Integrity Monitoring for Bluetooth Low Energy Beacons RSSI Based Indoor Positioning

Yao, Haiyun; Hong, Soondo; Liang, Xinlian; Yan, Hongji

doi:10.1109/access.2020.3038894

Cited by 20 publications

(10 citation statements)

References 42 publications

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“…Tags transmit wireless signals to nearby readers which will be picked up by readers. There are many types of wireless networks widely used to establish network between the tags and readers to implement IPS, as compared in Table I [7][8][9][10]. BLE is one of the wireless networks that is widely used for IPS, mainly due to low cost and low power consumption [10].…”

Section: Related Workmentioning

confidence: 99%

“…There are many types of wireless networks widely used to establish network between the tags and readers to implement IPS, as compared in Table I [7][8][9][10]. BLE is one of the wireless networks that is widely used for IPS, mainly due to low cost and low power consumption [10]. In addition, Bluetooth is easier to deploy and implemented as compared to RFID, UWB, ultrasonic and infrared systems [10].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Location Accuracy Optimization in Bluetooth Low Energy (BLE) 5.1-Based Indoor Positioning System (IPS)—A Machine Learning Approach

Philips,

Salami,

Ramiah

et al. 2023

IEEE Access

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Indoor Positioning System (IPS) is a technology used to locate and track objects or people inside buildings, by using sensors, wireless networks, or other means to determine their position. IPS has many applications in various fields such as healthcare, retail, logistics, and security. Achieving IPS of high location accuracy is yet to be explored further. In this experimental research, an IPS based on Bluetooth Low Energy (BLE) 5.1 protocol is implemented and two optimization techniques, parameters calibration and application of Machine Learning Algorithm (MLA) are proposed to improve location accuracy. In Stage 1 of this experiment, the measured Root Mean Square Error (RMSE) value before optimization yielded location accuracy of 0.670m. In Stage 2, four different parameters which include elevation angle, tag height, data rate and walking pace are calibrated and tested. Besides, in Stage 2, three different algorithms which include Support Vector Regression (SVR), Decision Tree (DT) and K-Nearest Neighbor (KNN) are evaluated. As a result, parameters calibration decreased RMSE value down to 0.219m. Additionally, among all three MLAs, KNN illustrated the lowest RMSE value of 0.631m. In Stage 3, the lowest RMSE value of 0.015m is obtained by combining parameters calibration and MLA approaches which improved location accuracy up to 98.5%. The developed framework is operational at our industry partner, ams OSRAM's LED wafer fabrication cleanroom facility.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Location Accuracy Optimization in Bluetooth Low Energy (BLE) 5.1-Based Indoor Positioning System (IPS)—A Machine Learning Approach

Philips,

Salami,

Ramiah

et al. 2023

IEEE Access

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“…BLE is a wireless communication technology [146] that is being increasingly used in IoT applications because of its simplicity, ubiquity in most current electronic devices, and low power operation [147], [148]. Applications such as retail [125], environmental monitoring [126], indoor localization [149], museum guiding [150], or inventory management [151] are using BLE beacons. The IoT sensor device presented in [127] measures light intensity, temperature, and relative humidity of the environment.…”

Section: A Solar-powered Environmental Ble Beacon Case Study System mentioning

confidence: 99%

Energy Harvesting Techniques for Internet of Things (IoT)

et al. 2021

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The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

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“…By utilizing the widely deployed light emitting diode (LED), visible light positioning (VLP) can simultaneously provide high-accuracy localization and illumination services. Compared with other existing indoor positioning technologies, such as wireless fidelity (WiFi) positioning [1], radio frequency identification (RFID) [2], magnetic information [3], bluetooth [4], and ultra-wideband (UWB) [5], VLP exhibits the advantages of high positioning accuracy, license-free spectrum, energy-efficient, low multi-path effects, high security, and no electromagnetic interference, etc. Furthermore, VLP can be widely applied in electromagnetic sensitive areas, where RF radiation is potentially hazardous or even forbidden, e.g., hospitals, nuclear power plants, and mines.…”

Section: Introductionmentioning

confidence: 99%

Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp

Zhang

et al. 2024

IEEE Internet Things J.

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In this paper, we consider a practical indoor 3D mobile online visible light positioning (VLP) system, where the orientation of the UE is arbitrary. Based on the received signal strength (RSS) of multiple photo-detectors (PDs), we formulate the 3D VLP problem as a non-linear least squares (NLS) optimization problem, and then propose a sequential quadratic programming (SQP) positioning algorithm to efficiently calculate UE's location. To obtain more accurate positioning solutions, we further leverage the advantages of deep learning and develop a stochastic gradient descent (SGD) based VLP algorithm, and achieve an average positioning error of 1.77cm, which significantly outperforms existing RSS VLP localization methods. Moreover, we design a 3D mobile online VLP system prototype by using a portable RaspberryPi 4 Model B as the positioning signal processor and data memory, and establish the first publicly available 3D VLP measured dataset including both RSS and orientation. The proposed positioning schemes are implemented and evaluated via the designed prototype system, which can achieve centimeter-level positioning accuracy (below 1 cm in certain condition).

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Integrity Monitoring for Bluetooth Low Energy Beacons RSSI Based Indoor Positioning

Cited by 20 publications

References 42 publications

Location Accuracy Optimization in Bluetooth Low Energy (BLE) 5.1-Based Indoor Positioning System (IPS)—A Machine Learning Approach

Location Accuracy Optimization in Bluetooth Low Energy (BLE) 5.1-Based Indoor Positioning System (IPS)—A Machine Learning Approach

Energy Harvesting Techniques for Internet of Things (IoT)

Centimeter-Level 3-D Mobile Online Visible Light Positioning System With Single LED Lamp

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