无线传感器网络中一种新的分布式定位方案研究

Node localization in wireless sensor networks (WSN) is treated as a functional dual of target tracking from a novel perspective in the paper. Different from the traditional tracking problem in WSN, using the static location-ware node to estimate the moving target, the mobile node is used to help unknown nodes to accurately discover their positions. A new node localization scheme virtual beacons-energy ratios localization (VB-ERL) and its refinements for the WSN are presented. In the scheme, the mobile node moves in the surveillant field based on the Gauss-Markov mobility model and periodically broadcasts the information packets. Each static unknown node receives the virtual beacons and energy in its sensing range, and estimates its location by finding the intersection of a set of hyper-spheres. Simulation results show the proposed scheme is efficient.     

Segment Based Camera Calibration

The basic idea of calibrating a camera system in previous approaches is to determine camera parmeters by using a set of known 3D points as calibration reference.In this paper,we present a method of camera calibration in whih camera parameters are determined by a set of 3D lines.A set of constraints is derived on camea parameters in terms of perspective line mapping.Form these constraints,the same perspective transformation matrix as that for point mapping can be computed linearly.The minimum number of calibration lines is 6.This result generalizes that of Liu,Huang and Faugeras^[12] for camera location determination in which at least 8 line correspondences are required for linear computation of camera location.Since line segments in an image can be located easily and more accurately than points,the use of lines as calibration reference tends to ease the computation in inage preprocessing and to improve calibration accuracy.Experimental results on the calibration along with stereo reconstruction are reported.     

Energy-efficient multipath routing in wireless sensor network considering wireless interference

Due to the energy and resource constraints of a wireless sensor node in a wireless sensor network (WSN), design of energy-efficient multipath routing protocols is a crucial concern for WSN applications. To provide high-quality monitoring information, many WSN applications require high-rate data transmission. Multipath routing protocols are often used to increase the network transmission rate and throughput. Although large-scale WSN can be supported by high bandwidth backbone network, the WSN remains the bottleneck due to resource constraints of wireless sensors and the effects of wireless interference. In this paper, we propose a multipath energy-efficient routing protocol for WSN that considers wireless interference. In the proposed routing protocol, nodes in the interference zone of the discovered path are marked and not allowed to take part in the subsequent routing process. In this way, the quality of wireless communication is improved because the effects of wireless interference can be reduced as much as possible. The network load is distributed on multiple paths instead of concentrating on only one path, and node energy cost is more balanced for the entire wireless network. The routing protocol is simulated in NS2 software. Simulation result shows that the proposed routing protocol achieves lower energy cost and longer network lifetime than that in the literature.     

Magic Sets Revisited

This paper distinguishes among three kinds of linear recursions:canonical strongly linear recursion(CSLR),non-interdependent linear recursion(NILR)and interdependent linear recurstion(ILR)and presents an optimal algorithm for each.First,for the CSLRs,the magic-set method is refined in such a way that queries can be evaluated efficiently.Then,for the NILRs and ILRs,the concept of query dependency graphs is introduced to partition the rules of a program into a set of CSLRs and the computation is elaborated so that the oplimization for CSLRs can also be applied.     

基于凸优化算法的无人水下航行器协同定位

In this paper, a cooperative localization algorithm for autonomous underwater vehicles (AUVs) is proposed. A ``parallel" model is adopted to describe the cooperative localization problem instead of the traditional ``leader-follower" model, and a linear programming associated with convex optimization method is used to deal with the problem. After an unknown-but-bounded model for sensor noise is assumed, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements. Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space. Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs. Simulation results are presented for a typical localization example of the AUV formation. The results show that our positioning method offers a good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, and this validates that it is an economical and practical positioning approach compared with the traditional approach.     

Power efficient scheme for self localization in wireless sensor networks

With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power.     

A Fast Scalable Classifier Tightly Integrated with RDBMS

In this pager,we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS).In addition to high classification accuracy,the unique features of the approach include its high training speed ,linear scalability,and simplicity in implementation.More importantly,the major computation required in the approach can be implemented using standard functions provided by the modern realtional DBMS.Besides,with the effective rule pruning strategy,the algorithm proposed in this paper can produce a compact set of classification rules,The results of experiments conducted for performance evaluation an analysis are presented.     

A top-down positioning scheme for underwater wireless sensor networks

In order to increase the localization coverage while keeping the localization error small in a unique network architecture in which there are not evenly distributed anchor nodes with great ability of communication or additional infrastructure,a Top-down Positioning Scheme(TPS)for underwater acoustic sensor networks is proposed.By defining node’s confidence reasonably,TPS insures the quality of the new reference nodes.TPS also refines the nodes which have just been positioned via the gradient method and helps non-localized nodes search for more reference nodes via the new scheme for 3D Euclidean distance estimation.By comparing the new scheme for 3D Euclidean distance estimation with the existing scheme,the new scheme is shown to have greater ability to estimate two-hop Euclidean distance in 3D space.Simulation results show that TPS which integrates node’s confidence defined reasonably,the gradient method,and the new scheme for 3D Euclidean distance estimation can increase the localization coverage ratio,while keeping the localization error small.     

Approximate K-Median of Location Streams with Redundancy and Inconsistency

Data streams produced by positioning systems such as Global Positioning System (GPS) or RFID readers can be considered as location streams[12]. Location streams are usually generated in a distributed fashion by a large scale distributed system covering a wide range of areas. Computing on distributed location streams is both practically useful and theoretically challenging. The results of computation could be used to schedule the traffic in a metropolis to avoid traffic jam, dispatch taxis to serve the passengers more quickly and display the current position of goods in supply chain management, etc. Since location streams are usually generated with very high rate in uncertain ways over hostile environments, the collected updates of location are probably redundant and inconsistent in a wide positioning system. To process distributed location streams with redundancy and inconsistency, this paper proposes a novel method based on min-wise hash. With this method, redundant updates of distributed location streams can be effiectively filtered out, while the true location could be derived from inconsistent ones. Consequently, globally uniform samples can be obtained. Based on the uniform samples, an algorithm for computing the approximate k-median of huge number of moving objects is presented in this paper. Furthermore, it is demonstrated that sketch-based methods are not necessarily effiective in processing location streams with redundancy and inconsistency. In addition to theoretical analysis, some extensive experiments are conducted to validate the efficiency and effiectiveness of the proposed approach.     

Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor (SLCA) semantics. For a given query Q with m Key words we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor (LCA) node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.     

无线传感器加权质心定位算法研究

无线传感器网络的节点自定位的技术主要有基于测距(Range-Based)的定位技术和距离无关(Range-Free)定位技术。该文主要研究了基于测距的无线传感器网络定位算法。在传统质心定位算法中,引入相对RSSI加权定位实现未知节点的位置估计。该方法每个锚节点的权值使用RSSI的相对位置值,每个锚节点的权值可以按线性或指数加权。     

Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

基于核的RSSI定位

定位在无线传感网络的应用中有非常重要的作用,但是目前定位技术的准确度不够理想,RSSI是一种能实际使用确定传感网络中节点间距离的技术。在无线传感网络中引进了核,核是一个拥有三个或更多有着固定位置关系传感节点的物体。核能定位其他核或节点,同时核也能被传感网络定位。通过模拟发现使用核能有效提高定位的准确度。     

基于阶次序列加权的无线传感器定位算法

针对WSN中节点的定位误差的问题,提出了一种阶次序列加权的无线传感器定位算法。首先根据节点定位中的信号传输采用Shadowing模型,其次根据锚节点建立Voronoi图,将Voronoi多边形的顶点作为参考点,然后建立参考点与锚节点之间的阶次序列,通过选择N个未知节点序列与最优序列加权估计未知节点的位置,仿真结果表明,相对于参比算法,算法不仅提高了传感器节点的定位精度,而且降低了算法的计算复杂度,在WSN节点定位方面具有较高的应用价值。     

三维无线传感器网络节点的序列重心定位算法

节点自定位技术是无线传感器网络的关键技术之一。三维序列重心算法利用锚节点两两之间的垂直平分面将定位空间分为边、面和体三类区域,缩小了未知节点可能存在的范围,并在所在范围内再次求出离未知节点最近三点组成的三角形的重心作为未知点位置的估计。该算法改善了二维序列算法误差较大的问题,且不需要增加硬件设施来实现特殊的功能。仿真结果表明,该算法可以达到较高的定位精度,能够满足三维空间中未知节点定位的应用需要。     

基于WSN路由节点度模型的楼宇走廊定位算法

针对现有的无线传感器网络(WSN)定位方法应用于结构复杂的楼宇走廊时,存在定位精度较低的问题,提出一种基于WSN路由节点度模型的楼宇走廊定位算法.该算法在路由节点度模型的基础上,先采用基于支持向量回归(SVR)的方法,用少量锚节点定位普通路由节点,达到间接增加锚节点覆盖率的目的;然后采用基于中垂线分割的方法定位随机分布在区域内的未知节点和移动终端.仿真表明:与传统SVR定位算法和核岭回归定位算法相比,所提出的算法精度提高了定位精度,满足室内定位精度要求(1 m~3 m),且降低了对锚节点数量的需求,可运用于楼宇走廊WSN定位.     

基于RSSI的测距差分修正定位算法

为了抑制RSSI误差对无线传感器节点自身定位精度的影响,以三边定位算法为基础,定义了个体差异差分系数、距离差分系数和距离差分定位方程,把离目标节点最近的信标节点作为参考节点对基于RSSI的测距进行差分修正,并将差分法和质心法相结合提出了一种测距差分修正定位算法。该定位算法无需增加额外硬件开销,容易实现,定位误差可小于2.5m,适合于处理能力和能量有限的无线传感器网络节点。     

一种基于边松弛的大规模WSN分簇定位算法

针对大规模WSN定位问题中,基于半定规划的分簇定位算法在分簇不均匀及节点密度较大时,部分簇会出现定位计算复杂度过高的问题,提出了一种新的基于边松弛的分簇定位算法-EES-Cluster.该算法通过对每一个网络簇子图进行边的松弛预处理,减少了边的数目;在网络分簇数目较少时,能有效降低定位过程的计算复杂度,同时较好地保持较高的定位精度,减小簇头节点信息融合的功耗.仿真实验及分析表明,EES-Cluster算法能有效降低分簇定位算法的计算复杂度,提高大规模WSN的定位效率.     

缩小采样区域的蒙特卡罗移动节点定位算法

针对无线传感器网络中移动节点定位问题,提出一种适用于未知节点移动而信标节点固定的改进蒙特卡罗定位算法,充分利用信标节点与未知节点间的测距误差来缩小采样区域,提高采样效率。仿真结果表明,改进算法在信标节点密度、连通度和节点最大运动速度等不同情况下均能提高定位精度,减少采样次数和计算量,延长网络的生存周期。