Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach

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Abstract

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) is widely used as a reliable, low-power, and low-cost communication technology for many industrial Internet-of-Things (IoT) networks. In many applications, Quality-of-Service (QoS) requirements are different for heterogeneous nodes, necessitating non-equal parameter settings per node. This results in a very large configuration space making space exploration complex and time-consuming. Moreover, network state and QoS requirements may change over time. Thus, run-time configuration mechanisms are needed for making decisions about proper node settings to consistently satisfy diverse and dynamic QoS requirements. In this paper, we propose a run-time decentralized self-optimization framework based on Deep Reinforcement Learning (DRL) for parameter configuration of a multi-hop TSCH network. DRL adopts neural networks as approximate functions to speed up the process of converging to QoS-satisfying configurations. Simulation results show that our proposed framework enables the network to use the right configuration settings according to the diverse QoS demands of different nodes. Moreover, it is shown that the convergence time of the learning framework is in the order of a few minutes which is acceptable for many IoT applications.
Original languageEnglish
Article number10114919
Pages (from-to)16869-16880
Number of pages12
JournalIEEE Internet of Things Journal
Volume10
Issue number19
Early online date3 May 2023
DOIs
Publication statusPublished - 1 Oct 2023

Funding

This work was supported by the Electronic Component Systems for European Leadership Joint Undertaking through SCOTT European Project (www.scott-project.eu) under Grant 737422.

FundersFunder number
SCOTT European Project737422
Electronic Components and Systems for European Leadership

    Keywords

    • DRL
    • IEEE 802.15 Standard
    • IEEE 802.15.4 TSCH
    • Internet of Things
    • IoT
    • Optimization
    • Q-learning
    • QoS
    • Quality of service
    • Standards
    • WSN
    • Wireless sensor networks
    • Internet of Things (IoT)
    • Deep reinforcement learning (DRL)
    • Quality-of-Service (QoS)
    • IEEE 802.15.4 time-slotted channel hopping (TSCH)
    • wireless sensor network (WSN)

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