research-article

Parameter Self-Adaptation for Industrial Wireless Sensor-Actuator Networks

Authors:

Mo ShaAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 3

Article No.: 28, Pages 1 - 28

https://doi.org/10.1145/3388240

Published: 26 June 2020 Publication History

Abstract

Wireless sensor-actuator network (WSAN) technology is gaining rapid adoption by industrial Internet of Things applications in recent years. A WSAN typically connects sensors, actuators, and controllers in industrial facilities, such as steel mills, oil refineries, chemical plants, and infrastructures implementing complex monitoring and control processes. IEEE 802.15.4–based WSANs operate at low power and can be manufactured inexpensively, which makes them ideal where battery lifetime and costs are important. Recent studies have shown that the selection of network parameters has a significant effect on network performance. However, the current practice of parameter selection is largely based on experience and rules of thumb involving a coarse-grained analysis of expected network load and dynamics or measurements during a few field trials, resulting in non-optimal decisions in many cases. In this work, we develop P-SAFE (Parameter Selection and Adaptation FramEwork), which optimally selects the network parameters based on the application quality-of-service demands and adapts the parameter configuration at runtime to consistently satisfy the dynamic requirements. We implement P-SAFE and evaluate it on three physical testbeds. Experimental results show that our solution can significantly better meet the application quality-of-service demand compared to the state of the art.

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Cited By

Shi JMa ACheng XSha MXi P(2024)Adapting Wireless Network Configuration From Simulation to Reality via Deep Learning-Based Domain AdaptationIEEE/ACM Transactions on Networking10.1109/TNET.2023.333534632:3(1983-1998)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2023.3335346
Tian WMeng XWang JYan H(2022)Optimization of Distribution Automation System Based on Artificial Intelligence Wireless Network TechnologyJournal of Sensors10.1155/2022/16466672022(1-8)Online publication date: 17-Sep-2022
https://doi.org/10.1155/2022/1646667

Index Terms

Parameter Self-Adaptation for Industrial Wireless Sensor-Actuator Networks
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Networks

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 3

SI: Evolution of IoT Networking Architectures papers

August 2020

259 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3408328

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Publication History

Published: 26 June 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 February 2020

Received: 01 May 2019

Published in TOIT Volume 20, Issue 3

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Cited By

Shi JMa ACheng XSha MXi P(2024)Adapting Wireless Network Configuration From Simulation to Reality via Deep Learning-Based Domain AdaptationIEEE/ACM Transactions on Networking10.1109/TNET.2023.333534632:3(1983-1998)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2023.3335346
Tian WMeng XWang JYan H(2022)Optimization of Distribution Automation System Based on Artificial Intelligence Wireless Network TechnologyJournal of Sensors10.1155/2022/16466672022(1-8)Online publication date: 17-Sep-2022
https://doi.org/10.1155/2022/1646667

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