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DTSTAMP:20260921T095222Z
UID:037673A8-4A08-4939-AC9D-BC0A9B79B69C
DTSTART;TZID=Europe/Rome:20260916T090000
DTEND;TZID=Europe/Rome:20260918T180000
DESCRIPTION:Special Session framework: Held in the framework of DS-RT 2026 
 - The 30th International Symposium on Distributed Simulation and Real-Time
  Applications.\n\nGoals of the Session: The primary objective of this sess
 ion is to explore the fundamental shift from centralized simulation models
  to distributed\, real-time Digital Twins (DTs) that operate across the en
 tire computing continuum. As Digital Twins evolve to represent increasingl
 y complex\, large-scale systems—such as smart cities\, global supply cha
 ins\, and autonomous fleets—the underlying network architecture must evo
 lve from a passive transport layer to an active\, &quot;intelligence-aware&quot; inf
 rastructure.\n\nTopics of Interest include\, but are not limited to:\n\n-\
 nArchitectures and frameworks for the Edge-to-Cloud-Continuum in DTs\n\n-\
 nDigital Twins for Networking (DT4NW): performance\, reliability\, and sec
 urity\n\n-\nSAGIN (Space-Air-Ground Integrated Networks) for DT connectivi
 ty\n\n-\nDistributed AI and Federated Learning for intelligent DT modeling
 \n\n-\nReal-time state synchronization and ultra-low latency protocols\n\n
 -\nIntelligence at the edge and autonomous decision-making in DTs\n\n-\nDy
 namic resource orchestration and task offloading for DT workloads\n\n-\nNe
 twork Slicing and QoS/QoE provisioning for mission-critical DTs\n\n-\nSecu
 rity\, privacy-preserving techniques\, and trust management\n\n-\nInterope
 rability standards and multi-domain Digital Twins\n\n-\nDigital-Twin-as-a-
 Service (DTaaS) and cloud-native simulation\n\n-\nPredictive &quot;What-if&quot; ana
 lysis and real-time optimization via DTs\n\n-\nAI-native network managemen
 t and self-healing continua\n\n-\nAI-native network and physical layers de
 sign through DT feedback\n\n-\nNetwork abstraction within Digital Twin eco
 systems\n\n-\nIntegrated sensing\, communication\, and computation (ISCC) 
 modeling\n\n-\nProof-Of-Concept and DT prototyping for innovative applicat
 ions\n\nBldg: Department of Mathematics “TULLIO LEVI-CIVITA”\, Via Tri
 este 63\, Padova\, Veneto\, Italy
LOCATION:Bldg: Department of Mathematics “TULLIO LEVI-CIVITA”\, Via Tri
 este 63\, Padova\, Veneto\, Italy
ORGANIZER:daniele.tarchi@unifi.it
SEQUENCE:19
SUMMARY:Special Session on Next-Generation Networking and Computing Continu
 um for Large-Scale Digital Twins (DS-RT 2026)
URL;VALUE=URI:https://events.vtools.ieee.org/m/562059
X-ALT-DESC:Description: &lt;br /&gt;&lt;p data-path-to-node=&quot;5\,7\,2\,0&quot;&gt;&lt;strong dat
 a-path-to-node=&quot;5\,7\,2\,0&quot; data-index-in-node=&quot;0&quot;&gt;Special Session framewo
 rk:&lt;/strong&gt; Held in the framework of DS-RT 2026 - The 30th International 
 Symposium on Distributed Simulation and Real-Time Applications.&lt;/p&gt;\n&lt;p da
 ta-path-to-node=&quot;5\,7\,2\,1&quot;&gt;&lt;strong data-path-to-node=&quot;5\,7\,2\,1&quot; data-i
 ndex-in-node=&quot;0&quot;&gt;Goals of the Session:&lt;/strong&gt; The primary objective of t
 his session is to explore the fundamental shift from centralized simulatio
 n models to distributed\, real-time Digital Twins (DTs) that operate acros
 s the entire computing continuum. As Digital Twins evolve to represent inc
 reasingly complex\, large-scale systems&amp;mdash\;such as smart cities\, glob
 al supply chains\, and autonomous fleets&amp;mdash\;the underlying network arc
 hitecture must evolve from a passive transport layer to an active\, &quot;intel
 ligence-aware&quot; infrastructure.&lt;/p&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,2&quot;&gt;&lt;str
 ong data-path-to-node=&quot;5\,7\,2\,2&quot; data-index-in-node=&quot;0&quot;&gt;Topics of Intere
 st include\, but are not limited to:&lt;/strong&gt;&lt;/p&gt;\n&lt;ul data-path-to-node=&quot;
 5\,7\,2\,3&quot;&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,0\,0&quot;&gt;Architectures 
 and frameworks for the Edge-to-Cloud-Continuum in DTs&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p
  data-path-to-node=&quot;5\,7\,2\,3\,1\,0&quot;&gt;Digital Twins for Networking (DT4NW)
 : performance\, reliability\, and security&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-
 to-node=&quot;5\,7\,2\,3\,2\,0&quot;&gt;SAGIN (Space-Air-Ground Integrated Networks) fo
 r DT connectivity&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,3\,0
 &quot;&gt;Distributed AI and Federated Learning for intelligent DT modeling&lt;/p&gt;\n&lt;
 /li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,4\,0&quot;&gt;Real-time state synchr
 onization and ultra-low latency protocols&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-t
 o-node=&quot;5\,7\,2\,3\,5\,0&quot;&gt;Intelligence at the edge and autonomous decision
 -making in DTs&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,6\,0&quot;&gt;D
 ynamic resource orchestration and task offloading for DT workloads&lt;/p&gt;\n&lt;/
 li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,7\,0&quot;&gt;Network Slicing and QoS
 /QoE provisioning for mission-critical DTs&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-
 to-node=&quot;5\,7\,2\,3\,8\,0&quot;&gt;Security\, privacy-preserving techniques\, and 
 trust management&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,9\,0&quot;
 &gt;Interoperability standards and multi-domain Digital Twins&lt;/p&gt;\n&lt;/li&gt;\n&lt;li
 &gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,10\,0&quot;&gt;Digital-Twin-as-a-Service (DTa
 aS) and cloud-native simulation&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\
 ,7\,2\,3\,11\,0&quot;&gt;Predictive &quot;What-if&quot; analysis and real-time optimization 
 via DTs&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,12\,0&quot;&gt;AI-nati
 ve network management and self-healing continua&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-
 path-to-node=&quot;5\,7\,2\,3\,13\,0&quot;&gt;AI-native network and physical layers des
 ign through DT feedback&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3
 \,14\,0&quot;&gt;Network abstraction within Digital Twin ecosystems&lt;/p&gt;\n&lt;/li&gt;\n&lt;l
 i&gt;\n&lt;p data-path-to-node=&quot;5\,7\,2\,3\,15\,0&quot;&gt;Integrated sensing\, communic
 ation\, and computation (ISCC) modeling&lt;/p&gt;\n&lt;/li&gt;\n&lt;li&gt;\n&lt;p data-path-to-
 node=&quot;5\,7\,2\,3\,16\,0&quot;&gt;Proof-Of-Concept and DT prototyping for innovativ
 e applications&lt;/p&gt;\n&lt;/li&gt;\n&lt;/ul&gt;
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