A New Node Self-Localization Algorithm Based RSSI for Wireless Sensor Networks

Feng, Chengxu; Liu, Zhong

doi:10.1109/iccis.2013.423

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Centroid approximation is one of the simplest solutions of the trilateration algorithm [64]. This method works reasonably well in an indoor environment when the anchors are in LOS with the target.…”

Section: Trilateration Based Localization Modelmentioning

confidence: 99%

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Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

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Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

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Section: Trilateration Based Localization Modelmentioning

confidence: 99%

“…It uses simple distance approximation or distance scaling between the reference locations of the selected anchors and the tag for localization. Mathematically, it is given as Equation (4) [64]: xfalse^Tt=∑k=1|k|xAkdAk,Tt∑k=1|k|1dAk,Tt,yfalse^Tt=∑k=1|k|yAkdAk,Tt∑k=1|k|1dAk,Tt.…”

Section: Trilateration Based Localization Modelmentioning

confidence: 99%

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

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“…In order to evaluate the algorithm performance, the MATLAB 2009a platform is used to simulate APIT, RAPIT [10], MIX-APIT [11], and ASG-APIT algorithm. The simulation scenario is a 1000*1000m square region with 300 nodes deployed.…”

Section: A Simulation Environmentmentioning

confidence: 99%

“…In order to reduce InToOut or OutToIn error, Zeng [9] proposed a new PIT test method based on cosine theorem. In [10,11], a weighted APIT algorithm based on RSSI was presented in two algorithms respectively, and the unknown nodes that cannot be located will be confirmed by modified DV-Hop algorithm in [11]. However, the two algorithms both ignore the different credibility of the areas in which a node resides and fault tolerance of OutToIn error.…”

Section: Introductionmentioning

confidence: 99%

A Weighted APIT Localization Algorithm based on Vector Similarity

He¹,

Kang²

2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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To reduce incorrect decisions of PIT test (Point-In-Triangulation Test) and localization error of APIT algorithm, a weighted APIT algorithm is proposed based on vector similarity. Firstly, for better distance precision, the estimated distances between nodes computed from RSSI attenuation model are corrected by the least square method. Secondly, a center of gravity method is proposed for PIT test, which decreases InToOut and OutToIn error. Finally, a weight is introduced based on vector similarity for the different reliability of triangular regions. The simulation result shows that the positioning error of ASG-APIT algorithm decreased by 43%, 17% and 13% respectively, compared with APIT, MIX-APIT and RAPIT algorithm.

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History-based multi-node collaborative localization in mobile wireless ad hoc networks

Chen

Guo

et al. 2016

2016 IEEE International Conference on Communications (ICC)

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A New Node Self-Localization Algorithm Based RSSI for Wireless Sensor Networks

Cited by 8 publications

References 6 publications

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

A Weighted APIT Localization Algorithm based on Vector Similarity

History-based multi-node collaborative localization in mobile wireless ad hoc networks

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