Abstract We have previously shown that the geographic routing’s greedy packet forwarding distance (PFD), in dissimilarity values of its average measures, characterizes a mobile ad hoc network’s (MANET) topology by node size. In this article, we demonstrate a distribution-based analysis of the PFD measures that were generated by two representative greedy algorithms, namely GREEDY and ELLIPSOID. The result shows the potential of the distribution-based dissimilarity learning of the PFD in topology characterizing. Characterizing dynamic MANET topology supports context-aware performance optimization in position-based or geographic packet routing.

Distribution-based packet forwarding distance dissimilarity learning for topology characterizing in geographic routing

Characterizing the topology of MANETs provides the means for packet routing protocols to perform adaptively and efficiently in the particular environments. We show that the geographic routing’s greedy packet forwarding distance dissimilarity distributions in relation to node size characterizes MANET topologies and supports efficient multi-greedy forwarding. The models we described, based on the average greedy packet forwarding distance measures, showed distinct distribution patterns of the dissimilarity indices when applied to the example multi-greedy routing environment consisting of the ELLIPSOID and the GREEDY forwarding metrics. The scheme demonstrates the potential for adaptive forwarding performance to improve successful packets delivery in environments of high node-size variations such as VANETs.

/pdf/topology-characterizing-using-packet-forwarding-distance-fld52bat.pdf

G. Oladeji-Atanda

Papers

Topology characterizing using packet forwarding distance dissimilarity in multi-greedy geographic routing

Distribution-based packet forwarding distance dissimilarity learning for topology characterizing in geographic routing