Beacon trains: Blazing a trail through dense BLE environments

Robin Kravets; Albert F. Harris; Roy Want

doi:10.1145/2979683.2979687

Beacon trains: Blazing a trail through dense BLE environments

Robin Kravets, Albert F. Harris, Roy Want

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Bluetooth Low Energy (BLE) was designed as a low power alternative to classic Bluetooth. However, the use of BLE in dense, Internet of Things (IoT) deployments results in high collision rates and wasted energy. In response, we present an in-depth evaluation of the effects of having a high density of both transmitting tags and scanning devices in IoT environments. Based on our evaluation, we introduce Beacon Train Mode, an additional mode targeting dense IoT deployments with large numbers of both tags and scanning devices. Our results show that although active scanning breaks down when there are 5 or more scanning devices, beacon trains scale to any number of scanning devices.

Original language	English (US)
Title of host publication	Proceedings of the 11th ACM Workshop on Challenged Networks, CHANTS 2016
Publisher	Association for Computing Machinery
Pages	69-74
Number of pages	6
ISBN (Print)	9781450342568
DOIs	https://doi.org/10.1145/2979683.2979687
State	Published - Oct 3 2016
Event	11th ACM Workshop on Challenged Networks, CHANTS 2016 - New York, United States Duration: Oct 3 2016 → Oct 7 2016

Publication series

Name	Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

Other

Other	11th ACM Workshop on Challenged Networks, CHANTS 2016
Country/Territory	United States
City	New York
Period	10/3/16 → 10/7/16

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Software

Online availability

10.1145/2979683.2979687

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Kravets, R., Harris, A. F., & Want, R. (2016). Beacon trains: Blazing a trail through dense BLE environments. In Proceedings of the 11th ACM Workshop on Challenged Networks, CHANTS 2016 (pp. 69-74). (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM). Association for Computing Machinery. https://doi.org/10.1145/2979683.2979687

Beacon trains: Blazing a trail through dense BLE environments. / Kravets, Robin; Harris, Albert F.; Want, Roy.
Proceedings of the 11th ACM Workshop on Challenged Networks, CHANTS 2016. Association for Computing Machinery, 2016. p. 69-74 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kravets, R, Harris, AF & Want, R 2016, Beacon trains: Blazing a trail through dense BLE environments. in Proceedings of the 11th ACM Workshop on Challenged Networks, CHANTS 2016. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Association for Computing Machinery, pp. 69-74, 11th ACM Workshop on Challenged Networks, CHANTS 2016, New York, United States, 10/3/16. https://doi.org/10.1145/2979683.2979687

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abstract = "Bluetooth Low Energy (BLE) was designed as a low power alternative to classic Bluetooth. However, the use of BLE in dense, Internet of Things (IoT) deployments results in high collision rates and wasted energy. In response, we present an in-depth evaluation of the effects of having a high density of both transmitting tags and scanning devices in IoT environments. Based on our evaluation, we introduce Beacon Train Mode, an additional mode targeting dense IoT deployments with large numbers of both tags and scanning devices. Our results show that although active scanning breaks down when there are 5 or more scanning devices, beacon trains scale to any number of scanning devices.",

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AB - Bluetooth Low Energy (BLE) was designed as a low power alternative to classic Bluetooth. However, the use of BLE in dense, Internet of Things (IoT) deployments results in high collision rates and wasted energy. In response, we present an in-depth evaluation of the effects of having a high density of both transmitting tags and scanning devices in IoT environments. Based on our evaluation, we introduce Beacon Train Mode, an additional mode targeting dense IoT deployments with large numbers of both tags and scanning devices. Our results show that although active scanning breaks down when there are 5 or more scanning devices, beacon trains scale to any number of scanning devices.

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Beacon trains: Blazing a trail through dense BLE environments

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