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Channel-Adaptive Probabilistic Broadcast in Route Discovery Mechanism of MANETs

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Broadcasting is the backbone of the route discovery process in on-demand routing protocols in Mobile Ad-hoc Networks (MANETs). Pure flooding is the simplest and most common broadcasting technique for route… Click to show full abstract

Broadcasting is the backbone of the route discovery process in on-demand routing protocols in Mobile Ad-hoc Networks (MANETs). Pure flooding is the simplest and most common broadcasting technique for route discovery in on-demand routing protocols. In pure flooding, the route request (RREQ) packet is broadcasted and each receiving node rebroadcasts it. This continues until the RREQ packet arrives at the destination node. The obvious drawback of pure flooding is excessive redundant traffic that degrades the system performance. This is commonly known as broadcast storm problem (BSP). To address BSP, various probabilistic broadcast schemes have been proposed in the literature where a node broadcasts a RREQ packet with a certain probability. However, these schemes do not consider the effects of thermal noise and co-channel interference which cannot be ignored in realistic MANETs, and therefore, these schemes do not perform well in real life MANETs. This paper presents a novel Channel Adaptive Probabilistic Broadcast (CAPB) scheme that adapts the rebroadcast probability dynamically to the current SINR (Signal to Interference plus Noise Ratio) and node density in the neighborhood. The proposed scheme and two related state of the art (SoA) schemes from the literature are implemented in the standard AODV routing protocol to replace the pure flooding based broadcast. Extensive ns-2 simulation results show that the proposed scheme outperforms the standard AODV, and the two competitors in terms of routing overhead, throughput, end-to-end delay and energy consumption significantly in noisy MANETs.

Keywords: broadcast; route discovery; pure flooding; route; probabilistic broadcast

Journal Title: Journal of Communications Software and Systems
Year Published: 2019

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