Module Code: H9FEC
Long Title Fog and Edge Computing
Title Fog and Edge Computing
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Horacio Gonzalez-Velez
Module Author: DAVID TRACEY
Departments: School of Computing
Specifications of the qualifications and experience required of staff

PhD or MSc degree in Computer Science, Computing or Electronic Engineering with experience of Cloud technologies, distributed systems and Internet of Things.  

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe the key architectures and concepts in the Internet of Things and in Fog and Edge Computing
LO2 Describe the key architectures and applications in cloud computing, big data and how they relate to edge computing
LO3 Critically evaluate research publications on cloud services and edge computing and deliver oral presentations on selected ones.
LO4 Implement software using standard open-source cloud and edge computing software for data analytics.
LO5 Demonstrate in-depth knowledge of different types of hardware and software systems used in fog and edge computing
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Bachelor’s degree in Computer Science, Computing or Electronic Engineering.


Module Content & Assessment

Indicative Content
Introduction to the Internet of Things
Overview of issues in IoT and Architectural approaches to IoT and Edge Computing, RESTFul Architectural Style, P2P systems
Wireless and Mobile technologies
Introduction to wireless fundamentals to understand the importance of power, Role of a MAC layer, Routing techniques such as RPL, NB-IoT and 5G
Big Data Systems and IoT
Overview of Big Data systems, Use of NoSQL databases in IoT and Fog, Focus on time-series databases for IoT and Fog
Services for Distributed Systems
Redis and the role of caching, Kafka, Zookeeper, Kubernetes
Embedded Systems and Constrained Devices
Introduction to programming on IoT devices and constrained environments, Use of embedded OS such as Contiki or RIOT, Review of CoAP and MQTT
Data models for IoT
OMA LWM2M, IETF WoT Architecture
Cloud IoT Services
Review of IoT services from Amazon, Google and Microsoft
Fog and Edge Architectures
Fog and Edge Architectures, e.g. OpenFog Reference Architecture, Network Function Virtualization (NFV) and SDN (Software Defined Networking), Recommendations of the National Institute of Standards and Technology (NIST)
Middleware for Fog and Edge
Focus on data aggregation
Security Issues for IoT, Fog and Edge
NIST recommendations, Typical security attacks and relevance to IoT devices
Introduction to Data Analytics
Introduction to Data Analytics approaches for Edge, e.g Amazon Sagemaker for Anomaly Detection
Assessment Breakdown%
End of Module Assessment60.00%


Full Time

Assessment Type: Project % of total: 40
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
This will include a review of selected research papers and an implementation of a small example of a system using this research, using open-source software as required. This will be demonstrated to the lecturer, with marks awarded for the implementation, demonstration and understanding shown of the research performed.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
Questions covering a range of content to ensure the fundamental aspects of the course have been understood
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Attendance at lecture 24 Every Week 24.00
Tutorial Review of lecture content and work for assessment 24 Every Week 24.00
Independent Learning Time Study and work on assessment 202 Per Semester 16.83
Total Weekly Contact Hours 48.00

Module Resources

This module does not have any book resources
Recommended Article/Paper Resources
Supplementary Article/Paper Resources
This module does not have any other resources
Discussion Note: