Analysis of Bluetooth low energy technology in indoor environments

Sousa, Fernando Sequeira; Capovilla, C. E.; Casella, Ivan R. S.

doi:10.1109/isce.2016.7797368

Cited by 2 publications

(2 citation statements)

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“…Nevertheless, the high power consumption of classic Bluetooth makes it impractical for some emerging IoT use-cases that require low-power transmissions for small and battery-limited devices [37]. To this end, Bluetooth Low Energy (BLE) has been introduced in Bluetooth 4.0 specifically for low-powered IoT devices [38]- [40]. Unlike classic Bluetooth optimized for continuous data streaming, BLE is optimized for short burst data transmissions.…”

Section: A Short-range Technologiesmentioning

confidence: 99%

IoT Connectivity Technologies and Applications: A Survey

et al. 2020

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The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

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Section: A Short-range Technologiesmentioning

confidence: 99%

IoT Connectivity Technologies and Applications: A Survey

et al. 2020

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“…The constraints of UWB include high infrastructural requirements, energy consumption, and high user costs [4]. Apple's low-power iBeacon technology [11] has been widely used for indoor location services with its low power consumption and low cost. Magnetic fields are mainly used in narrow one-dimensional spaces such as corridors [12,13].…”

Section: Introductionmentioning

confidence: 99%

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Xiang

Cui

et al. 2023

Remote Sensing

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Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services.

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Analysis of Bluetooth low energy technology in indoor environments

Cited by 2 publications

References 1 publication

IoT Connectivity Technologies and Applications: A Survey

IoT Connectivity Technologies and Applications: A Survey

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

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