BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250603T140235Z UID:28ECFA06-5357-471B-A1B8-BE875151A14E DTSTART;TZID=America/New_York:20250602T190000 DTEND;TZID=America/New_York:20250602T200000 DESCRIPTION:To support a wide range of vertical applications with limited r esources\, future connected systems rely on highly efficient distributed c ollaboration. However\, with the growing complexity of tasks and resources as well as the dynamic evolution of network topologies\, efficiently sele cting appropriate capabilities and resources within connected systems to e nsure effective task completion is becoming a significant challenge. To ad dress this\, we propose using trust as the unified evaluation framework to enable task-oriented resource and capability selection\, and present deta iled strategies on how trust evaluation can facilitate effective task comp letion in various types of connected systems. First\, a rapid trust evalua tion mechanism is introduced for highly dynamic Internet of Vehicles syste ms to enable timely and efficient collaborator selection. Additionally\, t ask-specific accurate trust evaluation is explored through the use of Gene rative AI and machine learning techniques to facilitate effective task com pletion. Moreover\, a trusted multi-task and multi-collaborator matching f ramework is developed using hypergraphs to uncover device dependencies und er specific tasks. Furthermore\, a spatio-temporal trust evaluation method is proposed for multi-hop collaboration\, leveraging Large Language Model (LLM)-enabled agents to facilitate privacy-preserving collaborator select ion. Finally\, several open challenges are discussed to highlight future r esearch directions.\n\nVirtual: https://events.vtools.ieee.org/m/487171 LOCATION:Virtual: https://events.vtools.ieee.org/m/487171 ORGANIZER:jche629@uwo.ca SEQUENCE:23 SUMMARY:Trusted Collaboration in Connected Intelligent Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/487171 X-ALT-DESC:Description: <br /><p>To support a wide range of vertical applic ations with limited resources\, future connected systems rely on highly ef ficient distributed collaboration. However\, with the growing complexity o f tasks and resources as well as the dynamic evolution of network topologi es\, efficiently selecting appropriate capabilities and resources within c onnected systems to ensure effective task completion is becoming a signifi cant challenge. To address this\, we propose using trust as the unified ev aluation framework to enable task-oriented resource and capability selecti on\, and present detailed strategies on how trust evaluation can facilitat e effective task completion in various types of connected systems. First\, a rapid trust evaluation mechanism is introduced for highly dynamic Inter net of Vehicles systems to enable timely and efficient collaborator select ion. Additionally\, task-specific accurate trust evaluation is explored th rough the use of Generative AI and machine learning techniques to facilita te effective task completion. &nbsp\;Moreover\, a trusted multi-task and m ulti-collaborator matching framework is developed using hypergraphs to unc over device dependencies under specific tasks. Furthermore\, a spatio-temp oral trust evaluation method is proposed for multi-hop collaboration\, lev eraging Large Language Model (LLM)-enabled agents to facilitate privacy-pr eserving collaborator selection. Finally\, several open challenges are dis cussed to highlight future research directions.&nbsp\;</p> END:VEVENT END:VCALENDAR