基于网格的移动无线传感网生存时间优化算法

摘要

为克服陆地静态无线传感网和水下无线传感网因节点能耗分布不均衡而出现的能量空穴问题，和具有单一移动 Sink 节点的无线传感网数据收集时延过长问题，该文提出基于网格的移动无线传感网生存时间优化算法(Grid-based Lifetime Optimization Algorithm, GLOA)。GLOA算法考虑多个Sink节点的移动，将监测区域分成多个大小相同的网格。根据网格潜能值确定Sink节点移动的锚点，将锚点分配给不同的Sink节点，建立路径选择优化模型并获得Sink节点的最短移动路径，采用移动收集方法或静态收集方法循环收集数据。仿真结果表明：与Ratio_w或TPGF算法相比，GLOA算法能延长网络生存时间，降低和均衡节点能耗。与LOA_SMSN算法相比， GLOA算法能降低数据收集时延。在一定的条件下，比Ratio_w, TPGF和LOA_SMSN算法更优。%In order to overcome the energy hole problem due to the uneven distribution of energy consumption in the static terrestrial Wireless Sensor Networks (WSNs) and underwater WSNs, and overcome the long data gathering delay problem in WSNs with single mobile sink node, the Grid-based Lifetime Optimization Algorithm (GLOA) is proposed for the mobile WSNs. In the GLOA algorithm, the movement of multiple sink nodes is considered. The monitoring region is divided into many grids of the same size. The anchor points are identified according to the grid potential value. Anchor points are assigned to different sink nodes. The path selection optimization model is proposed and shortest mobile path is obtained. The mobile method or static gathering method is used to cyclically gather data. The simulation results show that compared with Ratio_w algorithm or TPGF algorithm, the GLOA algorithm is able to prolong the network lifetime, reduce and balance the node energy consumption. Compared with the LOA_SMSN algorithm, the GLOA algorithm is able to decrease the data gathering delay. Under specified conditions, the proposed algorithm outperforms Ratio_w, TPGF or LOA_SMSN algorithms.