Ad-hoc网络中基于博弈论和粒子群优化的协作算法

Cooperation algorithm based on game theory and particle swarm optimization for Ad-hoc networks

为了促使Ad-hoc网络中的"自私"节点进行合作,提出了一种基于博弈论和粒子群优化的协作算法(Nash Bargaining of game theory and particle swarm optimization,NGPSO)在算法的第一阶段,源节点通过对中继节点转发的数据进行价格补偿,从而达到使中继节点参与合作的目的。将源节点的最优出价归结为纳什谈判问题,得到具有帕累托最优的激励价格,保证源节点和中继节点在合作中同时获得最佳收益;在算法的第二阶段,中继节点在获得源节点的最优出价后,通过粒子群优化算法得到最优的转发功率,使其合作收益增益最大。仿真表明,和随机价格激励相比,所提出的NGPSO算法能使源节点和中继节点达到最优收益;和中继节点固定功率转发相比,所提出的NGPSO算法,能显著提高源节点的能量效率和中继节点的收益,同时在适当设置中继节点转发功率的搜索空间时,可以保证总的能量效率。

To stimulate the selfish nodes of Ad-hoc networks to participate in cooperation, a cooperation algorithm based on Nash Bargaining of game theory and particle swarm optimization （NGPSO） is proposed. In the first stage of the proposed algorithm, the relay node is paid by the source node for forwarding source nodes＇ data, then cooperation between the source node and the relay node can be reached. We model the optimal bid of the source node as Nash bargain, and Nash equilibrium of the optimal bid which is Pareto efficient is given. Consequently, the optimal bid can guarantee that the source node and the relay node obtain optimal revenue. In the second stage of the proposed algorithm, after obtaining the optimal bid of the source node, the relay node determines optimal transmit power through particle swarm optimization to maximize its own cooperative gain. Simulation results show that, compared to random price incentive mechanisms, the NGPSO algorithm can make the source node and the relay node obtain optimal revenue. Meanwhile, the proposed algorithm improves the co operative gain of the relay node and the energy efficiency of the source node compared to the algorithm where the relay node uses constant transmit power. Furthermore, when the relay node appropriately sets its search space, the total energy efficiency of the whole system can be ensured.