能量获取传感网络图像压缩传输的波束成形节点选择方法

Node selection method of image compress transmission based on collaborative beamforming for wireless sensor network

图像监测与传输的高能耗直接影响着传感网络持续监测性能,提高传感网络的传输能效性能具有重要的实际意义。为有效提升图像监测与传输的性能,研究面向图像压缩传输的能量获取传感网络的波束成形节点选择方法。首先根据能量获取的能量预测模型、波束成形的传感网络传输以及图像压缩传输模型,以最小化非目标接收基站的平均旁瓣幅值为优化目标,以节点选择数量为约束条件,建立面向图像传输的波束成形节点选择优化模型。然后提出基于改进蚁群算法(IACO)的图像压缩传输波束成形节点选择算法,提出方法中的启发函数不仅考虑能量获取能量和图像压缩与传输能量,而且在信息素更新公式中也结合剩余能量和非目标接收基站的平均旁瓣性能。通过实验仿真分析,在非目标接收节点的平均旁瓣性能和能量利用率方面,提出的IACO算法都优于标准蚁群算法(ACO)及随机节点算法,有效验证了提出IACO算法有效性。

The high-energy consumption of image monitoring and transmission directly affects the performance of continuous monitoring for wireless sensor network,so it is of great practical significance to improve the energy efficiency and transmission performance.To effectively improve the image monitoring and transmission performance,the node selection method of collaborative beamforming toward image compression and transmission for energy harvesting sensor networks is studied.Firstly,according to the models of energy prediction model,collaborative beamforming of sensor network,and image compression and transmission,the optimization model of node selection is established for image transmission.In this model,the minimization of the average sidelobe amplitude for non-received base station(BS)is defined as the optimization objective and the required number of node selection is referred to as constraint conditions.Then,the node selection algorithm based on improved ant colony algorithm(IACO)for image compression is proposed.The proposed IACO method not only considers the harvested energy,the image compression and transmission energy in the heuristic function,but also combines residual energy and average sidelobe performance of non-target receiving BS in the pheromone updating formula.Through the experimental simulation analysis,the proposed IACO algorithm is better than the standard ant colony algorithm(ACO)and random node algorithm in terms of average sidelobe performance of non-target receiving nodes and energy utilization,which effectively verifies the effectiveness of the proposed IACO algorithm.