An Effective Area-Based Localization Algorithm for Wireless Networks

Lasla, Noureddine; Younis, Mohamed; Ouadjaout, Abdelraouf; Badache, Nadjib

doi:10.1109/tc.2014.2366744

Cited by 39 publications

(17 citation statements)

References 19 publications

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“…Localization in WSNs is regarded as an emerging technology for numerous systems [ 13 ]. Range-based [ 14 , 15 ] and range-free [ 16 , 17 , 18 , 19 , 20 ] are two main types of existing methods on localization work. Lately, other methods which combine range-based or range-free principle are also available [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Localization with Graph Diffusion Property

Chen¹,

Yin²,

Gao³

et al. 2017

Sensors

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Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%.

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Section: Related Workmentioning

confidence: 99%

Localization with Graph Diffusion Property

Chen¹,

Yin²,

Gao³

et al. 2017

Sensors

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“…Therefore, this study focuses on RSSI-based indoor localization techniques, which are readily accessed from Android smartphones. Typically, Wi-Fi positioning techniques can be coarsely divided into two categories: fingerprinting-based [14][15][16][17][18][19] and ranging-based [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. The former can be implemented with machine learning methods [10,[34][35][36][37] and machine learning-free methods [10,38].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

Luo

et al. 2020

IJGI

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Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

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“…At present, indoor positioning technology is roughly categorized into wireless and inertial positioning technology. Wireless positioning technologies include infrared [ 3 ], ultrasonic [ 4 ], Bluetooth [ 5 ], Wi-Fi [ 6 ], ZigBee [ 7 ], Radio Frequency Identification (RFID) [ 8 ], Ultra Wideband (UWB) [ 9 ], visual [ 10 ], and wireless network positioning technology [ 11 ]. These positioning technologies are affected by external factors such as non-line-of-sight factors and multipath factors [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

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An Effective Area-Based Localization Algorithm for Wireless Networks

Cited by 39 publications

References 19 publications

Localization with Graph Diffusion Property

Localization with Graph Diffusion Property

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

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