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Performance Model for Advanced Neighbor Discovery Process in Bluetooth Low Energy 5.0-Enabled Internet of Things Networks
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Publication Year
2020-12-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Transactions on Industrial Electronics, Vol.67, pp.10965-10974
Keyword
BLE 4.2BLE 5.0Bluetooth low energy (BLE)Internet of Things (IoT)neighbor discovery process (NDP)
Mesh Keyword
Bluetooth low energies (BLE)Bluetooth low energies (BTLE)Neighbor discoveryOperational conditionsOperational environmentsPerformance analysisPerformance ModelSignal collisions
All Science Classification Codes (ASJC)
Control and Systems EngineeringElectrical and Electronic Engineering
Abstract
The neighbor discovery process (NDP) plays a key role in Bluetooth low energy (BLE)-enabled applications. However, when a massive number of BLE devices are involved in the NDP, signal collisions become severer, which degrade the NDP performance seriously. To solve the problem, BLE 5.0 specifies some extended features such as advanced NDP (A-NDP). The previous works on the performance models for the NDP have focused on the basic NDP (B-NDP). Though some works considered the performance evaluations on A-NDP, they have done on simulations or testbed-based experiments. Likewise, so far, the performance analysis for A-NDP has been insufficient. In this article, we propose an analytical model to evaluate A-NDP performances, such as the signal collision probability, the discovery delay, the energy consumption, and so on. With the proposed analysis model, the performances of A-NDP are analyzed from the viewpoint of various operational environments for BLE-enabled Internet-of-Things services, and compared to those of B-NDP with extended features of Bluetooth 5.0. Besides, it showed that A-NDP is not always better than B-NDP, and we discuss the operational conditions where A-NDP or B-NDP operate effectively, respectively.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/31557
DOI
https://doi.org/10.1109/tie.2019.2962401
Fulltext

Type
Article
Funding
Manuscript received May 15, 2019; revised October 6, 2019 and November 26, 2019; accepted December 3, 2019. Date of publication January 1, 2020; date of current version August 18, 2020. This work was supported in part by the Ministry of Science and ICT, Korea, under the Information Technology Research Center Support Program IITP-2019-2018-0-01431, supervised by the Institute for Information and Communications Technology Promotion. (Corresponding author: Byeong-hee Roh.) The authors are with the Department of Computer Engineering, Ajou University, Suwon 16499, Korea (e-mail: shanyang166@gmail.com; bhroh@ajou.ac.kr).
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SHAN GAOYANGSHAN, GAOYANG
Department of Software and Computer Engineering
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