scholarly journals NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2991 ◽  
Author(s):  
Jingyu Hua ◽  
Yejia Yin ◽  
Weidang Lu ◽  
Yu Zhang ◽  
Feng Li
Keyword(s):  
Hypothesis Test ◽  
Intermediate Position ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Algorithms ◽  
Anchor Nodes ◽  
Positioning Algorithm ◽  
Non Line Of Sight

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

scholarly journals A Combined Localization Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaogang Qi ◽  
Xiaoke Liu ◽  
Lifang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Vehicle Tracking ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Environment And Health ◽  
Anchor Nodes ◽  
Positioning Algorithm

Wireless sensor networks (WSNs) are widely used in various fields to monitor and track various targets by gathering information, such as vehicle tracking and environment and health monitoring. The information gathered by the sensor nodes becomes meaningful only if it is known where it was collected from. Considering that multilateral algorithm and MDS algorithm can locate the position of each node, we proposed a localization algorithm combining the merits of these two approaches, which is called MA-MDS, to reduce the accumulation of errors in the process of multilateral positioning algorithm and improve the nodes’ positioning accuracy in WSNs. It works in more robust fashion for noise sparse networks, even with less number of anchor nodes. In the MDS positioning phase of this algorithm, the Prussian Analysis algorithm is used to obtain more accurate coordinate transformation. Through extensive simulations and the repeatable experiments under diverse representative networks, it can be confirmed that the proposed algorithm is more accurate and more efficient than the state-of-the-art algorithms.

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

scholarly journals Improvement of Localization Algorithm for Wireless Sensor Networks Based on DV-Hop

2019 ◽  
Vol 15 (06) ◽  
pp. 53 ◽  
Author(s):  
Xin Qiao ◽  
Fei Chang ◽  
Jing Ling
Keyword(s):  
Estimation Method ◽  
Likelihood Estimation ◽  
Mean Value ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Anchor Node ◽  
Jump Distance ◽  
Anchor Nodes ◽  
Unknown Node ◽  
Positioning Algorithm

In order to solve the problem that the DV-Hop localization algorithm has large errors in the wireless sensor network environment, this paper uses the minimum mean square criterion to determine the average hop distance of anchor nodes, and then calculates the mean value of the original average hop distance, which ensures that the improved average hop distance is closer to the real average hop distance of the whole network. The estimated distances between nodes are calculated by using the correction value corresponding to the average jump distance of the anchor node; in the positioning stage, when the anchor node is small, the estimated coordinates of unknown nodes are obtained by the minimum-maximum method; when the number of anchor nodes is large, the coordinates of unknown nodes are calculated by the maximum likelihood estimation method; this not only reduces the amount of calculation, but also the accuracy is more stable. This step is not only suitable for DV-Hop algorithm, but also can be used to estimate the coordinates when the distance between the unknown node and the anchor node is known. However, this improved method is only applicable to the premise that the simulation area is not large, so this improvement has its scope of adaptation, according to the needs of choice. Finally, the unknown node coordinates are iteratively optimized by using the quasi Newton method. Simulation results show that the proposed positioning algorithm has higher accuracy and better stability.

scholarly journals A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2945 ◽  
Author(s):  
Long Cheng ◽  
Liang Feng ◽  
Yan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Particle Filter ◽  
Residual Analysis ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Main Body ◽  
Localization Algorithm ◽  
Mobile Localization ◽  
Non Line Of Sight

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF.

scholarly journals MNCE: Multi-Hop Node Localization Algorithm for Wireless Sensor Network Based on Error Correction

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 269
Author(s):  
Yinghui Meng ◽  
Yuewen Chen ◽  
Qiuwen Zhang ◽  
Weiwei Zhang
Keyword(s):  
Error Correction ◽  
Large Error ◽  
Least Square ◽  
Localization Algorithm ◽  
Target Node ◽  
Node Localization ◽  
Simulation Experiments ◽  
Connectivity Information ◽  
Localization Algorithms ◽  
Anchor Nodes

Considering the problems of large error and high localization costs of current range-free localization algorithms, a MNCE algorithm based on error correction is proposed in this study. This algorithm decomposes the multi-hop distance between nodes into several small hops. The distance of each small hop is estimated by using the connectivity information of adjacent nodes; small hops are accumulated to obtain the initial estimated distance. Then, the error-correction rate based on the error-correction concept is proposed to correct the initial estimated distance. Finally, the location of the target node is resolved by total least square methods, according to the information on the anchor nodes and estimated distances. Simulation experiments show that the MNCE algorithm is superior to the similar types of localization algorithms.

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.

Advanced Sequence-Based Localization Algorithm in Wireless Sensor Networks

2014 ◽  
Vol 651-653 ◽  
pp. 387-390 ◽  
Author(s):  
Fu Bin Zhou ◽  
Shao Li Xue
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Measuring Devices ◽  
Localization Algorithms ◽  
Positioning Algorithm ◽  
Range Free

As an important application of Internet of Things , Wireless Sensor Networks utilized in surveillance and other case.Localization of nodes in wireless sensor networks is the prerequisite and base of target tracking in some surveillance applications, so localization error of sensor nodes is a key. However, due to limited energy, unreliable link and limited communication ranges of sensor nodes, high accurate positioning is difficult to achieve, which made it hot and full of challenging for wireless sensor nodes to localize without any auxiliary facilities. Range-based localization algorithm , could achieve good accuracy but require measuring devices, thus it is not appropriate for large-scale wireless sensor networks.So range-free localization algorithms are more popular.This paper analyses the algorithms in range-free localization,and proposed Advanced Sequence-Based Localization algorithm to improve the performance of positioning algorithm in wireless sensor network.

A Localization Algorithm of Multi-Hop Three Dimensional AOA with Space-Based Angle Transmission and its Application

2013 ◽  
Vol 756-759 ◽  
pp. 3562-3567
Author(s):  
Qing Zhang Chen ◽  
Yun Feng Ni ◽  
Xing Hua Li ◽  
Rong Jie Wu ◽  
Yan Jing Lei ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Terrain Modeling ◽  
Terrain Model ◽  
Anchor Nodes ◽  
Slow Progress ◽  
Positioning Algorithm

Wireless sensor node's localization is a funda-mental technology in Wireless Sensor Networks. There are only quite a few study on three-dimensional (3D) localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization. Based on the study of the existing two-dimensional positioning algorithm and the application of terrain modeling, localization algorithm for sensor nodes in (3D) condition has been focus on as well as the application of terrain model. Using the idea proposed by representative algorithm--APS multi-hop AOA (Angle of Arrival), this paper proposed a new algorithm named Multi-hop Three Dimensional AOA With Space-based Angle Trans-mission (MSAT3D AOA). Using this technology, target nodes can use information of anchor nodes which are more than one hop away form. This paper also combined MSAT3D AOA algorithm with Delaunay triangulation algorithm for terrain modeling.

Research on Improved Multidimensional Scaling Localization Algorithm for Wireless Sensor Network

2010 ◽  
Vol 44-47 ◽  
pp. 3932-3936
Author(s):  
Liang Tao ◽  
Shuai Xu ◽  
Hai Yong Chen ◽  
He Xu Xun
Keyword(s):  
Multidimensional Scaling ◽  
Search Algorithm ◽  
Correction Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Wireless Sensor Nodes ◽  
Anchor Nodes ◽  
Hardware Configuration ◽  
Positioning Algorithm

Wireless sensor networks, which are energy limited, low hardware configuration and proneness to invalidation, puts a high demand on the positioning algorithm. Therefore the improved multidimensional scaling (IMDS) algorithm is proposed. In IMDS, firstly, local positioning areas (LPA) are established by an adaptive search algorithm. So the centralized multidimensional scaling (MDS) algorithm is changed into a distributed one. Then the shortest path distances between nodes on LPA are corrected with the geometric correction method (GCM) and adjusting weight correction method (AWCM). The distances between nodes become more accurate. Finally, with information of the public nodes of LPA and anchor nodes, we get the wireless sensor nodes coordinates through coordinate transformation by the SMACOF algorithm and the classical MDS algorithm.

Optimization of the Wireless Sensor Nodes Localization Algorithm Based on Genetic Algorithm

2014 ◽  
Vol 6 (4) ◽  
pp. 55-64
Author(s):  
Tan Zhi ◽  
Zhang Yuting
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Initial Population ◽  
Localization Algorithm ◽  
Node Localization ◽  
Anchor Nodes ◽  
Reference Weight ◽  
Positioning Algorithm

The node localization technology is a foundation for practical application in wireless sensor networks. According to DV-HOP positioning algorithm in wireless sensor network low precision, the defect of inaccurate positioning, this paper presents an optimization algorithm of improved DV-HOP based on genetic algorithm. The algorithm is to redefine the scope of initial population, the reference weight, redesigned the fitness function and selection of anchor nodes. The simulation results show that compared with the traditional DV - HOP algorithm, the algorithm without any increase in the node hardware overhead on the basis of significantly higher positioning accuracy.

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