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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20250307T001747Z
UID:BA4C29C9-6CE6-4B04-BA4D-952BC24FEF3D
DTSTART;TZID=Etc/UTC:20241219T120000
DTEND;TZID=Etc/UTC:20241219T130000
DESCRIPTION:Special Presentation by Dr. Ayush Dwivedi (Tampere U.\, Finland
 )\n\nHosted by the Future Networks Artificial Intelligence &amp; Machine Learn
 ing (AIML) Working Group\n\nDate/Time: Thursday\, December 19th\, 2024 @ 1
 2:00 UTC\n\nTopic:\n\nMachine Learning Ideas for Sensor Networks\n\nAbstra
 ct:\n\nThis talk explores the application of machine learning (ML) techniq
 ues to address the challenges in scaling sensor networks while maintaining
  accuracy and energy efficiency. First\, we discuss ML-based approaches to
  optimize data transmission\, significantly reducing communication overhea
 d and extending battery lifetimes. Second\, we examine the impact of such 
 optimizations on enhancing overall network capacity. Finally\, the role of
  ML in sensor calibration is analyzed\, highlighting its potential to impr
 ove accuracy and scalability.\n\nSpeaker:\n\nDr. Ayush Dwivedi a postdocto
 ral research scholar at Tampere University\, Finland\, with a PhD from the
  International Institute of Information Technology\, Hyderabad\, India. Hi
 s research focuses on next-generation wireless communication\, including n
 on-terrestrial networks\, stochastic geometry\, satellite-based IoT\, and 
 sensor networks for smart cities. He has been an active member of the IEEE
  INGR Satellite Working Group for several years\, contributing to ideas on
  satellite-based sensor networks.\n\nVirtual: https://events.vtools.ieee.o
 rg/m/448221
LOCATION:Virtual: https://events.vtools.ieee.org/m/448221
ORGANIZER:c.polk@comsoc.org
SEQUENCE:12
SUMMARY:Machine Learning Ideas for Sensor Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/448221
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in
 \;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/1ed7b
 41b-d342-437e-ab42-77dbcf7f4dbc&quot;&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-
 top: 12.0pt\;&quot;&gt;Special Presentation by&lt;strong&gt; Dr. Ayush Dwivedi (Tampere 
 U.\, Finland)&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt
 \;&quot;&gt;Hosted by the Future Networks&lt;strong&gt; Artificial Intelligence &amp;amp\; M
 achine Learning (AIML) Working Group&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; st
 yle=&quot;margin-top: 12.0pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; font-f
 amily: Copperplate\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme
 -font: minor-fareast\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: 
 minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-
 bidi-language: AR-SA\;&quot;&gt;Date/Time&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-size: 1
 2.0pt\; font-family: &#39;Calibri&#39;\,sans-serif\; mso-ascii-theme-font: minor-l
 atin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-f
 areast\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: Arial\;
  mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-
 language: ZH-TW\; mso-bidi-language: AR-SA\;&quot;&gt;: &lt;strong&gt;Thursday\, Decembe
 r 19&lt;sup&gt;th&lt;/sup&gt;\, 2024 @ 12:00 UTC&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNor
 mal&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt
 \; font-family: Copperplate\;&quot;&gt;Topic&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span styl
 e=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n
 &lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\;&quot;&gt;Machine Lea
 rning Ideas for Sensor Networks&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; 
 style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; fo
 nt-family: Copperplate\;&quot;&gt;Abstract&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=
 &quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p
  class=&quot;MsoNormal&quot;&gt;&lt;span style=&quot;font-size: 12.0pt\; font-family: &#39;Calibri&#39;
 \,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family
 : PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font:
  minor-latin\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bi
 di\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-lan
 guage: AR-SA\;&quot;&gt;This talk explores the application of machine learning (ML
 ) techniques to address the challenges in scaling sensor networks while ma
 intaining accuracy and energy efficiency. First\, we discuss ML-based appr
 oaches to optimize data transmission\, significantly reducing communicatio
 n overhead and extending battery lifetimes. Second\, we examine the impact
  of such optimizations on enhancing overall network capacity. Finally\, th
 e role of ML in sensor calibration is analyzed\, highlighting its potentia
 l to improve accuracy and scalability.&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;span style=
 &quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;&lt;u&gt;Speaker&lt;/u&gt;:&lt;/span&gt;&lt;/s
 trong&gt;&lt;/p&gt;\n&lt;table style=&quot;border-collapse: collapse\; width: 100%\;&quot; borde
 r=&quot;1&quot;&gt;&lt;colgroup&gt;&lt;col style=&quot;width: 24.3015%\;&quot;&gt;&lt;col style=&quot;width: 75.7635%
 \;&quot;&gt;&lt;/colgroup&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td&gt;&lt;img src=&quot;https://events.vtools.ieee.o
 rg/vtools_ui/media/display/723677ff-f5c7-4bdb-b656-5dd7c34c799f&quot;&gt;&lt;/td&gt;\n&lt;t
 d&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 6.0pt\;&quot;&gt;&lt;span style=&quot;mso-no-p
 roof: yes\;&quot;&gt;&lt;span style=&quot;font-size: 12.0pt\; font-family: &#39;Calibri&#39;\,sans
 -serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMin
 gLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor
 -latin\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; m
 so-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language:
  AR-SA\; mso-no-proof: yes\;&quot;&gt;Dr. Ayush Dwivedi a postdoctoral research sc
 holar at Tampere University\, Finland\, with a PhD from the International 
 Institute of Information Technology\, Hyderabad\, India. His research focu
 ses on next-generation wireless communication\, including non-terrestrial 
 networks\, stochastic geometry\, satellite-based IoT\, and sensor networks
  for smart cities. He has been an active member of the IEEE INGR Satellite
  Working Group for several years\, contributing to ideas on satellite-base
 d sensor networks.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-t
 op: 6.0pt\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;
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