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:20251022T234121Z UID:01F3DC27-B36F-4BC8-AE49-568803ECB802 DTSTART;TZID=America/New_York:20251022T183000 DTEND;TZID=America/New_York:20251022T193000 DESCRIPTION:The rapid evolution of communication and computing technologies and their integration with Artificial Intelligence (AI) have enabled a pl ethora of emerging vertical applications\, leading to increasingly complex tasks. Effectively completing such tasks for application-oriented value r ealization requires fulfilling diverse task-specific needs\, which in turn rely on the tailored provisioning of heterogeneous services through effic ient resource allocation. However\, the limited onboard resources and serv ice capabilities of individual machines make it impractical for them to co mplete tasks independently. As a result\, these considerations necessitate seamless collaboration among interconnected machines for application-orie nted value realization by dynamically matching the comprehensive task comp letion requirements with resources and capabilities of potential collabora tors through proper collaborator selection.\n\nTo address the related chal lenges\, this thesis first introduces a new concept of task-specific trust as a holistic evaluation metric and task completion mechanism\, guiding c ollaborator selection process to ensure their resources and capabilities a re precisely aligned with diverse task-specific requirements\, thereby ena bling needs fulfillment and ultimately realizing application-oriented valu e. To realize this objective\, dynamic trust provisioning approaches are p roposed to select collaborators for effective task completion by capturing task-specific needs\, profiling the task owner’s resources and capabili ties\, and evaluating their external collaboration conditions.\n\nIn pursu it of value realization in time-sensitive tasks from Internet-of-Vehicles (IoV)\, we propose a Rapid Trust Evaluation (RTE) mechanism to enable rapi d and accurate collaborator selection for fulfilling the timeliness need o f task completion. RTE accelerates collaborator selection in cold-start si tuations by leveraging indirect trust\, generated from trusted recommendat ions of network servers or reliable vehicles. To improve accuracy without sacrificing timeliness\, an adaptive aggregation scheme progressively inco rporates task owners’ collaboration experiences and environment observat ions as direct experiential and capability trust\, with all trust factors related weights dynamically adjusted across different IoV collaboration st ages.\n\nBeyond time-sensitive tasks\, the proposed task-specific trust is further applied to completing complex tasks with diverse requirements\, w here value is realized through selecting collaborators that fulfill multi- dimensional task-specific needs. These needs are modeled as distinct metri cs\, termed Value of Task Completion\, through which task-specific trust g uides collaborator selection. A trust-guided bipartite graph matching prob lem between tasks and collaborators is then formulated\, where matching co mplexity is reduced by initially clustering tasks into limited categories and subsequently arranging them by priorities.\n\nTo further reduce risks for task completion in highly dynamic systems\, task-specific trust is int egrated with Graph Neural Networks (GNNs) as a new trust prediction method that effectively leverages historical information. However\, intensified resource contention under limited resources and concurrent tasks limits it s effectiveness in fulfilling task-specific needs and thereby undermines v alue realization. To address this\, a trusted collaborator group formation and task allocation strategy is proposed for concurrent task completion u nder long-term fairness. The concurrent task orchestration is formulated a s a long-term average task utility maximization problem under α-fairness\ , solved via a hierarchically matching strategy that is supported by task- specific trust prediction and fairness-aware Deep Reinforcement Learning ( DRL)\, to form and match trusted collaborator groups in a coarse-to-fine m anner.\n\nVirtual: https://events.vtools.ieee.org/m/508033 LOCATION:Virtual: https://events.vtools.ieee.org/m/508033 ORGANIZER:fchen324@uwo.ca SEQUENCE:8 SUMMARY:Dynamic Trust Provisioning for Collaborative Task Completion and Va lue Realization in Future Connected Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/508033 X-ALT-DESC:Description: <br /><div class="x_elementToProof" data-olk-copy-s ource="MessageBody">The rapid evolution of communication and computing tec hnologies and their integration with Artificial Intelligence (AI) have ena bled a plethora of emerging vertical applications\, leading to increasingl y complex tasks. Effectively completing such tasks for application-oriente d value realization requires fulfilling diverse task-specific needs\, whic h in turn rely on the tailored provisioning of heterogeneous services thro ugh efficient resource allocation. However\, the limited onboard resources and service capabilities of individual machines make it impractical for t hem to complete tasks independently. As a result\, these considerations ne cessitate seamless collaboration among interconnected machines for applica tion-oriented value realization by dynamically matching the comprehensive task completion requirements with resources and capabilities of potential collaborators through proper collaborator selection.</div>\n<div class="x_ elementToProof">&nbsp\;</div>\n<div class="x_elementToProof">To address th e related challenges\, this thesis first introduces a new concept of task- specific trust as a holistic evaluation metric and task completion mechani sm\, guiding collaborator selection process to ensure their resources and capabilities are precisely aligned with diverse task-specific requirements \, thereby enabling needs fulfillment and ultimately realizing application -oriented value. To realize this objective\, dynamic trust provisioning ap proaches are proposed to select collaborators for effective task completio n by capturing task-specific needs\, profiling the task owner&rsquo\;s res ources and capabilities\, and evaluating their external collaboration cond itions.</div>\n<div class="x_elementToProof">&nbsp\;</div>\n<div class="x_ elementToProof">In pursuit of value realization in time-sensitive tasks fr om Internet-of-Vehicles (IoV)\, we propose a Rapid Trust Evaluation (RTE) mechanism to enable rapid and accurate collaborator selection for fulfilli ng the timeliness need of task completion. RTE accelerates collaborator se lection in cold-start situations by leveraging indirect trust\, generated from trusted recommendations of network servers or reliable vehicles. To i mprove accuracy without sacrificing timeliness\, an adaptive aggregation s cheme progressively incorporates task owners&rsquo\; collaboration experie nces and environment observations as direct experiential and capability tr ust\, with all trust factors related weights dynamically adjusted across d ifferent IoV collaboration stages.</div>\n<div class="x_elementToProof">&n bsp\;</div>\n<div class="x_elementToProof">Beyond time-sensitive tasks\, t he proposed task-specific trust is further applied to completing complex t asks with diverse requirements\, where value is realized through selecting collaborators that fulfill multi-dimensional task-specific needs. These n eeds are modeled as distinct metrics\, termed Value of Task Completion\, t hrough which task-specific trust guides collaborator selection. A trust-gu ided bipartite graph matching problem between tasks and collaborators is t hen formulated\, where matching complexity is reduced by initially cluster ing tasks into limited categories and subsequently arranging them by prior ities.</div>\n<div class="x_elementToProof">&nbsp\;</div>\n<div class="x_e lementToProof">To further reduce risks for task completion in highly dynam ic systems\, task-specific trust is integrated with Graph Neural Networks (GNNs) as a new trust prediction method that effectively leverages histori cal information. However\, intensified resource contention under limited r esources and concurrent tasks limits its effectiveness in fulfilling task- specific needs and thereby undermines value realization. To address this\, a trusted collaborator group formation and task allocation strategy is pr oposed for concurrent task completion under long-term fairness. The concur rent task orchestration is formulated as a long-term average task utility maximization problem under &alpha\;-fairness\, solved via a hierarchically matching strategy that is supported by task-specific trust prediction and fairness-aware Deep Reinforcement Learning (DRL)\, to form and match trus ted collaborator groups in a coarse-to-fine manner.</div> END:VEVENT END:VCALENDAR