ARTIFICIAL INTELLIGENCE OF THINGS - 2025/6

Module Overview

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. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics.

Module Overview:
The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, 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 AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

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

Module Availability

Semester 1

Prerequisites / Co-requisites

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 content

Indicative content includes the following.
- Introduction to the module, fundamental AIoT concepts, evolution from IoT to AIoT, state-of-the-art technologies, and emerging applications
- Cyber-physical systems, intelligent devices, sensors and actuators
- Physical and Link layer protocols for AIoT
- Architectures and radio access technologies for AIoT 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 (LPWANs) including LoRaWAN
- Cellular technologies for AIoT such as NB-IoT and 5G NR RedCap
- Machine learning/artificial intelligence for Internet of Things
- AI models for AIoT applications: Ensemble learning, multi-armed bandits for sequential decision-making, and lightweight models
- Localization and tracking for AIoT including AI-powered positioning

Assessment pattern

Alternative Assessment

N/A

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 examination.

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 AIoT 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 AIoT 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 AIoT technology.
• To offer hands-on experience through labs covering embedded programming and LoRaWAN, including the design, configuration, and deployment of LoRa-based communication systems.
• 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).
• Incorporate machine learning techniques such as ensemble learning, multi-armed bandits, and lightweight AI models (e.g., Decision Trees, Logistic Regression) to optimize AIoT applications in real-time data processing, anomaly detection, and intelligent decision-making.

Learning outcomes

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

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: EEEM086

Other information

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

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 2025/6 academic year.