Connectivity graphs are widely used in different branches of engineering, specially in ad hoc network analysis, decentralized control of unmanned air vehicles (UAV) and robot control. In large network simulations the extensive cost and the huge amount of memory required for performing the calculations is a major problem. Therefore methods should be developed that are fast and require less memory to determine the connectivity graph to expedite such calculations. In this paper we focus on approximate connectivity graphs for dynamic networks with moving agents. We use the velocity information of each agent to develop a fast algorithm to approximate the connectivity graph. 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 radio range 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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