By the same authors

From the same journal

Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing

Research output: Contribution to journalArticlepeer-review

Standard

Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks : A Q-Learning Approach for Graph Routing. / Künzel, Gustavo; Soares Indrusiak, Leandro; Pereira, Carlos Eduardo.

In: Industrial Informatics, IEEE Transactions on, Vol. 16, No. 8, 08.2020, p. 5617-5625.

Research output: Contribution to journalArticlepeer-review

Harvard

Künzel, G, Soares Indrusiak, L & Pereira, CE 2020, 'Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing', Industrial Informatics, IEEE Transactions on, vol. 16, no. 8, pp. 5617-5625. https://doi.org/10.1109/TII.2019.2941771

APA

Künzel, G., Soares Indrusiak, L., & Pereira, C. E. (2020). Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing. Industrial Informatics, IEEE Transactions on, 16(8), 5617-5625. https://doi.org/10.1109/TII.2019.2941771

Vancouver

Künzel G, Soares Indrusiak L, Pereira CE. Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing. Industrial Informatics, IEEE Transactions on. 2020 Aug;16(8):5617-5625. https://doi.org/10.1109/TII.2019.2941771

Author

Künzel, Gustavo ; Soares Indrusiak, Leandro ; Pereira, Carlos Eduardo. / Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks : A Q-Learning Approach for Graph Routing. In: Industrial Informatics, IEEE Transactions on. 2020 ; Vol. 16, No. 8. pp. 5617-5625.

Bibtex - Download

@article{0dc75453ae3d4ec59027756d1204d8b7,
title = "Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing",
abstract = "Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.",
author = "Gustavo K{\"u}nzel and {Soares Indrusiak}, Leandro and Pereira, {Carlos Eduardo}",
note = "{\textcopyright} 2019 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2020",
month = aug,
doi = "10.1109/TII.2019.2941771",
language = "English",
volume = "16",
pages = "5617--5625",
journal = "Industrial Informatics, IEEE Transactions on",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "8",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks

T2 - A Q-Learning Approach for Graph Routing

AU - Künzel, Gustavo

AU - Soares Indrusiak, Leandro

AU - Pereira, Carlos Eduardo

N1 - © 2019 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2020/8

Y1 - 2020/8

N2 - Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.

AB - Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.

U2 - 10.1109/TII.2019.2941771

DO - 10.1109/TII.2019.2941771

M3 - Article

VL - 16

SP - 5617

EP - 5625

JO - Industrial Informatics, IEEE Transactions on

JF - Industrial Informatics, IEEE Transactions on

SN - 1551-3203

IS - 8

ER -