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CE52003: Smart cities and the internet of things

[Page last updated: 16 August 2024]

Academic Year: 2024/25
Owning Department/School: Department of Chemical Engineering
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CWRI 100%
Assessment Detail:
  • Smart city project report (CWRI 100%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Learning Outcomes: Apply generative design principles to create innovative smart cities solutions.
Conceptualise the design and implementation of sensor networks and IoT solutions for robust data collection and assess their effectiveness for resolving targeted complex problems.
Implement AI/ML algorithms for data-driven decision support, and critically evaluate their impact.


Synopsis: Develop your knowledge of smart cities, focusing on the gathering of data through sensor networks and the 'Internet of Things' technology. You'll combine generative design, urban planning, and AI to create sustainable, efficient and smart solutions to complex problems. You'll also explore future trends and technological innovations to learn how to develop smarter, more connected and sustainable cities.

Aims: Develop a deep understanding of smart cities, focusing on the gathering of data through internet of things technology, as well as harnessing the power of generative design and AI to create sustainable, efficient and smart solutions for complex problems. Through a combination of theory and hands-on practical experience, you will gain the skills and knowledge necessary to play a pivotal role in the development of smarter, more connected and more sustainable cities.

Skills: Education for sustainable development, generative design, data driven decision making.

Content: Introduction to Smart Cities
IoT and sensor technologies
Data Analytics and Visualization
AI/ML approaches in Smart Cities
Generative design and urban planning
Sustainable infrastructure such as sustainable energy solutions, air pollution, waste management, water and wastewater systems
Future trends and technology innovation

Course availability:

CE52003 is a Must Pass Unit on the following courses:

Department of Electronic & Electrical Engineering
  • TEEE-AFM22 : MSc Artificial Intelligence for Engineering and Design
  • TEEE-AWM22 : MSc Artificial Intelligence for Engineering and Design

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.