一种基于网络密度分簇的移动信标辅助定位方法

现有移动信标辅助定位算法未充分利用网络节点分布信息,存在移动路径过长及信标利用率较低等问题。该文把网络节点分簇、增量定位与移动信标辅助相结合,提出了一种基于网络密度分簇的移动信标辅助定位算法(MBL(ndc))。该算法选择核心密度较大的节点作簇头,采用基于密度可达性的分簇机制把整个网络划分为多个簇内密度相等的簇,并联合使用基于遗传算法的簇头全局路径规划和基于正六边形的簇内局部路径规划方法,得到信标的优化移动路径。当簇头及附近节点完成定位后,升级为信标,采用增量定位方式参与网络其它节点的定位。仿真结果表明,该算法定位精度与基于HILBERT路径的移动信标辅助定位算法相当,而路径长度不到后者的50％。     

Extended Monte Carlo localization algorithm for mobile sensor networks

A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node＇s location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization （Ext-MCL） is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.     

基于自适应采样的移动WSN定位算法

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。     

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

Range-Free Mobile Sensor Localization and a Novel Obstacle Detection Technique

Mobile sensor localization is a challenging problem in wireless sensor networks. Due to mobility, it is difficult to find exact position of the sensors at any time instance. The aim of localization is to minimize positioning errors of the mobile sensors. In this paper we propose two range-free distributed localization algorithms for mobile sensors with static anchors. Both the algorithms depend on selection of beacon points. First we assume that mobile sensors move straight during localization which helps us to provide an upper bound on localization error. Certain applications may not allow sensors to move in a straight line. Obstacles may also obstruct path of sensors. Moreover beacon point selection becomes difficult in presence of obstacles. To address these issues, we propose another localization algorithm with an obstacle detection technique which selects correct beacon points for localization in presence of obstacles. Simulation results show improvements in performance over existing algorithms.     

Optimal trajectory determination of a single moving beacon for efficient localization in a mobile ad-hoc network

An ad hoc network of small robots (sensor nodes) adjusting their positions to establish network connectivity would be able to provide a communication infrastructure in an urban battlefield environment. A sensor node would be capable of moving to a particular position to establish network connectivity, provided it knows its current position, positions of other sensor nodes and the radio propagation characteristics of the sensor area.In this paper, we present a pseudo formation control based trajectory algorithm to determine the optimal trajectory of a moving beacon used in localization of the sensor nodes in real-time. The trajectory and the frequency of transmission of the GPS based position information of the moving beacon influences the accuracy of localization and the power consumed by the beacon to localize. Localization accuracy and reduction in the number of position information messages can be achieved, in real-time, by determining the optimal position from where the beacon should transmit its next position information. This will decrease the time required to localize, and power consumed by the beacon in comparison to random or predetermined trajectories.We first show that optimal position determination is a pseudo formation control problem. Next, we show the pseudo formation control problem formulated as an unconstrained optimization problem under the free space propagation model. We further present the modeling of the beacon incorporating the trajectory algorithm based on the pseudo formation control in a discrete event simulator. Simulation results, comparing the performance of localization with pseudo formation control based trajectory against random waypoint and predetermined trajectories for the beacon are presented. The simulation results show that the localization accuracy is significantly improved along with reduction in the number of position information messages transmitted when the beacon traverses along the pseudo formation control based trajectory.     

无线传感网节点自适应优化定位研究

文中提出了一种有效的基于RSSI的无线传感网节点自适应定位优化算法.该算法用对具体环境条件下RSSI测距的自适应校正及误差修正来提高测距精度,基于小世界网络理论遴选侯选锚节点,结合MDS-MAP算法进行定位.与同类算法相比,该算法能适应不同的应用环境,且定位稳定性和精确性均显著提高.     

Detecting and connecting disjoint sub-networks in wireless sensor and actor networks

Wireless sensor and actor networks (WSANs) can be considered as a combination of a sensor network and an actor network in which powerful and mobile actor nodes can perform application specific actions based on the received data from the sensors. As most of these actions are performed collaboratively among the actors, inter-actor connectivity is one of the desirable features of WSANs. In this paper, we propose a novel distributed algorithm for establishing a connected inter-actor network topology. Considering initially disjoint sets of actors, our algorithm first initiates a search process by using the underlying sensor network in order to detect the possible sub-networks of actors in the region. After these sub-networks are detected, our algorithm pursues a coordinated actor movement in order to connect the sub-networks and thus achieve inter-actor connectivity for all the actors. This coordinated movement approach exploits the minimum connected dominating set of each sub-network when picking the appropriate actor to move so that the connectivity of each sub-network is not violated. In addition, the approach strives to minimize the total travel distance of actors and the messaging cost on both sensors and actors in order to extend the lifetime of WSAN. We analytically study the performance of our algorithm. Extensive simulation experiments validate the analytical results and confirm the effectiveness of our approach.     

信标节点漂移情况下的无线传感器网络节点定位机制

针对信标节点漂移情况下的节点定位问题,提出了一种分布式的信标节点漂移检测方法,采用节点自评分和协商机制,自动寻找可能发生了漂移的信标节点,同时针对大量信标节点发生漂移后的定位覆盖率下降问题,构建普通节点的定位可信度模型,并在定位盲区内使用一些较为可靠的普通节点作为临时信标节点进行定位。仿真实验表明,该算法在误检测、定位误差方面性能优于传统算法,具有较低的通信开销、较高的实用性和灵活性。     

Connectivity properties of large-scale sensor networks

In wireless sensor networks, both nodes and links are prone to failures. In this paper we study connectivity properties of large-scale wireless sensor networks and discuss their implicit effect on routing algorithms and network reliability. We assume a network model of n sensors which are distributed randomly over a field based on a given distribution function. The sensors may be unreliable with a probability distribution, which possibly depends on n and the location of sensors. Two active sensor nodes are connected with probability p _e (n) if they are within communication range of each other. We prove a general result relating unreliable sensor networks to reliable networks. We investigate different graph theoretic properties of sensor networks such as k-connectivity and the existence of the giant component. While connectivity (i.e. k = 1) insures that all nodes can communicate with each other, k-connectivity for k > 1 is required for multi-path routing. We analyze the average shortest path of the k paths from a node in the sensing field back to a base station. It is found that the lengths of these multiple paths in a k-connected network are all close to the shortest path. These results are shown through graph theoretical derivations and are also verified through simulations.     

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSNs), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: (i) an optional 1-lag smoothing done almost in real-time, (ii) a novel low-cost communication protocol based on package approximation and censoring, (iii) higher robustness of the standard mixture importance sampling (MIS) technique, and (iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.     

Sequential Monte Carlo localization in mobile sensor networks

Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed in the past few years and they can be classified into two categories: range-based and range-free. Since range-based techniques need special hardware, which increases the localization cost, many researchers now focus on the range-free techniques. However, most of the range-free localization schemes assume that the sensor nodes are static, the network topology is known in advance, and the radio propagation is perfect circle. Moreover, many schemes need densely distributed anchor nodes whose positions are known in advance in order to estimate the positions of the unknown nodes. These assumptions are not practical in real network. In this paper, we consider the sensor networks with sparse anchor nodes and irregular radio propagation. Based on Sequential Monte Carlo method, we propose an alterative localization method—Sequential Monte Carlo Localization scheme (SMCL). Unlike many previously proposed methods, our work takes the probabilistic approach, which is suitable for the mobile sensor networks because both anchors and unknown nodes can move, and the network topology need not be formed beforehand. Moreover, our algorithm is scalable and can be used in large-scale sensor networks. Simulation results show that SMCL has better localization accuracy and it can localize more sensor nodes when the anchor density is low. The communication overhead of SMCL is also lower than other localization algorithms.

An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network

In many applications of wireless sensor network, the position of the sensor node is useful to identify the actuating response of the environment. The main idea of the proposed localization scheme is similar with most of the existing localization schemes, where a mobile beacon with global positioning system broadcast its current location coordinate periodically. The received information of the coordinates help other unknown nodes to localize themselves. In this paper, we proposed a localization scheme using mobile beacon points based on analytical geometry. Sensor node initially choose two distant beacon points, in-order to minimize its residence area. Later using the residence area, sensor node approximate the radius and half length of the chord with reference to one of the distant beacon point. Then the radius and half length of the chord are used to estimate the sagitta of an arc. Later, sensor node estimate its position using radius, half length of the chord, and sagitta of an arc. Simulation result shows the performance evaluation of our proposed scheme on various trajectories of mobile beacon such as CIRCLE, SPIRAL, S-CURVE, and HILBERT.     

Some Fundamental Results on Complex Network Problem for Large-Scale Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically constituted by a large number of connected wireless sensors (nodes), generally distributed at random on a given surface area. In such large-scale networks, the desired global system performance is achieved by gathering local information and decisions collected from each individual node. There exist two fundamental global issues on WSNs that we consider here, i.e. full network connectivity and network lifetime. Full connectivity can be obtained either by increasing transmission range, at the expense of consuming higher transmission power, or by increasing the number of sensors, i.e. by increasing network costs. Both of them are closely related to global network lifetime, in the sense that the higher the power consumption or the more sensors deployed the shorter the network lifetime [31]. So the main question is, how can one design large-scale random networks in order to have both global connectivity and maximum network lifetime? Although these questions have been addressed often in the past, a definite, simple predicting algorithm for achieving these goals does not exist so far. In this paper, we aim to discuss such a scheme and confront it with extensive simulations of random networks generated numerically. Specifically, we study the minimum number of nodes required to achieve full network connectivity, and present an analytical formula for estimating it. The results are in very good agreement with the numerical simulations as a function of transmission range. In addition, we study in detail several other statistical properties of large-scale WSNs, such as average path distance, clustering coefficient, degree distribution, etc., also as a function of the transmission range, both qualitatively and quantitatively. We discuss results on how to further improve network energy consumption from the original networks considered by switching off (deleting) some nodes at random but keeping whole network connectivity. The present results are expected to be useful for the design of more efficient large-scale WSNs.     

移动Sink传感网中基于IEEE 802.15.4的拓扑控制与路由

针对查询驱动的移动Sink无线传感器网络,基于IEEE 802.15.4标准,提出了一个联合簇树组网和移动路由的跨层协议设计方案。以能耗、网络连通度、负载均衡和子树深度等指标为评价因子,在多目标优化模型下定义了一个拓扑综合评价函数,构建稳定的最优簇树网络结构;改进了IEEE 802.15.4 MAC中基于信标帧调度的分布式睡眠同步算法,维护较低占空比下的网络同步与拓扑修正;利用拓扑形成过程中依"最早信标帧"、"最好链路"、"最小层次"3种准则确立的父子关系,建立移动Sink与N-Hop范围内节点的双向路由路径。最终将上述MAC层的拓扑控制方案与网络层的查询路由方法完整实现于TinyOS协议栈中。实验结果表明,评价函数能够有效地反映组网质量,N-Hop后验式路由结合睡眠同步机制,在"最好链路"拓扑下表现出较高的能量效率。     

Monte Carlo localization for mobile wireless sensor networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.     

A Theory of Network Localization

In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

传感网络中基于移动信标的网格扫描定位算法

节点定位是传感网络最基本的技术之一,对此提出一种基于移动信标的网格扫描定位算法(Mobile Beacon Grid-Scan,MBGS)。该算法在网格扫描定位算法基础上,利用一个移动信标巡航整个传感区域,产生大量的虚拟信标,提高网络信标覆盖率,然后普通节点利用这些信标信息减小其可能区域(Estimative Rectangle,ER),并把新可能区域网格坐标质心作为其最新估计坐标。仿真结果表明,与Bounding Box、质心定位算法以及传统的网格扫描定位算法相比,MBGS定位方法的定位精度更高,算法性能更加稳定。     

Moving anchor node localization algorithm based on network connectivity

In order to better solve the contradiction between precision of localization and the number of anchor nodes in wireless sensor network,a mobile anchor node localization technology based on connectivity was proposed.First,the coverage characteristic of the network nodes was analyzed,and a critical value was found between the mobile step and the anchor node communication radius,mobile anchor nodes＇ coverage characteristic would change when near this critical value.Second,a mobile anchor node followed a planning path to form a positioning area seamless coverage was used.Finally,when there was no need for high-precision technology,node position would been estimated according with the connectivity of the network and the receiving information of the node.The simulation results show that the proposed algorithm can realize coarse-grained localization,and paths perform complete localization.