BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Canada/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20211107T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210324T011401Z
UID:355EAAF6-3D4F-4C53-8F46-59F0134D8EE8
DTSTART;TZID=Canada/Eastern:20210323T190000
DTEND;TZID=Canada/Eastern:20210323T200000
DESCRIPTION:Benefiting from advances in the automobile industry and wireles
 s communication technologies\, the vehicular network has been emerged as a
  key enabler of intelligent transportation services. However\, with more a
 nd more services and applications\, mobile data traffic generated by vehic
 les has been increasing and the issue of the overloaded computing task has
  been getting worse. Because of the limitation of spectrum resources and v
 ehicles’ onboard computing/caching resources\, it is challenging to prom
 ote vehicular networking technologies to support the emerged services and 
 applications\, especially those requiring sensitive delay and diverse reso
 urces. To effectively address the above challenges\, two potential technol
 ogies\, multi-access edge computing (MEC) and unmanned aerial vehicle (UAV
 )\, can be exploited in vehicular networks. In this presentation\, I will 
 introduce how to adopt optimization and AI technologies for efficient reso
 urce slicing\, and therefore supporting various applications with satisfie
 d quality of service (QoS) requirements in MEC- and/or UAV-assisted vehicu
 lar networks. For a relatively simple vehicular network scenario with only
  terrestrial MEC servers\, a model-based method is applied for dynamic spe
 ctrum management\, including spectrum slicing\, spectrum allocating\, and 
 transmit power controlling. For a vehicular network supported by both terr
 estrial and aerial MEC servers\, an AI-based method is applied to effectiv
 ely manage the spectrum\, computing\, and caching resources while satisfyi
 ng the QoS requirements of different applications.\n\nVirtual: https://eve
 nts.vtools.ieee.org/m/265588
LOCATION:Virtual: https://events.vtools.ieee.org/m/265588
ORGANIZER:l5zhao@ryerson.ca
SEQUENCE:5
SUMMARY:Intelligent Multi-Dimensional Resource Slicing in MEC-Assisted Vehi
 cular Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/265588
X-ALT-DESC:Description: <br /><p>Benefiting from advances in the automobile
  industry and wireless communication technologies\, the vehicular network 
 has been emerged as a key enabler of intelligent transportation services. 
 However\, with more and more services and applications\, mobile data traff
 ic generated by vehicles has been increasing and the issue of the overload
 ed computing task has been getting worse. Because of the limitation of spe
 ctrum resources and vehicles&rsquo\; onboard computing/caching resources\,
  it is challenging to promote vehicular networking technologies to support
  the emerged services and applications\, especially those requiring sensit
 ive delay and diverse resources. To effectively address the above challeng
 es\, two potential technologies\, multi-access edge computing (MEC) and un
 manned aerial vehicle (UAV)\, can be exploited in vehicular networks. In t
 his presentation\, I will introduce how to adopt optimization and AI techn
 ologies for efficient resource slicing\, and therefore supporting various 
 applications with satisfied quality of service (QoS) requirements in MEC- 
 and/or UAV-assisted vehicular networks. For a relatively simple vehicular 
 network scenario with only terrestrial MEC servers\, a model-based method 
 is applied for dynamic spectrum management\, including spectrum slicing\, 
 spectrum allocating\, and transmit power controlling. For a vehicular netw
 ork supported by both terrestrial and aerial MEC servers\, an AI-based met
 hod is applied to effectively manage the spectrum\, computing\, and cachin
 g resources while satisfying the QoS requirements of different application
 s. &nbsp\;&nbsp\;&nbsp\;</p>
END:VEVENT
END:VCALENDAR