Joint Asynchronous Time and Localization of an Unknown Node in Wireless Sensor Networks

2017 ◽  
pp. 3-9
Author(s):  
Junhui Zhao ◽  
Lei Li ◽  
Yi Gong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Unknown Node

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.

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.

PACMLA: A Localization Algorithm Based on Permutation and Combination Midnormal for Wireless Sensor Networks

2013 ◽  
Vol 475-476 ◽  
pp. 564-568
Author(s):  
Wei Yong Jiang ◽  
Pin Wan ◽  
Yong Hua Wang ◽  
Dong Liang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Cost ◽  
Wireless Sensor ◽  
Data Sets ◽  
Localization Algorithm ◽  
Anchor Nodes ◽  
Unknown Node ◽  
Number Of Iterations ◽  
Range Free

Localization of sensors is one key technique in wireless sensor networks (WSN).Because the midnormal-based localization algorithm (MBLA) has shortcomings such as low accuracy, relatively large number of iterations, a localization algorithm based on permutation and combination midnormal (PACMLA) for WSN is proposed. Nodes are divided into anchor nodes and unknown nodes. In its own communication range, unknown node can communicate with anchor nodes. In PACMLA algorithm, the unknown node communicates with the anchor nodes in turn, and collects their coordinate information and RSSI value. Then by comparing the RSSI values received by unknown node, these RSSI values are formed an array in accordance with the order from small to large. Then starting from the first value of the RSSI array, each of these values and the value behind them will be combined into data sets. Finally, according to corresponding coordinate information of the RSSI value in the data sets, we will determine the position of the unknown node by Point In Which Side (PIWS) determination. In addition, our algorithm is a kind of Range-free algorithm, and it can cuts down the node energy cost. The experiment results illustrate that the PACMLA algorithm has lower error and higher accuracy.

An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks

2013 ◽  
Vol 401-403 ◽  
pp. 1800-1804 ◽  
Author(s):  
Shi Ping Fan ◽  
Yong Jiang Wen ◽  
Lin Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Distribution Area ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Sample Points ◽  
Unknown Node

There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.

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.

A Localization Algorithm of Nodes Based on Hypersphere Granular Computing in Wireless Sensor Networks

2014 ◽  
Vol 998-999 ◽  
pp. 1390-1393
Author(s):  
Yong He
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Training Set ◽  
Node Localization ◽  
Single Output ◽  
Node Location ◽  
Unknown Node

This paper proposes a node localization algorithm based on super chondritic calculation, super chondritic calculation scanning of a training set is able to complete the training process, the training has characteristics of fast, the problem of node localization for wireless sensor networks. Firstly, according to the related parameter information for the location of wireless sensor nodes, a multi input, multi output problem of training set, and then through the methods of grid division, location area, the original training set into the classification of multi input, single output training set, the super chondritic algorithm by scanning training, in order to get the relevant parameters, and the estimation of the unknown node location. Simulation results show that the proposed 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 Survey on Localization Algorithms in Wireless Sensor Networks

2014 ◽  
Vol 530-531 ◽  
pp. 15-18 ◽  
Author(s):  
Lu Gao ◽  
Zhong Min Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Main Idea ◽  
Wireless Sensor ◽  
Simple Analysis ◽  
Position Information ◽  
Localization Algorithms ◽  
Unknown Node ◽  
New Algorithms ◽  
Open Issues

In the applications based on Wireless Sensor Networks (WSNs) , localization is one of the most fundamental and important technologies if the accurate location information cant be determined, these application cant be accomplished. Then main idea in localization algorithms is that every unknown node in WSN can localize itself by position information from the landmarks or other nodes. in the last years localization is still a hot field and some new algorithms are proposed. In this paper, the localization algorithms are classified on the basis of the way to get position information, and a simple analysis is present. the paper are ended by discussing some open issues.

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