NAPF: Percolation driven probabilistic flooding for interference limited cognitive radio networks

In this paper, we argue that the traditional techniques for flooding and probabilistic flooding are not applicable to cognitive radio networks under the SINR regime. We identify the causes that i) degrade node outreach even with increasing deployment density under the SINR model and ii) lead to duplicate transmissions under the Boolean model. Further performance degradation occurs due to the additional constraints imposed by the primary users in such networks. To increase node outreach in interference-limited cognitive radio networks, we propose a modified version of probabilistic flooding that uses lower message overhead without compromising network connectivity. This is achieved by having just enough number of neighbors of a node to rebroadcast to others. The subset of neighbors that are selected to broadcast is decided on the number of neighbor a nodes has, their spatial orientation with respect to each other, and the interference they might cause. Identification of such subsets reduce duplicate retransmissions which in turn reduces interference. We use a localized clustering technique in conjunction with the concept of critical density from percolation theory such that each node decides its own rebroadcasting probability in a distributed manner. Through simulations, we compare the proposed technique with flooding and probabilistic flooding. Results validated that, the proposed technique reduces number of rebroadcasts and increases node outreach both under SINR and Boolean models.1