After enhancing the beaconing performance at both the MAC layer and link layer, we turn to the network layer performance in this chapter. Particularly, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this chapter, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, named ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resources are enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ will be assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the efficiency of the heuristic algorithm theoretically, which can achieve the near-optimal result. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates in accordance with the road safety demand, and the beaconing reliability can be guaranteed even under high-density vehicle scenarios.


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    Title :

    Safety-Aware and Distributed Beacon Congestion Control


    Additional title:

    Wireless Networks


    Contributors:
    Lyu, Feng (author) / Li, Minglu (author) / Shen, Xuemin (author)


    Publication date :

    2020-08-07


    Size :

    29 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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