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A cross-layer design for data dissemination in vehicular ad hoc networks

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Abstract

Data dissemination in vehicular ad hoc networks (VANETs) has attracted researchers’ attention in recent years. However, due to high vehicle density in urban areas and increasing data volume, wireless interference seriously impairs data dissemination performance of a VANET. To alleviate the interference problem, we propose a cross-layer design to exploit both spatial and temporal reusability of wireless spectrum in VANETs. In particular, the proposed cross-layer design integrates power control in the physical layer, time division in the MAC layer and data routing in the network layer. To implement the cross-layer design, we formulate an interference-aware power-control (IAPC) problem with the objective of maximizing data throughput in a VANET. For solving the IAPC problem, an integer linear programming formulation is proposed to derive the optimal solution. In addition, an efficient heuristic algorithm, named IAPC-Solver, is proposed to achieve a near-optimal solution. Extensive simulation experiments have been conducted on a widely adopted testbed, and the experimental results illustrate that the cross-layer design together with the proposed algorithms are effective for improving data dissemination performance of a VANET.

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Notes

  1. Data throughput is the total amount of data packets that are successfully received by the requesting vehicles during a time window.

  2. Collision is a phenomenon of wireless interference.

  3. As discussed in Sect. 4.2, we consider that in a scheduling period the position of a moving vehicle is not significantly changed.

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Acknowledgements

This work was supported in part by National Science Foundation of China under Grant No. 61572088. The work described in this paper was substantially supported by a Grant from City University of Hong Kong (Project Nos. 7004412 and 7004679).

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Correspondence to Kai Liu.

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Duan, Y., Lee, V.C.S., Lam, KY. et al. A cross-layer design for data dissemination in vehicular ad hoc networks. Neural Comput & Applic 31, 2869–2887 (2019). https://doi.org/10.1007/s00521-017-3234-y

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