Optimal weighted K‐nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment

Fang, Xuming; Jiang, Zonghua; Nan, Lei; Chen, Lijun

doi:10.1049/iet-com.2017.0515

Cited by 42 publications

(28 citation statements)

References 32 publications

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“…Since the accuracy of KNN-based algorithm using Euclidean distance is not high enough due to the ignorance of statistical regularities from the training set, an improved method combining the Manhattan distance with the WKNN algorithm was proposed to distinguish the influence of different reference nodes [23]. To obtain the optimized node location estimate, Fang et al [24] proposed an optimal WKNN (OWKNN) algorithm for wireless sensor network (WSN) fingerprint localization in a noisy environment. In order to eliminate incorrect neighboring reference points and to avoid selected reference points located only on one side of the test point, an improved neighboring reference points selection method [25] was proposed based on their physical distances to the test point, instead of the widely used positions of the reference points.…”

Section: Related Workmentioning

confidence: 99%

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Zhang

Jiang

2019

J Wireless Com Network

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Accurate detection of the unknown radio transmitter (URT) is crucial to combat illegal occupation of radio signal resources and protect communication system from harmful signal interference. The fingerprint positioning technique based on received signal strength (RSS) is famous for requiring no extra equipment, antenna arrays, and time synchronization. However, conventional RSS-based fingerprint positioning techniques that utilize K-nearest neighbor (KNN) method are confronted with problems when the positioning target is radio transmitter with unknown emission strength and frequency. Moreover, they not only cannot realize the precise localization of the URT but also only rely on pre-set reference points in the fingerprint database. In this paper, a new KNN-based geo-location approach using received signal strength difference (RSSD) information and virtual reference point is proposed to estimate an URT location. To obtain more accurate RSSD measurements, a RSSD-based filtering method by calculating the Euclidean distance between each sampling RSSD and the average value is devised to establish the fingerprint database. To achieve higher positioning accuracy, we combine KNN technique with the virtual reference (VR) point to propose RSSD-VRKNN algorithm. The simulation results show that the proposed scheme can obtain the best positioning performance compared with the conventional KNN and weighted K-nearest neighbor (WKNN) techniques. The performance and feasibility of our proposed algorithm are verified through extensive experiments.

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Section: Related Workmentioning

confidence: 99%

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Zhang

Jiang

2019

J Wireless Com Network

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“…Therefore, if there is a large deviation in distance estimation, the positioning error will be large. A fingerprint database positioning algorithm has been constructed to improve the positioning accuracy in the document [23][24][25] by collecting the positioning samples in advance. The positioning algorithm highly relies on the fingerprint database data, if the environment changes, the positioning accuracy will become poor.…”

Section: The Target Node Positioning Algorithmmentioning

confidence: 99%

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

Chen¹,

Wang²,

Ouyang³

et al. 2019

Preprint

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The node position information is critical in the wireless sensor network (WSN). However, the existing positioning algorithms commonly have low positioning accuracy because of noise interferences in communication. To solve this problem, this paper presents an iterative positioning model based on distance correction to improve the positioning accuracy of the target node in WSN. First, the log-distance distribution model of received signal strength indication (RSSI) ranging is built and the noise impact factor is derived based on the model. Second, the initial position coordinates of the target node are obtained based on the triangle centroid localization algorithm, thereby calculating the distance deviation coefficient under the influence of noise. Then, the ratio of the distance measured by the log-normal distribution model to the median distance deviation coefficient is taken as the new distance between the anchor node and the target node. Based on the new distance, the triangular centroid positioning algorithm is used again to calculate the target node coordinates. Finally, the iterative positioning model is constructed, and the distance deviation coefficient is updated repeatedly to update the positioning result until the set number of iterations is reached. Experiment results show that the proposed iterative positioning model can improve positioning accuracy effectively.

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“…Aiming at the low efficiency of fingerprint matching, an improved algorithm built on the Nearest Neighbor (NN) method is proposed, and a strategy of data classification is proposed based on the weighted K Nearest Neighbor (KNN) method [13], [14]. The improved algorithm effectively improves the matching efficiency of position fingerprint and also improves the indoor positioning accuracy [15]. Radiofrequency fingerprint database is established by partitioning a distinctive indoor space and measuring the wireless signal intensity of each point.…”

Section: Introductionmentioning

confidence: 99%

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

Lu¹

2020

IEEE Access

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Indoor positioning technology plays an important role in the production and life of today's society. Modern people's clothing, food, housing, and transportation are increasingly closely related to Location Based Service. In this paper, an indoor location algorithm based on the radio frequency fingerprint matching is offered. In the offline phase, by deploying a small number of radio signal intensity location regions, the location selection mechanism is used to predict the location of mobile terminals, which can avoid the instability of radio frequency signals caused by time-lapse and the impact of changes in Access Point (AP) facilities. In the online stage, the location engine receives real-time Radio Frequency fingerprints and updates the fingerprint location by mapping time and equipment. Finally, several main localization algorithms are simulated and compared to the aspect of location accuracy. The proposed new method reduces the location error, improves the location accuracy by 1 to 2 meters, and achieves 100% within 2.5 meters.

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Optimal weighted K‐nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment

Cited by 42 publications

References 32 publications

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Detection of an unknown radio transmitter using an enhanced K-nearest neighbor algorithm based on virtual reference point and RSSD information

Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

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