Minor Sensor and Network Devices

Coordinated by: Cécile Belleudy and Jean-Marc Ribero, Université Côte d'Azur, LEAT
 

FORMAT

Classroom

LOCATION

Campus SophiaTech, Templiers

Prerequisites

Basics in programming language

Capacity

20 students

About this minor

Summary

Learning Outcomes

The first objective of this module is to program a connected system. The case study is to program a Cherokey robot with the Arduino language to go from point A to point B, avoiding all obstacles in its path and being as fast as possible. It will have to cross the circuit first independently and then with a Bluetooth command. 
​The second objective is to design a sensor network for data collecting and integrating machine learning algorithms for making decision for smart environment (home, building, …).​ 

​​The evolution of sensor technology and communication protocols has made it possible to realize connected objects whose application domains are in full expansion. 
​This teaching unit aims to provide theoretical and practical bases for the development of connected objects by the design of the Cherokey 4WD arduino mobile robot. It is a versatile mobile robot that is compatible with popular microcontrollers such as the arduino UNO, arduino MEGA 2560, Romeo, etc. 
​In order to address the network aspect, sensor nodes (M5Stack family) will monitor physical data and forward the collected information to a central node (Raspberry Pi) in charge of decision for actuator nodes. The central node will rely on machine learning algorithms (KNN, RF, SVM, CNN, …) for making centralized decisions. The efficiency of different algorithm of ML will be compared. 

​This course will be divided into two parts: 

  • ​Robot building and controlling and Bluetooth communication
    • A first step will be devoted to understanding how the robot works, discovering the Arduino language and its functions.
    • ​The second step is dedicated to the programming the Cherokey Robot and to find the function that will allow the robot to avoid obstacles. 
    • ​The last step will be to use the bluetooth function and the camera to move the robot. 
  • ​Design of sensor network
    • the first step is to build a sensor network and a data collection infrastructure based on WIFI communication, a broker (MQTT) and nodered.
    • The second step is to integrate machine learning (ML) algorithms for making decision bases on collected data. The different steps of the ML deployment will be studied
    • ​At the end, the student will develop a solution for societal challenges for example for the management of energy (heating, electricity, …), water consumption, … for smart environment (home, building, …).​ 
Lecturer
Evaluation
  • Oral presentation (50 % of the final grade) - 11/12/2025 (duration: 15+10 min) - SophiaTech Campus, Templiers, room F201
  • Final written report (50 % of the final grade) - Submission deadline (on Moodle): 15/12/2025

SCHEDULE FALL 2025

Mind the evaluation modalities and deadlines in the "Evaluation" tab above.

Date

Time slot

Course titleCourse title

Lecturer

Room

9/10/2025 9h00-12h00 Introduction/ Arduino Environment JM Ribero Campus SophiaTech Templiers, room F201
16/10/2025 9h00-12h00 Concept of robot and first tests JM Ribero Campus SophiaTech Templiers, room F201
23/10/2025 9h00-12h00 Mini-project - Tutorial JM Ribero Campus SophiaTech Templiers, room F201
06/11/2025 9h00-12h00 Mini-project JM Ribero Campus SophiaTech Templiers, room F201
13/11/2025 9h00-12h00 Sensor Communication C. Belleudy Campus SophiaTech Templiers, room B211
20/11/2025 9h00-12h00 Sensor Network Building C. Belleudy Campus SophiaTech Templiers, room B211
04/12/2025 9h00-12h00 AI integration and making decision  C. Belleudy Campus SophiaTech Templiers, room B211
11/12/2025 9h00-12h00 Evaluation : oral presentation JM Ribero and C. Belleudy Campus SophiaTech Templiers, room F201