合作博弈下无线传感器网络功率控制策略

Power control strategy of wireless sensor network under cooperative game

摘要:

无线传感器网络在动态变化的信道和干扰环境工作时,为获得较高的信干噪比,节点会提高发射功率,致使节点间的干扰不断增大,为抵消其带来的消极影响,节点将继续增加发射功率,这将导致网络环境逐渐恶化,同时过多浪费节点能量.针对以上问题,本文提出一种合作博弈下无线传感器网络功率控制策略,为使节点能够更加精准的根据周围环境信息动态调节发射功率,算法引入节点间距离作为干扰权重因子以修正有效干扰模型,进而改进信干噪比模型；基于合作博弈理论将节点信息传输速率和自身剩余能量整合,建立合作博弈下的效用函数,在对不同效用权重因子下的归一化信息传输速率、发射功率方差值、信干噪比和网络效用4种结果进行综合权衡后,得出适当的效用权重因子值,并证明效用函数存在纳什均衡解,通过算法多次迭代后得出使网络效用达到最高时的节点最优发射功率.仿真结果表明,本文算法得出的最优发射功率方差小,算法收敛速度快,网络在节点较低发射功率时即可获得较高的信干噪比,网络生存周期得以延长,实现更高的网络效用.

Abstract:

In order to obtain a higher signal-to-noise ratio, the transmitted power of nodes will be improved, wireless sensor networks work in dynamically changing channel conditions and interference levels environments at the same time, causing the interference between the nodes to increase, the node will continue to increase the transmit power to offset the negative impact, which will lead to a gradual deterioration of the network environment and waste too much node energy. Aiming at the above problems, this paper proposes a wireless sensor network power control strategy under cooperative game. In order to enable the node to dynamically adjust the transmit power according to the surrounding environment information, the algorithm introduces the distance between nodes as the interference weight factor to correct the effective interference model. The signal-to-noise ratio model is improved. Based on the cooperative game theory, the node information transmission rate and its residual energy are integrated, and the utility function under the cooperative game is established. The normalized information transmission rate and the transmission power variance value under different utility weight factors are used. After the four balances of the signal-to-noise ratio and the network utility are comprehensively weighed, the appropriate utility weight factor value is obtained, and the utility function has a Nash equilibrium solution. After several iterations of the algorithm, the optimal transmission power set of the whole network is obtained. The simulation results show that the optimal transmit power variance obtained by the algorithm is small, and the algorithm converges fast. The network can obtain a higher signal-to-noise ratio when the node has lower transmit power, and the network life cycle can be extended to achieve higher performance. The nodes utility can be improved.