Novel iBeacon Placement for Indoor Positioning in IoT

Rezazadeh, Javad; Subramanian, Ramprasad; Sandrasegaran, Kumbesan; Kong, Xiaoying; Moradi, Marjan; Khodamoradi, Farshad

doi:10.1109/jsen.2018.2875037

Cited by 65 publications

(28 citation statements)

References 16 publications

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“…Also, they recommended placing beacons at the corners and one in the center of the area, with a beacon at least every 6 m. In their experiments, they tested a grid deployment. Newer proposals include the work of Rezazadeh et al [101], which analyzed in an environment the vertical and horizontal positions of beacons to increase the measured intensity of each beacon and thus increase the chances of seeing at least three of them at any position.…”

Section: An Overview Of Ips Solutionsmentioning

confidence: 99%

A Meta-Review of Indoor Positioning Systems

Mendoza-Silva

Torres-Sospedra

Huerta

2019

Sensors

204

130

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An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys.

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Section: An Overview Of Ips Solutionsmentioning

confidence: 99%

A Meta-Review of Indoor Positioning Systems

Mendoza-Silva

Torres-Sospedra

Huerta

2019

Sensors

204

130

View full text Add to dashboard Cite

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“…Compared with other ranging-based algorithms, RSSI can be easily obtained by mobile device without any additional hardware [5]. In [6], a novel placement was proposed to enhance the accuracy of indoor localization, which has given a great inspiration to the author of this paper. In [7] and [8], researchers proposed some novel methods to estimate the propagation model of actual enviroment, which reveals that positioning performance would be improved by modifying the propagation model.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning System With Cellular Network Assistance Based on Received Signal Strength Indication of Beacon

You

2020

IEEE Access

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With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon's RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m−0.5m and limits the maximum error within 0.8 m. INDEX TERMS Bluetooth beacon, received signal strength indication, indoor positioning, cellular network, median-Kalman filter, ranging model.

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“…Unfortunately, the ubiquitous Global Navigation Satellite System (GNSS) cannot be used as a positioning system in greenhouses due to large indoor positioning errors [10]. Indoor positioning is an important part of Internet of Things (IoT) and plays an important role in improving most services in 2 of 15 IoT [11,12]. Moreover, many of the other commercialized indoor positioning systems use Ultra-Wide Band (UWB) signals that only have a 100 mm accuracy [13], and can interfere with wireless systems in Industrial Scientific Medical and Mobile cellular bands [14].…”

Section: Introductionmentioning

confidence: 99%

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse

Huang

Tsay

Shiigi

et al. 2020

Sensors

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Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse.

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Novel iBeacon Placement for Indoor Positioning in IoT

Cited by 65 publications

References 16 publications

A Meta-Review of Indoor Positioning Systems

A Meta-Review of Indoor Positioning Systems

Indoor Positioning System With Cellular Network Assistance Based on Received Signal Strength Indication of Beacon

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse

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