A centroid localization algorithm for wireless sensor networks based on finite element method

Tian, Jun; Zhang, Tianqi; Feng, Jiali; Xu, Yuqi; Cheng, Lin; Wang, Youming

doi:10.1109/itaic.2019.8785735

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Te model-based methods utilize geometrical methods to measure the distances of several known access points (APs), while the fngerprint-based ones utilize the received signal that has pattern diferences in diferent positions for indoor localization. Model-based localization methods including the centroid determination method [15], AOA, and TOA [16].…”

Section: Related Workmentioning

confidence: 99%

PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU

Wang

Yao

et al. 2023

Mobile Information Systems

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With the increasing popularization and development of WiFi devices, nowadays WiFi-based indoor localization has become a hot topic. Traditional Wi-Fi-based localization technologies which utilize received signal strength indication suffer from indoor multi-path effects and result in localization performance degradation. Therefore, choosing the appropriate characteristic of the WiFi signal is crucial for indoor localization. To improve the localization accuracy, we propose PLAP, a passive localization method using amplitude and phase of channel state information (CSI). Specifically, Hampel filter is used to process the amplitude signals and linear transformation is employed for calibrating phases. To extract representative features from calibrated amplitude and phase signals, we developed a deep learning framework which combines a convolutional neural network (CNN) and a bi-directional Gated recurrent unit (BGRU) to estimate the location of an objective. The experimental results show that the proposed PLAP outperforms other baselines with real-world evaluation.

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Section: Related Workmentioning

confidence: 99%

PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU

Wang

Yao

et al. 2023

Mobile Information Systems

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“…The range-based localization algorithm gets the distance from sensor nodes to different sensor nodes using measuring information, such as received signal strength indicator (RSSI) [5,11,12], time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) [13]. Rangefree localization methods include centroid algorithm [14,15], bounding box algorithm [16,17], gird Scan algorithm [18,19], DV-Hop algorithm [20, 21], amorphous algorithm [22,23], APIT algorithm [24, 25], MDS-MAP algorithm [26,27], and so on. The ranged-free methods estimate location of unknown nodes based on node connectivity and hop count information [28].…”

Section: Related Workmentioning

confidence: 99%

Research on improved localization algorithms RSSI-based in wireless sensor networks

Qiao

Hou

Gao

et al. 2021

Meas. Sci. Technol.

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The localization problem is a key supporting technology in wireless sensor network (WSN). The performance of commonly used positioning algorithms in WSN in terms of node density, beacon node ratio and different terrain is firstly analyzed and compared in this paper. The analysis shows that the magnitude of the positioning error of the trilateration method based on the received signal strength indicator (RSSI) ranging technology is 10 −15 , which is far lower than other positioning algorithms, but the node proportion that cannot be located is 20%-50% or so, which is much higher than other algorithms. Therefore, an improved trilateration positioning method based on RSSI ranging (ITRSS) is proposed, that is, the unknown nodes that have been located is used as new beacon nodes to locate other unknown nodes. MATLAB simulation shows that the positioning error of the proposed ITRSS method is only reduced by an order of magnitude, moreover, the proportion of nodes that cannot be located is reduced to about 1%, which is only some remote individual node out of the communication range in the area. The Zigbee network established by the CC2530 chip is used for preliminary experimental verification. The experimental results show that the nodes that could not be located at first can be located, and the positioning error does not exceed the set threshold of 3 m. Thus shows the proposed ITRSS method is of feasibility and effectiveness, which help to improve the positioning capability of the entire WSN network.

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Improvements of Centroid Localization Algorithm for Wireless Sensor Networks

Hadir

Zine-Dine

Bakhouya

2020

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)

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A centroid localization algorithm for wireless sensor networks based on finite element method

Cited by 5 publications

References 16 publications

PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU

PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU

Research on improved localization algorithms RSSI-based in wireless sensor networks

Improvements of Centroid Localization Algorithm for Wireless Sensor Networks

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