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。     

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.     

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％。     

Wireless Node Localization under Hostile Radio Environment using Smart Antenna

This paper presents a resilient localization scheme for wireless sensor networks (WSNs). It suits well in estimation of node position under a corrupted radio environment. Position computation is based on information of angle-of-arrivals (AoA) and references obtained from a few mobile anchors. In the network, anchors are equipped with smart antennas and global positioning system receivers. They broadcast signals in a synchronous and periodic fashion. The neighboring nodes having the signals with received signal strength values above a prescribed threshold level, respond with their respective IDs. Anchors evaluate AoA information from these signals using estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm. Next, they forward beacon messages, containing their references and estimated angles, to the corresponding nodes and move along random trajectories. After receiving three sets of such data, at least, nodes can initiate selective segregation of the inconsistent position estimations. Simulation results attaining higher degree of localization accuracy validate its competency over the existing schemes.

     

LIP: an efficient lightweight iterative positioning algorithm for wireless sensor networks

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.     

Path planning using a mobile anchor node based on trilateration in wireless sensor networks

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

无线传感器网络中非GPS的定位算法

无线传感器网络中节点自定位一直是一个具有挑战性的研究课题。全球定位系统(GPS)是一种传统的定位技术,但是定位的准确性低且网络花费较大。通过分析现有的节点自定位算法,认为六边形节点自定位算法是比较优秀的非GPS算法,该算法基于蜂窝交叠的思想,节点仅使用简单的连接矩阵和信标帧中的定位数据就能自定位,且定位准确性高,最后提出了今后要做的工作。     

A GPS-less, outdoor, self-positioning method for wireless sensor networks

One challenging issue in sensor networks is to determine where a given sensor node is physically located. This problem is especially crucial for very small sensor nodes. This paper presents a GPS-less, outdoor, self-positioning method for wireless sensor networks. In our method, a set of nodes, called reference points (RPs), are deployed in the sensor network with overlapping regions of coverage. The RP periodically broadcasts beacon frames which contain localization data. The sensor node collects the beacon frames from RPs and process the data in the frame; it can then easily localize itself. The analysis of positioning accuracy is given to show how well a sensor node can correctly localize itself. In the optimal transmitting power, the worst-case accuracy for all data points is within 28.87% of the separation-distance between two adjacent RPs and the average accuracy is within 15.51%. The simulation results also show the robustness of the proposed method. Finally, we have implemented our positioning method on a sensor network test bed and the actual measurement show that the method can achieve average accuracy within 17.9% of the separation-distance between two adjacent RPs in an outdoor environment.     

Monte Carlo localization of wireless sensor networks with a single mobile beacon

One of the most important tasks in sensor networks is to determine the physical location of sensory nodes as they may not all be equipped with GPS receivers. In this paper we propose a localization method for wireless sensor networks (WSNs) using a single mobile beacon. The sensor locations are maintained as probability distributions that are sequentially updated using Monte Carlo sampling as the mobile beacon moves over the deployment area. Our method relieves much of the localization tasks from the less powerful sensor nodes themselves and relies on the more powerful beacon to perform the calculation. We discuss the Monte Carlo sampling steps in the context of the localization using a single beacon for various types of observations such as ranging, Angle of Arrival (AoA), connectivity and combinations of those. We also discuss the communication protocol that relays the observation data to the beacon and the localization result back to the sensors. We consider security issues in the localization process and the necessary steps to guard against the scenario in which a small number of sensors are compromised. Our simulation shows that our method is able to achieve less than 50% localization error and over 80% coverage with a very sparse network of degree less than 4 while achieving significantly better results if network connectivity increases.     

Non‐convex model for sensor network localization based on connectivity

In this paper, a new model utilizing all the information derived from connectivity‐based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non‐convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two‐objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non‐convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two‐level range connectivity‐based sensor network localization method is proposed to enrich the connectivity information. The two‐level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one‐level and two‐level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two‐level range connectivity compared with the usual one‐level range connectivity‐based localization. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed object tracking using moving trajectories in wireless sensor networks

Most recent research on object tracking sensor networks has focused on collecting all data from the sensor network into the sink, which delivers the predicted locations to the corresponding nodes in order to accurately predict object movement. The communication cost of this centralized scenario is higher than that of a distributed method. Centralized data collection affects the freshness of the data and increases latency in movement trajectory prediction. In addition, due to the large amount of packets being sent and received, sensor node energy is quickly exhausted. Although this data collection method might result in higher accuracy for prediction, the sensor network lifetime is not reduced. In this paper, a distributed object tracking method is proposed using the network structure of convex polygons, called faces. The nodes in the faces cooperate to find the trajectories of an object and then these trajectories are used to predict the objects’ movement. The proposed method, based on trajectory tree construction, can reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and make prediction of nodes moving direction accurately than the existing approaches.     

多移动信标辅助的分布式节点定位方法

为提高移动信标辅助定位算法的定位精度,避免重复扫描待定位节点,提出了一种使用多个移动信标的定位方法。这些信标在遍历网络时保持一定相对位置关系,使用TDoA技术测距并为未知节点提供距离信息辅助其定位。提出了基于最优覆盖策略的2种移动信标路径规划方法。仿真结果表明,所提出的算法具有较高的定位精度,且所使用的移动路径性能较高。     

Computational intelligence based localization of moving target nodes using single anchor node in wireless sensor networks

Wireless Sensor Networks (WSNs) have tremendous ability to interact and collect data from the physical world. The main challenges for WSNs regarding performance are data computation, prolong lifetime, routing, task scheduling, security, deployment and localization. In recent years, many Computational Intelligence (CI) based solutions for above mentioned challenges have been proposed to accomplish the desired level of performance in WSNs. Application of CI provides independent and robust solutions to ascertain accurate node position (2D/3D) with minimum hardware requirement (position finding device, i.e., GPS enabled device). The localization of static target nodes can be determined more accurately. However, in the case of moving target nodes, accurate position of each node in network is a challenging problem. In this paper, a novel concept of projecting virtual anchor nodes for localizing the moving target node is proposed using applications of Particle Swarm Intelligence, H-Best Particle Swarm Optimization, Biogeography Based Optimization and Firefly Algorithm separately. The proposed algorithms are implemented for range-based, distributed, non-collaborative and isotropic WSNs. Only single anchor node is used as a reference node to localize the moving target node in the network. Once a moving target node comes under the range of a anchor node, six virtual anchor nodes with same range are projected in a circle around the anchor node and two virtual anchor nodes (minimum three anchor nodes are required for 2D position) in surrounding (anchor and respective moving target node) are selected to find the 2D position. The performance based results on experimental mobile sensor network data demonstrate the effectiveness of the proposed algorithms by comparing the performance in terms of the number of nodes localized, localization accuracy and scalability. In proposed algorithms, problem of Line of Sight is minimized due to projection of virtual anchor nodes.     

SALA: Smartphone-Assisted Localization Algorithm for Positioning Indoor IoT Devices

This paper proposes a Smartphone-Assisted Localization Algorithm (SALA) for the localization of Internet of Things (IoT) devices that are placed in indoor environments (e.g., smart home, smart office, smart mall, and smart factory). This SALA allows a smartphone to visually display the positions of IoT devices in indoor environments for the easy management of IoT devices, such as remote-control and monitoring. A smartphone plays a role of a mobile beacon that tracks its own position indoors by a sensor-fusion method with its motion sensors, such as accelerometer, gyroscope, and magnetometer. While moving around indoor, the smartphone periodically broadcasts short-distance beacon messages and collects the response messages from neighboring IoT devices. The response messages contains IoT device information. The smartphone stores the IoT device information in the response messages along with the message’s signal strength and its position into a dedicated server (e.g., home gateway) for the localization. These stored trace data are processed offline through our localization algorithm along with a given indoor layout, such as apartment layout. Through simulations, it is shown that our SALA can effectively localize IoT devices in an apartment with position errors less than 20 cm in a realistic apartment setting.     

Sensor Position Determination with Flying Anchors in Three-Dimensional Wireless Sensor Networks

This paper presents a range-free position determination (localization) mechanism for sensors in a three-dimensional wireless sensor network based on the use of flying anchors. In the scheme, each anchor is equipped with a GPS receiver and broadcasts its location information as it flies through the sensing space. Each sensor node in the sensing area then estimates its own location by applying basic geometry principles to the location information it receives from the flying anchors. The scheme eliminates the requirement for specific positioning hardware, avoids the need for any interaction between the individual sensor nodes, and is independent of network densities and topologies. The performance of the localization scheme is evaluated in a series of simulations performed using ns-2 software and is compared to that of the Centroid and Constraint range-free mechanisms. The simulation results demonstrate that the localization scheme outperforms both Centroid and Constraint in terms of a higher location accuracy, a reduced localization time, and a lower beacon overhead. In addition, the localization scheme is implemented on the Tmote Sky for validating the feasibility of the localization scheme.     

Geometrical Localization Algorithm for Three Dimensional Wireless Sensor Networks

The issue of localization has been addressed in many research areas such as vehicle navigation systems, virtual reality systems, user localization in wireless sensor networks (WSNs). In this paper, we have proposed an efficient range-free localization algorithm: Geometrical Localization Algorithm (GLA) for large scale three dimensional WSNs. GLA uses moving anchors to localize static sensors. GLA consists of beacon message selection, circular cross section selection. Three beacon messages are used to compute the center of circular cross section using vector method and perpendicular bisector method. The static sensors are localized with help of the center of circular cross section and geometrical rules for sphere. GLA is simulated in SINALGO software and results have been compared with existing methods namely chord selection and point localization. GLA outperforms both the compared methods in terms of average localization time and beacon overhead.     

应用三维弹簧系统模型的锚节点部署优化

为使随机部署的三维无线传感器网络中锚节点的分布更加合理,提高未知节点定位精度,针对锚节点部署进行优化。通过构建弹簧系统模型,将锚节点抽象为通过弹簧相连接的点,使部分锚节点在合力作用下进行伸缩运动,达到提高网络性能的目的。当锚节点部署优化完成后,应用近似三角形内点测试( APIT)和DV-HOP( Distance Vector-hop)算法测试优化前后的节点定位精度。仿真结果表明,三维空间下的锚节点经过弹簧系统模型的部署优化后,锚节点网络覆盖率和定位覆盖率均得到了提高,网络平均连通度有所提升,且定位精度显著提高。     

Distributed localization using mobile beacons in wireless sensor networks

A new distributed node localization algorithm named mobile beacons-improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.     

Sensitivity-based data fusion for optical localization of a mobile robot

Acoustic-based techniques are the standard for localization and communication in underwater environments, but due to the challenges associated with this medium, it is becoming increasingly popular to find alternatives such as using optics. In our prior work we developed an LED-based Simultaneous Localization and Communication (SLAC) approach that used the bearing angles, needed for establishing optical line-of-sight for LED-based communication between two beacon nodes and a mobile robot, to triangulate and thereby localize the position of the robot. Our focus in this paper is on how to optimally fuse measurement data for optical localization in a network with multiple pairs of beacon nodes to obtain the target location. We propose the use of a sensitivity metric, designed to characterize the level of uncertainty in the position estimate with respect to the bearing angle error, to dynamically select a desired pair of beacon nodes. The proposed solution is evaluated with extensive simulation and experimentation, in a setting of three beacons nodes and one mobile node. Comparison with multiple alternative approaches demonstrates the efficacy of the proposed approach.     

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.