamental knowledge to large groups of students.
• Seminars: Presenting expert guest speakers or student-led research presentations, fostering interactive engagement with diverse perspectives in the field.
• Practical Lab Sessions: Engaging in hands-on experiments and completing research tasks in specialized facilities, applying theoretical understanding to real-world scenarios.
• Research-led Projects: Conducting individual or group research culminating in a capstone project, developing independent research skills and critical analysis.
• Group Work: Fostering collaboration, communication, and problem-solving abilities in team