AdaptaBLE: Data Rate and Transmission Power Adaptation for Bluetooth Low Energy

2019 ◽  
Author(s):  
Eunjeong Park ◽  
Myung-Sup Lee ◽  
Saewoong Bahk
Keyword(s):  
Data Rate ◽  
Low Energy ◽  
Transmission Power ◽  
Bluetooth Low Energy ◽  
Power Adaptation

scholarly journals Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Juan Aznar-Poveda ◽  
Antonio-Javier Garcia-Sanchez ◽  
Esteban Egea-Lopez ◽  
Joan Garcia-Haro
Keyword(s):  
Reinforcement Learning ◽  
Data Rate ◽  
Learning Approach ◽  
Transmission Power ◽  
Power Adaptation ◽  
V2v Communications

scholarly journals Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3282 ◽  
Author(s):  
Umair Mujtaba Qureshi ◽  
Zuneera Umair ◽  
Gerhard Petrus Hancke
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Transmission Power ◽  
Pass Filter ◽  
Bluetooth Low Energy ◽  
Localization Accuracy ◽  
Localization Model ◽  
Localization Precision ◽  
The Impact ◽  
Wireless Indoor Localization

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

scholarly journals BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 12 ◽  
Author(s):  
Germán Mendoza-Silva ◽  
Miguel Matey-Sanz ◽  
Joaquín Torres-Sospedra ◽  
Joaquín Huerta
Keyword(s):  
Open Source ◽  
Indoor Positioning ◽  
Research Field ◽  
Low Energy ◽  
The Other ◽  
Transmission Power ◽  
Bluetooth Low Energy ◽  
Practical Applications ◽  
Office Space ◽  
Supporting Materials

RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.

scholarly journals Performance Evaluation of Bluetooth Low Energy for High Data Rate Body Area Networks

2016 ◽  
Vol 90 (1) ◽  
pp. 121-141 ◽  
Author(s):  
José Augusto Afonso ◽  
António José F. Maio ◽  
Ricardo Simoes
Keyword(s):  
Performance Evaluation ◽  
Body Area Networks ◽  
Data Rate ◽  
Low Energy ◽  
High Data Rate ◽  
Body Area ◽  
Bluetooth Low Energy ◽  
High Data

RFID, NFC, Beacons, and the Infrastructures of Logistical Locative Media

2020 ◽  
pp. 474-486
Author(s):  
Jordan Frith
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Near Field ◽  
Low Energy ◽  
Near Field Communication ◽  
Rfid Tags ◽  
Bluetooth Low Energy ◽  
Locative Media ◽  
Frequency Identification ◽  
Machine Readable

The phrase the Internet of things was originally coined in a 1999 presentation about attaching radio frequency identification (RFID) tags to individual objects. These tags would make the objects machine-readable, uniquely identifiable, and, most importantly, wirelessly communicative with infrastructure. This chapter evaluates RFID as a piece of mobile communicative infrastructure, and it examines two emerging forms: near-field communication (NFC) and Bluetooth low-energy beacons. The chapter shows how NFC and Bluetooth low-energy beacons may soon move some types of RFID to smartphones, in this way evolving the use of RFID in payment and transportation and enabling new practices of post-purchasing behaviors.

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