SVM-based indoor localization in Wireless Sensor Networks

Chriki, Amira; Touati, Haifa; Snoussi, Hichem

doi:10.1109/iwcmc.2017.7986446

Cited by 92 publications

(47 citation statements)

References 12 publications

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“…The one-versus-all [33] method and the one-versus-one method [34] are two commonly adopted techniques to solve multi-class classification problems using binary classifiers. In [23], the authors proved that the one-versus-one method performs better in indoor localization. So, in our system, we use the one-versus-one SVM to solve the position classification problem.…”

Section: Position Classifiermentioning

confidence: 99%

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Zhang

Qin

Xue

et al. 2020

Sensors

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Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

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Section: Position Classifiermentioning

confidence: 99%

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Zhang

Qin

Xue

et al. 2020

Sensors

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“…In recent years, the widespread popularity of indoor Wi-Fi has given rise to various indoor localization technologies based on Wi-Fi [3], which includes localization based on the received signal strength indication (RSSI) and channel state information (CSI). Particularly, Horus [4] system adopts RSS data to estimate position with a probabilistic approach.…”

Section: Introductionmentioning

confidence: 99%

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

Dang

Tang

Hao

et al. 2020

J Wireless Com Network

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The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

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“…However, in many special occasions, most people do not carry equipment at any time. Such as at home or in sensitive areas, the objects may not carry any electronic devices or power them off [7]. Thus, device-free indoor human behavior detection is in need, which detects and tracks the objects that do not carry any electronic devices nor participate actively in the process [8].…”

Section: Introductionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

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SVM-based indoor localization in Wireless Sensor Networks

Cited by 92 publications

References 12 publications

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

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