Reference : Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics
Scientific journals : Article
Engineering, computing & technology : Computer science
Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics
Turcanu, Ion mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Sommer, Christoph mailto [Technische Universit├Ąt Dresden - TU Dresden > Chair of Networked Systems Modeling]
In press
IEEE Vehicular Technology Magazine
Institute of Electrical and Electronics Engineers
New York
[en] Information-Centric Vehicular Networking ; Fog Computing ; Sidelink Networking
[en] High-fidelity content distribution and other emerging applications of 5G and beyond-5G mobile broadband networking can put massive load on the core and Radio Access Network (RAN). To address this, direct Device to Device (D2D) communication has recently become a first-class citizen of these networks. While Information-Centric Vehicular Networking (ICVN) based on fog computing can indeed exploit such D2D links to alleviate the load on the RAN by proactively seeding content in the network, it has been shown that such seeding can cause even more load if performed where not needed. In addition, trying to determine where to seed content often causes additional load, negating the benefit of seeding. In this work, we therefore propose to adaptively seed fog nodes based on a purely virtual clustering approach. Here, vehicles are unaware of clustering decisions, thus no longer requiring an explicit exchange of control messages. We show that the benefit of such an adaptive approach goes beyond simply being able to flexibly trade off performance metrics versus each other: instead, it can consistently lower the load on the RAN link. We also show that this property even holds if node location information is only available as coarsely-grained as macro-scale grid cells.

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