<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized approach for providing Quality of Service (QoS) routing for heavy volume IoT data transmissions this paper proposes a machine learning-based routing algorithm with a multi-agent environment. The overall routing process is divided into two phases: route discovery phase and route maintenance phase. The route discovery or path finding phase is performed using rank calculation and Q-routing. Q-routing is performed with Q-Learning reinforcement machine learning approach, for selecting the next hop node. The proposed routing protocol first creates a Destination Oriented Directed Acyclic Graph (DODAG) using Q-Learning. The second phase is route maintenance. In this paper, we also propose an approach for route maintenance that considerably reduces control overheads as shown by the simulation and has shown less delay in routing convergence.</p>
</abstract>

The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized approach for providing Quality of Service (QoS) routing for heavy volume IoT data transmissions this paper proposes a machine learning-based routing algorithm with a multi-agent environment. The overall routing process is divided into two phases: route discovery phase and route maintenance phase. The route discovery or path finding phase is performed using rank calculation and Q-routing. Q-routing is performed with Q-Learning reinforcement machine learning approach, for selecting the next hop node. The proposed routing protocol first creates a Destination Oriented Directed Acyclic Graph (DODAG) using Q-Learning. The second phase is route maintenance. In this paper, we also propose an approach for route maintenance that considerably reduces control overheads as shown by the simulation and has shown less delay in routing convergence.

A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

PESIT Bangalore South Campus, Bangalore, India and affiliated to Visvesvaraya Technological University

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT"}

The Routing Protocol for Low power and lossy networks (RPL) is used as a routing protocol in IoT applications. In an endeavor to bring out an optimized...

A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

Data publikacji: 09 gru 2021

Zakres stron: 45 - 61

Otrzymano: 09 maj 2021

Przyjęty: 08 lis 2021

DOI: https://doi.org/10.2478/cait-2021-0042

Słowa kluczowe
QoS routing, multi-agent system, Internet of Things (IoT), reinforcement learning, RPL routing

© 2021 T. C. Jermin Jeaunita et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

Data publikacji: 09 gru 2021

Zakres stron: 45 - 61

Otrzymano: 09 maj 2021

Przyjęty: 08 lis 2021

DOI: https://doi.org/10.2478/cait-2021-0042

Słowa kluczoweQoS routing, multi-agent system, Internet of Things (IoT), reinforcement learning, RPL routing

© 2021 T. C. Jermin Jeaunita et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
QoS routing, multi-agent system, Internet of Things (IoT), reinforcement learning, RPL routing