Yukuan Ding
ABSTRACT
For the widely used Bluetooth Low-Energy (BLE) neighbor discovery, the parameter configuration of neighbor discovery directly decides the results of the trade-off between discovery latency and power consumption. Therefore, it requires evaluating whether any given parameter configuration meets the demands. The existing solutions, however, are far from satisfactory due to unsolved issues. In this paper, we propose Blender, a simulation framework that produces a determined and full probabilistic distribution of discovery latency for a given parameter configuration. To capture the key features in practice, Blender provides adaption to the stochastic factors such as the channel collision and the random behavior of the advertiser. Evaluation results show that, compared with the state-of-art simulators, Blender converges closer to the traces from the Android-based realistic estimations. Blender can be used to guide parameter configuration for BLE neighbor discovery systems where the trade-off between discovery latency and power consumption is of critical importance.
- Bluetooth specifications. https://www.bluetooth.com/, 2022.Google Scholar
- Bluetooth low energy devices market volume worldwide 2013--2020. https://www.statista.com/statistics/750569/worldwide-bluetooth-low-energy-device-market-volume/, 2022.Google Scholar
- Tong Li, Kai Zheng, Ke Xu, Rahul Arvind Jadhav, Tao Xiong, Keith Winstein, and Kun Tan. Revisiting acknowledgment mechanism for transport control: Modeling, analysis, and implementation. IEEE/ACM Transactions on Networking, 29(6):2678--2692, 2021.Google ScholarDigital Library
- Huawei contact shield. https://developer.huawei.com/consumer/en/doc/development/system-Guides/contactshield-introduction-0000001057494465, 2020.Google Scholar
- Exposure notification. https://www.google.com/covid19/exposurenotifications, 2020.Google Scholar
- Meng Shen, Yaqian Wei, and Tong Li. Bluetooth-based covid-19 proximity tracing proposals: An overview. arXiv2008.12469, 2020.Google Scholar
- S Gallo, Laura Galluccio, Giacomo Morabito, and Sergio Palazzo. Rapid and energy efficient neighbor discovery for spontaneous networks. In Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, pages 8--11, 2004.Google ScholarDigital Library
- Zainab R Zaidi, Marius Portmann, and Wee Lum Tan. Analysis of link break detection using hello messages. In Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, pages 143--150, 2011.Google ScholarDigital Library
- Dimitrios Amaxilatis, Georgios Oikonomou, and Ioannis Chatzigiannakis. Adaptive neighbor discovery for mobile and low power wireless sensor networks. In Proceedings of the 15th ACM International Conference on Modeling, analysis and simulation of wireless and mobile systems, pages 385--394, 2012.Google ScholarDigital Library
- Prabal Dutta and David Culler. Practical asynchronous neighbor discovery and rendezvous for mobile sensing applications. In ACM SenSys, pages 71--84, 2008.Google ScholarDigital Library
- Arvind Kandhalu, Karthik Lakshmanan, and Ragunathan Rajkumar. U-connect: a low-latency energy-efficient asynchronous neighbor discovery protocol. In ACM/IEEE IPSN, pages 350--361, 2010.Google ScholarDigital Library
- Mehedi Bakht, Matt Trower, and Robin Hilary Kravets. Searchlight: Won't you be my neighbor? In ACM Mobicom, pages 185--196, 2012.Google Scholar
- Wei Sun, Zheng Yang, Keyu Wang, and Yunhao Liu. Hello: A generic flexible protocol for neighbor discovery. In IEEE INFOCOM, pages 540--548, 2014.Google ScholarCross Ref
- Lin Chen, Ruolin Fan, Kaigui Bian, Mario Gerla, Tao Wang, and Xiaoming Li. On heterogeneous neighbor discovery in wireless sensor networks. In IEEE INFOCOM, pages 693--701, 2015.Google ScholarCross Ref
- Philipp H Kindt and Samarjit Chakraborty. On optimal neighbor discovery. In ACM SIGCOMM, pages 441--457. 2019.Google ScholarDigital Library
- Philipp Kindt, Daniel Yunge, Robert Diemer, and Samarjit Chakraborty. Precise energy modeling for the bluetooth low energy protocol. arXiv preprint arXiv:1403.2919, 2014.Google Scholar
- Andreina Liendo, Dominique Morche, Roberto Guizzetti, and Franck Rousseau. Ble parameter optimization for iot applications. In IEEE ICC, pages 1--7, 2018.Google ScholarCross Ref
- Antonio Del Campo, Lorenzo Cintioni, Susanna Spinsante, and Ennio Gambi. Analysis and tools for improved management of connectionless and connection- oriented ble devices coexistence. MDPI Sensors, 17(4):792, 2017.Google ScholarCross Ref
- Philipp H Kindt, Marco Saur, Michael Balszun, and Samarjit Chakraborty. Neighbor discovery latency in ble-like protocols. IEEE TMC, 17(3):617--631, 2017.Google Scholar
- Wha Sook Jeon, Made Harta Dwijaksara, and Dong Geun Jeong. Performance analysis of neighbor discovery process in bluetooth low-energy networks. IEEE ToVT, 66(2):1865--1871, 2016.Google Scholar
- Bingqing Luo, Feng Xiang, Zhixin Sun, and Yudong Yao. Ble neighbor discovery parameter configuration for iot applications. IEEE Access, 7:54097--54105, 2019.Google ScholarCross Ref
- Texas Instruments. Measuring bluetooth low energy power consumption. In Application Note AN092, 2010.Google Scholar
- Jia Liu, Canfeng Chen, and Yan Ma. Modeling neighbor discovery in bluetooth low energy networks. IEEE communications letters, 16(9):1439--1441, 2012.Google ScholarCross Ref
- Hojin Lee, Dongseok Ok, Joonsub Han, Iksoon Hwang, and Kangtae Kim. Performance anomaly of neighbor discovery in bluetooth low energy. In IEEE ICCE, pages 341--342, 2016.Google ScholarCross Ref
- Keuchul Cho, Gisu Park, Wooseong Cho, Jihun Seo, and Kijun Han. Performance analysis of device discovery of bluetooth low energy (ble) networks. Computer Communications, 81:72--85, 2016.Google ScholarDigital Library
- Bingqing Luo, Yudong Yao, and Zhixin Sun. Performance analysis models of ble neighbor discovery: A survey. IEEE Internet of Things Journal, 8(11):8734--8746, 2020.Google ScholarCross Ref
- Johanna Nieminen, Carles Gomez, Markus Isomaki, Teemu Savolainen, Basavaraj Patil, Zach Shelby, Minjun Xi, and Joaquim Oller. Networking solutions for connecting bluetooth low energy enabled machines to the internet of things. IEEE network, 28(6):83--90, 2014.Google ScholarCross Ref
- Mira Weller, Jiska Classen, Fabian Ullrich, Denis Waßmann, and Erik Tews. Lost and found: Stopping bluetooth finders from leaking private information. In ACM WiSec, page 184--194, 2020.Google ScholarDigital Library
- Apple airtag. https://www.apple.com/sg/airtag/, 2021.Google Scholar
- Hui Xie and Tong Li. Revisiting loss recovery for high-speed transmission. In IEEE WCNC, pages 1987--1992, 2022.Google ScholarDigital Library
- Tong Li, Ke Xu, Hanlin Huang, Xinle Du, and Kai Zheng. Wip: When rdma meets wireless. In IEEE WoWMoM, pages 177--180, 2022.Google Scholar
- Tong Li, Kezhi Wang, Ke Xu, Kun Yang, Chathura Sarathchandra Magurawalage, and Haiyang Wang. Communication and computation cooperation in cloud radio access network with mobile edge computing. CCF Transactions on Networking, 2(1):43--56, 2019.Google ScholarCross Ref
- Android samples. https://github.com/android/connectivity-samples/tree/main/BluetoothAdvertisements, 2021.Google Scholar
- Android ble documentation. https://developer.android.com/reference/android/bluetooth/le/ScanSettings, 2022.Google Scholar
Index Terms
- Blender: Toward Practical Simulation Framework for BLE Neighbor Discovery
Recommendations
A discovery scheme based on carrier sensing in self-organizing Bluetooth Low Energy networks
Bluetooth Low Energy (BLE) gets lots of attention from researchers as one of the most prominent solutions for short range communications. But, the BLE still has many challenging issues which must be resolved before deploying it for the technologies. In ...
Comments