Postgraduate Courses
IOTA
Internet of Things
- IOTA 5101Fog/Edge/Cloud Computing for IoT[3-0-0:3]BackgroundBackgrounds in communication and networking, computer systems, and distributed architectures are preferredDescriptionThis course introduces students to the latest research on fog computing, mobile edge computing, and cloud computing. How IoT applications can benefit from the computation and caching resources provided at different parts of the Internet will be discussed and the tradeoff among different options will be analyzed. Challenges in deploying IoT applications will be discussed and proposed solutions will be explored.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the characteristics and service requirements of IoT applications.
- 2.Understand the common cloud/edge/fog computing paradigms and system architectures.
- 3.Design and evaluate distributed file storage/caching system.
- 4.Demonstrate a good understanding of common distributed computing primitives like data/model parallelism and MapReduce.
- 5.Understand the basic machine learning principles and common distributed ML frameworks.
- 6.Design and evaluate mobile edge computing platforms for particular IoT applications.
- 7.Demonstrate a good understanding on the security/privacy issues in IoT and state-of-the-art solutions.
- 8.Demonstrate the ability to propose and solve research problems in IoT, and implement and evaluate IoT systems.
- IOTA 5102Localization for IoT[3-0-0:3]BackgroundBackgrounds in digital signal processing and statistical signal processingDescriptionThis course introduces students to the fundamentals and latest research on localization for Internet of Things, including GPS, indoor positioning based on ultra-wideband communications, and simultaneous localization and communications in 5G/6G, et al. Apart from electromagnetic waves, the localization based on acoustic signal will also be introduced.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate good understanding of the GPS, in terms of framework and signal processing.
- 2.Design and evaluate algorithms for triangularization and trilateration, including indoor and outdoor applications.
- 3.Implement and evaluate classic algorithms such as MUSIC and ESPRIT, for AoA, propagation delay, and Doppler estimation.
- 4.Apply and justify proper signal propagation models for specific localization applications.
- 5.Develop/apply proper localization systems for given applications.
- IOTA 5103Emerging Wireless Technologies for IoT[3-0-0:3]BackgroundGeneral knowledge of linear algebra, probabilities, and basic concepts in machine learning (ML) are essential, while exposure to communications, networking, and signal processing as well as experiences in ML implementations are desirable.DescriptionWith the proliferation of IoT devices and applications, successful delivery of latency-critical and energy-constrained services pose new challenges for the next-generation wireless communications. In this course, basic principles and techniques for designing wireless systems will first be covered, then followed by introduction to the state-of-the-art of emerging technologies, in which we will investigate sustainable, scalable and smart solutions for IoT.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Master basic knowledge of wireless communication and design principles of wireless communications systems for IoT
- 2.Master basic theory and classical algorithms on convex optimization
- 3.Demonstrate a good understanding of basic solution for large-scale optimization problems
- 4.Develop a comprehensive understanding of theory and state-of-the-art applications for self-sustained wireless communications in IoT
- 5.Develop a comprehensive understanding of theory and state-of-the-art applications for massive connectivity in IoT
- 6.Develop a comprehensive understanding of theory and state-of-the-art applications for AI endogenous communications design in IoT
- 7.Demonstrate the ability to identify challenges and solutions for a particular IoT research problem
- IOTA 5501Convex and Nonconvex Optimization[3-0-0:3]Exclusion(s)ELEC 5470, IEDA 5470BackgroundGood knowledge of calculus and linear algebra, and exposure to probability. Exposure to communications, signal processing, automatic control, and machine learning is helpful but not required. Capable of reading and dissecting scientific papers.DescriptionConvex and nonconvex optimization have experienced significant developments and have found widespread applications in various fields. This course covers theory and algorithms for convex and nonconvex optimization and their recent applications in communications, networking, signal processing, machine learning, etc.
- IOTA 6101Internet of Things Seminar I[0 credit]DescriptionA series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Exemplify a wide spectrum of IoT- related research topics.
- 2.Explain up-to-date IoT-related research knowledge.
- 3.Raise pertinent research questions on IoT-related topics.
- IOTA 6102Internet of Things Seminar II[0-1-0:1]DescriptionA series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Continuation of IOTA 6101. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Exemplify a wide spectrum of IoT- related research topics.
- 2.Explain up-to-date IoT-related research knowledge.
- 3.Raise pertinent research questions on IoT-related topics.
- IOTA 6900Independent Study[1-3 credit(s)]DescriptionAn independent research project carried out under the supervision of a faculty member on an Internet of Things topic.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Demonstrate an understanding of individual discipline knowledge related to the chosen topic.
- 2.Apply the learnt disciplinary knowledge in an integrated manner to the chosen topic.
- IOTA 6910Special Topics in Internet of Things[3-0-0:3]DescriptionAdvanced topics in Internet of Things(IoT): IoT in finance; IoT in manufacturing; IoT in healthcare; IoT in security and privacy; IoT in digital society; ethical issues in IoT and digital society ethics; modeling and optimization for IoT; signal processing for IoT. The course may be repeated for credit if different topics are studied.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the current research trend in IoT area
- 2.Demonstrate a good understanding of theory and application on particular IoT research topics
- 3.Demonstrate the ability to propose and solve research problems on particular IoT research topics
- IOTA 6990MPhil Thesis ResearchDescriptionMaster's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Internet of Things.
- 2.Communicate research findings effectively in written and oral presentations.
- IOTA 7990Doctoral Thesis ResearchDescriptionOriginal and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Internet of Things.
- 2.Communicate research findings effectively in written and oral presentations.
