Performance of some metaheuristic algorithms for localization in wireless sensor networks
Performance of some metaheuristic algorithms for localization in wireless sensor networks
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DOI：
10.1002/nem.714
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发表时间：
2009-09
影响因子：
1.5
通讯作者：
A. Gopakumar;L. Jacob
中科院分区：
文献类型：
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作者：
A. Gopakumar;L. Jacob
在本文中，我们提出了两种新的和计算效率的元启发式算法的基础上禁忌搜索（TS）和粒子群优化（PSO）的原则，在分布式无线传感器网络（WSN）环境中定位传感器节点。无线传感器网络定位问题被公式化为一个非线性优化问题，由噪声距离测量产生的均方距离误差作为目标函数。与梯度下降方法不同，TS和PSO方法都确保目标函数的最小化，而不会使解陷入局部最优。我们进一步实施了一个改进阶段，误差传播控制，以改善结果。所提出的算法的性能进行了比较，并与其他基于模拟退火的无线传感器网络定位。通过各种仿真研究了测距误差、锚节点密度和锚节点位置的不确定性对定位性能的影响。仿真结果表明，TS和PSO方法具有更好的精度、计算效率和收敛特性。此外，所提出的方法的有效性进行了验证，从文献中报道的实验传感器网络收集的数据。版权所有© 2008约翰威利父子有限公司.
In this paper we propose two novel and computationally efficient metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) principles for locating the sensor nodes in a distributed wireless sensor network (WSN) environment. The WSN localization problem is formulated as a non‐linear optimization problem with mean squared range error resulting from noisy distance measurement as the objective function. Unlike gradient descent methods, both TS and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. We further implement a refinement phase with error propagation control for improvement of the results. The performance of the proposed algorithms are compared with each other and also against simulated annealing based WSN localization. The effects of range measurement error, anchor node density and uncertainty in the anchor node position on localization performance are also studied through various simulations. The simulation results establish better accuracy, computational efficiency and convergence characteristics for TS and PSO methods. Further, the efficacy of the proposed methods is verified with data collected from an experimental sensor network reported in the literature. Copyright © 2008 John Wiley & Sons, Ltd.