Connectivity graphs are widely used in different branches of engineering, especially 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. Connectivity probability and propagation-based link reliability approaches are some other calculations which use connectivity-based calculations. Therefore, methods should be developed that are fast and require less memory to determine the connectivity-based calculations in order to expedite such calculations. In this paper, we focus on applications which use distance between nodes as a base in calculations. Dynamic networks in which moving agents produce varying connectivity graphs in time increase this type of calculations. We used mean degree distribution versus radio range instead of degree distribution for a specified radio range to investigate congestion/interference of networks when the approximate method is used. We introduce a modification factor for connectivity distance, R in the approximate Manhattan measure to improve accuracy in a wide range of R. The results for two random distributions of agents based on Monte Carlo simulations are compared to the present real methods to show the superiority of our approach.

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