Research on an Improved Self-Correcting Location Algorithm for Wireless Local Region

2011 ◽  
Vol 128-129 ◽  
pp. 1325-1328
Author(s):  
Zheng Zhang ◽  
Min Jiang ◽  
Guo Cheng Wan ◽  
Xiao Wei Liu
Keyword(s):  
Local Region ◽  
Wireless Sensor ◽  
Main Function ◽  
Test Results ◽  
Localization Algorithm ◽  
Back Testing ◽  
Ranging Error ◽  
Exact Position ◽  
Location Algorithm ◽  
Node Position

Node position technology is an internal demand of wireless sensor network, however, in most project account; it’s hard to obtain the nodes' exact position because of uncontrollability in the network deployment process. For this purpose, an improved self-correcting location algorithm which based on RSSI multilateral ranging was proposed in this paper. Other than the primary multilateral localization algorithm, a correcting node which adjacent to the blind node was imported into the pending location region. The main function is to reduce the influence caused by unilateral ranging error in the calculation of multilateral positioning and improves the positioning precision. Detailedly introduced the principle of self-correcting location algorithm, moreover, in order to check the improved algorithm’s location performance, a back testing was done via CC2430/CC2431 wireless nodes. Test results show that improved algorithm enhances the system’s accuracy and stability.

scholarly journals A Localization Algorithm of Wireless Sensor Network Based on Statistical Uncorrelated Vector

2017 ◽  
Vol 13 (07) ◽  
pp. 57
Author(s):  
Min Wang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Inner Product ◽  
Localization Algorithm ◽  
Product Matrix ◽  
Ranging Error ◽  
Location Algorithm ◽  
The Impact

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-ansi-language: EN-US; mso-fareast-font-family: 宋体; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">For exploring wireless sensor network - a self-organized network, a new node location algorithm based on statistical uncorrelated vector (SUV) model, namely SUV location algorithm, is proposed. The algorithm, by translating the node coordinate, simplifies the solution to double center coordinate matrix, and gets the coordinate inner product matrix; then it uses statistical uncorrelated vectors to reconstruct the coordinates of the inner product matrix and remove the correlation of inner matrix of coordinates caused by the ranging error, so as to reduce the impact of ranging error on subsequent positioning accuracy. The experimental results show that the proposed algorithm does not consider the network traffic, bust still has good performance in localization. At last, it is concluded that reducing the amount of communication of sensor nodes is beneficial to prolong the service life of the sensor nodes, thus increasing the lifetime of the whole network.</span>

scholarly journals Application of Ranging Difference Location Algorithm in Wireless Sensor Network Location

2017 ◽  
Vol 13 (07) ◽  
pp. 81
Author(s):  
Bo Xiang
Keyword(s):  
Sensor Network ◽  
Hyperbolic Equations ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Font Size ◽  
Network Location ◽  
Error Location ◽  
Ranging Error ◽  
Localization Performance ◽  
Location Algorithm

<span style="color: black; font-family: 'Times New Roman',serif; font-size: 10pt; mso-ansi-language: EN-US; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-bidi-language: AR-SA; mso-themecolor: text1;" lang="EN-US">A kind of location technologies are developed used in the sensor network monitoring, the existing RSSI location algorithm are analyzed, and a localization method is proposed based on the SSDOA ranging difference method. hyperbolic equations are constructed and Chan's algorithm is used to solve the coordinates of unknown nodes. The simulation model of ranging error location algorithm is established by MATLAB. By analyzing the effect of ranging accuracy on positioning accuracy, the positioning performance of classical RSSI localization algorithm is compared. The results show that the WSN localization algorithm based on the SSDOA has a good localization performance and can meet the localization requirements of some WSN applications. Based on the above finding, It is concluded that Chan's algorithm is suitable for wireless sensor networks based on TDOA localization algorithm.</span>

scholarly journals Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Sana Messous ◽  
Hend Liouane
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Average Distance ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Multihop Wireless ◽  
Distance Vector ◽  
Node Position

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

Gravitational Particle Swarm Optimization Localization Algorithm for Wireless Sensor Network Nodes

2014 ◽  
Vol 556-562 ◽  
pp. 4622-4627
Author(s):  
Shu Wang Zhou ◽  
Ming Lei Shu ◽  
Ming Yang ◽  
Ying Long Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Method Of Least Squares ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Ranging Error ◽  
Anchor Nodes ◽  
Localization Approach

A range-based localization approach which named gravitational particle swarm optimization localization algorithm (GL) has been proposed. This algorithm considered the influence from the position of anchor nodes to the localization results, GL can directly searched out the coordinates of unknown nodes by the distance from anchor nodes to unknown nodes. As is shown in the experiment results, GL not only has high positioning accuracy, but also overcomes the defect that location error increases rapidly as the ranging error increases, compares with normal schemes (such as method of least squares, ML ) GL’s accuracy can improve 40% as the ranging error is 35%.

The Weighted Localization Algorithm Based on RSSI Calibration

2012 ◽  
Vol 236-237 ◽  
pp. 1010-1014
Author(s):  
Ning Hui He ◽  
Hong Sheng Li ◽  
Guang Rong Bian
Keyword(s):  
Wireless Sensor Network ◽  
Signal Strength ◽  
The Other ◽  
Wireless Sensor ◽  
Network Node ◽  
Localization Algorithm ◽  
Node Localization ◽  
Positioning Accuracy ◽  
Location Algorithm ◽  
Beacon Node

This paper introduces the first wireless sensor network node localization in two ways, one is based on the RSSI ranging approach, and the other is based on the weighted centric location algorithm, as a result of environmental factors, the same RSSI value the distance ，but the corresponding values are not always the same, these two methods do not consider the environmental impact to the RSSI value. Therefore, we consider combining the distance and signal strength information as a reference to correct each beacon node weights， in order to improve positioning accuracy.

The Improved Personnel Location Algorithm Based on RSSI

2013 ◽  
Vol 712-715 ◽  
pp. 2003-2006
Author(s):  
Sheng Mei Zhou ◽  
Ting Lei Huang
Keyword(s):  
Signal Strength ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Matlab Simulation ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Location Algorithm ◽  
Non Line Of Sight

In the process of that based on the RSSI received signal strength indicator technique, resulting in the positioning accuracy is so low, since the simple RSSI, multipath, diffraction and non line of sight and other factors. In order to achieve higher accuracy node localization in wireless sensor, the paper is proposed based on the probability of recycling triangle centroid location algorithm in the RSSI technique,The probability of the cycle to handle triangle centroid localization algorithm. Through the Matlab simulation, compared with the traditional triangle centroid localization algorithm, the error is significantly reduced and positioning accuracy improved when the anchor point number exceeds a certain number.

Research on the Indoor Location Algorithm Based on RFID

2013 ◽  
Vol 421 ◽  
pp. 518-522
Author(s):  
Yi Zhang ◽  
Qi Shan Hou ◽  
Yuan Luo
Keyword(s):  
Nearest Neighbor ◽  
Computational Cost ◽  
Wireless Sensor ◽  
The Novel ◽  
Localization Algorithm ◽  
Target Node ◽  
K Value ◽  
Location Accuracy ◽  
Indoor Location ◽  
Location Algorithm

An improved localization algorithm was proposed to solve the problem of low location accuracy and large computational cost in nearest neighbor (KNN) algorithm for LANDMARC system. The novel algorithm combined RFID and wireless sensor networks. It divided location area into several sub areas, utilized the sensor network to locate the target node to corresponding sub area, removed the reference nodes far from the target node, narrowed down the selection scope of the k value, used KNN algorithm to calculate the coordinate of target node in the sub area and applied the Taylor series iteration to improve the accuracy. Experiment results shows that the proposed algorithm improves the location accuracy evidently.

A Wireless Sensor Network Localization Method Based on Dynamic Path Loss Exponent

2012 ◽  
Vol 433-440 ◽  
pp. 4530-4535
Author(s):  
Gang Zhu Qiao ◽  
Jian Chao Zeng
Keyword(s):  
Path Loss ◽  
Space Environment ◽  
Communication Model ◽  
Localization Algorithm ◽  
Network Localization ◽  
Path Loss Exponent ◽  
Rf Signals ◽  
Wireless Signal ◽  
Location Algorithm ◽  
Node Position

The path loss exponent shows the effect of space environment on the RF signals in wireless communication model. In most RSSI based location method the path loss exponent is assigned a fixed empirical value which can not reflect the actual environmental impact of the wireless signal, which lead to low position accuracy and considerable positioning error. Aiming at some complex and rapidly changing environment a path loss exponent dynamic acquired algorithm is proposed, which can calculate the actual path loss exponent with the distance and the RSSI value information between adjacent beacon nodes. On basis of the path loss exponent dynamic acquired algorithm a path loss exponent dynamic acquired based localization algorithm is proposed which can estimate the blind node position with the actual path loss exponent, and can improve the adaptability to the environment of the RSSI location algorithm. The simulation shows that the positioning accuracy of proposed method is significantly improved and the effect of proposed method is more precise than the common RSSI method under the same environment.

A Refined Concentric Anchor-Beacons Location Algorithm for Wireless Sensor Networks Based on Received Signal Strength Indicator

2013 ◽  
Vol 347-350 ◽  
pp. 1860-1863
Author(s):  
Kun Zhang ◽  
Can Zhang ◽  
Chen He ◽  
Xiao Hu Yin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Location Algorithm ◽  
Algorithm Base

As the development of technology, the wireless sensor networks (WSN) have a wide spread usage. And people pay more attention on the localization algorithm, as the key technology of WSN, there have been many method of self-localization. The concentric anchor-beacons (CAB) location algorithm is one of the most practical one, which is a range-free WSN localization algorithm. In order to further improve the accuracy of localizing nodes, an improved CAB location algorithm base on Received Signal Strength Indicator (RSSI) is proposed. The RSSI is used to measure the distance between two anchors and compare with the practical distance. Then the environment between two anchors can be simulated. At last the communication radius of anchors can be optimized. And the common area of the anchors in the process of localizing nodes can be reduced. Then the accuracy is improved. By simulation, the localization accuracy is improved when the anchors numbers is more than a certain percentage.

Research of Location Technology Based on Wireless Sensor Network

2013 ◽  
Vol 860-863 ◽  
pp. 2817-2824
Author(s):  
Mei Lin Zheng ◽  
Pei Pei Zhai ◽  
Xu Zou
Keyword(s):  
Software Design ◽  
Distance Measurement ◽  
Wireless Sensor ◽  
Basic System ◽  
Localization Algorithm ◽  
Node Localization ◽  
Interface Circuit ◽  
Localization Algorithms ◽  
Ranging Error ◽  
Positioning Algorithm

The node localization algorithms based on distance measurement are mainly studied in this paper.A stepwise weighted positioning algorithm based on distance measurement is designed on the basis of analyzing several typical positioning algorithms,static nodes positioning under natural environment is achieved. On this basis,with STMS32 selected as a core chip in the paper,the basic system circuit and peripheral interface circuit of node device is designed,and then using the development software BeeKit and CodeWarrior for node software design,localization algorithm is testedthrough experiment.After analyzing the results,the algorithm is mostly influenced by the RSSI ranging error,the coarse positioning of node can be achieved.

Sign in / Sign up

Export Citation Format

Share Document