Abstract
Opportunistic Sensor Network (OSN) is different from traditional networks because it frequently splits into several parts and connections are characterized by their opportunistic nature. To re-describe the connectivity of OSN, Divided Area Random Graph (DARG) model is introduced according to the opportunistic nature of connections. DARG divides the radio communication area of each node into three parts: communication area, probabilistic communication area and none-communication area. After calculating the communication probability of each area, total probability formula is adopted to calculate the direct communication probability between nodes. Then, Probabilistic Path Matrix (PPM) is employed to capture the communication probability caused by ”Storing-Carrying-Forwarding” communication pattern which is very typical in OSN. Finally, based on PPM, Network Connection Mean Probability (NCMP) is defined to determine how well the network is connected. It is helpful for many applications to characterize how well the network is connected based on this model. Theoretical analyses and simulations results show that DARG is more practical than conventional random graphs and NCMP can describe OSN connectivity better.
This work is supported by The National Natural Science Foundation of China under grant NO.61262020 and Aeronautical Science Foundation of China under grant ON.2010ZC56008.
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Shu, J., Zeng, L., Liu, L. (2014). Random Graph Model for Opportunistic Sensor Networks Based on Divided Area. In: Sun, L., Ma, H., Hong, F. (eds) Advances in Wireless Sensor Networks. CWSN 2013. Communications in Computer and Information Science, vol 418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54522-1_18
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DOI: https://doi.org/10.1007/978-3-642-54522-1_18
Publisher Name: Springer, Berlin, Heidelberg
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