Distributed Range-Free Localization Algorithm for 3D Wireless Sensor Networks under Irregular Radio Propagation Model

Xing, Jianping; Wang, Dehua; Liu, Yang

doi:10.1007/978-3-642-23214-5_40

Cited by 3 publications

(2 citation statements)

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“…To report the origin of objects, node localization is necessary so that group querying of sensors, routing, and questions about the network coverage [1] can be assisted or answered. Over the decade, a number of solutions have been provided by researches for the problem of node localization (for example, [2]- [6], [9], [11], [13]- [16], [18]- [21], [23], [25]- [26], [33]- [34], [36]). In general, mostly there is a trade-off between the accuracy of localization, the computational complexity, and energy efficiency (for example, [24], [28][29][30][31][32]) of the techniques depending on their application requirements.…”

Section: Introductionmentioning

confidence: 99%

Localization for Wireless Sensor Networks: A Neural Network Approach

Kumar¹,

Sharma²,

Vans³

2016

IJCNC

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Section: Introductionmentioning

confidence: 99%

Localization for Wireless Sensor Networks: A Neural Network Approach

Kumar¹,

Sharma²,

Vans³

2016

IJCNC

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“…Node localization is needed for reporting the origin of the events, assisting the group querying of sensors, routing, and for answering questions about the network coverage [4]. Over the years, researchers have proposed various solutions [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] to the problem of node localization. Depending on the application environment, tradeoff between the localization accuracy and the computational complexity of the techniques are mostly considered.…”

Section: Introductionmentioning

confidence: 99%

Localization with RSSI Values for Wireless Sensor Networks: An Artificial Neural Network Approach

Kumar

Lee

2014

Proceedings of International Electronic Conference on Sensors and Applications

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Collecting the data and forwarding it to its destination is an important function of a sensor network. For many applications, it is also very important to include the location information to the collected data. Localization techniques in wireless sensor networks (WSNs), which is a way of determining the sensor node locations can be utilized for obtaining such information. In this paper, we present an artificial feed-forward neural network based approach for node localization using the RSSI values of the anchor node beacons. The method was applied in a 5 x 4 m indoor environment with beacons installed at each of the corners. Five different training algorithms have been evaluated to obtain the training algorithm that gives the best results for this scenario. The multi-layer Perceptron (MLP) neural network has been obtained using the Matlab software and implemented using the Arduino programming language on the mobile node to evaluate its performance in real time environment. An average 2D localization error of 30 cm is obtained using a 12-12-2 feed-forward neural network structure. The method proposed can be used to implement a trained neural network using other programming languages on other platforms.

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