| Module Code: |
H8IOTRTA |
|
Long Title
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IoT Real Time Analytics
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|
Title
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IoT Real Time Analytics
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| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Coordinator: |
Dominic Carr |
| Module Author: |
Dominic Carr |
| Departments: |
School of Computing
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| Specifications of the qualifications and experience required of staff |
Master’s degree or PhD in a computing or cognate discipline. May have industry experience also.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Design and implement an IoT system which produces streams of real-time data |
| LO2 |
Select and implement appropriate algorithms for context sensitive real-time analytical processing |
| LO3 |
Utilize industry standard analytics engines |
| LO4 |
Demonstrate proficiency in IoT device level, and server side, programming |
| Dependencies |
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).
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| No recommendations listed |
Co-requisite Modules
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| No Co-requisite modules listed |
| Entry requirements |
Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
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Module Content & Assessment
| Indicative Content |
|
Introduction to IoT
What is it? What is it useful for? Why does it present an analytics challenge?What are the ethical implications?
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Data Stream algorithms and their applications 1
Sampling, filtering, correlation, frequency analysis, anomaly / tampering testing,
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Data Stream algorithms and their applications 2
Prediction, clustering, event triggering, merging streams, stream correlation e.g. necessity to view temperature, humidity, and status of windows as conjoined
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IoT Development
working with IoT development boards, attaching sensors, writing programs using sensors, pre-processing the data at the node level, inter-node communication
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IoT Development 2
working with IoT development boards, attaching sensors, writing programs using sensors, pre-processing the data at the node level, inter-node communication
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Publishing Sensor Data
How to publish to WWW e.g. NodeRED, IFTTT, Google Cloud, Dweet, AWS IoT or other platforms such as ThingWorx
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Publishing Sensor Data 2
Detailed work through of sensor to cloud with an industry standard platform
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Utilizing Analytics Dashboards
Such as AWS IoT conditional triggers to actuate a response to identified events, thingworx, IFTTT, Dweet triggers
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Building your own Analytics suite
Create a REST web service to receive and store data, program triggers to handle events, allow for the customization and creation of other triggers, link to actuation e.g. reprogram IoT device, send message to user (Twilio perhaps)
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Implementation of Data Stream algorithms in our system
Implementation of Data Stream algorithms in our system
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Utilization
Utilization of existing systems such as Apache Spark, Flink, Pulsar, Heron, Akka, Samza or Apache Storm.
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| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
Yes |
Assessment Description:
There will be formative assessment throughout the module which will guide the students for other assessments. They will receive feedback on in class labs and online submissions. |
|
| Assessment Type: |
Assignment |
% of total: |
50 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
The assignment will assess students ability to implement an IoT system which produces a stream of real-time sensory observations. This will be done with physical hardware such a Raspberry PI and a sensor kit publishing to a service such as Dweet or AWS IoT.
The learner will have to select and implement appropriate algorithms for context sensitive real-time analytical processing, additionally they must utilize Utilize industry standard analytics engines to analyse the data produced from real sensory streams (may also use repositories of sensor data through ‘playback’) |
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| Assessment Type: |
Project |
% of total: |
50 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
Develop a project which builds upon the functionality described in the assignments using a selection of student developed and industry standard hardware and software |
|
| No End of Module 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.
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Reassessment Description
The repeat strategy for this module is a project. Learners will be afforded an opportunity to repeat the project at specified times throughout the year and all learning outcomes will be assessed in the repeat project.
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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 |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
| Tutorial |
Other hours (Practical/Tutorial) |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning (hours) |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
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Ellis, Byron.. (2014), Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, Wiley.
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DeLoach, Don.. (2017), The Future of IoT: Leveraging the Shift to a Data Centric World, Wiley.
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Kolozali, Sefki, et al.. (2014), A knowledge-based approach for real-time iot data stream annotation and processing, Internet of Things (iThings),.
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Tönjes, Ralf, et al.. (2014), Real time iot stream processing and large-scale data analytics for smart city applications, poster session, European Conference on Networks and Communications.
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Gubbi, Jayavardhana, et al.. (2013), Internet of Things (IoT): A vision, architectural elements, and future directions, 7 (, " Future generation computer systems 29.
| | Supplementary Book Resources |
|---|
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Hwang, H.. (2017), Big-Data Analytics for Cloud, IoT and Cognitive Computing.
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Slama, D,. (2017), Enterprise IoT: Strategies and Best Practices for Connected Products and Services.
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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