Improved path loss prediction model for short range indoor positioning using bluetooth low energy

Viswanathan, Subha; Srinivasan, Sreedevi

doi:10.1109/icsens.2015.7370397

Cited by 26 publications

(11 citation statements)

References 5 publications

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“…In this modality, different devices or sensors, provided with the BLE interface and placed in a structured environment, are programmed to send broadcast messages (described in Section 2.2.1 ) so that listener devices (e.g., user’s mobile device) can receive them. In this way, it is possible to send some pieces of information about the surrounding area [ 25 ], or to detect the user position [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ], or to measure the distance between sensor devices in the environment [ 33 , 34 ], or also to detect the presence of devices, and so people [ 22 , 24 , 35 , 36 , 37 , 38 ]. Moreover, thanks to its versatility and low power consumption, BLE has also been applied in Internet of Things (IoT) technologies [ 39 ], for example transmitting Internet Protocol Version 6 (IPv6) packets over Low power Wireless Personal Area Networks [ 40 , 41 , 42 ] (6LowPAN) in health monitoring application [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Tosi

Taffoni

Santacatterina

et al. 2017

Sensors

157

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Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Tosi

Taffoni

Santacatterina

et al. 2017

Sensors

157

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“…Regarding [66,[74][75][76][77][78]. Bluetooth is a standard based on a wireless radio system and it is designed for short-range wireless communications.…”

Section: Body Motion and Daily Activities Monitoringmentioning

confidence: 99%

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Rodrigues

Postolache

Cercas

2020

Sensors

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Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

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“…However, estimation error and bias with pedestrian's true walking direction exist in smart phone heading. Thus, we calculate the current pedestrian location as follows: (13) where N k+1 and E k+1 are the current north and east coordinates, respectively; N k and E k are the previous north and east coordinates, respectively; s k is the distance scale factor; D is the pedestrian's moved distance; ϕ k is the smart phone heading; and dω N , dω E , dω s and dω ϕ are the north coordinate error, the east coordinate error, the scale factor error and the heading error, respectively. We set the state vector as…”

Section: Gnss/dgnss Positioning and Noise Evaluationmentioning

confidence: 99%

“…Beacon RSSI is mostly used to compute the distance between the receiver and beacon; although several models have been proposed, there is still large error produced due to the effect of multipath and environment interference. In sum, the well-known models used in previous studies are the free space propagation model, log distance path loss model, Rappaport's path loss model and Friis' transmission model [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

Luo

et al. 2019

Remote Sensing

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This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % .

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Improved path loss prediction model for short range indoor positioning using bluetooth low energy

Cited by 26 publications

References 5 publications

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone

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