A Cluster-Based Beaconing Approach in VANETs: Near Optimal Topology Via Proximity Information

Abstract

A key component for safety applications in Vehicular ad-hoc network (VANET) is the use of periodic beacon messages which provide vehicles with a real-time vehicle proximity map of their surroundings. Based on this map, safety applications can be used for accident prevention by informing drivers about evolving hazardous situations. In order to allow synchronized and cooperative reactions, the target of this work is to design a beacon dissemination process that provides a real-time, broad and coordinated map under the challenging VANET conditions. In order to obtain the desired map, we consider an aggregation-dissemination based scheme for a beacon dissemination process that based on top of a cluster-based topology. To this end, we propose the Distributed Construct Underlying Topology (D-CUT) algorithm tailed specifically to provide an optimized topology for such beacon dissemination process. To deal with the heavy load of beacon messages required for an accurate and broad map, we propose a topology that allows the execution of extensive but reliable spatial bandwidth reuse. Our D-CUT algorithm exploits the real-time and coordinated map for constructing an adaptive and robust topology to deal with the dynamic nature of the VANET environment. We present theoretically provable bounds demonstrating the ability of the algorithm to deal with the dynamic nature of the VANET environment supported by simulation results.

Appendices

Appendix 1—list of abbreviations

CA :

Clustering Assignment

D-CUT:

Distributed Construct Underlying Topology

GNOCA :

Geographically Near-Optimal CA

JC1 :

the first join condition

JC2 :

the second join condition

MMIDP :

Max-Min Inter-Distance Pair

SC1 :

the first split condition

SC2 :

the second split condition

SJC :

the spit join condition

VANET:

Vehicular ad-hoc network

Appendix 2—an example of the D-CUT convergence process

In the following we provide a detailed example of the D-CUT convergence process.

The D-CUT gets as an input 9 small scrappy clusters (see Fig. 17a). Next we show how D-CUT converges to the GNOCA in three iterations that match the height of the corresponding SBT.

Fig. 17

An example of D-CUT convergence process

Throughout the convergence process 14 inter-distances are involved. Those inter-distances are associated to the class A ₁ , A ₂ , and A ₃ as the following: {d ₁ , d _2, d ₃ , d ₄ , d ₅ , d ₆ , d ₈ , d ₉ , d ₁₀ , d ₁₂ , d ₁₄ } ∈ A ₁ ;{d ₁₃ } ∈ A ₂ ; {d ₇ , d ₁₁ } ∈ A _3.

In first iteration (see Fig. 17b) the inter-distances {d ₁ , d ₃ , d ₅ , d ₈ , d ₁₀ , d ₁₂ , d ₁₄ } (which belong to A ₁ ) satisfy the SJC and are classified as inner-gap (Lemma 2). In addition, the inter-distance d ₁₃ that belong to A ₂ is classified as an inter-cluster gap (Lemma 4).

In the second iteration (see Fig. 17c) the inter-distances {d ₂ , d ₆ , d ₉ } that belong to A ₁ satisfy the SJC and are classified as inner-gap. At the end of this iteration (after all inter-distances that belong to A ₁ are classified as inner-gap), the CA produced by the D-CUT algorithm meets Objective 1 (Lemma 3).

In the third iteration (see Fig. 17d), the inter-distance d ₁₁ that belongs to A ₃ satisfies the JC1 and is classified as an inner-gap. In addition, the inter-distance d ₇ that belongs to A ₃ is classified as an inter-cluster gap (since F(d ₇ , d _f ) = false; see Lemma 5). At the end of this iteration the D-CUT algorithm produces the GNOCA (Lemma 6).