Abstract
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.
Highlights
DTNs refers to Delay Tolerant Networks, a particular category of the ad-hoc networks characterized by consistent low density levels which result in an intermittent connectivity between nodes [1]
The proposed Firefly Algorithm (FA)–Glowworm Swarm Optimization (GSO) Vehicular DTN (VDTN) scheme is compared to ProPHET and GeoSpray routers
We propose an extension of the bio-inspired DTN routing through the application of a new swarm-based forwarding strategy for VDTNs founded on the stochastic search of two combined swarm-inspired techniques
Summary
DTNs refers to Delay Tolerant Networks, a particular category of the ad-hoc networks characterized by consistent low density levels which result in an intermittent connectivity between nodes [1]. The probabilistic, or prediction-based, VDTN forwarding is one of the most effective routing policies in DTN-based networks [8] This DTN routing mode consists of exploiting the forwarding historic of nodes to predict their future routing abilities in order to make SCF forwarding decision and define the appropriate buffer management policy [9]. We propose an extension of the bio-inspired DTN routing through the application of a new swarm-based forwarding strategy for VDTNs founded on the stochastic search of two combined swarm-inspired techniques. This manuscript is organized as follows: Section 2 gathers together the literature works in probabilistic VDTN routing field.
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