Internet of Things and Edge Computing
Code: 45679Credits: 5
| Degree programme | Type | Course |
|---|---|---|
| Telecommunication Engineering | OP | 2 |
Contact lecturer
- Name :
- Marc Codina BarberÃ
- Email :
- marc.codina@uab.cat
Teaching staff
- Jordi Carrabina Bordoll
Group languages
You can consult this information at the end of the document.
Prerequisites
This course is scheduled in the third semester (Year 2, Semester 1) of the Master's in Telecommunication Engineering (MUET). It is highly recommended that students have previously passed the compulsory first-year course Artificial Intelligence Applied to Next-Generation Mobile Communications (Code 45625). Students are expected to have a solid background in data preprocessing, machine learning evaluation metrics, supervised and unsupervised learning algorithms, deep learning structures (including CNNs, RNNs, and Transformers), and basic deployment of AI models on embedded platforms. This foundational knowledge allows this course to bypass introductory AI concepts and focus on advanced distributed AI paradigms (Edge AI + Cloud AI) across IoT networks.
Objectives
The primary objective of this course is to empower students to design, implement, and evaluate complete Internet of Things (IoT) architectures across the entire signal and value chain, from end physical devices to cloud servers and user interfaces and dashboards.
An additional focus of this curriculum is the deployment of distributed Artificial Intelligence (AI) across the network topology—optimizing the partition and orchestration between localized Edge AI inference and centralized Cloud AI analytics.
The course places specific emphasis on:
- Heterogeneous Last-Mile Communications & Interoperability: Critically selecting and integrating diverse access technologies (LoRaWAN, NB-IoT, BLE, Zigbee, Wi-Fi 6) based on application constraints.
- Processing Platform Optimization: Conducting systematic performance-cost analyses to select processing hardware (FPGAs, advanced microcontrollers, low-power SoCs) appropriate for edge computation.
- End-to-End Energy Efficiency & Sustainability: Engineering systems with ultra-low power design techniques, sleep modes, and energy-harvesting technologies across the entire communication chain.
- Regulatory, Economic, and Standardisation: Integrating essential compliance, intellectual property, standardisation norms, and cost of ownership (TCO) principles to prepare commercial-grade IoT deployments.
Learning outcomes
- (CA36) Solve the interoperability of heterogeneous networks with local, access, and backbone networks in the implementation of data services.
- (CA37) Use programmable integrated circuits to design advanced electronic systems at the component and periphery level.
- (CA38) Implement energy-efficient systems in fixed and mobile communications environments, with a focus on sustainability and responsible use of energy resources.
- (KA33) Describe the operation and organisation of the IoTs' next-generation networks and protocols, component and infrastructure models, as well as the intermediary software and associated services.
- (KA34) Explain the economic and project management principles applied to the standardisation of last-mile networks and protocols, illustrating their impact on the efficiency and quality of telecommunications services.
- (KA35) Identify the applicable standards in the professional practice in the design of complete IoT solutions.
- (SA49) Select the last-mile communications protocols that best suit each type of application and the components that enable their implementation.
- (SA50) Develop complete IoT systems consisting of devices, edge, cloud, and user interface.
- (SA51) Evaluate the performance of the systems in terms of cost, speed (bandwidth and latency) and power consumption.
Contents
Unit 1: Next-Generation IoT & Edge Architectures
Layered IoT infrastructure models: Device, Edge (Periphery), Fog, and Cloud layers.
Middleware architectures and service-oriented frameworks for scalable device management.
Topologies and architectural partitioning for distributed Artificial Intelligence: coordinating local Edge AI computation with centralized Cloud AI services.
Unit 2: Connectivity Technologies & Interoperability
Technical study, physical layer characteristics, and MAC-layer protocols of last-mile access technologies: LoRaWAN, NB-IoT, BLE, Zigbee, and Wi-Fi 6.
Interoperability mechanisms in heterogeneous networks, interfacing low-power sensor networks with local backhauls and IP networks.
Communication payload optimization for distributed AI (e.g., edge feature extraction and compression vs. raw signal streaming).
Unit 3: Edge Computing with Advanced Hardware
Hardware architectures for edge computing devices using advanced programmable integrated circuits: FPGAs, high-performance ARM-based microcontrollers, and heterogeneous Systems-on-Chip (SoCs).
Local real-time data processing, filtering, and embedded inference acceleration (Edge AI) to reduce cloud latency and communication overhead.
Unit 4: Energy Efficiency & Sustainability in IoT
Low-power hardware design principles and software execution strategies: deep sleep modes, power gating, dynamic voltage and frequency scaling (DVFS), and Energy Harvesting technologies.
Systematic power-performance analysis (PPA) comparing localized computation energy (Edge AI) versus wireless transmission energy (Cloud AI).
Unit 5: End-to-End Development & Metric Benchmarking
Practical end-to-end integration: configuring physical nodes (sensors/actuators), edge gateways, cloud brokers/databases, and user interface dashboards.
Quantitative system benchmarking: calculating and optimizing trade-offs among unit cost, latency, communication throughput, and long-term energy consumption under real-world traffic.
Unit 6: Regulation, Economics, & IoT Project Management
Regulatory compliance frameworks, standardisation bodies (IEEE, 3GPP, ETSI), and radio spectrum allocations for IoT.
Economic analysis of IoT deployments: Total Cost of Ownership (TCO) models, Capital Expenditures (CapEx) vs. Operational Expenditures (OpEx) calculations, and bills of materials (BOM).
Impact of IoT and Edge AI on telecommunication service quality, reliability, and business sustainability.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Type: Autonomous | |||
| Freelance Work and Project Development | 42.5 | 1.7 | CA36, CA37, CA38, KA33, KA34, SA49, SA50, SA51 |
| Tutoring and project monitoring | 10 | 0.4 | KA35, SA50, SA51 |
| Type: Guided | |||
| Lectures: Oral presentation given by the professor with the aim of transmitting knowledge about the subject. | 20 | 0.8 | KA33, KA34, KA35, SA51 |
| Seminars and use cases | 12.5 | 0.5 | CA38, SA49, SA51 |
| Type: Supervised | |||
| Laboratory Sessions | 15 | 0.6 | CA36, CA37, SA50, SA51 |
The course will be mainly driven by the lectures, which will use ad hoc material (presentations, documents, links, tools and other resources) available in the virtual campus (VC) of the UAB (https://cv.uab.cat). Students will deliver exercises on specific subjects (on the Virtual Campus).
As a new common policy at UAB, generative AI tools will be allowed while the students must add the tool name and the used prompts. The focus on its evaluation will extend to the entire development process with potential follow-up interviews.
Laboratory work will let the students to apply and experiment with the concepts acquired on edge computing platforms and public cloud infrastructures, widely used in industry. In the case of the labs, the AI tools are not allowed at the classroom since they usually make serious errors for unexperienced users. Specially for the labs, we expect that master students prepared them before the on-site session. Attendance will be mandatory for all sessions. Any lack of attendance must be communicated in advance to the teacher in charge, attaching the corresponding reasonable justified reasons.
Annotation: Within the schedule set by the center or master program, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Final Exam | 30% | 2 | 0.08 | KA33, KA34, KA35 |
| IoT and Edge Project (Memory and Defense) | 45% | 15 | 0.6 | CA36, CA37, CA38, SA49, SA50, SA51 |
| Laboratories (Reports and monitoring) | 25% | 8 | 0.32 | CA36, CA37, SA49 |
Averages and Minimums: To successfully pass the course and allow grading averaging, students must obtain a minimum grade of 4.0 out of 10 in both the Final Written Exam and the IoT and Edge Project. A final overall weighted grade of 5.0 out of 10 or higher is required to pass. Specially during the labs, the teacher will evaluate the skills, development and correctness of the preparation and results of every lab during the classroom.
Recovery (Retake): If a student fails to achieve the minimum 4.0 on the Final Written Exam, they are eligible to sit for a theory retake exam at the end of the semester, provided they have participated in at least 2/3 of the continuous assessment activities. Because the Laboratories and the Project involve continuous peer-collaboration and practical lab resource utilization, they are strictly non-recoverable.
Attendance & Evaluation: Unjustified absence from more than 20% of the practical laboratory sessions, or failure to deliver and defend the final project, will lead to the grade of 'Not Evaluable' (Non-assessable).
Plagiarism & Misconduct: Copying, plagiarism, cheating, or letting others copy in any assessment activity will result in a grade of 0 for that activity. These penalties are non-recoverable and will lead to an automatic suspension of the course with a maximum grade of 3.0/10.
Generative AI Policy: The use of generative AI tools is permitted exclusively for support tasks (e.g., debugging code syntax, searching documentation, or styling text). It is strictly prohibited to generate core designs, code logic, or reports directly. Students must explicitly declare in their reports which parts were assisted by AI, which tools were used, and provide a critical reflection on how the tool influenced their learning. Failure to disclose AI usage will be treated as academic misconduct (grade of 0).
Bibliography
Recommended Books:
- Edward A. Lee and Sanjit A. Seshia, Introduction to Embedded Systems: A Cyber-Physical Systems Approach, 2nd Edition, MIT Press, 2017.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016 (Essential background for AI concepts).
- V.S. Chakravarthi, A Practical Approach to VLSI System on Chip (SoC) Design: A Comprehensive Guide, Springer, 2020.
Software
Development & Simulation Software:
- Firmware & Microcontrollers: EdgeImpulse, Arduino IDE
- Edge AI & Cloud Analytics: Python 3.12 (PyTorch), Node-RED, and MQTT/CoAP Brokers.
- Cloud Dashboards & Middleware: AWS IoT Core, ThingsBoard, or Adafruit IO.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE