Communications for Precision Agriculture and Farming

Year:
2nd year
Semester:
S1
Programme main editor:
(I2CAT)
Onsite in:
Remote:
ECTS range:
3 ECTS

Professors

img
Professors
Athanasios Iossifides
IHU
img
Professors
Stefano Giordano
UULM

Prerequisites:

Basic knowledge of wireless, mobile communications and networking.

Pedagogical objectives:

The purpose of this course is to delve into the most significant communication techniques and protocols available for Precision Agriculture and Farming. It offers an end-to-end understanding of the ecosystem and the appropriate communication and networking options that can be used in different settings and setups based on their specific KPIs and requirements.

Evaluation modalities:

Written exam, assignments, and short in-class quizzes. A project assignment to perform after the STC execution will also be evaluated.

Description:

The course provides the IoT architectures and communication models that can be used for precision agriculture and farming. It describes the contemporary IoT protocol stack, the networking (including physical and medium access control) and application layer protocols, their pros and cons, their combinations to deliver end-to-end communication, and their suitability for different use cases, systems and device types. In addition, it provides relevant end-to-end examples as well as future trends and directions.

Topics:

  • Introduction to IoT for precision agriculture and farming (1-2 hours).
  • IoT systems (architectures), devices, communication models and protocol stacks (1-2 hours).
  • Radio access and networking protocols for IoT including, RFID, Bluetooth (LE), IEEE 802.15.4 family (ZigBee, WirelessHART, Wi-SUN, etc.), IEEE 802.11, LoRa, SigFox, LTE-M, NB-IoT, 5G (8-10 hours).
  • Application layer protocols (MQTT, CoAP, etc.).
  • Service composition, virtualization, visualization and digital twins.
  • Use cases.

Future directions and conclusion.

Required teaching material

Survey papers from international literature [computing/cloud resources] Access to the Moodle platform of AI4CI. Virtual machines with preconfigured lab environment in the AI4CI cloud. [devices] Laptop or desktop computer with network access [Additional] Zoom or other platform for synchronous lectures Lessons’ recording

Teaching volume:
lessons:
25 hours
Exercices:
Supervised lab:
5 hours
Project:

Devices:

  • Laboratory-Based Course Structure
  • Open-Source Software Requirements