Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D* algorithm

Kamsing, Patcharin; Tortceka, Peerapong; Manuthasna, Shariff; Hematulin, Warunyu; Chooraks, Tachodom; Phisannupawong, Thaweerath; Sanzkarak, Sitthirak; Munakhud, Sookaseam; Somjit, Thananorn

doi:10.23919/icact51234.2021.9370734

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Then, the data collected by the Bluetooth Hubs are combined through the time stamp of the sensor, and the signal strength data originally uploaded in a single entry are now combined into the signal strength data set received by the Hubs at the same time, providing the necessary data for the model. (3) Two machine learning models: K-nearest Neighbor (KNN) algorithm [ 25 , 46 , 47 ] and Support Vector Machine (SVM) [ 48 ], are used and compared. (4) Signal entries (data) are divided into the training data and the test data, and the RSSI signal strength is used as the feature value and the cell number as the label.…”

Section: Methodsmentioning

confidence: 99%

Bluetooth-Based Healthcare Information and Medical Resource Management System

Chang

et al. 2023

Sensors

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This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker’s burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

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Section: Methodsmentioning

confidence: 99%

Bluetooth-Based Healthcare Information and Medical Resource Management System

Chang

et al. 2023

Sensors

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“…Separate datasets are created for training and testing purposes. Accordingly, the output data D and each element t are defined by Equation (7).…”

Section: Movement Path Generationmentioning

confidence: 99%

“…Recently, Wi-Fi fingerprinting has been used to build indoor positioning systems [1][2][3][4][5][6][7][8][9][10][11]. In this approach, a dataset from a Wi-Fi fingerprinting system uses received signal strength indication (RSSI) information from access points (APs) collected at a specific location point as data to determine location.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells

Shin,

Lee,

Hwang

et al. 2023

Applied Sciences

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Recently, various application fields utilizing Wi-Fi fingerprint data have been under research. However, fingerprint data collected from a specific location does not include relevant information, such as continuity. Therefore, most indoor positioning technologies predict the user’s location based on location signals collected at specific points within the indoor space, without taking into account the user’s movements. Hence, there is a need for technology that improves the accuracy of indoor positioning while moving. This paper proposes a technique to generate movement path data by applying the concepts of “BB” and “Grid Cell” from computer vision to Wi-Fi fingerprint data. This approach represents data points as bounding boxes (BBs), then establishes grid cells and clusters of these BBs to generate adjacency information. Subsequently, movement path data are created based on this information. We utilized the movement path information generated from the dataset as training data for machine learning and introduced an enhanced indoor positioning technology. First, the experiments in this paper assessed the performance of the proposed technology based on the number of paths in the LSTM model. Second, the performance of clustering methods was compared through experiments. Finally, we evaluated the performance of various machine learning models. The experimental results confirmed a maximum accuracy of 94.48% when determining the location based on route information. Clustering performance improved accuracy by up to 3%. In comparative experiments with machine learning models, accuracy improved by up to 2.8%.

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“…However, satellite signals are usually attenuated or interrupted by ceiling or other obstacles, and that inevitably leads to a sharp decline in indoor positioning accuracy and continuity [1]. To fill the gap of GPS signals, various indoor positioning techniques, such as WiFi [2], Bluetooth [3], RFID [4], and UWB [5] have been developed to provide indoor positioning services.…”

Section: Introductionmentioning

confidence: 99%

Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

Wang¹,

Liang²,

Li³

et al. 2022

IEEE Access

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In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original particle swarm optimization (PSO) used in visible light positioning (VLP) systems. In the proposed IPSO algorithm, an adaptive particle initialization method based on Min-Max algorithm is used to adjust the number of particles and ensure that there are always particles near the target node (TN). Moreover, a nonlinear decreasing strategy of inertia weight is designed to ensure the stability of particle velocity during the iterative process. Simulation results show that, compared with the original PSO algorithm, the averaged positioning accuracy of the proposed IPSO-Min-Max algorithm is enhanced significantly at the expense of limited time consumption. What's more, we also find that for the proposed IPSO-Min-Max algorithm the increase of particle generation spacing will reduce the positioning delay but with the penalty in positioning accuracy. Therefore, it is necessary to select an appropriate particle spacing value according to specific requirements.INDEX TERMS Visible light positioning (VLP), particle swarm optimization (PSO), received signal strength (RSS), Min-Max algorithm.

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Wi-Fi Received Signal Strength-based Indoor Localization System Using K-Nearest Neighbors fingerprint integrated D* algorithm

Cited by 5 publications

References 7 publications

Bluetooth-Based Healthcare Information and Medical Resource Management System

Bluetooth-Based Healthcare Information and Medical Resource Management System

Enhanced Clustering and Indoor Movement Path Generation from Wi-Fi Fingerprint Data Using Bounding Boxes and Grid Cells

Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

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