UAV-Assisted Reactive Routing for Urban VANETs

International audienceVehicular ad hoc networks (VANETs) are characterized by frequent path failures due to the high mobility caused by the sudden changes of vehicles direction. The routing paths between two different vehicles should be established with this challenge in mind. It must be stable and well connected in order to guarantee a reliable and safe delivery of packets. The aim of this work is to present a new reactive routing technique providing effective and well-regulated communication paths. These discovered paths are created based on a robust flooding discovery process involving UAVs (Un-manned Aerial Vehicles) to ensure the connectivity when the network is sparsely connected. The evaluation of this technique is performed using NS-2 simulator and its performances are compared with on-demand protocols dedicated for VANET. Simulation results show clearly that our approach gives interesting outcomes ensuring a high delivery ratio with a minimum delay. This hybrid communication between the vehicles and UAVs is attractive to initiate more smart connected nodes in the near future

A novel collaborative IoD-assisted VANET approach for coverage area maximization

Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes. © 2013 IEEE

U2RV: UAV-assisted reactive routing protocol for VANETs

When it comes to keeping the data routing robust and effective in Vehicular Ad hoc Networks (VANETs), stable and durable connectivity constitutes the keystone to ensure successful point-to-point communication. Since VANETs can comprise all kinds of mobile vehicles moving and changing direction frequently, this may result in frequent link failures and network partitions. Moreover, when VANETs are deployed in a city environment, another problem arises, that is, the existing obstructions (e.g., buildings, trees, hoppers, etc.) preventing the line-of-sight between vehicles, thus degrading wireless transmissions. Therefore, it is more complicated to design a routing technique that adapts to frequent changes in the topology. In order to settle all these problems, in this work, we design a flooding scheme that automatically reacts at each topology variation while overcoming the present obstacles while exchanging data in ad hoc mode with drones that are commonly called Unmanned Aerial Vehicles (UAVs). Also, the aim of this work is to explore well-regulated routing paths providing a long lifetime connectivity based on the amount of traffic and the expiration time of each discovered path, respectively. A set of experiments is carried out using simulation, and the outcomes are confronted with similar protocols based on a couple of metrics. The results clearly show that the assistance of UAVs to vehicles is capable to provide high delivery ratios and low delivery delays while efficiently extending the network connectivity

A Novel Multimodal Collaborative Drone-Assisted VANET Networking Model

Drones can be used in many assistance roles in complex communication situations and play key roles as aerial wireless relays to help terrestrial network communications. Although a great deal of emphasis has been placed on the drone-assisted networks, the majority of existing works often focus on routing protocols and do not fully exploit the drones’ superiority and flexibility. To fill in this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed according to the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones simultaneously, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on a real-world floating car data of Chengdu city in China, extensive experiments are carried out to examine the performance of the proposed MNA-optimized drone-assisted VANET considering the processed mobility models. The results demonstrate that our scheme outperforms its counterparts in terms of the average number of hops, improved average packet delivery ratio, and throughput of the global test space