Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks

2004 ◽  
pp. 866-875 ◽  
Author(s):  
C. Nerguizian ◽  
C. Despins ◽  
S. Affès
Keyword(s):  
Neural Networks ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Fingerprinting Technique ◽  
Indoor Geolocation

scholarly journals NLOS Identification in WLANs Using Deep LSTM with CNN Features

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4057 ◽  
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jeongsik Choi ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Channel State Information ◽  
Recurrent Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Channel State ◽  
State Information ◽  
Channel Conditions

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

scholarly journals Fingerprint Database Variations for WiFi Positioning

2013 ◽  
Vol 11 (10) ◽  
pp. 3101-3107
Author(s):  
Nelson Acosta ◽  
Juan Toloza ◽  
Carlos Kornuta
Keyword(s):  
Mobile Device ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Relative Precision ◽  
Received Signal Strength Indicator ◽  
Positioning Systems ◽  
Fingerprinting Technique ◽  
Euclidian Distance ◽  
Fingerprint Database

Indoor positioning systems calculate the position of a mobile device (MD) in an enclosed environment with relative precision. Most systems use WiFi infrastructure and several positioning techniques, where the most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the fingerprinting technique to calculate the error window obtained with the Euclidian distance as main metric. Build variations are presented for the Fingerprint database analyzing various statistical values to compare the precision achieved with different indicators.

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

2.4 GHz RF Received Signal Strength Based Node Separation in WSN Monitoring Infrastructure for Millet and Rice Vegetation

2021 ◽  
pp. 1-1
Author(s):  
Pankaj Pal ◽  
Rashmi Priya Sharma ◽  
Sachin Tripathi ◽  
Chiranjeev Kumar ◽  
Dharavath Ramesh
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Strength Based
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