Mobility-aware proactive edge caching for connected vehicles using federated learning
Yu, Z; Hu, J; Min, G; et al.Zhao, Z; Miao, W; Hossain, MS
Date: 31 August 2020
Journal
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers
Publisher DOI
Abstract
Content Caching at the edge of vehicular networks
has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive
vehicular applications for intelligent transportation. The existing
content caching schemes, when used in vehicular networks,
face two distinct challenges: ...
Content Caching at the edge of vehicular networks
has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive
vehicular applications for intelligent transportation. The existing
content caching schemes, when used in vehicular networks,
face two distinct challenges: 1) Vehicles connected to an edge
server keep moving, making the content popularity varying and
hard to predict. 2) Cached content is easily out-of-date since
each connected vehicle stays in the area of an edge server
for a short duration. To address these challenges, we propose
a Mobility-aware Proactive edge Caching scheme based on
Federated learning (MPCF). This new scheme enables multiple
vehicles to collaboratively learn a global model for predicting
content popularity with the private training data distributed on
local vehicles. MPCF also employs a Context-aware Adversarial
AutoEncoder to predict the highly dynamic content popularity.
Besides, MPCF integrates a mobility-aware cache replacement
policy, which allows the network edges to add/evict contents in
response to the mobility patterns and preferences of vehicles.
MPCF can greatly improve cache performance, effectively protect
users’ privacy and significantly reduce communication costs.
Experimental results demonstrate that MPCF outperforms other
baseline caching schemes in terms of the cache hit ratio in
vehicular edge networks.
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0