In this paper we focus on dynamic networks, where moving agents produce varying connectivity graphs in time. We use the velocity information of every agent to develop a fast algorithm to approximate the connectivity graph of dynamic networks. Also we used mean degree distribution instead of degree distribution to investigate congestion/interference of network when approximate method is used. We introduced a modification factor for connectivity distance, R in Manhattan measure to increase the accuracy in a wide range of R. The results for two random distributions of agents based on Monte Carlo simulation are compared to the present real connectivity method to show the superiority of our approach.

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