dc.contributor.author | Yu, Z | |
dc.contributor.author | Hu, J | |
dc.contributor.author | Min, G | |
dc.contributor.author | Zhao, Z | |
dc.contributor.author | Miao, W | |
dc.contributor.author | Hossain, MS | |
dc.date.accessioned | 2020-08-17T10:30:58Z | |
dc.date.issued | 2020-08-31 | |
dc.description.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: 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hard to predict. 2) Cached content is easily out-of-date since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. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Published online 31 August 2020 | en_GB |
dc.identifier.doi | 10.1109/TITS.2020.3017474 | |
dc.identifier.grantnumber | EP/M013936/2 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/122491 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission | en_GB |
dc.subject | Content Caching | en_GB |
dc.subject | Edge Computing | en_GB |
dc.subject | Federated Learning | en_GB |
dc.subject | Deep Learning | en_GB |
dc.subject | Vehicular Networks | en_GB |
dc.title | Mobility-aware proactive edge caching for connected vehicles using federated learning | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-08-17T10:30:58Z | |
dc.identifier.issn | 1524-9050 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Intelligent Transportation Systems | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2020-08-13 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2020-08-13 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-08-14T23:31:04Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-09-04T12:42:41Z | |
refterms.panel | B | en_GB |