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PRODID:IEEE vTools.Events//EN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
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BEGIN:VEVENT
DTSTAMP:20260325T065424Z
UID:E95DF4AC-933F-45D8-B55A-9FD5E8E9F71A
DTSTART;TZID=Asia/Kolkata:20260320T163000
DTEND;TZID=Asia/Kolkata:20260320T173000
DESCRIPTION:Title: Robust task allocation algorithms for dynamic IoT system
 s\nSpeaker: Mr. Vikky Masih\, PhD Scholar\, MFSDSAI Department\nDate: 20 M
 arch 2026\nTime: 4:30 PM – 5:30 PM\nVenue: Seminar Room\, 2nd Floor\, Ne
 w Extension Building\, EEE Department\n\nThe abstract of the talk is provi
 ded below:\nIn Multi-access edge computing based IoT networks\, task sched
 uling requires balancing a critical trade-off between energy consumption a
 nd deadline violations. Standard reinforcement learning approaches are hig
 hly vulnerable to performance degradation when faced with unpredictable ta
 sk arrival rates. To address this\, we propose a novel provably robust rei
 nforcement learning-based task allocation algorithm that maintains stable 
 performance despite environmental volatility.\n\nSpeaker(s): Vikky \, \n\n
 Seminar Room\, EEE Department \, Guwahati\, Assam\, India\, 781039
LOCATION:Seminar Room\, EEE Department \, Guwahati\, Assam\, India\, 78103
 9
ORGANIZER:br.manoj@gmail.com
SEQUENCE:45
SUMMARY:Robust task allocation algorithms for dynamic IoT systems
URL;VALUE=URI:https://events.vtools.ieee.org/m/548969
X-ALT-DESC:Description: <br /><div class="x_elementToProof"><span data-olk-
 copy-source="MessageBody">Title:&nbsp\;</span>Robust task allocation algor
 ithms for dynamic IoT systems</div>\n<div class="x_elementToProof">Speaker
 :&nbsp\;Mr. Vikky Masih\, PhD Scholar\, &nbsp\;MFSDSAI&nbsp\;Department</d
 iv>\n<div class="x_elementToProof">Date:&nbsp\;20 March 2026</div>\n<div c
 lass="x_elementToProof">Time:&nbsp\;4:30 PM &ndash\; 5:30 PM</div>\n<div c
 lass="x_elementToProof">Venue:&nbsp\;Seminar&nbsp\;Room\, 2nd Floor\, New 
 Extension&nbsp\;Building\, EEE Department</div>\n<div class="x_elementToPr
 oof">&nbsp\;</div>\n<div class="x_elementToProof">&nbsp\;</div>\n<div clas
 s="x_elementToProof">\n<div class="x_elementToProof" data-olk-copy-source=
 "MessageBody">The abstract of the talk is provided&nbsp\;below:</div>\n<di
 v class="x_elementToProof">In Multi-access edge computing&nbsp\; based IoT
  networks\, task scheduling requires balancing a critical trade-off betwee
 n energy consumption and deadline violations. Standard reinforcement learn
 ing approaches are highly vulnerable to performance degradation when faced
  with unpredictable task arrival rates. To address this\, we propose a nov
 el provably robust reinforcement learning-based task allocation algorithm 
 that maintains stable performance despite environmental volatility.</div>\
 n</div>
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