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DTSTART:20210314T030000
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DTSTAMP:20210331T004708Z
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DTSTART;TZID=Canada/Eastern:20210330T190000
DTEND;TZID=Canada/Eastern:20210330T200000
DESCRIPTION:Connected and automated vehicles (CAVs)\, which enable informat
 ion exchange and content delivery in real time\, are expected to revolutio
 nize current transportation systems. However\, the emerging CAV applicatio
 ns such as content delivery pose stringent requirements on latency\, throu
 ghput\, and global connectivity. To empower multifarious CAV content deliv
 ery\, heterogeneous vehicular networks (HetVNets)\, which integrate the te
 rrestrial networks with aerial networks and space networks\, can guarantee
  reliable\, flexible\, and globally seamless service provisioning. In addi
 tion\, edge caching can facilitate content delivery by caching popular fil
 es in the HetVNet access points (APs) to relieve the backhaul traffic with
  a lower delivery delay. In this talk\, we investigate the content caching
  and delivery schemes in the caching-enabled HetVNet. First\, we study the
  content caching in terrestrial HetVNets with intermittent network connect
 ions. A coding-based caching scheme is designed and a matching-based conte
 nt placement algorithm is proposed to minimize the content delivery delay.
  Second\, UAV-aided caching is considered to assist vehicular content deli
 very in aerial-ground vehicular networks (AGVN) and a joint caching and tr
 ajectory optimization (JCTO) problem is investigated to jointly optimize c
 ontent caching\, content delivery\, and UAV trajectory. To enable real-tim
 e decision-making in highly dynamic vehicular networks\, we propose a deep
  supervised learning scheme to solve the JCTO problem. Third\, we investig
 ate caching-assisted cooperative content delivery in space-air-ground inte
 grated vehicular networks (SAGVNs)\, where the vehicle-to-AP association\,
  bandwidth allocation\, and content delivery ratio are jointly optimized. 
 To address the tightly coupled optimization variables\, we propose a load-
  and mobility-aware cooperative delivery scheme to solve the joint optimiz
 ation problem with the consideration of user fairness\, load balancing\, a
 nd vehicle mobility.\n\n350 Victoria Street\, Toronto\, Ontario\, Canada\,
  M5B 2K3\, Virtual: https://events.vtools.ieee.org/m/265745
LOCATION:350 Victoria Street\, Toronto\, Ontario\, Canada\, M5B 2K3\, Virtu
 al: https://events.vtools.ieee.org/m/265745
ORGANIZER:l5zhao@ryerson.ca
SEQUENCE:2
SUMMARY:Content Caching and Delivery in Heterogeneous Vehicular Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/265745
X-ALT-DESC:Description: <br /><p>Connected and automated vehicles (CAVs)\, 
 which enable information exchange and content delivery in real time\, are 
 expected to revolutionize current transportation systems. However\, the em
 erging CAV applications such as content delivery pose stringent requiremen
 ts on latency\, throughput\, and global connectivity. To empower multifari
 ous CAV content delivery\, heterogeneous vehicular networks (HetVNets)\, w
 hich integrate the terrestrial networks with aerial networks and space net
 works\, can guarantee reliable\, flexible\, and globally seamless service 
 provisioning. In addition\, edge caching can facilitate content delivery b
 y caching popular files in the HetVNet access points (APs) to relieve the 
 backhaul traffic with a lower delivery delay. In this talk\, we investigat
 e the content caching and delivery schemes in the caching-enabled HetVNet.
  First\, we study the content caching in terrestrial HetVNets with intermi
 ttent network connections. A coding-based caching scheme is designed and a
  matching-based content placement algorithm is proposed to minimize the co
 ntent delivery delay. Second\, UAV-aided caching is considered to assist v
 ehicular content delivery in aerial-ground vehicular networks (AGVN) and a
  joint caching and trajectory optimization (JCTO) problem is investigated 
 to jointly optimize content caching\, content delivery\, and UAV trajector
 y. To enable real-time decision-making in highly dynamic vehicular network
 s\, we propose a deep supervised learning scheme to solve the JCTO problem
 . Third\, we investigate caching-assisted cooperative content delivery in 
 space-air-ground integrated vehicular networks (SAGVNs)\, where the vehicl
 e-to-AP association\, bandwidth allocation\, and content delivery ratio ar
 e jointly optimized. To address the tightly coupled optimization variables
 \, we propose a load- and mobility-aware cooperative delivery scheme to so
 lve the joint optimization problem with the consideration of user fairness
 \, load balancing\, and vehicle mobility.</p>
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