Automatic identification of rigidly linked 6DoF sensors

Fountain, Jake; Smith, Shamus P.

doi:10.1109/vr.2016.7504710

Cited by 2 publications

(2 citation statements)

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“…These characteristics of the BMH algorithm are proper to our data packet matching module. [21]. This technique allows for subsequent automatic alignment of the sensor systems and increasing the usability of modular sensor systems.…”

Section: Packet Matching Algorithmsmentioning

confidence: 99%

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Pattern Matching Based Sensor Identification Layer for an Android Platform

Min

Kim

Heo

et al. 2018

Wireless Communications and Mobile Computing

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As sensor-related technologies have been developed, smartphones obtain more information from internal and external sensors. This interaction accelerates the development of applications in the Internet of Things environment. Due to many attributes that may vary the quality of the IoT system, sensor manufacturers provide their own data format and application even if there is a well-defined standard, such as ISO/IEEE 11073 for personal health devices. In this paper, we propose a client-server-based sensor adaptation layer for an Android platform to improve interoperability among nonstandard sensors. Interoperability is an important quality aspect for the IoT that may have a strong impact on the system especially when the sensors are coming from different sources. Here, the server compares profiles that have clues to identify the sensor device with a data packet stream based on a modified Boyer-Moore-Horspool algorithm. Our matching model considers features of the sensor data packet. To verify the operability, we have implemented a prototype of this proposed system. The evaluation results show that the start and end pattern of the data packet are more efficient when the length of the data packet is longer.

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Section: Packet Matching Algorithmsmentioning

confidence: 99%

“…Sequential scheme compares the profile data packet with a sample data packet per a byte. In the case of sliding window [21], we set the window size as 1/3 of the profile data packet. Clustering algorithm [22] selects a sample data packet that has the highest similarity to the profile data packet.…”

Section: Modeling and Analysismentioning

confidence: 99%