Probabilistic route discovery for Wireless Mobile Ad Hoc Networks (MANETs)

Mobile wireless ad hoc networks (MANETs) have become of increasing interest in view of their promise to extend connectivity beyond traditional fixed infrastructure networks. In MANETs, the task of routing is distributed among network nodes which act as both end points and routers in a wireless multi-hop network environment. To discover a route to a specific destination node, existing on-demand routing protocols employ a broadcast scheme referred to as simple flooding whereby a route request packet (RREQ) originating from a source node is blindly disseminated to the rest of the network nodes. This can lead to excessive redundant retransmissions, causing high channel contention and packet collisions in the network, a phenomenon called a broadcast storm. To reduce the deleterious impact of flooding RREQ packets, a number of route discovery algorithms have been suggested over the past few years based on, for example, location, zoning or clustering. Most such approaches however involve considerably increased complexity requiring additional hardware or the maintenance of complex state information. This research argues that such requirements can be largely alleviated without sacrificing performance gains through the use of probabilistic broadcast methods, where an intermediate node rebroadcasts RREQ packets based on some suitable forwarding probability rather than in the traditional deterministic manner. Although several probabilistic broadcast algorithms have been suggested for MANETs in the past, most of these have focused on “pure” broadcast scenarios with relatively little investigation of the performance impact on specific applications such as route discovery. As a consequence, there has been so far very little study of the performance of probabilistic route discovery applied to the well-established MANET routing protocols. In an effort to fill this gap, the first part of this thesis evaluates the performance of the routing protocols Ad hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR) augmented with probabilistic route discovery, taking into account parameters such as network density, traffic density and nodal mobility. The results reveal encouraging benefits in overall routing control overhead but also show that network operating conditions have a critical impact on the optimality of the forwarding probabilities. In most existing probabilistic broadcast algorithms, including the one used here for preliminary investigations, each forwarding node is allowed to rebroadcast a received packet with a fixed forwarding probability regardless of its relative location with respect to the locations of the source and destination pairs. However, in a route