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The dynamic nature of topology makes it challengeable to estimate link loss rates in mobile ad hoc network (MANET). Firstly, simulation results based on existing mobility models have the unrealistic movement scenarios which may not correctly reflect true MANET performance. Secondly, it is difficult to identify MANET topology under these models due to phantasmagoric movements. This paper presents the circle movement mobility model (CMMM), which is superior to previous models, and its topology identification algorithm to characterize the dynamic MANET topology. Moreover, we present a loss inferring algorithmic based on modeling and computational methodology of factor graphs, which iteratively updates the estimates of link losses. The inference is a process based on unicast back-to-back packets probes sent from a sender to pairs of receivers. Without internal nodes' cooperations, the inference can be calculated using only information recorded at the end hosts. NS2 simulations show that the proposed algorithms exhibit good performance.

The link performance (loss rate and delay probability) inference is important for evaluating mobile ad hoc network (MANET). The crucial step is MANET topology identification. Since the majority of existing mobility models (MM) may not correctly reflect the true MANET performance, a novel MM based on circle movement is presented. With help of the model, we identify the MANET topology and its live time. MANET internal links performance inference process, which consists of unicast back-to- back packets probes sent from a sender to pairs of receivers, and the performance inference algorithms are presented. NS2 simulations validate the presented model and algorithms.

The internal link performance inference is an increasingly important role in evaluating network. In this paper, we explore the use of end-to-end unicast traffic measurements, which consist of back-to-back packets sent from a sender to pairs of receivers, to estimate the delay probability distribution of mobile ad hoc network (MANET) internal links. Since the majority of existing mobility models for MANET do not provide realistic movement scenarios, we propose a novel mobility model based on circle movement. It can be applied to military patrol or search and rescue activities. Using this model, we can infer the stable topology within the identifiable time slot. We then extend the exiting inference algorithm of delay probability distribution in wired network for MANET delay tomography. NS2 simulations with different network scenarios validate the proposed models and inference arithmetic.