Behavioral epidemic analysis on Random Graph model for smart wireless networks

Abstract

In distributed wireless networks, nodes are expected to spend their own resources so as to relay other nodes messages. This setup is governed by the trade-off between connectivity across the network and minimum interference, which maps to the standard Random Geometric Graph model. Even though smart network helps here, problem is that cognitive nodes may violate rules of cooperation with selfish intention of reaping the benefits from other nodes without bearing its cost. This leads to a behavioral epidemic causing nodes to follow non-cooperative strategy bringing down the network performance. In this paper, we examine the optimal way to choose control nodes using a central coordinator. We show by analysis that contrary to the general intuition, there exists a range of interest where random selection of control nodes outperforms that by highest degree ordering for the number of control nodes. We then simulate the mechanisms of inspections and counter-epidemic for these two control node selection procedures and verify the result.