Internet of objects and embedded concurrent systems

Lecturer(s): René CHALON, Alexandre SAIDI
Course ⋅ 12 hStudy ⋅ 8 h


The Internet of Things is based on the continuous progress of microelectronic and network technologies that allow the deployment of distributed services on networks of interconnected communicating objects.

This module will first provide an overview of the Internet of Things, from the norms, standards and technologies on which it is based, to the applications and security issues.

In a second step, the students will be made aware of the notions of concurrent programming as well as of real-time and embedded systems. In this way, students will acquire the necessary knowledge to understand the principles of realization of highly connected systems (IoT) as well as their processing.


Internet of Things, Web of Things, connected devices, smart city, Ambiant Intelligence, home automation, Bluetooth, Zigbee, 6LoWPAN, PLC, Concurrent programming, Real-time computing, Embedded computing, Mobile computing


  • Context, uses and fields of application of IoT: smart cities, ambient intelligence, Big Data (2h)
  • Technologies of connected objects (2h)
  • Security and physical safety of connected objects (2h)
  • Network aspects and identification of objects (2h)
  • Notions on concurrent programming, mutual exclusion mechanisms, concurrent schemes (2h)
  • Requirements of real time systems and kernels, embedded and mobile computing, robotics (2h)

Labs :

  • Programming of a communicating object with sensors based on Arduino (4 h)
  • Realization of some parallel calculations, realization of a concurrent system for example for the regulation of pressure, humidity and temperature of a sensitive room (4h)

Learning Outcomes

  • Understand the field of connected objects, their technologies and applications as well as the notions of concurrency/parallelism.
  • Design an application based on the exploitation of data from distributed sensors
  • Simulations of concurrent systems for handling and processing data from multiple sensors


Final mark = 50% Knowledge + 50% Know-how Knowledge N1 = 100% final exam Know-how N2 = 100% continuous assessment