Probabilistic flooding (parameterized by a forwarding probability) has frequently been considered in the past, as a means of limiting the large message overhead associated with traditional (full) flooding approaches that are used to disseminate globally information in unstructured peer-topeer and other networks. A key challenge in using probabilistic flooding is the determination of the forwarding probability so that global network outreach is achieved while keeping the message overhead as low as possible. By showing that a probabilistic flooding network generated by applying probabilistic flooding to a connected random graph network can be bounded by properly parameterized random graph networks and invoking random graph theory results, bounds on the value of the forwarding probability are derived guaranteeing global network outreach with high probability, while significantly reducing the message overhead. Bounds on the average number of messages – as well as asymptotic expressions- and on the average time required to complete network outreach are also derived, illustrating the benefits of the properly parameterized probabilistic flooding scheme. 1.

Abstract — In this paper we study the feasibility of disseminating reports about resources via vehicular ad-hoc networks. Each disseminated report represents information about a spatial-temporal event, such as the availability of a parking slot at a particular time. The simple flooding algorithm is used for dissemination in a VANET. The feasibility is analyzed by comparing the effectiveness of VANET with that of client-server. The performance measure integrates throughput and response time, the two traditional measures for evaluation of data dissemination algorithms. The comparison is based on realistic simulation of vehicle mobility in a real road network and of the 802.11 protocol. The comparison enables determining the superior architecture (VANET or client-server) for a given environment. I.

Probabilistic flooding has been frequently considered as a suitable dissemination information approach for limiting the large message overhead associated with traditional (full) flooding approaches that are used to disseminate globally information in unstructured peer-to-peer and other networks. A key challenge in using probabilistic flooding is the determination of the forwarding probability so that global network outreach is achieved while keeping the message overhead as low as possible. In this paper, by showing that a probabilistic flooding network, generated by applying probabilistic flooding to a connected random graph network, can be (asymptotically) “bounded ” by properly parameterized random graph networks and by invoking random graph theory results, asymptotic values of the forwarding probability are derived guaranteeing (probabilistically) successful coverage, while significantly reducing the message overhead with respect to traditional flooding. Asymptotic expressions with respect to the average number of messages and the average time required to complete network coverage are also derived, illustrating the benefits of the properly parameterized probabilistic flooding scheme. Simulation results support the claims and expectations of the analytical results and reveal certain aspects of probabilistic flooding not covered by the analysis.