Abstract. Despite a considerable number of topology generation algorithms for simulation of wireless multihop networks it is difficult to find one with output similar to real networks [13]. In this paper, we propose NPART -a Node Placement Algorithm for Realistic Topologies whose created topologies resemble networks encountered in reality. The algorithm is flexible since it is sufficient to provide it with different input data to obtain different topologies.To demonstrate its quality and adaptivity, we compare topologies created by PART algorithm with topology samples from open wireless multihop networks in Berlin and Leipzig. Compared with real topologies, the generated topologies have almost identical node degree distribution, similar number of cut edges and vertices, and distribution of component sizes after bridge removal. The importance of node placement algorithm is demonstrated by comparing ns-2 simulation results for grid and uniform node placement with NPART generated topologies. Simulation results show that quality of node placement model plays as important role in simulation outcome as the accuracy of wireless signal propagation model. To our best knowledge, this is the first node placement algorithm for wireless multihop networks capable of creating topologies that have properties observed in user initiated networks.

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We extend the Breadth First Search (BFS) algorithm to use it for cut-edge(bridge) detection in graphs. The changes in the algorithm are tailored such that the algorithm can be applied in wireless multihop networks: e.g., it fully utilizes the broadcasting nature of the wireless medium. The distributed BFS algorithm (flooding) is widely used for route discovery and information dissemination in wireless multihop networks (WMNs) so the overhead introduced by our bridge detection algorithm is limited -the network is already performing the distributed BFS and we reuse the information from it to detect the bridges.We verify our detection algorithm on the data sampled from Berlin's free multi-hop wireless network. Detection precision varies depending on the algorithm parameters but for the representative algorithm configurations it stabilizes around 75%. Analysis of the data samples indicated that due to unreliability of wireless links and frequent occurrence of bridges the route discovery mechanism cannot find the route between two nodes although a valid route exists. We use our bridge detection algorithm to improve the route discovery success ratio from about 47% to approximately 90% by utilizing unicast of route discovery messages over the bridges. We verified by using fault injection the robustness of our approach as precision and route discovery remained high even for frequent node failures in the network.

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