Comparative analysis of RSSI-based indoor localization when using multiple antennas in Wireless Sensor Networks

Hamdoun, Safa; Rachedi, Abderrezak; Benslimane, Abderrahim

doi:10.1109/mownet.2013.6613811

Cited by 22 publications

(18 citation statements)

References 10 publications

(16 reference statements)

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“…They discovered that the performance of distance estimation in outdoor environment is better than that in indoor environment. Hamdoun et al [30] proposed the RSSI-based location algorithm by using multiple antennas at the transmitter side, the receiver side or at the both sides to calculate the location error in indoor environment. They found that the location performance for multiple antennas at the both sides is better than that for antennas at either the transmitter or the receiver side.…”

Section: Related Workmentioning

confidence: 99%

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

Miao

Zhang

2018

Journal of Sensors

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The range measurement is the premise for location, and the precise range measurement is the assurance of accurate location. Hence, it is essential to know the accurate internode distance. It is noted that the path loss model plays an important role in improving the quality and reliability of ranging accuracy. Therefore, it is necessary to investigate the path loss model in actual propagation environment. Through the analysis of experiments performed at the wheat field, we find that the best fitted parametric exponential decay model (OFPEDM) can achieve a higher distance estimation accuracy and adaptability to environment variations in comparison to the traditional path loss models. Based on the proposed OFPEDM, we perform the RSSI-based location experiments in wheat field. Through simulating the location characteristics in MATLAB, we find that for all the unknown nodes, the location errors range from 0.0004 m to 5.1739 m. The location error in this RSSI-based location algorithm is acceptable in the wide areas such as wheat field. The findings in this research may provide reference for location estimation in large-scale farmland.

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Section: Related Workmentioning

confidence: 99%

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

Miao

Zhang

2018

Journal of Sensors

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“…Therefore, the values of an, bn, cn, and dn may be slightly higher than the respective actual values, and consequently, the four circles may not intersect at one common point (Figure 10a). The received signal strength indication (RSSI) algorithm [50][51][52][53][54][55][56], such as quadrilateral localization (Figure 10a), provides an alternative to solve this problem and to improve the accuracy and precision. The plane OXY is perpendicular to the gravity direction, the origin O is the location of anchor A, and the x-axis is the projection direction from anchor A to anchor B.…”

Section: Estimation Algorithm Of a Tree Positionmentioning

confidence: 99%

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Sun

Fang

Weng

et al. 2019

Sensors

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Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.

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“…Konumlandırma fazında ise alınan sinyal bu harita üzerindeki sinyaller ile eşlenmeye çalışılır. Sonuçta en olası konum seçilir [18]. Çapa düğüm tabanlı konumlandırma yapan birçok yöntem bulunmaktadır.…”

Section: Mesafeye Tabanlı Yöntemlerden Ilki Varış Süresiunclassified

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Kanmaz

Aydın

2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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 New approaches in indoor positioning system  Anchor node-based positioning  k-means ++ use of clustering method in positioning With the great need for indoor positioning technology, new techniques have been searched for, and studies are being carried out on methods that can achieve the success of GPS (Global Positioning System) outdoor positioning. However, due to the objects and architectural structure of the interior, it is difficult to break down the signals and give the correct results to this method. Many methods have been tried and appropriate solutions are being sought to overcome this problem. A wireless sensor network is one of the technologies used for this. When positioning in sensor networks, anchor nodes that have their own position information are used, and their features such as power, arrival time, arrival angle and how many different nodes are used while the signal is transmitted can be used for positioning. One of these methods, Dv-hop method, has been studied in this study and it has been compared with different methods in terms of performance. It has been observed that when this comparison is made it is necessary to use an appropriate clustering method to create an appropriate topology when node points distributed around the center facilitate transmission. At this point k means ++ clustering method was proposed and it was seen that the accuracy rate could be increased up to 50% when probing.

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Comparative analysis of RSSI-based indoor localization when using multiple antennas in Wireless Sensor Networks

Cited by 22 publications

References 10 publications

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

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