1,537 research outputs found

    Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)

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    This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios

    SCALABLE MULTI-HOP DATA DISSEMINATION IN VEHICULAR AD HOC NETWORKS

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    Vehicular Ad hoc Networks (VANETs) aim at improving road safety and travel comfort, by providing self-organizing environments to disseminate traffic data, without requiring fixed infrastructure or centralized administration. Since traffic data is of public interest and usually benefit a group of users rather than a specific individual, it is more appropriate to rely on broadcasting for data dissemination in VANETs. However, broadcasting under dense networks suffers from high percentage of data redundancy that wastes the limited radio channel bandwidth. Moreover, packet collisions may lead to the broadcast storm problem when large number of vehicles in the same vicinity rebroadcast nearly simultaneously. The broadcast storm problem is still challenging in the context of VANET, due to the rapid changes in the network topology, which are difficult to predict and manage. Existing solutions either do not scale well under high density scenarios, or require extra communication overhead to estimate traffic density, so as to manage data dissemination accordingly. In this dissertation, we specifically aim at providing an efficient solution for the broadcast storm problem in VANETs, in order to support different types of applications. A novel approach is developed to provide scalable broadcast without extra communication overhead, by relying on traffic regime estimation using speed data. We theoretically validate the utilization of speed instead of the density to estimate traffic flow. The results of simulating our approach under different density scenarios show its efficiency in providing scalable multi-hop data dissemination for VANETs

    SDDV: scalable data dissemination in vehicular ad hoc networks

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    An important challenge in the domain of vehicular ad hoc networks (VANET) is the scalability of data dissemination. Under dense traffic conditions, the large number of communicating vehicles can easily result in a congested wireless channel. In that situation, delays and packet losses increase to a level where the VANET cannot be applied for road safety applications anymore. This paper introduces scalable data dissemination in vehicular ad hoc networks (SDDV), a holistic solution to this problem. It is composed of several techniques spread across the different layers of the protocol stack. Simulation results are presented that illustrate the severity of the scalability problem when applying common state-of-the-art techniques and parameters. Starting from such a baseline solution, optimization techniques are gradually added to SDDV until the scalability problem is entirely solved. Besides the performance evaluation based on simulations, the paper ends with an evaluation of the final SDDV configuration on real hardware. Experiments including 110 nodes are performed on the iMinds w-iLab.t wireless lab. The results of these experiments confirm the results obtained in the corresponding simulations

    Connectivity Investigation of Channel Quality-Based Adaptive Gossip Flooding Mechanism for AODV

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    To address the “broadcast storm” problem associated with flooding-based route discovery mechanism of reactive routing protocols, probabilistic approaches are suggested in the literature. In the earlier work, Gossip flooding mechanism of Haas et.al. was extended with signal quality, to propose channel quality based adaptive gossip flooding mechanism for AODV (CQAG-AODV). Following the cross-layer design principle, CQAG-AODV algorithm tried to discover robust routes, as well as address the “broadcast storm” problem by controlling the rebroadcast probability of Route request (RREQ) packets on the basis of signal strength experienced at the physical layer. This paper investigates the connectivity of CQAG-AODV through theoretical and simulation analysis. Results show that, by accounting the signal strength in the route discovery process, not only does the proposed algorithm floods  a lesser number of route requests and controls the broadcast storm, but also maintains a higher level of connectivity to offer high packet delivery ratio; independent of network density and node mobility. Moreover, due to controlled routing overhead and robust route discovery, channel quality based adaptive flooding mechanism offers fringe benefit of energy efficiency as well. CQAG-AODV thus proves its suitability in a variety of use cases of multi-hop ad hoc networks including WSNs and VANETs

    Efficient Information Dissemination in VANETs

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    Dynamic speed adaptive classified (D-SAC) data dissemination protocol for improving autonomous robot performance in VANETs

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    In robotics, mechanized and computer simulation for accurate and fast crash detection between general geometric models is a fundamental problem. The explanation of this problem will gravely improve driver safety and traffic efficiency, vehicular ad hoc networks (VANETs) have been employed in many scenarios to provide road safety and for convenient travel of the people. They offer self-organizing decentralized environments to disseminate traffic data, vehicle information and hazardous events. In order to avoid accidents during roadway travels, which are a major burden to the society, the data, such as traffic data, vehicle data and the road condition, play a critical role. VANET is employed for disseminating the data. Still the scalability issues occur when the communication happens under high-traffic regime where the vehicle density is high. The data redundancy and packet collisions may be high which cause broadcast storm problems. Here the traffic regime in the current state is obtained from the speed of the vehicle. Thus the data reduction is obtained. In order to suppress the redundant broadcast D-SAC data, dissemination protocol is presented in this paper. Here the data are classified according to its criticality and the probability is determined. The performance of the D-SAC protocol is verified through conventional methods with simulation
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