Cheepsync: a time synchronization service for resource constrained bluetooth le advertisers

Sridhar, Shreya; Misra, Prasant; Gill, Gurinder Singh; Warrior, Jay

doi:10.1109/mcom.2016.7378439

Cited by 36 publications

(15 citation statements)

References 15 publications

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“…To synchronize the data from the sensors, we record the wall clock time for each data sample from the sensors. Then, using the offset between the sensors, we align the sensor readings using the approach in [ 38 ].…”

Section: Human Activity Recognition Datasetmentioning

confidence: 99%

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Bhat

Tran

Shill³

et al. 2020

Sensors

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Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.

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Section: Human Activity Recognition Datasetmentioning

confidence: 99%

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Bhat

Tran

Shill³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Although the receiver-receiver messaging scheme eliminates the transmitter side errors at the cost of increased communication overhead, Coefficient Exchange Synchronization Protocol (CESP) [ 94 ] offers an optimized solution that can be used in different IoT deployments. A similar approach is presented in the work by Sridhar et al [ 95 ], where low-power Bluetooth LE beacons are used to transmit time information to high-end receivers. In the proposed scheme, the high-end receiver is used for translating the time information of each receiver’s time information.…”

Section: Time Synchronization Messagingmentioning

confidence: 99%

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Yi̇ği̇tler

Badihi

Jäntti

2020

Sensors

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Internet of Things (IoT) is expected to change the everyday life of its users by enabling data exchanges among pervasive things through the Internet. Such a broad aim, however, puts prohibitive constraints on applications demanding time-synchronized operation for the chronological ordering of information or synchronous execution of some tasks, since in general the networks are formed by entities of widely varying resources. On one hand, the existing contemporary solutions for time synchronization, such as Network Time Protocol, do not easily tailor to resource-constrained devices, and on the other, the available solutions for constrained systems do not extend well to heterogeneous deployments. In this article, the time synchronization problems for IoT deployments for applications requiring a coherent notion of time are studied. Detailed derivations of the clock model and various clock relation models are provided. The clock synchronization methods are also presented for different models, and their expected performance are derived and illustrated. A survey of time synchronization protocols is provided to aid the IoT practitioners to select appropriate components for a deployment. The clock discipline algorithms are presented in a tutorial format, while the time synchronization methods are summarized as a survey. Therefore, this paper is a holistic overview of the available time synchronization methods for IoT deployments.

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“…The heart rate sensor was linked at low voltage to decrease power consumption. The monitoring unit was supplied by power via Universal Serial Bus (USB) cable from the laptop to power the Arduino Mega and wireless protocol nRF24L01 [20].…”

Section: Experiments Configurationmentioning

confidence: 99%

Ultrasound Sensor-Based Wireless Power Transfer for Low-Power Medical Devices

Mahmood

Mohammed

Gharghan

2019

JLPEA

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Ultrasonic power transfer (UPT) is a promising method for wireless power transfer technology for low-power medical applications. Most portable or wearable medical devices are battery-powered. Batteries cannot be used for a long time and require periodic charging or replacement. UPT is a candidate technology for solving this problem. In this work, a 40-KHz ultrasound transducer was used to design a new prototype for supplying power to a wearable heart rate sensor for medical application. The implemented system consists of a power unit and heart rate measurement unit. The power unit includes an ultrasonic transmitter and receiver, rectifier, boost converter and super-capacitors. The heart rate measurement unit comprises measurement and monitoring circuits. UPT-based transfer power and efficiency were achieved using 1-, 4- and 8-Farad (F) super-capacitors. At 4 F, the system achieved 69.4% transfer efficiency and 0.318 mW power at 4 cm. In addition, 97% heart rate measurement accuracy was achieved relative to the benchmark device. The heart rate measurements were validated with statistical analysis. Our results show that this work outperforms previous works in terms of transfer power and efficiency with a 4-cm gap between the ultrasound transmitter and receiver.

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Cheepsync: a time synchronization service for resource constrained bluetooth le advertisers

Cited by 36 publications

References 15 publications

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Ultrasound Sensor-Based Wireless Power Transfer for Low-Power Medical Devices

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