Module code: EEEM048

Module Overview

Expected prior/parallel learning: Basic knowledge of hardware systems and module EEE2047 (Object-Oriented Programming and C++) or equivalent knowledge of C++ or 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 world with the cyber world. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems.

The advanced IoT module is designed to provide a comprehensive understanding of how machine communications contribute to creating smart, artificial intelligence-driven environments focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms.¿Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.  

Module provider

Computer Science and Electronic Eng

Module Leader

SHOJAFAR Mohammad (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 95

Lecture Hours: 20

Laboratory Hours: 10

Guided Learning: 10

Captured Content: 20

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative content includes the following.

  • Introduction to the module, basic concepts in the Internet of Things domain, and the state-of-the-art technologies that are used to enable the Internet of Things systems, services, and applications

  • Cyber-physical systems, smart devices, sensors and actuators

  • Physical and Link layer protocols for Internet of Things

  • Architectures and radio access technologies for Internet of Things via satellites

  • Software platforms and services

  • Intelligent data processing and semantic technologies

  • Reliability, Security, Privacy, and Trust issues and solutions

  • Enabling technologies for low power wide area networks

  • Localization and tracking for Internet of Things

  • Machine learning/artificial intelligence for Internet of Things


Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 20
Examination Invigilated (Closed book) examination within a 2-hour window 80

Alternative Assessment


Assessment Strategy

The assessment strategy for this module is designed to allow students 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 an Internet of Things system using a common platform and will also evaluate the ability to critically analyse an existing work in a related area.

Summative assessment for this module consists of the following:

  • A programming assessment in implementing a sensor node application.

  • Invigilated (Closed book) examination within a 2-hour window.

Formative assessment and feedback: For the module, students will receive formative assessment/feedback in the following ways.

  • During lectures, e.g., question and answer sessions

  • During supervised computer laboratory sessions

  • During the seminar and class discussions

  • Via the marking of written reports

  • Via assessed coursework

Module aims

  • 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.
  • To explore the modularity of IoT systems and their applications in the fields of communication systems and computer vision, robotics, and machine learning.
  • To provide a cross-layer understanding of practical communication systems, including physical, MAC (Medium Access Control), and network layers, with an emphasis on security and satellite IoT technology.
  • To offer hands-on experience through 20-hour labs covering embedded programming.
  • 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).
  • To incorporate machine learning techniques in the context of IoT applications.
  • The module also aims to provide opportunities for students to learn about the Surrey Pillars listed below.

Learning outcomes

Attributes Developed
001 Explain the key concepts of the Internet of Things and its enabling technologies KC M1, M2
002 Describe the principles of design and development of Internet-of-Things systems and applications KCPT M5, M6
003 Describe and evaluate theoretical concepts and apply them to practical examples and use-cases KCPT M1, M3, M7
004 Describe and discuss recent developments, protocols, and technologies for Internet of Things PT M10, M14
005 Apply software development concepts such as machine learning and techniques for embedded Internet of Things systems PT M12, M16, M17
006 Select appropriate radio access technology for various Internet of Things applications KCPT M5, M6

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

Learning and teaching strategy is designed to achieve the following aims:

This module is designed to provide up-to-date knowledge to enhance and extend the students’ theoretical and practical skills in related areas and improve their analytical and problem-solving skills. This will be achieved through lectures, classroom practices, and discussions designed to provide the students with fundamental knowledge of the related areas. The lectures will explore various technologies, methods and techniques, use cases, and common practices in the lectures, where students will also learn 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 complement the coursework assignment. The coursework comprises a programming assignment and also writing an essay report. Students will be expected to investigate the relevant literature, write a report, learn practical skills, develop a basic system, and demonstrate it on a common platform.

Learning and teaching methods include the following:

  • Lectures to provide the fundamental knowledge 

  • Class discussion to encourage interaction and participation

  • Programming lab sessions 

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.

Reading list
Upon accessing the reading list, please search for the module using the module code: EEEM048

Other information

This module has a capped number and may not be available to exchange students. Please check with the International Engagement Office email:

The EEEM048 module contributes to the Surrey Pillars as follows: 

  • Sustainability: EEEM048 enables the deep understanding of IoT systems joint with future internet and system technologies such as LTE and IoT-narrow band, CoAP,4G and 5G generations that play an important role for the postgraduates to link the IoT design to such technologies. Besides, by applying IoT network capabilities, they can cover the different environmental catastrophic events like fire in the forest, understand the Earth’s ecological effects and recognise how to manage the data through IoT network.


  • Global and cultural intelligence: IoT models and their related infrastructure-linked systems are a global system, and EEEM048 helps to understand its details. Therefore, this module promotes the critical thinking (concerning the working of g of IoT networks) of our postgraduates as global citizens who can engage effectively and ethically with people from diverse backgrounds.


  • Digital capabilities: The skills that EEEM048 provides will enable postgraduates and their organisations to be able to participate actively in society and professional life, within a digital world, that will play a critical role in providing future services such as critical event detection, real-time analysis the data through critical infrastructure applications like remote surgency and smart grid and smart homes. 


  • Employability: The importance of understanding IoT systems and related engineering skills cannot be underestimated. As such, EEEM048 provides professionally focused learning (e.g., hands-on using IoT devices and related practical skills using Cooja and ContikiOS software and programming with C language on real IoT devices) that nurtures career-ready postgraduates.


  • Resourcefulness and resilience: EEEM048, with the gained IoT systems skills helps to produce resourceful and resilient students who can respond positively and effectively to opportunities, challenges, difficulties and setbacks. Future IoT networks are expected to be large-scale and adaptable to cater for varying user demands and network and security threats. EEEM048 will help with a deep to understand of the enabling technologies for a future network of network IoT models in the industries and smart cities.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Electronic Engineering (by short course) MSc(EEE SHORT COURSES OPTIONAL) 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 2024/5 academic year.