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IOTA

Internet of Things

IOTA 5001
Social and Web Computing
[3-0-0:3]
Description
This course will introduce students to the fundamentals of Social and Web Computing, as well as providing detailed coverage of recent research in this space. It will consist of two major parts. First, students will learn about fundamental social computing theories, alongside computational methodologies that can be used to understand human interactions online. Second, students will be exposed to a range of recent applied research that has employed these methodologies. There will be an empirical focus in the course, and students will be exposed to a range of measurement research capturing how social and web systems work in-the-wild. Students will learn about social network analysis and relevant APIs, alongside related aspects of the Web, user privacy and online advertisement.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a clear understanding of how graph theoretical principles can model social interactions.
- 2.
  Demonstrate a clear understanding of how online communities form and how they can be identified from social graph structures.
- 3.
  Demonstrate a clear understanding of how information propagates (e.g. virality) through communities and what can accelerate or impede its transmission.
- 4.
  Demonstrate a clear understanding of how social graphs underpin various other online domains, such as video sharing and the advertisement industry.
- 5.
  Demonstrate a clear understanding of the associated security and privacy issues that arise from data retention within social and web platforms, as well as how these risks can be mitigated.
- 6.
  Demonstrate an appreciation of how social computing platforms can interact with and underpin other domains such as IoT.
IOTA 5002
Data-driven Modeling: Learning from Sensor Data
[3-0-0:3]
Background
Basic knowledge of linear algebra, numerical analysis, signal processing, and machine learning is encouraged.
Description
Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the basic concept of data-driven modeling.
- 2.
  Understand how to analyze and explain data towards enhancing their understanding and ability to model physical, biological, and engineering systems.
- 3.
  Understand the applications of the data-driven modeling for diverse disciplines.
- 4.
  Acquire hand-on skills for using contemporary machine learning/data analysis tools for constructing models, extracting patterns, and making predictions.
- 5.
  Demonstrate research ability on mini-project of data-driven modeling.
IOTA 5003
Wireless Connectivity for Mobile Autonomous Things
[3-0-0:3]
Co-list with
INTR 5220
Exclusion(s)
INTR 5220
Description
This course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Grasp the fundamentals of digital communications.
- 2.
  Understand the requirements of connected autonomous things.
- 3.
  Learn the dynamics associated with mobile autonomous things.
- 4.
  Analyze and evaluate wireless connectivity solutions under dynamics.
- 5.
  Learn the scalability and complexity issues associated with mobile autonomous things.
- 6.
  Analyze and evaluate wireless connectivity solutions addressing scalability and complexity issues.
- 7.
  Learn the heterogeneity associated with mobile autonomous things.
- 8.
  Analyze and evaluate wireless connectivity solutions accommodating heterogeneity.
IOTA 5004
Introduction to Physics-informed Machine Learning
[3-0-0:3]
Background
Basic knowledge of linear algebra, numerical analysis, engineering, and machine learning is encouraged.
Description
Machine learning has emerged as a powerful tool for tackling various problems in engineering and science. Typically via the use of large volume of data, deep neural nets can be trained for this end. However, for engineering and science problems, big data is not enough, and is not always available. This course will introduce the newly emerged paradigm and research trend called “physics-informed machine learning”, where physical laws or physical prior knowledge can be enforced into the architecture of machine learning models, to boost the training and promote the trained models to be more physically consistent and generalizable. At the end of the course, students are expected to understand the principles and methods of physics-informed machine learning, and to have hands-on implementations with Python.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the basic concept of physics-informed machine learning.
- 2.
  Understand how to analyze and explain data towards enhancing their understanding and ability to model physical, biological, and engineering systems.
- 3.
  Understand the applications of physics-informed machine learning for diverse disciplines.
- 4.
  Acquire hand-on skills for using contemporary machine learning/data analysis tools for constructing models, extracting patterns, and making predictions.
- 5.
  Demonstrate research ability on mini-project of physics-informed machine learning.
IOTA 5005
Introduction to Energy Harvesting Technology
[3-0-0:3]
Previous Course Code(s)
IOTA 6910C
Background
Fundamentals of dynamics and vibration; basic knowledge of numerical analysis and circuit analysis.
Description
The distributed power supply for millions and billions of remote & off-grid electronics (such as wireless IOT node sensors) is challenging. Harvesting sustainable energy from the ambient environment provides the possibility of designing battery-free devices. This course will introduce the vibration energy harvesting technology developed in the past two decades to students. As the fundamentals, commonly used energy transduction mechanisms will be first introduced to the students. The students will also learn various modeling methods, including lumped parameter modeling, equivalent circuit modeling, and finite element modeling.
IOTA 5006
Distributed Systems: Concepts and Applications
[3-0-0:3]
Description
This course teaches the design and implementation of efficient, scalable, and fault-tolerant distributed systems. Topics include models for distributed communication and computing, synchronization, and consensus algorithms. The course will also cover relevant applications, including platforms for distributed Machine Learning such as Ray, and large-scale data and stream processing systems such as Apache Flink and Google Dataflow.
IOTA 5101
Fog/Edge/Cloud Computing for IoT
[3-0-0:3]
Background
Backgrounds in communication and networking, computer systems, and distributed architectures are preferred
Description
This 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 5102
Fundamentals of Localization Technologies
[3-0-0:3]
Background
Backgrounds in digital signal processing and statistical signal processing
Description
This course introduces students to the fundamentals and latest research on localization technologies, including GPS, indoor positioning based on ultra-wideband communications, and simultaneous localization and communications in 5G/6G, et al. Apart from electromagnetic waves, localization based on acoustic signals will also be introduced. The course will provide students with the fundamental knowledge required to understand, analyze and develop localization technologies.
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 5103
Emerging Wireless Technologies for IoT
[3-0-0:3]
Background
General 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.
Description
With 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 knowledge of wireless communications will first be introduced, and optimization/AI assisted techniques to combat these challenges will also be briefly covered, following introduction to the state-of-the-art beyond-5G (B5G) technologies, in which we will investigate sustainable, scalable and AI endogenous wireless solutions for IoT.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a good understanding of basic knowledge of wireless communication and design principles of wireless communications systems for IoT.
- 2.
  Demonstrate a general understanding of application of optimization to wireless technology design.
- 3.
  Demonstrate a general understanding of application of deep learning to wireless technology design.
- 4.
  Develop a good understanding of design principles and state-of-the-art technologies for self-sustained wireless communications in IoT.
- 5.
  Develop a good understanding of design principles and state-of-the-art technologies for scalable wireless communications in IoT.
- 6.
  Develop a good understanding of design principles and state-of-the-art technologies for AI endogenous communications design in IoT.
- 7.
  Demonstrate the ability to identify challenges and solutions for a particular IoT research problem
IOTA 5104
Fundamentals of Discrete-Time Signal Processing
[3-0-0:3]
Background
Students should understand the basics of calculus, probability and linear algebra.
Description
This course introduces students to the fundamentals of discrete-time signal processing, for both linear time invariant (LTI) and non-LTI systems. For LTI systems, the topics include the sampling theorem, Fourier transform, convolution, and spectrum analysis, which lays the foundation for OFDM in wireless communications. Advanced topics will also be covered for time-variant systems, such as Heisenberg transform, Wigner distribution and Fractional Fourier transform, and their applications in radar, sonar and the OTFS modulation in wireless communications.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate good understanding of the fundamentals of DSP for LTI systems.
- 2.
  Demonstrate good understanding of delay-Doppler domain signal modeling and processing.
- 3.
  Implement classic algorithms such as FFT.
- 4.
  Design and evaluate DSP algorithms.
- 5.
  Develop/apply proper DSP algorithms for given applications.
IOTA 5105
Fundamentals of Wireless Communications
[3-0-0:3]
Background
Students are expected to have a general background in EE-related subjects with good knowledge of probability theory and linear algebra, and preferably with some basic understanding of digital signal processing.
Description
This course lays theoretical foundation for students with general EE background to pursue research advances involving wireless communication-based systems, e.g., 4G/5G mobile communication, IoT and WLAN. In this course, concepts of wireless channels, its modelling as well as channel capacity for point-to-point/multi-user/MMO communications will be systematically introduced. Along with understanding of these theories, the principles and state-of-the-art technologies to combat fading and interference including general diversity techniques, OFDM, and multiple access schemes will be conveyed. Finally, a few advanced topics for 5G-and beyond (B5G) communication system design will be briefly introduced, such as massive MIMO and reconfigurable intelligent surface (RIS).
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate good understanding of the basic properties of wireless channels.
- 2.
  Demonstrate good understanding of the challenges in designing wireless communication systems.
- 3.
  Learn important technologies developed to combat these challenges (e.g., MIMO, OFDM and NOMA).
- 4.
  Learn how to evaluate the performance of wireless communication systems using relevant metrics.
- 5.
  Design and implement (via simulations) classical signal-processing schemes for given problems.
IOTA 5106
Introduction to Communication Networks
[3-0-0:3]
Background
General knowledge of linear algebra and probability.
Description
This course introduces students to different types of communication networks, with a focus on Internet technologies. Topics covered include error control, flow control, medium access control, routing, congestion control, packet scheduling, queueing theory, and network optimization. The course will provide students with the fundamental knowledge required to understand, analyze, and optimize the performance of communication networks. It is suitable for students who do not have an existing background in communication networks.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a good understanding of the architecture and the principles of operation in communication networks.
- 2.
  Demonstrate a good understanding of the protocols, including error control, flow control, medium access control, routing, congestion control, and packet scheduling.
- 3.
  Master the basic theory and models of queueing theory, Markov chains, and queueing networks.
- 4.
  Master the basic theory and models of linear programming, convex optimization, and network optimization.
- 5.
  Master the basic theory and models of multimedia networks and fluid-flow analysis.
- 6.
  Demonstrate the ability to identify problems and solutions in related research fields.
IOTA 5107
Advanced Networked Systems for AI and Multimedia Systems
[3-0-0:3]
Background
Internet networking
Description
This course focuses on relevant current research topics in Networked Systems such as Networking for AI, Multimedia Communication, Data-Center Networks, Software-Defined Networking, Dataplane Programmability, Information-Centric Networking, Quantum Internet Communication, Privacy Preserving Communication, Constrained (IoT) Networks. This course will provide a structured introduction to the state-of-the-art and recent research in the field of networked systems. It will further introduce students to recent development efforts in the academic community as well as in Internet standardization.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a good understanding of relevant advanced networking concepts.
- 2.
  Demonstrate a good understanding of current research challenges in networking.
- 3.
  Use the concepts and technologies presented in the course for building networked systems.
- 4.
  Conduct independent research on relevant topics in networked systems.
- 5.
  Present insights and their own research results on a scientific level.
IOTA 5108
Incremental Learning and Adaptive Signal Processing
[3-0-0:3]
Co-list with
INTR 5320
Exclusion(s)
INTR 5320
Description
This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand the basic concepts and procedures of incremental learning.
- 2.
  Learn how to derive the optimal linear solutions for many applications such as channel equalization, beamforming, etc.
- 3.
  Understand common incremental algorithms such as Kalman filter, least mean squares (LMS), recursive least squares (RLS), etc.
- 4.
  Learn how to solve classification problems with incremental learning techniques.
- 5.
  Learn non-linear learning techniques such as neural networks.
- 6.
  Understand the parameter learning process and learn typical algorithms for parameter learning.
IOTA 5109
Wireless Sensor and Actuator Networks Toward Swarm Intelligence
[2-1-0:3]
Description
This course teaches the basic concepts of wireless sensor and actuator networks (WSANs), and how swarm intelligence is applied over WSAN. The course content includes the typical architecture of the hardware WSAN devices, the general communication mechanism and examples of applications using WSANs to realize swarm intelligence.
IOTA 5202
Efficient Machine Learning for Resource Constrained Environments
[3-0-0:3]
Background
General knowledge of artificial intelligence, deep learning.
Description
This course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance.
IOTA 5501
Convex and Nonconvex Optimization I
[3-0-0:3]
Background
Good 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.
Description
This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Master fundamental convex and nonconvex optimization theory.
- 2.
  Master fundamental convex and nonconvex optimization algorithms.
- 3.
  Understand emerging optimization applications in machine learning, signal processing, communications, etc.
- 4.
  Have skills to recognize, formulate, and solve optimization problems.
- 5.
  Apply optimization theory and methods in frontier research.
IOTA 5502
Convex and Nonconvex Optimization II
[3-0-0:3]
Background
Good knowledge of calculus, linear algebra, and fundamental optimization theory and algorithms, 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.
Description
This course covers advanced theory, algorithms, and applications for convex and nonconvex optimization, including: subgradient methods, localization methods, decomposition methods, proximal methods, alternating direction methods of multipliers, conjugate direction methods, successive approximation methods, convex-cardinality problems, low-rank optimization problems, neural networks, semidefinite programming and relaxation, robust optimization, discrete optimization, stochastic optimization, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Master advanced convex and nonconvex optimization theory.
- 2.
  Master advanced convex and nonconvex optimization algorithms.
- 3.
  Understand emerging optimization applications in machine learning, signal processing, communications, etc.
- 4.
  Recognize, formulate, and solve optimization problems.
- 5.
  Apply optimization theory and methods in frontier research.
IOTA 5503
Cybersecurity and Privacy
[3-0-0:3]
Previous Course Code(s)
IOTA 6910A
Description
This course covers fundamental aspects of cybersecurity and privacy. The course will equip students with the ability to understand and analyze security technologies. This course will then explain how these technologies are used and deployed in practical real-world environments, before exploring how recent attacks have discovered new vulnerabilities. The course will emphasize the value of empirical observations and give students insight into how these vulnerabilities can be measured in-the-wild. This course is ideal for students who are interested in understanding cybersecurity risks in a broad range of technologies.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Demonstrate a clear understanding of the fundamentals of cryptography.
- 2.
  Demonstrate a clear understanding of how cryptographic technologies are used in practical systems.
- 3.
  Demonstrate the ability to formally evaluate secure protocols.
- 4.
  Demonstrate a clear understanding of the challenges faced in deploying real security solutions.
- 5.
  Demonstrate a clear understanding of multiple applied security use cases, including in the field of network and information security.
- 6.
  Demonstrate a clear understanding of the associated security and privacy issues that arise specifically in information systems.
IOTA 5504
Approximate Computing - Introduction to Numerical Analysis
[3-0-0:3]
Background
Fundamentals of calculus, linear algebra, differential equations, and programming (Matlab).
Description
This course is an introduction to the algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis. The course will help students develop a basic understanding of numerical algorithms and the skills to implement algorithms to solve mathematical problems on the computer.
IOTA 5505
Statistics for Inference, Learning and Data Processing
[3-0-0:3]
Background
Basic knowledge of calculus and linear algebra, and any undergraduate first course in probability and statistics (for non-mathematical majors).
Description
This course introduces the fundamentals and advanced topics of statistics from its modeling, analysis and inference that covers multivariate distribution along with dimension-reduction techniques, concentration inequalities, classification/clustering, to applications in statistical signal processing, including estimation theory and methods, detection theory and methods, and advanced algorithms such as Markov Chain Monte Carlo (MCMC) and expectation maximization (EM), etc.
IOTA 6101
Internet of Things Seminar I
[0 credit]
Description
A 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 6102
Internet of Things Seminar II
[0-1-0:1]
Description
A 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 6900
Independent Study
[1-3 credit(s)]
Description
An 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 6910
Special Topics in Internet of Things
[3-0-0:3]
Description
Advanced 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 6990
MPhil Thesis Research
Description
Master'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 7990
Doctoral Thesis Research
Description
Original 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.

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