RL-IoT: Reinforcement Learning to Interact with IoT Devices

05/03/2021
by   Giulia Milan, et al.
0

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

07/22/2019

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

The Internet of Things (IoT) extends the Internet connectivity into bill...
12/06/2019

Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop

Rapid advancements in the Internet of Things (IoT) have facilitated more...
01/25/2021

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

The Internet of Things (IoT) is becoming an indispensable part of everyd...
04/29/2021

Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers

This paper proposes a distributed Reinforcement Learning (RL) based fram...
07/13/2021

On the Analysis of MUD-Files' Interactions, Conflicts, and Configuration Requirements Before Deployment

Manufacturer Usage Description (MUD) is an Internet Engineering Task For...
10/31/2021

On multiple IoT data streams processing using LoRaWAN

LoraWAN has turned out to be one of the most successful frameworks in Io...
03/30/2020

Increasing negotiation performance at the edge of the network

Automated negotiation has been used in a variety of distributed settings...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.