Wireless and Mobile Communications

Credits
6
Types
Specialisation complementary (Information Technologies)
Requirements
Department
AC
The course integrates IoT and AI (without a mathematical orientation). It combines a 14-week theory track (low-power communications, protocols, security, sensors, edge-cloud, AI, generative AI and TinyML) with a 12-week applied track where teams develop a full use case: idea, competitor analysis, business model and proof of concept.

Teachers

Person in charge

  • Jorge García Vidal (jorge@ac.upc.edu)

Weekly hours

Theory
3
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
6

Competences

Technical Competences of each Specialization

Information technology specialization

  • CTI1 - To define, plan and manage the installation of the ICT infrastructure of the organization.
    • CTI1.1 - To demonstrate understanding the environment of an organization and its needs in the field of the information and communication technologies.
    • CTI1.2 - To select, design, deploy, integrate and manage communication networks and infrastructures in a organization.
    • CTI1.3 - To select, deploy, integrate and manage information system which satisfy the organization needs with the identified cost and quality criteria.
    • CTI1.4 - To select, design, deploy, integrate, evaluate, build, manage, exploit and maintain the hardware, software and network technologies, according to the adequate cost and quality parameters.
  • CTI2 - To guarantee that the ICT systems of an organization operate adequately, are secure and adequately installed, documented, personalized, maintained, updated and substituted, and the people of the organization receive a correct ICT support.
    • CTI2.1 - To manage, plan and coordinate the management of the computers infrastructure: hardware, software, networks and communications.
  • CTI3 - To design solutions which integrate hardware, software and communication technologies (and capacity to develop specific solutions of systems software) for distributed systems and ubiquitous computation devices.
    • CTI3.1 - To conceive systems, applications and services based on network technologies, taking into account Internet, web, electronic commerce, multimedia, interactive services and ubiquitous computation.
    • CTI3.2 - To implement and manage ubiquitous systems (mobile computing systems).
    • CTI3.4 - To design communications software.

    • Transversal Competences

    Sustainability and social commitment

  • G2 [Avaluable] - To know and understand the complexity of the economic and social phenomena typical of the welfare society. To be capable of analyse and evaluate the social and environmental impact.
    • G2.3 - To take into account the social, economical and environmental dimensions, and the privacy right when applying solutions and carry out project which will be coherent with the human development and sustainability.

    • Objectives

    1. knowledge of specific problems in the radio transmission
      Related competences: CTI1.2, CTI2.1, CTI1.3, CTI1.4, CTI3.2,
    2. know the technology of wireless networks
      Related competences: CTI1.2, CTI2.1, CTI1.1, CTI1.3, CTI1.4, CTI3.2,
    3. knowing the value chain of Internet of Things and its integration with Artificial Intelligence.
      Related competences: CTI1.1, G2.3,
    4. To know the basic processing techniques for IoT sensors and integration of AI tools on IoT nodes.
      Related competences: CTI3.1, CTI3.4, CTI3.2, G2.3,
    5. understand the business models, development costs, marketing, competition, etc., associated with the development of an IoT application
      Related competences: G2.3,
    6. work together to develop a design work
      Related competences: G2.3,
    7. knowledge on IoT technologies
      Related competences: CTI3.1, CTI1.2, CTI2.1, CTI1.3, CTI1.4, CTI3.2,
    8. known auxiliary technologies: positioning, secure mobile payments, advertising insertion, etc.
      Related competences: CTI3.1, CTI3.4, CTI1.1, CTI3.2,

    Contents

    1. Foundations & device constraints
      Introduction to IoT, its applications and verticals, and the real constraints of low-power nodes and microcontrollers (battery, memory, compute) that shape every later technology decision.
    2. Short- and long-range communications
      RFID, NFC, 802.15.4 and LoRaWAN: low-power wireless communication technologies, their network topologies, and the trade-offs between range, power and data rate.
    3. Protocol stacks & security
      6LoWPAN, Zigbee, CoAP and MQTT as communication stacks and application protocols, together with the security risks specific to constrained IoT devices and how to mitigate them.
    4. Sensing, positioning & edge-cloud architecture
      IMU sensors, absolute and relative positioning (taught conceptually), and the IoT-edge-cloud continuum: where to process data considering latency, bandwidth, privacy and cost.
    5. Artificial intelligence & generative AI
      Conceptual introduction to machine learning and generative AI (including LLMs): how models learn, what they can do, and their practical capabilities and limitations.
    6. AI-IoT integration & TinyML
      AI-IoT integration (edge vs. cloud inference, predictive maintenance), TinyML for running AI on constrained devices.

    Activities

    Activity Evaluation act


    Topic 1

    Introduction to IoT, its applications and verticals, and the real constraints of low-power nodes and microcontrollers (battery, memory, compute) that shape every later technology decision.
    • Theory: The Mobile Internet before and after the iPhone. Value chain IM: Contents. On-line services. Distribution networks. Interface with the user. Devices. Applications. Networks.
    • Autonomous learning: Study the issues of class
    Objectives: 3
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    3h

    item 2

    RFID, NFC, 802.15.4 and LoRaWAN: low-power wireless communication technologies, their network topologies, and the trade-offs between range, power and data rate.
    • Theory: RFID, NFC, 802.15.4 and LoRaWAN: low-power wireless communication technologies, their network topologies, and the trade-offs between range, power and data rate.
    • Autonomous learning: Study
    Objectives: 1
    Contents:
    Theory
    8h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    18h

    item 3

    6LoWPAN, Zigbee, CoAP and MQTT as communication stacks and application protocols, together with the security risks specific to constrained IoT devices and how to mitigate them.
    • Theory: 6LoWPAN, Zigbee, CoAP and MQTT as communication stacks and application protocols, together with the security risks specific to constrained IoT devices and how to mitigate them.
    • Autonomous learning: study
    Objectives: 2
    Contents:
    Theory
    9h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Use case: IoT Application Design

    The use-case sessions (1 hour weekly, 12 weeks) accompany the theory track with an applied project. Teams of 3 students develop their own IoT+AI product: defining the idea and tech stack, analyzing competitors and business model, building a proof of concept of one instructor-assigned aspect, and finishing with an investor-pitch-style final presentation.
    • Laboratory: classes based on case
    • Autonomous learning: design of the application
    Objectives: 3 4 5
    Theory
    0h
    Problems
    0h
    Laboratory
    15h
    Guided learning
    0h
    Autonomous learning
    22h

    item 4

    IMU sensors, absolute and relative positioning (taught conceptually), and the IoT-edge-cloud continuum: where to process data considering latency, bandwidth, privacy and cost.
    • Theory: IMU sensors, absolute and relative positioning (taught conceptually), and the IoT-edge-cloud continuum: where to process data considering latency, bandwidth, privacy and cost.
    • Autonomous learning: Study
    Objectives: 2
    Contents:
    Theory
    8h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    15h

    Partial Review

    Exam topics 1-3
    Objectives: 1 2 3
    Week: 7
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    item 5

    Conceptual introduction to machine learning and generative AI (including LLMs): how models learn, what they can do, and their practical capabilities and limitations.
    • Theory: Conceptual introduction to machine learning and generative AI (including LLMs): how models learn, what they can do, and their practical capabilities and limitations.
    • Autonomous learning: Study
    Objectives: 7
    Theory
    9h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    item 6

    AI-IoT integration patterns (edge vs. cloud inference, predictive maintenance), TinyML for running AI on constrained devices, and real end-to-end case studies.
    • Theory: Type of mobility. Support for mobility level 3 (IP phone). Support for mobility in cellular networks. Nomadic mobility. Protocols to support mobility transparent to Level 2.
    • Autonomous learning: study
    Objectives: 2
    Contents:
    Theory
    6h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    5h

    Final Exam

    Review of issues 1-6
    Objectives: 1 2 3 4 5 6 7 8
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    * Classroom sessions
    * Lab classes (python programming)
    * Case-based sessions (mobile app design)

    Evaluation methodology

    Ep: Mideterm exam : 0 <= Ep <= 10
    Ef: Final exam: 0 <= Ef <= 10
    Ec: Use case: 0 <= Ec <= 1.


    Final mark = 0,7xMAX (Ef, 0, 75 x Ef +0,25 x Ep) + 3xEc

    Bibliography

    Basic

    Previous capacities

    Basic knowledge of TCP / IP networks and network protocols.
    Basic knowledge of probability and linear algebra