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

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 cooperative 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.