Localization Algorithm For Sensor Networks Research Articles

Distributed Localization Algorithm for Large-Scale Wireless Sensor Network

Aiming at the inherent deficiencies of large-scale wireless sensor network for distributed positioning algorithm, the DV-hop algorithm is mainly studied and improved, and two kinds of improved algorithms DV-hop and DV-hop+Lastdist DV- hop-MinMax are proposed. The DV-hop+Lastdist improves the positioning accuracy of the node by only adding a message variable, while the DV-hop-MinMax effectively reduces the costs of floating-point operations and power consumption, and can select the appropriate algorithm according to actual application environment. Finally, the paper gives the method to realize OMNeT++ in the simulation environment, and carries out the simulation analysis.

AOA and TDOA-Based A Novel Three Dimensional Location Algorithm in Wireless Sensor Network

Combining angle measurement algorithm based on wave interference in two-dimensional space with positioning algorithm based on time difference of arrival, this study proposes a Wireless Sensor Networks localization algorithm for three-dimensional space. First, the algorithm measures the angle and distance of beacon nodes and test nodes, and then calculates the precise coordinates of the test node. The algorithm complexity is low, just needing at least three beacon nodes. As it uses distributed computing, so it has high positioning accuracy. Simulation results show that the algorithm can accurately calculate the position of the sensor nodes in three-dimensional space.

A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks

With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.

An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks

This paper presents a hybrid localization algorithm for wireless sensor networks (WSNs) that simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurement...

An improved multihop-based localization algorithm for wireless sensor network using learning approach

Multihop range-free localization methods could obtain relatively reasonable location estimation in the isotropic network; however, during the practical application, it is often affected by various anisotropic factors such as the radio irregularity and barriers, which can significantly reduce its performance. In this paper, we propose a new approach for multihop localization in wireless sensor network based on nonlinear mapping and learning algorithm. The proposed method is simple, efficient, higher accuracy and no need to set complex parameter in that only hop-counts information and position information of the beacons are used for the localization. The proposed approach is composed of two steps: firstly, this algorithm uses kernel function to define the connectivity information (hop-counts) between nodes, then, learning method is used to guide and build the inter-node localization model; secondly, the hop-counts between the unknown nodes and beacons are used to estimate the coordinate of unknown nodes. We evaluate our algorithm under various anisotropic network and real environment, and analyze its performance. We also compare our approach with several available advanced approaches, and demonstrate the superior performance of our proposed algorithm in terms of location estimation adaptability.

Improvements of DV-Hop localization algorithm for wireless sensor networks

Based on the improvements of the original DV-Hop localization algorithm, three new localization algorithms (iDV-Hop1, iDV-Hop2, and Quad DV-Hop) are presented in this article. In iDV-Hop1 and iDV-Hop2, all steps of the original DV-Hop are kept, and several steps based on geometry improvements of the localization problem are added in order to obtain better localization accuracy. The third algorithm (Quad DV-Hop) formulated the localization problem as bounded least squares problem, to be solved by quadratic programming. Simulations are carried out on the four different types of network topology by varying nodes communication range, number of anchor nodes and number of nodes. Comparison of our algorithms with the original DV-Hop and Improved DV-Hop algorithms are given. It is shown that iDV-Hop1 algorithm can significantly reduce the localization error (up to three times) in scenarios with irregular topologies compared to DV-Hop and Improved DV-Hop. In scenarios with regular topologies, iDV-Hop2 and Quad DV-Hop showed better performance compared to DV-Hop and Improved DV-Hop (up to 11 % lower localization error).

A locality-based range-free localization algorithm for anisotropic wireless sensor networks

Location discovery is one of the fundamental services in Internet of things built with wireless sensor networks (WSNs). In this paper, we propose AnSuper, a novel range-free localization algorithm that provides accurate location estimates in anisotropic WSNs. In AnSuper, every anchor first selects a set of friendly anchors to which its distance estimates can be accurately obtained. These anchors are then broadcasted to neighboring common nodes, with which they can obtain accurate distance estimates to the anchors by using a novel locality-based algorithm and consequently calculate their positions. We discuss how to suppress error accumulation in the localization process. AnSuper can handle multiple anisotropy factors, including uneven nodal distribution and irregularity of the deployment region. Simulation results show that, compared with state-of-the-art solutions, AnSuper improves localization accuracy by more then 30 %.

A Single Chord Localization Algorithm for Wireless Sensor Networks

This paper presents a novel range-free single chord localization algorithm with low energy consumption and high accuracy for wireless sensor networks. A mobile anchor node and a mobile reference node are employed to periodically broadcast beacon messages and reference messages, respectively. Each sensor node is equipped with a received signal strength indicator for comparing the signal strength. By using the edge function and the result of comparing signal strength, each sensor node only needs one chord on the communication circle for localization. Compared with previous approaches that require multiple chords, less beacon messages are required by the sensor nodes. In the proposed algorithm, the localization error mainly depends on the move distance interval. However, for the other approaches, large localization errors can be observed according to the angle between chords which may descend the localization accuracy dramatically. Analysis results indicate that the proposed algorithm is superior to other approaches in energy consumption and localization accuracy.

An Improved CPE Localization Algorithm for Wireless Sensor Networks

An Improved CPE Localization Algorithm for Wireless Sensor Networks

Bilateral localization algorithm for wireless sensor networks with communication holes

Bilateral localization algorithm for wireless sensor networks with communication holes

Analysis of Five Typical Localization Algorithms for Wireless Sensor Networks

In this paper, the self-localization problem is studied. It is one of the key technologies in wireless sensor networks (WSNs). And five localization algorithms: Centroid algorithm, Amorphous algorithm, DV-hop algorithm, APIT algorithm and Bounding Box algorithm are discussed. Simulation of those five localization algorithms is done by MATLAB. The simulation results show that the positioning error of Amorphous algorithm is the minimum. Considering economy and localization accuracy, the Amorphous algorithm can achieve the best localization performance under certain conditions.

A Hybrid Localization Algorithm for Wireless Sensor Networks

Abstract The continuously widening range of Wireless Sensor Networks (WSNs) applications requires exact node location which needs efficient and error free localization methods. Localization methods developed in the past are completely based on very fine numerical computation of various network parameters such as transmission range, propagation shape, transmitted or received power, sending or arrival time, connectivity information etc. These parameters are prone towards environmental situation and presence of obstacles in environment. Recently, research in localization is focused towards minimization of localization error in the available techniques. In this paper, the cause and behavior of errors in AOA and RSSI localization techniques have been mathematically analyzed. Based on the error analysis of both the existing techniques, a hybrid localization algorithm is proposed. The hybrid localization algorithm is based on existing Angle of Arrival (AOA) and Received signal strength indicator (RSSI). The algorithm is named as Minimum AOA Error with Minimum RSSI Error (MAE with MRE). Analysis of results obtained through simulation show that the hybrid localization algorithm performs better than AOA and RSSI techniques in terms of error percentage probability with varying number of known sensors, unknown sensors and shadowing effect percentage.

Wireless Sensor Network Localization Based On A Mobile Beacon And Tsvm

Abstract In this paper a new wireless sensor network localization algorithm, based on a mobile beacon and TSVM (Transductive Support Vector Machines) is proposed, which is referred to as MTSVM. The new algorithm takes advantage of a mobile beacon to generate virtual beacon nodes and then utilizes the beacon vector produced by the communication between the nodes to transform the problem of localization into one of classification. TSVM helps to minimize the error of classification of unknown fixed nodes (unlabeled samples). An auxiliary mobile beacon is designed to save the large volumes of expensive sensor nodes with GPS devices. As shown by the simulation test, the algorithm achieves good localization performance.

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

AbstractLocalization 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.

An Improved Bounding Box Localization Algorithm Based on Optimum Node Selection

This paper mainly discusses the problem of localization algorithm for wireless sensor network. For improving the disadvantages of high positioning error and Low coverage, an improved algorithm is proposed based on the optimum node selection determined by the received RSSI strength and virtual anchor nodes. It uses anchor node to locate which selected within 0.5 communication scope of the node by RSSI signal strength. Nodes Completed positioning upgraded to virtual anchor nodes .Complete positioning upgraded to virtual anchor nodes to help neighbors to locate. It reduces the positioning error and improves the positioning accuracy. Simulation experiment shows that the positioning performance of improved algorithm is better than traditional Bounding Box algorithm.

A Barycentric Coordinate Based Distributed Localization Algorithm for Sensor Networks

This paper studies the problem of determining the sensor locations in a large sensor network using only relative distance (range) measurements. Based on a generalized barycentric coordinate representation, our work generalizes the DILOC algorithm to the localization problem under arbitrary deployments of sensor nodes and anchor nodes. First, a criterion and algorithm are developed to determine a generalized barycentric coordinate of a node with respect to its neighboring nodes, which do not require the node to be inside the convex hull of its neighbors. Next, for the localization problem based on the generalized barycentric coordinate representation, a necessary and sufficient condition for the localizability of a sensor network with a generic configuration is obtained. Finally, a new linear iterative algorithm is proposed to ensure distributed implementation as well as global convergence to the true coordinates.

English

English

Optimization of DV-hop localization algorithm in hybrid optical wireless sensor networks

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Node Self-localization Algorithm Based on Received Signal Strength in Sensor Networks

The traditional wireless sensor network localization algorithm can be divided into the positioning algorithm based on distance and has nothing to do with the distance algorithm, due to the positioning algorithm based on distance is not suitable for the application of low power consumption and low cost areas and distance has nothing to do with poor precision problems of algorithm, this paper proposes a node self-localization algorithm based on received signal strength. Based on node location database in the grid topology analysis of the known conditions, and from the Angle of practical evaluates the localization algorithm scalability and fault tolerance. Finally with the traditional wireless network positioning algorithm do the experiment comparisons. The experimental results show that the proposed algorithm is less than the traditional algorithm in the interference in the environment of high precision, low cost, less energy consumption.

Energy Efficient Mobility Prediction based Localization Algorithm for Mobile Sensor Networks

Aim of study is to develop energy efficient mobility prediction based localization algorithm for mobile sensor networks. Mobile sensors are being deployed in the environment like underwater sea monitoring, where they can move to any location to meet coverage requirements. Sensor networks choose to follow data centric approach to track the exact location of the nodes. Location information should be identified for application tasks and network operations. Novel localization algorithms need to be developed for mobile sensor networks with high accuracy and minimal energy consumption. The proposed localization algorithm for mobile sensor networks uses the mobility prediction localization and Mobility Prediction Localization (MPL) algorithm with Maximum Likelihood Estimation (MLE) technique for low and high accuracy respectively. The performance analysis shows that proposed MPL and MPL along with MLE achieve accuracy above 95.5% in the simulated environment. This study also analyses the number of anchor nodes to be deployed to achieve desired level of accuracy. The proposed algorithm uses minimal number of nodes for tracking while rest are in sleep mode for energy saving, thereby increasing lifetime of the nodes.