An Accurate GPS-Based Localization in Wireless Sensor Networks: A GM-WLS Method

Cheng, Bo; Du, Rong; Yang, Bo; Yu, Wenbin; Chen, Cailian; Guan, Xinping

doi:10.1109/icppw.2011.32

Cited by 22 publications

(15 citation statements)

References 15 publications

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“…The high-resolution range-independent localization (HiRLoc) [14] and modified HiRLoc [7] scheme used omni directional antennas. Recently, the use of artificial intelligence in WSNs [8][9][10]15] has been on the rise. The following paragraph briefly describes some of these algorithms.…”

Section: ⅱ Related Workmentioning

confidence: 99%

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Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

Kumar¹,

Jeon²,

Lee³

2014

The Journal of Korea Information and Communications Society

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BSTRACTOne of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position)

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Section: ⅱ Related Workmentioning

confidence: 99%

“…Positioning System (GPS) receiver [15,16] , which can be used to determine the exact location of the sensor…”

Section: ⅰ Introductionmentioning

confidence: 99%

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

Kumar¹,

Jeon²,

Lee³

2014

The Journal of Korea Information and Communications Society

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“…Positioning System (GPS: [4], [19]) can be used for accurate localization. However, use of GPS requires an external hardware to be attached to the sensor nodes leading to an increase in cost and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Localization for Wireless Sensor Networks: A Neural Network Approach

Kumar¹,

Sharma²,

Vans³

2016

IJCNC

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“…Cheng and Du [9] took into account that the unbelievable GPS observations may cause positioning errors, so they gave the observations different weights using weighted least square method. The observations which are nearer can get a larger weight, and it helps to achieve more accurate position information.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Localization Algorithm for Maritime Search and Rescue Wireless Sensor Network

Yang

Liu

et al. 2013

International Journal of Distributed Sensor Networks

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Maritime search and rescue (MSR), as the last defense line for the life at sea, plays a great role in shipping industry and ocean environment protection. MSR is attracting more and more attention from technical and scientific aspects which are expected to help locate the victim goals quickly, precisely, and efficiently. So far, there have been some new techniques applied to MSR, such as computer vision. However, these techniques have a common drawback that they are too dependent on the effort of the search and rescue party, so that the victim goals can only passively wait for their search. To change this kind of passive status, wireless sensor network (WSN) is the deficiencies of the current maritime search and rescue. To make WSN localization suitable for the MSR, an improved microelectromechanical systems MEMS aided algorithm on the basis of triangle and centroid algorithm is proposed to locate and track the search targets in real time and more precisely. Finally, the simulation results show that the proposed method and algorithm are feasible and efficient.

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An Accurate GPS-Based Localization in Wireless Sensor Networks: A GM-WLS Method

Cited by 22 publications

References 15 publications

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks

Localization for Wireless Sensor Networks: A Neural Network Approach

Real-Time Localization Algorithm for Maritime Search and Rescue Wireless Sensor Network

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