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传感器网络的粒子群优化定位算法 总被引:1,自引:0,他引:1
无线传感器网络定位问题是一个基于不同距离或路径测量值的优化问题。由于传统的节点定位算法采用最小二乘法求解非线性方程组时很容易受到测距误差的影响,为了提高节点的定位精度,将粒子群优化算法引入到传感器网络定位中,提出了一种传感器网络的粒子群优化定位算法。该算法利用未知节点接收到的锚节点的距离信息,通过迭代方法搜索未知节点位置。仿真结果表明,该算法有效地抑制了测距误差累积对定位精度的影响,提高了节点的定位精度。 相似文献
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一种降低定位误差的无线传感器网络节点定位改进算法 总被引:4,自引:0,他引:4
本文针对无线传感器网络节点的定位精度问题,提出了一种采用误差修正的方法来降低累积距离误差和定位误差的传感器网络节点定位改进算法,给出了该算法的基本原理与实现方法.该算法在不增加原算法通信量及计算复杂度的基础上提高了定位精度.仿真结果显示,在同等条件下,本文提出的算法定位精度提高了5~10%. 相似文献
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节点定位是无线传感器关键技术之一,针对固定多锚节点方法定位精度低的缺陷,为了提高无线传感器的定位精度,提出了一种基于改进单锚节点的无线传感器网络节点定位算法(SFOA-SVM)。首先采用单移动锚节点在无线传感器网络中移动,构建无线传感器定位模型的学习样本,然后采用SVM构建节点定位模型,并采用渔夫捕鱼算法模拟渔夫捕鱼行为找到最优SVM参数,最后采用仿真实验测试节点的定位性能。结果表明,相对于其它定位算法,SFOA-SVM提高了无线传感器节点的定位精度,具有一定的实际应用价值。 相似文献
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提出了一种基于映射扩散的无线传感器网络节点定位算法,适用于规模较大、参考节点较少的无线传感器网络.该算法在传感器网络中首先随机选择一个节点作为"起始节点",然后根据扩散算法选择3个"一级节点",然后以每个一级节点为中心,逐级外推,直至覆盖网络中的所有节点.仿真结果证明,该算法可以快速准确进行定位,可降低和均衡所有节点的能耗,提高定位精度. 相似文献
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节点自定位是无线传感器网络的关键技术之一。当前对无线传感器网络定位的研究主要集中静态节点定位,移动无线传感器网络定位研究相对较少。研究了基于序列蒙特卡罗方法的移动无线传感器网络定位。针对蒙特卡罗定位采用固定样本数,计算量大的缺点,根据蒙特卡罗定位盒(MCB)算法的锚盒子大小动态设置样本数,提出一种自适应采样蒙特卡罗盒定位算法。仿真表明,该算法在保持定位精度的同时有效地减小了采样次数,节约了计算量。 相似文献
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为了提高节点定位精度,解决定位误差较大的问题,提出了基于元胞蝙蝠算法的无线传感器网络节点定位算法,以此来获得更高的定位精度。首先将元胞自动机的思想融入蝙蝠算法,采用了改进的元胞限制竞争选择小生境技术和灾变机制,使得该算法在寻优过程中能够跳出局部极值,避免早熟现象,更快地收敛到全局最优解。通过标准测试函数的验证,表明了该改进算法在收敛深度和广度上的优势。之后将元胞蝙蝠算法应用到无线传感器网络节点定位上来提高定位精度。实测实验中,该算法在测试环境下平均定位误差在0.4 m以内,相比于改进PSO算法,获得更好的定位效果。 相似文献
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在无线传感器网络中,监测到时间之后关心的一个重要问题就是该事件发生的位置。传感器节点能量有限、可靠性差、节点规模大且随机布放、无线模块通信距离有限,对定位算法和定位技术提出了很高的要求。针对随机布放、节点配置低的无线传感器网络,提出一种新的RSSI-Hop定位方法,该方法可以在不增加硬件开销的基础上,有效降低节点能量消耗,较准确地估算未知节点到参考节点之间的距离,减少累积误差,提高定位的准确性。其主要思想是,节点信息根据RSSI强弱,估算各节点到信标节点之间的距离。实验表明,新算法比以前的算法定位更准确。 相似文献
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水声传感器网络(UASNs)节点由于洋流等因素长时间作用会出现位置偏移,故需要修正其位置信息。在水声传感器网络节点定位中将自主式水下潜器(AUV)作为移动锚点辅助定位可有效降低定位成本,但在AUV辅助定位过程中AUV的能量利用率仍有待提升。为了进一步提高AUV的能量利用率,该文提出一种面向水声传感网的AUV辅助定位动态路径规划方法。该方法中将节点位置修正过程看成节点位置信息熵减少的过程。在AUV动态路径规划时根据定位过程的节点位置信息和预计AUV能耗,规划AUV下一步移动目标位置。使用贪婪算法选取使信息增益期望和移动消耗能量比值最大的位置作为AUV下一步移动目标位置。仿真结果表明,该算法能够在保证节点定位精度的基础上有效提高AUV能量利用率。 相似文献
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经典MDS-MAP算法在无线传感器网络定位中存在误差较大及计算量随网络规模增大而急剧增加的缺点。该文设计了基于自身和邻居节点剩余能量大小的成簇方法,形成的簇具有适当节点连接度和簇大小,降低了下一步定位算法的计算量和误差。然后对于仅有连通信息的簇内节点,利用时间差测距方法获得簇首与其他单跳节点间距离。提出多跳节点间距离误差校正算法,利用相邻节点的几何关系及节点连接度信息,获得簇内多跳间隔节点距离。采用多维标度技术计算各簇内节点相对坐标,融合簇间坐标并通过锚节点转换为绝对坐标,最终实现节点的定位。所提方法通过能量分簇及多跳间隔节点加权几何距离校正算法,相对于经典多维标度算法定位提供更准确的节点间距离信息,能够在进一步提高定位精度的基础上降低无线传感器网络定位功耗。 相似文献
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如何实现高效的分布式声源定位是无线传感器网络研究的热点。通过一种基于声源信号能量的分布式声源定位算法,采用交互方向的拉格朗日乘子方法将最大似然声源定位问题拆分到单个传感器节点,通过桥接传感器节点实现传感器节点之间的信息融合。由于采用声源信号衰减模型,交互方向拉格朗日乘子方法中的最优化目标函数成为非凸函数,导致定位算法容易陷入局部最优,为此提出了多重网格搜索方法。仿真结果表明,新算法与现有的分布式声源定位算法相比,具有可并行实现,可应用于任意网络拓扑,不易陷于局部最优等优点。 相似文献
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Guangjie Han Chenyu Zhang Tongqing Liu Lei Shu 《Wireless Communications and Mobile Computing》2016,16(6):682-702
Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy. 相似文献
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Wireless sensor networks (WSNs) are increasingly being used in remote environment monitoring, security surveillance, military applications, and health monitoring systems among many other applications. Designing efficient localization techniques have been a major obstacle towards the deployment of WSN for these applications. In this paper, we present a novel lightweight iterative positioning (LIP) algorithm for next generation of wireless sensor networks, where we propose to resolve the localization problem through the following two phases: (1) initial position estimation and (2) iterative refinement. In the initial position estimation phase, instead of flooding the network with beacon messages, we propose to limit the propagation of the messages by using a random time-to-live for the majority of the beacon nodes. In the second phase of the algorithm, the nodes select random waiting periods for correcting their position estimates based on the information received from neighbouring nodes. We propose the use of Weighted Moving Average when the nodes have received multiple position corrections from a neighbouring node in order to emphasize the corrections with a high confidence. In addition, in the refinement phase, the algorithm employs low duty-cycling for the nodes that have low confidence in their position estimates, with the goal of reducing their impact on localization of neighbouring nodes and preserving their energy. Our simulation results indicate that LIP is not only scalable, but it is also capable of providing localization accuracy comparable to the Robust Positioning Algorithm, while significantly reducing the number of messages exchanged, and achieving energy savings. 相似文献
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Very Low Energy Consumption Wireless Sensor Localization for Danger Environments with Single Mobile Anchor Node 总被引:1,自引:0,他引:1
In wireless sensor networks (WSN), it is very important for sensor nodes to locate with low energy consumption and high accuracy,
especially in a dangerous environment. This paper describes a range-free layered localization scheme using one mobile anchor
node which can transmit gradient signals, and whose moving track is a straight-line along the x-axis. And this paper proposes
a sleep/wake mechanism called sensor sleep-time forecasting to save energy consumption during localization. The relationship, between the key factors in localization algorithm and the
average location error, is analyzed in detail. Simulation results show that the scheme performs better than other range-free
mechanisms—the average location error is less than 0.7 m, and it is independent on sensor nodes density or sensor nodes radio
range, the accuracy of the algorithm can be adjusted in different occasions, and the algorithm beacon overhand is small and
average localization time is short.
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Lili Zhang (Corresponding author)Email: |
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Many improved DV-Hop localization algorithm have been proposed to enhance the localization accuracy of DV-Hop algorithm for wireless sensor networks. These proposed improvements of DV-Hop also have some drawbacks in terms of time and energy consumption. In this paper, we propose Novel DV-Hop localization algorithm that provides efficient localization with lesser communication cost without requiring additional hardware. The proposed algorithm completely eliminates communication from one of the steps by calculating hop-size at unknown nodes. It significantly reduces time and energy consumption, which is an important improvement over DV-Hop—based algorithms. The algorithm also uses improvement term to refine the hop-size of anchor nodes. Furthermore, unconstrained optimization is used to achieve better localization accuracy by minimizing the error terms (ranging error) in the estimated distance between anchor node and unknown node. Log-normal shadowing path loss model is used to simulate the algorithms in a more realistic environment. Simulation results show that the performance of our proposed algorithm is better when compared with DV-Hop algorithm and improved DV-Hop—based algorithms in all considered scenarios. 相似文献