Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Optimal design of ad hoc injection networks by using genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dynamic multi-hop clustering for mobile hybrid wireless networks
Proceedings of the 2nd international conference on Ubiquitous information management and communication
A vehicular mobility model based on real traffic counting data
Nets4Cars/Nets4Trains'11 Proceedings of the Third international conference on Communication technologies for vehicles
LSWTC: A local small-world topology control algorithm for backbone-assisted mobile ad hoc networks
LCN '10 Proceedings of the 2010 IEEE 35th Conference on Local Computer Networks
The impact of mobility on Mobile Ad Hoc Networks through the perspective of complex networks
Journal of Parallel and Distributed Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. In order to face this new multi-objective optimisation problem, the NSGAII algorithm was used on realistic instances in the city-centre of Luxembourg. The accurate tradeoff solutions found by NSGAII (assuming global knowledge of the network) will permit to better know and understand the problem. This will later ease the design of decentralised solutions to be used in real environments, as well as their future validation.