Improved Localization Algorithm Based on Proportion of Differential RSSI

2012 ◽  
Vol 192 ◽  
pp. 401-405 ◽  
Author(s):  
Kai Sheng Zhang ◽  
Ya Ming Xu ◽  
Wu Yang ◽  
Qian Zhou
Keyword(s):  
Energy Resource ◽  
Simple Algorithm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Advantages And Disadvantages ◽  
Unknown Node ◽  
Resource Networks

How to enhance the accuracy of sensor node self-localization for limited energy resource networks is an important problem in the study of wireless sensor networks (WSNs). Concerning the advantages and disadvantages of some main algorithms for senor node self-localization, an easy and simple algorithm is proposed to locate the unknown node itself. The algorithm is to improve weight centroid localization (WCL), by the way of determining weight through using the proportion of differential received signal strength indicator (RSSI) that are derived from unknown node and criterion nodes. In contrast to WCL, the algorithm has the strengths of less computation and better determination of weight, and the determination of weight shows more distinguished distinction in the effect on the localization of unknown node, which is caused by various beacon nodes. Simulations demonstrate that the algorithm has a higher localization accuracy than WCL

Sequence Localization Algorithm Based on 3D Voronoi Diagram in Wireless Sensor Network

2014 ◽  
Vol 644-650 ◽  
pp. 4422-4426 ◽  
Author(s):  
Xi Yang ◽  
Jun Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Weighted Estimate ◽  
Localization Algorithm ◽  
Simulation Experiments ◽  
Localization Accuracy ◽  
3D Space ◽  
Unknown Node

For nodes’ self-localization in wireless sensor networks (WSN), a new localization algorithm called Sequence Localization algorithm based on 3D Voronoi diagram (SL3V) is proposed, which uses 3D Voronoi diagram to divide the localization space.It uses the polyhedron vertices as the virtual beacon nodes and constructs the rank sequence table of virtual beacon nodes. Then it computes Kendall coefficients of the ranks in the optimal rank sequence table and that of the unknown node. Finally, it realizes the weighted estimate of the unknown node by normalization processing Kendall coefficients. Simulation experiments prove that itcan obviously improve the localization accuracy compared with the traditional 2D sequence-based localization and can satisfy the need of localization for 3D space.

A Collaborative Multilateral Localization Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 303-306 ◽  
pp. 201-205
Author(s):  
Shao Ping Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Unknown Node

Localization technology is one of the key supporting technologies in wireless sensor networks. In this paper, a collaborative multilateral localization algorithm is proposed to localization issues for wireless sensor networks. The algorithm applies anchor nodes within two hops to localize unknown nodes, and uses Nelder-Mead simplex optimization method to compute coordinates of the unknown nodes. If an unknown node can not be localized through two-hop anchor nodes, it is localized by anchor nodes and localized nodes within two hops through auxiliary iterative localization method. Simulation results show that the localization accuracy of this algorithm is very good, even in larger range errors.

Research on RSSI and Triangles Deformation Based APIT Localization Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 998-999 ◽  
pp. 1305-1310
Author(s):  
Fei Liu ◽  
Guang Zeng Feng
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Sparse Network ◽  
Unknown Node

The localization accuracy of traditional APIT localization algorithm for wireless sensor network depends on the Approximate Perfect Point-In-Triangulation Test (APIT), and the localization error can be promoted in sparse network. We design one improved localization algorithm (RTD-APIT) based on APIT by using the RSSI and the triangles deformation. RTD-APIT uses the RSSI to improve the APIT for achieving the preliminary location of unknown node, and expand or deform the triangles for solving the Point-In-Triangulation (PIT) problem well and enhancing the localization. Simulation shows RTD-APIT can reduce the localization error effectively, and it also promote the localization coverage.

Iterated Hybrid Localization Algorithm for Random Wireless Sensor Networks Based on Centroid and DV-Hop Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1854-1857 ◽  
Author(s):  
Xin Hua Nie ◽  
Zhong Ming Pan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Hybrid Algorithm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Advantages And Disadvantages ◽  
Centroid Algorithm ◽  
Initial Location ◽  
Unknown Node

Localization has been a major challenge in Wireless Sensor Networks (WSNs), especially for the applications requiring the accurate position of the sensed information. In this paper, we propose a new localization algorithm based on the Centroid algorithm and the DV-Hop algorithm to improve the positioning accuracy without increasing any extra hardware for sensor nodes. This paper firstly analyzed the advantages and disadvantages of the centroid algorithm and the DV-Hop algorithm. Then we put forward an iterated hybrid algorithm, which is comprised of three steps. Firstly, obtaining the initial location of each unknown node by using the centroid algorithm; secondly, computing the distances among each unknown node to the anchor nodes based on the DV-Hop algorithm; finally, Taylor Series Expansion (TSE) algorithm is utilized to estimate coordinate of each unknown node. Simulation results show that our iterated hybrid algorithm has better positioning accuracy.

scholarly journals Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Huanqing Cui ◽  
Yongquan Liang ◽  
Chuanai Zhou ◽  
Ning Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Large Scale ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Unknown Node

Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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.

scholarly journals Localization Algorithm Based on Iterative Centroid Estimation for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Jiang ◽  
Xin Wang ◽  
Li Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Anchor Node ◽  
Centroid Estimation ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Unknown Node ◽  
Received Signal Strength Indication

According to the application of range-free localization technology for wireless sensor networks (WSNs), an improved localization algorithm based on iterative centroid estimation is proposed in this paper. With this methodology, the centroid coordinate of the space enclosed by connected anchor nodes and the received signal strength indication (RSSI) between the unknown node and the centroid are calculated. Then, the centroid is used as a virtual anchor node. It is proven that there is at least one connected anchor node whose distance from the unknown node must be farther than the virtual anchor node. Hence, in order to reduce the space enclosed by connected anchor nodes and improve the location precision, the anchor node with the weakest RSSI is replaced by this virtual anchor node. By applying this procedure repeatedly, the localization algorithm can achieve a good accuracy. Observing from the simulation results, the proposed algorithm has strong robustness and can achieve an ideal performance of localization precision and coverage.

scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks

2012 ◽  
Vol 2 (2) ◽  
pp. 16-35 ◽  
Author(s):  
Shrawan Kumar ◽  
D. K. Lobiyal
Keyword(s):  
Wireless Sensor Networks ◽  
Linear Programming ◽  
Sensor Networks ◽  
Problem Formulation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Location Accuracy ◽  
Localization Accuracy ◽  
Error Terms

Obtaining precise location of sensor nodes at low energy consumption, less hardware requirement, and little computation is a challenging task. As one of the well-known range-free localization algorithm, DV-Hop can be simply implemented in wireless sensor networks, but it provides poor localization accuracy. Therefore, in this paper, the authors propose an enhanced DV-Hop localization algorithm that provides good localization accuracy without requiring additional hardware and communication messages in the network. The first two steps of proposed algorithm are similar to the respective steps of the DV-Hop algorithm. In the third step, they first separate error terms (correction factors) of the estimated distance between unknown node and anchor node. The authors then minimize these error terms by using linear programming to obtain better location accuracy. Furthermore, they enhance location accuracy of nodes by introducing weight matrix in the objective function of linear programming problem formulation. Simulation results show that the performance of our proposed algorithm is superior to DV-Hop algorithm and DV-Hop–based algorithms in all considered scenarios.

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