A Feature-Scaling-Based &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$k$&lt;/tex-math&gt; &lt;/inline-formula&gt;-Nearest Neighbor Algorithm for Indoor Positioning Systems

Liu, Dong; Zhang, Baoxian; Li, Cheng

doi:10.1109/jiot.2015.2495229

Cited by 125 publications

(14 citation statements)

References 21 publications

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“…Moreover, they are proposed for positioning the static objects which are not competent for the moving objects. On the other hand, there are some works are suitable for both F-RFID and M-RFID, e.g., k-nearest-neighbor-based localization [24,25] and multilateration-based localization [26], but their localization accuracy are needed to be improved. In this paper, we propose a new approach based on the M-RFID model to tracking indoor humans with high-accuracy and in real-time.…”

Section: Related Workmentioning

confidence: 99%

MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking

et al. 2020

Sensors

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Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been proposed. However, tracking mobile RFID readers in real-time has been a daunting task, especially for achieving high localization accuracy. In this paper, we propose a new Mobile RFID (M-RFID)-based Localization approach for Indoor Human Tracking, named MRLIHT. Based on the M-RFID model where RFID readers are equipped on the moving objects (human beings) and RFID tags are fixed deployed in the monitoring area, MRLIHT implements the real-time indoor location tracking effectively and economically. First, based on the readings of multiple tags detected by an RFID reader simultaneously, MRLIHT generates the response regions of tags to the reader. Next, MRLIHT determines the potential location region of the reader where two algorithms are devised. Finally, MRLIHT estimates the location of the reader by dividing the potential location region of the reader into finer-grained grids. The experimental results demonstrate that the proposed MRLIHT performs well in both accuracy and scalability.

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Section: Related Workmentioning

confidence: 99%

MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking

et al. 2020

Sensors

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“…As a result, the problem of determining the position of IoT devices has received significant attention, in particular in indoor environments where Global Navigation Satellite Systems (GNSS) are not available. Several approaches have been proposed, relying on a single wireless technology, such as WiFi [8][9][10], Bluetooth [11,12], or RFID [13]. A review of how suitable different wireless technologies are as the underlying technology for indoor positioning in IoT is provided in [14].…”

Section: Introductionmentioning

confidence: 99%

“…In the broad context of wireless networks, a vast literature exists on algorithms and metrics to be used in the online phase, ranging from low-complexity k-Nearest Neighbor (kNN) algorithms operating on Euclidean distance to more complex, hierarchical algorithms adopting probabilistic metrics [19]. In the specific context of IoT, in [8] an improved version of the kNN fingerprinting algorithm was proposed, but no specific provision was given on the hardware and processing requirements for its deployment in IoT systems.…”

Section: Introductionmentioning

confidence: 99%

ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things

2019

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Seamless location awareness is considered a cornerstone in the successful deployment of the Internet of Things (IoT). Support for IoT devices in indoor positioning platforms and, vice versa, availability of indoor positioning functions in IoT platforms, are however still in their early stages, posing a significant challenge in the study and research of the interaction of indoor positioning and IoT. This paper proposes a new indoor positioning platform, called ThingsLocate, that fills this gap by building upon the popular and flexible ThingSpeak cloud service for IoT, leveraging its data input and data processing capabilities and, most importantly, its native support for cloud execution of Matlab code. ThingsLocate provides a flexible, user-friendly WiFi fingerprinting indoor positioning service for IoT devices, based on Received Signal Strength Indicator (RSSI) information. The key components of ThingsLocate are introduced and described: RSSI channels used by IoT devices to provide WiFi RSSI data, an Analysis app estimating the position of the device, and a Location channel to publish such estimate. A proof-of-concept implementation of ThingsLocate is then introduced, and used to show the possibilities offered by the platform in the context of graduate studies and academic research on indoor positioning for IoT. Results of an experiment enabled by ThingsLocate with limited setup and no coding effort are presented, focusing on the impact of using different devices and different positioning algorithms on positioning accuracy.

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“…However, when using Wi-Fi signals for positioning, due to the numerous Wi-Fi APs, the data preprocessing will be labor-intensive and time-consuming, especially when measuring large areas. Additionally, it cannot be used in rural areas since most of these areas are not covered by .The first fingerprint positioning system relied on the k-nearest neighbors (KNN) algorithm to find the best matches from the fingerprint database [12]. Weighted k-nearest neighbors (WKNN) was proposed to enhance the robustness of the fingerprint positioning system [13].…”

mentioning

confidence: 99%

“…The first fingerprint positioning system relied on the k-nearest neighbors (KNN) algorithm to find the best matches from the fingerprint database [12]. Weighted k-nearest neighbors (WKNN) was proposed to enhance the robustness of the fingerprint positioning system [13].…”

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confidence: 99%

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Lei

Zhang

2020

Sensors

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In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.technologies require additional devices, making these systems impractical. Additionally, in signal NLOS propagation, the performance of the positioning systems will be greatly reduced [5].In contrast, wireless fingerprint positioning technology has received much attention owing to its simplicity and practicality with existing infrastructure and hardware. Many complex LTE signal cues are hidden in the surrounding environment, and the goal of wireless fingerprint-based localization is to discover these cues and use them effectively to determine UE positions [6]. Compared to satellite navigation positioning systems and other wireless positioning technologies, fingerprint-based positioning techniques have many merits. First, low-energy consumption sensors in UE require much less energy. Second, most of the fingerprint-based positioning technologies require no additional devices or infrastructure. Finally, fingerprint-based positioning can be achieved by using ubiquitous smartphones and signals from LTE base stations (BSs) [6,7]. Fingerprint positioning technology works in two phases: an offline training phase and an online matching phase [8]. During the training phase, signal measurements can be used, which are usually received signal strength indicator (RSSI), reference signal receivin...

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A Feature-Scaling-Based <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-Nearest Neighbor Algorithm for Indoor Positioning Systems

Cited by 125 publications

References 21 publications

MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking

MRLIHT: Mobile RFID-Based Localization for Indoor Human Tracking

ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

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