Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms

Belay, Abebe; Yayeh, Yirga; Berie, Getaneh; Lin, Hsin‐Piao; Yen, Lei; Li, Yun Ruei

doi:10.1109/wocc.2018.8372704

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…The Nearest Neighbor in Signal Strength (NNSS) algorithm [5,[40][41][42][43] is a classification algorithm that searches for the most similar data in the sample database to an unknown data point. It assigns a class label based on the closest match in the database.…”

Section: K Nearest Neighbor Algorithmmentioning

confidence: 99%

“…Reference [40] proposed the INTRI indoor positioning method, combining fingerprint matching and trilateration to enhance indoor positioning accuracy. References [41][42][43][44][45] utilized adjusted K-nearest neighbor (KNN) parameters to improve indoor positioning precision. Xie, Y. et al introduced an indoor positioning method using KNN with Spearman distance [46].…”

Section: Introductionmentioning

confidence: 99%

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Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

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Section: K Nearest Neighbor Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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“…The linear relationship between

and the logarithm of the distance value

may be observed from Equation (5). Hence, the

curve can be obtained through the use of linear regression, and the environmental attenuation factor, n , can be ascertained based on the slope and intercept of this curve [ 18 ].…”

Section: Indoor Localization System Designmentioning

confidence: 99%

Indoor Localization System Based on RSSI-APIT Algorithm

Shen,

Xu,

Shen

2023

Sensors

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An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system’s localization capabilities. In a complex environment of 100 m2 in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m.

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“…When a mobile device is in the area covered by these nodes, the position of the device can be obtained on the basis of the RSS received by the nodes and the weights acquired by the network during the training stage. The output of the system is a vector of two elements for a position estimated in 2D or a vector of three elements for a position estimated in 3D [174].…”

Section: Ai-based Techniques 1) Annsmentioning

confidence: 99%

“…Similarly, the approximate location is known. In such a case, k is the adaptation parameter for best performance, but it is fundamentally dependent on the data [174].…”

Section: ) K-nnmentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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Object identification and localization in indoor and outdoor environments are paramount issues in object-human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate stateof-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.INDEX TERMS Indoor and outdoor positioning systems; identification and localization; unstructured environment; multi-sensor system; multi-object; positioning and localization techniques.

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Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms

Cited by 14 publications

References 5 publications

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Indoor Localization System Based on RSSI-APIT Algorithm

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

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