A fuzzy multi-metric QoS-balancing gateway selection algorithm in a clustered VANET to LTE advanced hybrid cellular network

G El Mouna Zhioua, N Tabbane… - IEEE Transactions …, 2014 - ieeexplore.ieee.org
IEEE Transactions on Vehicular Technology, 2014ieeexplore.ieee.org
Intelligent transportation systems are currently attracting the attention of the research
community and the automotive industry, which both aim to provide not only more safety in
the transportation systems but other high-quality services and applications for their
customers as well. In this paper, we propose a cooperative traffic transmission algorithm in a
joint vehicular ad hoc network-Long Term Evolution Advanced (LTE Advanced) hybrid
network architecture that elects a gateway to connect the source vehicle to the LTE …
Intelligent transportation systems are currently attracting the attention of the research community and the automotive industry, which both aim to provide not only more safety in the transportation systems but other high-quality services and applications for their customers as well. In this paper, we propose a cooperative traffic transmission algorithm in a joint vehicular ad hoc network-Long Term Evolution Advanced (LTE Advanced) hybrid network architecture that elects a gateway to connect the source vehicle to the LTE Advanced infrastructure under the scope of vehicle-to-infrastructure (V2I) communications. The originality of the proposed fuzzy quality-of-service (QoS)-balancing gateway selection (FQGwS) algorithm is the consideration of QoS traffic class constraints for electing the gateway. Our algorithm is a multicriteria and QoS-based scheme optimized by performing the fuzzy logic to make the decision on the appropriate gateway. Criteria are related to the received signal strength (RSS) and load of the cluster head (CH) and gateway candidates (GwCs), as well as the vehicle-to-vehicle link connectivity duration (LCD). Simulation results demonstrate that our algorithm gets better results than the deterministic scheme for gateway selection. Moreover, results show the efficiency of the FQGwS algorithm as it adapts its gateway selection decision to the cluster density and the relative velocity of the source node.
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