Beyond beaconing: Emerging applications and challenges of BLE

Yang, Jian; Poellabauer, Christian; Mitra, Pramita; Neubecker, Cynthia

doi:10.1016/j.adhoc.2019.102015

Cited by 52 publications

(24 citation statements)

References 65 publications

(82 reference statements)

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“…Then, for example for k = 1, scanInt seq can be expressed as (21). The corresponding scanWin seq is shown in (22) and the final result in (23).…”

Section: G Calculation Of Scanint Seqmentioning

confidence: 99%

“…The survey mostly emphasizes this aspect in solutions that change the routing or use a scatternet topology (connectionoriented). The also recently published work of Yang et al [22], surveys several built applications, the problems to adopt BLE in new areas, and current academic and industrial solutions that expand the capabilities of BLE, as can be the new mesh profile. However, once again, its energy optimization is not discussed.…”

Section: Introductionmentioning

confidence: 99%

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BMADS: BLE Mesh Asynchronous Dynamic Scanning

Perez-Diaz-de-Cerio

Valenzuela

García-Lozano

et al. 2021

IEEE Internet Things J.

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The Bluetooth Low Energy (BLE) mesh profile, currently standardized by the Bluetooth Special Interest Group (SIG), assumes that friend and relay nodes should be continuously scanning the channel ready to receive any incoming transmissions. This is very inefficient in terms of energy consumption. Hence, we present BMADS, an asynchronous dynamic scanning mechanism designed to reduce the overall energy consumption of the mesh network. BMADS is implemented as a new feature for the currently published BLE mesh specifications without modifying its core. This feature reduces the scanning cycles of the nodes, but still ensuring the reliability. To make this possible, BMADS sends a new control message sequence before transmitting the data. This sequence puts the nodes into continuous scan mode, preparing the network for data transmission. The proposal can be used in BLE sensor networks that can tolerate a certain transmission delay and require especially low energy consumption. BMADS allows a theoretical reduction in the energy consumption of the relay nodes up to 98 %.

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“…Then, for example for k = 1, scanInt seq can be expressed as (21). The corresponding scanWin seq is shown in (22) and the final result in (23).…”

Section: G Calculation Of Scanint Seqmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BMADS: BLE Mesh Asynchronous Dynamic Scanning

Perez-Diaz-de-Cerio

Valenzuela

García-Lozano

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…BLE has become one of the most frequently used short-range wireless communication systems for indoor localization purposes [ 6 ]. Utilizing the Bluetooth system for indoor localization has been a target of numerous studies including our own [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. In these works, in general, one or combination of three basic techniques is employed.…”

Section: Introductionmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

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“…BLE has ubiquitously diffused into most contemporary electronic devices, such as PCs, smartphones, hubs like Raspberry Pi, smartwatches, fitness trackers, etc. Though the applications of BLE started with indoor proximity detection and positioning using BLE beacons [1,2], the current applications span a wide range of use-cases like multimedia streaming [3], intra-vehicular wireless sensor network for automobiles (IVWSN) [4], smart cities, and so on [5,6].…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Data Packet Loss in Bluetooth Low Energy-Based Wearable Healthcare Ecosystem

et al. 2021

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Bluetooth Low Energy (BLE) plays a critical role in wireless data transmission in wearable technologies. The previous work in this field has mostly focused on optimizing the transmission throughput and power consumption. However, not much work has been reported on a systematic evaluation of the data packet loss of BLE in the wearable healthcare ecosystem, which is essential for reliable and secure data transmission. Considering that diverse wearable devices are used as peripherals and off-the-shelf smartphones (Android, iPhone) or Raspberry Pi with various chipsets and operating systems (OS) as hubs in the wearable ecosystem, there is an urgent need to understand the factors that influence data loss in BLE and develop a mitigation solution to address the data loss issue. In this work, we have systematically evaluated packet losses in Android and iOS based wearable ecosystems and proposed a reduced transmission frequency and data bundling strategy along with queue-based packet transmission protocol to mitigate data packet loss in BLE. The proposed protocol provides flexibility to the peripheral device to work with the host either in real-time mode for timely data transmission or offline mode for accumulated data transmission when there is a request from the host. The test results show that lowered transmission frequency and data bundling reduce the packet losses to less than 1%. The queue-based packet transmission protocol eliminates any remaining packet loss by using re-request routines. The data loss mitigation protocol developed in this research can be widely applied to the BLE-based wearable ecosystem for various applications, such as body sensor networks (BSN), the Internet of Things (IoT), and smart homes.

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Beyond beaconing: Emerging applications and challenges of BLE

Cited by 52 publications

References 65 publications

BMADS: BLE Mesh Asynchronous Dynamic Scanning

BMADS: BLE Mesh Asynchronous Dynamic Scanning

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Mitigation of Data Packet Loss in Bluetooth Low Energy-Based Wearable Healthcare Ecosystem

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