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         <title>Machine Learning Based Wireless Interference Estimation in a Robotic Force-Seeking Application</title>
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         <topic>Cooperating objects (Computer systems)</topic>
         <topic>Factory communications</topic>
         <topic>IEEE 802.11 (Standard)</topic>
         <topic>Industrial wireless</topic>
         <topic>Machine learning</topic>
         <topic>Robotics</topic>
         <topic>Wireless communication systems</topic>
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         <namePart>Kashef, Mohamed.</namePart>
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    <name type="personal">
         <namePart>Montgomery, Karl.</namePart>
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    <name type="personal">
         <namePart>Liu, Yongkang.</namePart>
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    <name type="personal">
         <namePart>Foufou, Sebti.</namePart>
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         <namePart>Material Measurement Laboratory</namePart>
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    <abstract>Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.</abstract>
    <note type="statement of responsibility">Richard Candell; Mohamed Kashef; Karl Montgomery; Yongkang Liu; Sebti Foufou.</note>
    <note>February 2022.</note>
    <note>Title from PDF title page (viewed January 4, 2023).</note>
    <note type="bibliography">Includes bibliographical references.</note>
    <note type="venue">Approved by the NIST Editorial Review Board on 2022-09-20</note>
    <note type="system details">Mode of access: World Wide Web.</note>
    <note type="system details">Systems requirements: Adobe Acrobat PDF reader.</note>
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         <topic>Cooperating objects (Computer systems)</topic>
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         <topic>Wireless communication systems</topic>
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         <topic>Robotics</topic>
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         <topic>Machine learning</topic>
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         <topic>IEEE 802.11 (Standard)</topic>
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         <topic>Industrial wireless</topic>
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              <title>NISTIR; NIST IR; NIST interagency report; NIST internal report; 8416</title>
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