%Aigaion2 BibTeX export from HES SO Valais Publications
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@ARTICLE{,
    author = {Manzo, Gaetano and Otalora, Sebastian and Marsan, Marco G Ajmone and Braun, Torsten and Nguyen, Hung and Rizzo, Gianluca},
     month = aug,
     title = {DeepFloat: Resource-Efficient DynamicManagement of Vehicular Floating Content},
   journal = {ITC 31- Networked Systems and Services},
      year = {2019},
  abstract = {Opportunistic communications are expected to playa  crucial  role  in  enabling  context-aware  vehicular  services.  Awidely  investigated  opportunistic  communication  paradigm  forstoring  a  piece  of  content  probabilistically  in  a  geographicalarea  is  Floating  Content  (FC).  A  key  issue  in  the  practicaldeployment of FC is how to tune content replication and cachingin  a  way  which  achieves  a  target  performance  (in  terms  ofthe  mean  fraction  of  users  possessing  the  content  in  a  givenregion  of  space)  while  minimizing  the  use  of  bandwidth  andhost memory. Fully distributed, distance-based approaches provehighly  inefficient,  and  may  not  meet  the  performance  target,while  centralized,  model-based  approaches  do  not  perform  wellin  realistic,  inhomogeneous  settings.In  this  work,  we  present  a  data-driven  centralized  approachto  resource-efficient,  QoS-aware  dynamic  management  of  FC.We  propose  a  Deep  Learning  strategy,  which  employs  a  Con-volutional  Neural  Network  (CNN)  to  capture  the  relationshipsbetween  patterns  of  users  mobility,  of  content  diffusion  andreplication, and FC performance in terms of resource utilizationand   of   content   availability   within   a   given   area.   Numericalevaluations  show  the  effectiveness  of  our  approach  in  derivingstrategies  which  efficiently  modulate  the  FC  operation  in  spaceand  effectively  adapt  to  mobility  pattern  changes  over  time},
Networking 
VANET
Floating Content
Machine Learning
}