INTERNET OF THINGS - 2022/3
Module code: EEEM048
Expected prior/parallel learning: Basic knowledge of hardware systems and module EEE3013 (Object Oriented Programming and C++), or equivalent knowledge of C++ or of Java programming.
Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical word with the cyber-world. The number of devices connected to the Internet already exceeds the number of people on earth and is estimated to grow to 50 billion devices by 2020. The resulting framework, which is known as the Internet of Things (IoT), incorporates a number of technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context-awareness and distributed systems. This module will provide an overview of the key concepts and enabling technologies for the Internet of Things, including: Wireless Sensor Networks, Platforms (Hardware, Software), Machine-to-Machine communications (M2M), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms.
Computer Science and Electronic Eng
SHOJAFAR Mohammad (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I160
Module cap (Maximum number of students): 93
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 11
Tutorial Hours: 6
Laboratory Hours: 8
Guided Learning: 10
Captured Content: 20
Prerequisites / Co-requisites
Indicative content includes the following.
- Introduction to the module, basic concepts in the Internet of Things domain, and the state of the art in technologies that are used to enable the Internet of Things systems, services and applications (3 hrs).
- Cyber-Physical systems, smart devices, sensors and actuators (3 hrs)
- Key applications, protocols and architectures (3 hrs)
- Networks and Communications (Wireless Multi-hop Networks (WMN), Mobile Ad-hoc Networks (MANET), Wireless Sensor Networks (WSN)) (3 hrs)
- Software platforms and services (3 hrs)
- Intelligent Data Processing and Semantic technologies (3 hrs)
- Connecting things to the Web (3 hrs)
- Reliability, Security, Privacy and Trust issues and solutions (3 hrs)
- Applications, System models, Standards, and Physical-Cyber-Social systems (3 hrs)
- Wrap-up and outlook (3 hrs)
|Assessment type||Unit of assessment||Weighting|
|Examination Online||ONLINE (OPEN BOOK) EXAM WITHIN A 4-HOUR WINDOW||80|
Not applicable: students failing a unit of assessment resit the assessment in its original format.
The assessment strategy for this module is designed to provide students with the opportunity to demonstrate the learning outcomes. The written examination will assess the knowledge, concepts and theory of key technologies, common protocols, and relevant techniques in the Internet of Things area, as well as the ability to analyse problems and apply the common solutions and techniques to solve different uses-case scenarios in this domain. The Assignment will assess the ability to design a basic Internet of Things system using a common platform and will also evaluate the ability to critically analyse an existing work in a related area.
Thus, the summative assessment for this module consists of:
- A programming assessment implementing a sensor node application
- 2 hour, open-book written examination
Formative assessment and feedback
For the module, students will receive formative assessment/feedback in the following ways.
- During lectures, by question and answer sessions
- During supervised computer laboratory sessions
- During the seminar and class discussions
- Via the marking of written reports
- Via assessed coursework
- introduce the fundamental concepts of the Internet of Things and its applications and architecture models, followed by an introduction to the technologies and mechanisms for sensing, actuation, processing and cyber-physical data communication.
- enable students to develop practical skills that can be transferred into a real-world environment.
- discuss semantic technologies, service oriented solutions and networking technologies that enable the integration of Internet of Things data and services into the cyber world (i.e. the Internet and the Web).
|1||Explain the key concepts of the Internet of Things and its enabling technologies|
|2||Describe the principles of design and development of Internet-of-Things systems and applications|
|3||Describe and evaluate theoretical concepts and apply them to practical examples and use-cases|
|4||Describe and discuss recent and evolving developments, protocols and technologies such as 6LowPAN, CoAp, ETSI M2M, and W3C SSN.|
|5||Understand the basic software development concepts and techniques for embedded Internet-of-Things systems.|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to achieve the following aims.
This module is designed to provide up-to-date knowledge that will enhance and extend the students’ theoretical and practical skills in related areas and will also improve their analytical and problem solving skills. This will be achieved through a set of lectures and classroom practices and discussions that are designed to provide fundamental knowledge of the related areas to the students. The lectures will explore various technologies, methods and techniques, use cases and common practices in the lectures, where students will also learn how the fundamental concepts and solutions that can be applied in solving the problems or extending the frontiers in related areas. The practical sessions will be conducted as lab experiments and will be complimented by the coursework assignment. The coursework comprises a programming assignment and also writing an essay report. Students will be expected to not only investigate the relevant literature and write a report, but also to learn practical skills and develop a basic system and demonstrate it on a common platform.
Learning and teaching methods include the following.
- Lectures to provide the fudamental knowledge (3 hrs lecture per week x 10 weeks)
- Class discussion to encourage interaction and partcipation (no specific time is devoted to this activity; however this will be a part of the activities during the lectures)
- Seminar to present and discuss the essay work (3 hours)
- Programming lab sessions (1 hour per week)
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: EEEM048
This module has a capped number and may not be available to ERASMUS and other international exchange students. Please check with the International Engagement Office email: firstname.lastname@example.org
Programmes this module appears in
|Electronic Engineering with Nanotechnology MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Entrepreneurship & Innovation Management MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Communication Systems MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electrical and Electronic Engineering MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Communications Networks and Software MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|5G and Future Generation Communication Systems MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Computer Vision, Robotics and Machine Learning MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering with Computer Systems MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Computer and Internet Engineering MEng||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|FinTech and Policy MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering with Professional Postgraduate Year MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.