Percorrer por autor "Boukra, Abdelmadjid"
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- Hybrid swarm-based geographic VDTN routingPublication . Azzoug, Youcef; Boukra, Abdelmadjid; Soares, V.N.G.J.Vehicular Delay Tolerant Network (VDTN) routing is referred to the hybridization of Delay Tolerant Networks (DTNs) with VANETs which mobilizes both knowledge-based and geography-based forwarding techniques. Numerous shortage are stated in existing VDTN routing protocols in both modes exposes such as the inaccurate location information and uncontrolled congestion due to bundles flooding. In this paper, we introduce a hybrid VDTN routing strategy combining a swarm-inspired algorithm, namely the Firefly Algorithm (FA) to enhance the decision-making of finding better next Store-Carry-and-Forward (SCF) relay vehicle in accordance with the use of geographical forwarding for better localization of closer nodes to the destination. Thus, the flooding of bundles is controlled by the movement of fireflies in early routing stages, then a reliable geographic routing is followed to better track closer SCF vehicles toward bundle’s destination. We implement our approach using the Opportunistic Network Environment (ONE) simulator and compare it with few common DTN routers, namely Spray-and-Wait (SnW), ProPHET and Epidemic (ER) routers; the simulation results shows superior balance between average delivery delays and delivery probability with a reasonable overheads ratio and flooding levels.
- A probabilistic VDTN routing scheme based on hybrid swarm-based approachPublication . Azzoug, Youcef; Boukra, Abdelmadjid; Soares, V.N.G.J.The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.