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Parameter Self-Adaptation for Industrial Wireless Sensor-Actuator Networks

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Published:26 June 2020Publication History
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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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            • 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
              Issue’s Table of Contents

              Copyright © 2020 ACM

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 26 June 2020
              • Online AM: 7 May 2020
              • Accepted: 1 March 2020
              • Revised: 1 February 2020
              • Received: 1 May 2019
              Published in toit Volume 20, Issue 3

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