Review of Indoor Positioning: Radio Wave Technology

Geok, Tan Kim; Aung, Khaing Zar; Aung, Moe Sandar; Min, Thu Soe; Abdaziz, Azlan; Liew, Chia Pao; Hossain, Ferdous; Tso, C.P.; Wong, H.Y.

doi:10.3390/app11010279

Cited by 78 publications

(29 citation statements)

References 241 publications

(352 reference statements)

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“…Indoor localization systems, regardless of the technology used, suffer from high position errors. In particular, RSSI for BLE technology is susceptible to noise and multipath effects, which significantly reduces its localization accuracy, and in addition, the LoS (loss of sight) problem has a great impact, especially in crowded rooms and interiors with obstacles 17 . The accuracy of RSSI calculations can be improved by calibration, use of dedicated devices and analysis of radio signal propagation.…”

Section: Related Workmentioning

confidence: 99%

Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

Filus

Nowak

Domańska

et al. 2022

Sci Rep

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Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity between static (anchors) and moving objects, evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one—Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

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Section: Related Workmentioning

confidence: 99%

Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

Filus

Nowak

Domańska

et al. 2022

Sci Rep

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“…Generalized Regression Neural Network and Kalman filter in 21 or Denoising Autoencoders in 3 ). A broader review of techniques related to indoor localization is presented in 17 .…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

Filus

Nowak

Domańska

et al. 2021

Preprint

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Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity of a moving object with respect to static points (anchors), evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one-Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

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“…Radio-frequency identification (RFID) technology is increasingly common around us, being used in package identification, healthcare, supply-chain management, controlling access within a building, and detecting objects or a person’s location during context build [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. From a system point of view, an RFID network consists of RFID tags, antenna, and reader, and the software component responsible with managing the information.…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of RFID-based applications is successful object identification [ 14 ]. Even though algorithms for advanced image processing have evolved, low environmental light or image stabilization can affect the process [ 7 ], which is why RFID can act as a complementary technology to improve object detection. For example, image processing can be very useful in monitoring patients taking their medication [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Availability of an RFID Object-Identification System in IoT Environments

Corches

Daraban

Miclea

2021

Sensors

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Through the latest technological and conceptual developments, the centralized cloud-computing approach has moved to structures such as edge, fog, and the Internet of Things (IoT), approaching end users. As mobile network operators (MNOs) implement the new 5G standards, enterprise computing function shifts to the edge. In parallel to interconnection topics, there is the issue of global impact over the environment. The idea is to develop IoT devices to eliminate the greenhouse effect of current applications. Radio-frequency identification (RFID) is the technology that has this potential, and it can be used in applications ranging from identifying a person to granting access in a building. Past studies have focused on how to improve RFID communication or to achieve maximal throughput. However, for many applications, system latency and availability are critical aspects. This paper examines, through stochastic Petri nets (SPNs), the availability, dependability, and latency of an object-identification system that uses RFID tags. Through the performed analysis, the optimal balance between latency and throughput was identified. Analyzing multiple communication scenarios revealed the availability of such a system when deployed at the edge layer.

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Review of Indoor Positioning: Radio Wave Technology

Cited by 78 publications

References 241 publications

Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

Cost-effective filtering of unreliable proximity detection results based on BLE RSSI and IMU readings using smartphones

Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

Availability of an RFID Object-Identification System in IoT Environments

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