Distributive Justice for Fair Auto-Adaptive Clusters of Connected Vehicles

Connected vehicles will likely use hybrid communication networks. Presumably a licence-free radio access technology (RAT) will be used for vehicle-to-vehicle (V2V) contact, complemented by a cellular network, with an associated usage cost. In previous work, we developed a self-adaptive clustering al...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2017 IEEE 2nd International Workshops on Foundations and Applications of Self Systems (FASW) s. 79 - 84
Hlavní autori: Garbiso, Julian, Diaconescu, Ada, Coupechoux, Marceau, Pitt, Jeremy, Leroy, Bertrand
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2017
Predmet:
Algorithm design and analysis
Base stations
Cellular networks
clustering
Clustering algorithms
Conferences
distributive justice
hybrid vehicular networks
Resource management
theory of commons
vanets
Voting
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Connected vehicles will likely use hybrid communication networks. Presumably a licence-free radio access technology (RAT) will be used for vehicle-to-vehicle (V2V) contact, complemented by a cellular network, with an associated usage cost. In previous work, we developed a self-adaptive clustering algorithm for reducing cellular access costs, while ensuring that clustering overheads do not saturate the V2V link. However, the vehicle in the role of Cluster Head (CH) is the only one to bear the communication costs in a cluster's lifetime. This means certain drivers may pay much more than others for the same service, which may in turn undermine the system's social acceptability. In this paper, we adopt the theory of distributive justice to ensure fairness over time, and hence make the system socially acceptable. We compare the proposed approach with our previous algorithm through simulations, analyzing network performance and specific fairness metrics. We show that the proposed approach improves fairness metrics significantly, while not affecting network performance.
DOI:10.1109/FAS-W.2017.124