一种改进的无线传感器网络优化定位算法

An Improved Optimization Localization Algorithm in WSNs

节点定位是无线传感器网络实际应用需要解决的关键问题。为了在提高定位精度的同时降低成本,提出了一种改进的粒子群优化定位算法。该算法首先提出前摄估计思想,完成未知节点的区域估计,缩小并限制可行解空间,以此加快粒子群的搜索速度;然后给出了竞争进化思想的数学模型,使用该模型和自适应权重在进一步加快收敛速度的同时增强了算法的全局和局部搜索能力。仿真结果表明,对比同类算法,该算法能更有效地利用锚节点信息,降低网络成本,在计算量显著减少的同时明显提高了定位精度,并且具有对测距误差鲁棒性强的优点。

Node localization of wireless sensor networks （WSNs） is a key problem in the practical applications. To improve the localization accuracy and reduce the cost, an improved localization algorithm based on particle swarm optimization （PSO） is proposed. In the algorithm, the idea of proactive estimate is introduced to estimate the area of nodes, reduce and restrict the solution space, so as to quicken the search speed of particles, and then the idea of competition evolution and adaptive weighting are used to enhance the global and local search ability when accelerating convergence speed. Simulation results show that, compared with other similar methods, the proposed algorithm can make more effective use of anchor node information, reduce the cost of network, and increase positioning accuracy while significantly reducing the calculation amount. Moreover the algorithm shows robust for communication ranging error.