Welcome to ELISE’s documentation!

ELISE is a framework built upon multidisciplinary research efforts of Software-Defined Networking (SDN), Wireless Sensor Networks (WSNs), and Machine Learning (ML). The aim of ELISE is to provide a holistic architecture to support run-time network reconfigurations and deployment of ML algorithms in WSNs. We currently support reinforcement learning to adapt the slotframe size of the TSCH protocol give a set of user requirements. A detail description of the project can be found in the paper.

A overview of the framework is shown below:

The ELISE project comprises two main components or repositories: data plane , control plane.

• Data plane: devices that reside on this plane runs on an embedded operating systems. Among the embedded operating systems available in the market, ELISE use Contiki-NG. However, to comply with SDWSN principles of making the network infrastructure run simple tasks and remove energy-intensive functions from sensor nodes, we have redesigned the protocol stack. Thus, we have added support to these functionalities in Contiki-NG-SDWSN.

• Control plane: In its core, it runs on Python. This plane provides support to all functionalities listed in the architecture including reinforcement learning, the code resides in SDWSN-Controller.

ELISE repository has all components added as submodules, and it also has scripts to automate experiments in the FIT-IoT-LAB platform.

Contents

• ELISE Home
• ELISE Installation
  • Set up the data plane
  • Set up the control plane
• Getting Started
  • Control plane
  • Data plane
• Tutorials
  • Simple SDWSN controller
  • Deploying a Wireless Sensor Network: A Step-by-Step Tutorial for Real-World Applications

Citing ELISE

If you wish to cite ELISE in your academic work, you can add this:

@article{ELISE,
title={ELISE: A Reinforcement Learning Framework to Optimize the Sloftframe Size of the TSCH Protocol in IoT Networks},
author={Jurado Lasso, Fabian Fernando and Barzegaran, Mohammadreza and Jurado, Jesus Fabian and Fafoutis, Xenofon},
year={2023},
publisher={TechRxiv},
doi={10.36227/techrxiv.23212442.v2}
}