基于麻雀搜索的WSN约束优化节点定位研究

Research on WSN Constrained Optimization Node Location Based on Sparrow Search

针对无线传感器网络节点定位难,测距误差大等问题,提出了基于麻雀约束优化节点定位算法。基于数学中的多维约束优化模型,麻雀搜索算法与该模型共同求解出最准确的节点位置。为了缩小节点定位时的搜索范围,设定了相应的约束适应度函数数学模型;然后利用麻雀搜索算法求解节点未知位置,能够更快速、准确的解决未知节点定位的问题。仿真结果表明,相较于已有算法,所提算法在无线传感器网络中定位误差更小,准确度高,且能够满足大部分节点定位的需求,在WSN中有很好的扩展性,具备了很高的实用性。

In order to solve the problem of wireless sensor network(WSN)node positioning difficulties and large-ranging errors,an optimized node positioning algorithm based on sparrow constraints is proposed.Based on the multi-dimensional constrained optimization model in mathematics,the Sparrow Search Algorithm(SSA)will work with the model to find the most accurate node position.First,in order to narrow the search range during node positioning,the corresponding constraint fitness function mathematical model is set.Then the SSA is used to solve the unknown posi-tion of the node,which can determine the position of the unknown node more quickly.The simulation results show that compared with the existing algorithm,the algorithm has smaller positioning errors in WSN,high accuracy,and can meet the needs of most node positioning.It has good scalability in WSN and has high Practicality.