Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning

Kołakowski, Marcin

doi:10.3390/s21186270

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…One of the primary advantages of Bluetooth is its low power consumption and suitability for IL. Various papers have exploited the simplicity of Bluetooth and the low cost [96], [97].…”

Section: B Bluetooth (Ble)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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“…One of the primary advantages of Bluetooth is its low power consumption and suitability for IL. Various papers have exploited the simplicity of Bluetooth and the low cost [96], [97].…”

Section: B Bluetooth (Ble)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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“…This is conducted by monitoring the intensity of signals from numerous beacons and utilizing trilateration to compute the device's position based on the beacons' known positions and signal strengths. One of the primary advantages of Bluetooth is its low power consumption and suitability for IL various paper exploited the simplicity of Bluetooth and the low cost [91,92].…”

Section: Bluetooth (Ble)mentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

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Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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“…Various technologies can be used for this task, such as cameras, LiDAR [ 6 ], and radio communication modules [ 7 ], etc. The fusion of these technologies was also widely used in related research [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints

Šarčević

Csík

Odry

2023

Sensors

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Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.

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Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning

Cited by 10 publications

References 37 publications

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints

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