BIG DATA AND IOT

SHE Level 4
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code MHI226694
Module Leader Yan Zhang
School School of Computing, Engineering and Built Environment
Subject Computing
Trimester
  • A (September start)

Summary of Content

In this module students will build knowledge and practical skills required to design, implement and deploy computer systems that include geographically-distributed devices. These types of systems have the potential to generate large volumes of data, have heterogeneous data-sources, need rapid responsiveness or support human-interaction. The module focusses on applications of the Internet of Things (IoT) and the associated technologies that are required to support the challenges of building such systems in a scalable and secure manner. Some of these challenges require technology solutions that fall within the sphere of what has come to be known as "Big Data" systems that employ appropriate data-engineering techniques. The module learning paths will be driven from the standpoint of case-studies for real-world problems and applications, with an exploration of the wide range of associated data. Students will explore secure IoT architectural patterns and industry-standard data-engineering techniques for realising such architectures. These techniques include appropriate aspects of data-analysis and human interaction. Students will gain experience in implement and deploying these techniques, by designing and building appropriate solutions, utilising industry-standard technologies. On completion of this module, students will be able to select, justify, implement and deploy appropriate architectural solutions for a range of IoT applications that include a representative range of real-world data and interaction.

Syllabus

-Real-world problems/applications as drivers for technology solutions. -IoT reference architectures and specific industry-vendor solutions. -IoT 'Security by Design' as a fundamental driver for IoT architectures. -IoT Connectivity, communication and messaging. -Understanding the heterogeneous nature of the data. -Ingesting, pre-processing and storing data and understanding trade-offs such as: performance, reliability, scalability and cost. -Analysing data: edge-processing, batch processing, real-time & interactive systems. -Cloud-based technologies and Cloud services to facilitate rapid-application building. -Data pipelines and orchestration of workflows. -Dashboards and visualisations. -Human interactive systems. Laboratory content will evolve as new technology opportunities become available. Examples of possible laboratory content are: -Connecting and securing an IoT device to a representative Cloud architecture. -IoT device sensing, control and human interaction. -Messaging between Device and Cloud. -Building Device applications and Cloud applications. -Using Cloud services including database services. -Implement analytics to detect patterns and anomalies or trigger actions based on rules. -Building dashboards. -Implementing human-interactive applications (such as voice-interactive).

Learning Outcomes

On successful completion of the module the student should be able to:1. Demonstrate a detailed understanding of the architectures and techniques used to create IoT systems that include heterogeneous data sources.2. Demonstrate an understanding of how to map representative IoT problems into secure IoT designs that utilise industry-standard technologies.3. Select and justify appropriate processing/analysis methods that are relevant to a representative set of data sources.4. Develop and test solutions that implement a representative range of IoT systems, using appropriate software and hardware technologies.

Teaching / Learning Strategy

The learning and teaching strategy for this module has been informed by the university's 'Strategy for Learning' design principles. The course material is introduced through lectures and laboratory/tutorial sessions that draw upon and extend the lecture material to deepen students' knowledge. The laboratory/tutorial sessions are designed as a set of formative exercises and a substantial summative exercise spanning several weeks. The formative exercises introduce a range of methodologies that allow students to gain confidence and build knowledge of the range of solutions that can be applied to particular problems. Summative exercises provide experience in real-world problem-solving and challenges students to demonstrate analytical, design & implementation skills and a capacity for divergent thinking. Tutorials (held with laboratory space) will be used to help explain and elaborate on both the lecture material and the laboratory exercises; these will include a range of case studies and live implementation examples that bring a global perspective to the subject matter. During all lab and tutorial sessions students receive formative feedback on their performance. Summative feedback and grades are also provided for the coursework assignments undertaken as part of the module, using GCULearn. GCU Learn is also used to provide the students with module specific Forums and Wikis to stimulate student and lecturer interaction outside of the normal lecture, laboratory and tutorial sessions. Flexible learning is encouraged and supported. All teaching materials and self-testing exercises are made available on GCULearn and links are provided to external materials such as podcasts, MOOCs, videos and relevant literature. All the computing resources used for laboratories are made available either by virtual machine images or low-resource-utilisation applications (made available to students for use on their own computers) or online using industry standard cloud computing services provided by major global computing industry vendors. Due to the availability of all material and computing facilities online, the module is suitable for use where Flexible and Distributed Learning (FDL) is required. Students can access complete laboratory facilities in their own time.

Indicative Reading

Internet-of-Things (IoT) Systems Architectures, Algorithms, Methodologies Serpanos, Dimitrios. author.; Wolf, Marilyn. author. Springer International Publishing : Imprint: Springer Availability: Online GCU Library 1st edition 2018. Analytics for the Internet of things (IoT) : intelligent analytics for your intelligent devices Minteer, Andrew, author. Publisher: Birmingham, England ; Mumbai, India : Packt Availability: Online in GCU Library 2017 IoT Security Foundation best practise Publications: <https://www.iotsecurityfoundation.org/best-practice-guidelines/> Node-Red Low-code programming for event-driven applications: <https://nodered.org/> Representative industry IoT solutions: -AWS IoT: <https://aws.amazon.com/iot/> -Watson IoT: <https://cloud.ibm.com/docs/services/IoT?topic=iot-platform-getting-started> -Google IoT: <https://cloud.google.com/iot/docs> -ThingWorx Industrial IoT Solutions Platform: <https://www.ptc.com/en/products/iiot/thingworx-platform> Voice Driven Web Apps: Introduction to the Web Speech API: <https://developers.google.com/web/updates/2013/01/Voice-Driven-Web-Apps-Introduction-to-the-Web-Speech-API> IBM Watson Assistant: <https://www.ibm.com/cloud/watson-assistant/> Alexa Voice Service: <https://developer.amazon.com/en-GB/alexa/alexa-voice-service>

Transferrable Skills

Specialist knowledge and application (A1) Critical thinking and problem solving (D1) Communication skills, written, oral and listening (D14) Effective information retrieval and research skills (D10)

Module Structure

Activity Total Hours
Practicals (FT) 36.00
Independent Learning (FT) 120.00
Assessment (FT) 20.00
Lectures (FT) 12.00
Tutorials (FT) 12.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 02 n/a 50.00 35% Practical coursework exercise
Course Work 01 n/a 50.00 35% Class Based Test