|Module Name||Urban Computing|
|ECTS Weighting ||5 ECTS|
|Semester Taught||Semester 1|
|Module Coordinator/s||Professor Melanie Bouroche|
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Describe the purpose, scope, and challenges associated with urban computing;
- Describe and reason about cyber-physical systems, including closing the feedback loop;
- Describe, compare and contrast existing approaches and associated challenges to data collection and management, including participatory and opportunistic sensing;
- Contrast, select and apply state of the art city-scale intelligent optimization techniques;
- Analyze, specify, design, implement and test a complete smart city application.
This module aims to provide both a theoretical and practical understanding of urban computing and associated cyber-physical concepts, principles, challenges and solutions. Urban computing is a process of acquisition, integration, analysis of and actuation upon, big and heterogeneous data generated by a diversity of sources in urban spaces, to improve the management of constrained urban resources, thereby enhancing the urban environment, human life quality, and city operation. Students will be exposed to the wide range of principles and challenges associated with urban computing, and how ubiquitous sensing, advanced data management and analytic models, and autonomic computing need to come together to address those. The module also aims to highlight some of the relevant ongoing research and innovation in the space taking place within Ireland and internationally.
Specific topics addressed in this module include:
- Gathering urban data, resources (environment/pollution/energy, human mobility and vehicular traffic, water) monitoring and data mining;
- Urban big data management and heterogeneous data management, knowledge fusion across heterogeneous data;
- Closing the feedback loop, model/analyze/plan/execute loop and associated requirements and challenges;
- Citizen engagement, including participatory and opportunistic sensing;
- Urban data visualization and decision support systems;
- Anomaly detection and event discovery in urban areas;
- Urban-scale ubiquitous/pervasive intelligent systems.
Teaching and Learning Methods
2 lectures per week.
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of Total||Week Set||Week Due|
|Examination||Take-Home Exam||L01, L02, L03, L04, L05||40%||N/A||N/A|
|Group Assignment||Smart City Case Study (report and presentation)||L01, L02, L03, L04, L05||15%||Week 1||Week 4|
|Individual Assignment||Sensor Data Collection||L01, L03, L05||10%||Week 1||Week 6|
|Individual Assignment||Sensor Data Gathering||L01, L03, L05||10%||Week 1||Week 8|
|Individual Assignment||Smart City Application||L05||25%||Week 1||Week 11|
Take-home exam (100%).
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||22 hours|
|Tutorial or seminar||0 hours|
|Independent Study (outside scheduled contact hours), broken down by:||94 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable)||40 hours|
|Completion of assessments (including examination, if applicable)||54 hours|
|Total Hours||116 hours|
Recommended Reading List
Prerequisite modules: N/A
Other/alternative non-module prerequisites: Students must be able to program a front and back end application.
Wednesdays, 13:00-14:00 – https://eu.bbcollab.com/guest/0e94d107b32649c5b81fdf04f37672f7
Fridays, 11:00-12:00 – https://eu.bbcollab.com/guest/fcfd86ccc0d948a1b380e507f643efec