认知网络能量感知及伽玛统计模型能量优化算法

Cognitive network energy optimization algorithm based on energy-aware and gamma statistical model

为了提升分簇无线传感器网络的能量效率并均衡节点的能量负载,提出了一种认知网络能量感知及伽玛统计模型能量优化算法。算法提出基于认知无线电的信道能量感知模型,可以得到网络休眠模式和运作模式下网络的能量分布方程。在多个中继网络场景中采用伽玛函数进行网络总能耗分析,并基于次级网络跳数与簇头总能耗关系提出能量优化策略,在均衡簇头能量负载的同时最小化网络总能耗量。实验仿真结果表明,在网络总能量消耗上,所提出的能量优化算法相比基于节能路由方案的认知无线电网络和基于多能量探测器的认知网络,节能效果分别提高了37.6%和12.2%,并且算法采用伽玛函数对网络能量分布的分析具有一定的准确性。

In order to enhance the energy efficiency and balance node of energy load in the clustering wireless sensor network, this paper proposed cognitive network energy optimization algorithm based on energy-aware and gamma statistical model. First, the algorithm based on cognitive radio channel energy perceptual model, it could get the energy distribution equation under the sleep mode and network operation mode. Next, it analysed the total energy consumption of network by using gamma function in multiple relay network scenarios, and it based on the number of network hops and the total energy consumption of cluster head to propose the energy optimization strategy, to balance the energy of cluster head while minimizing the total network energy consumption. Simulation results show, the proposed energy optimization algorithm compared based on cognitive radio networks saving routing scheme and multi-energy detector-based cognitive networks in the total energy consumption of network, the energy savings were increased by 37.6% and 12.2%, and the algorithm analysed energy distribution network by the gamma function with a certain accuracy.