Research On Cooperation Mechanism In Wireless Networks

With the development of wireless network technologies and the increasing requirement of high efficiency and reliability wireless applications, cooperation in wireless networks draws increasing attentions from researchers.Mobile ad hoc networks are deployed under the assumption that participating nodes are willing to forward other nodes' packets.However, for civilian applications where nodes are not owned by a single entity but are profit-oriented independent agents, cooperation cannot be taken for granted.Since forwarding data for other nodes can drain the battery of a node, for civilian applications where nodes are not owned by a single entity but are profit-oriented independent agents,cooperation cannot be taken for granted.Users that want to maximize their own welfare and do not contribute to the network are defined as selfish nodes or free riders.For the existence of such selfish nodes in the network, it is necessary to develop incentive mechanisms that avoid nodes behave selfishly.In this paper, we present a novel reputation computation model to discover and prevent selfish behaviors by combining familiarity values with subjective opinions.The familiarity value represents a node's familiar degree with another individual node.In our model, a node that queries another's reputation first accumulates subjective opinions from their common neighbors.The familiarity values are used to calculate the weighting factor that determines how much a node's recommending opinion impacts on the reputation computation result. The utilization of this familiarity allows nodes to obtain opinions with lower uncertainty values, which helps nodes to recognize selfish nodes much earlier and can decrease the convergence time for isolating selfish nodes.We evaluate the performance of our model based on ns-2 simulations to analyze the impact of different parameters on the network performance.The simulation results show that our model outperforms the pure subjective logic-based model and achieves up to 25% improvement in the convergence time.Although it takes less time to discover selfish nodes,the Familiarity-based model cannot guarantee the selfish nodes rejoin the network when they act cooperatively.For this reason, we introduce the DARWIN mechanism which presented by Jaramillo and Srikant. And we combine our model with DARWIN in order to overcome the defects of DARWIN.Through analysis we know that the new model can resist the selfish nodes'defamation behaviors and decrease their profit efficiently.