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Analytical Framework for Data Reception Latency Modeling in BLE 5.x Based Clustered Architecture
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Publication Year
2024-06-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Communications Letters, Vol.28, pp.1447-1451
Keyword
Bluetooth low energy (BLE)clusteringextended advertisingIndustrial IoT (IIoT)latency
Mesh Keyword
AdvertizingBluetooth low energyClusteringsExtended advertizingIndustrial IIoTIndustrial internet of thingIntelligent sensorsLatencyLower energies
All Science Classification Codes (ASJC)
Modeling and SimulationComputer Science ApplicationsElectrical and Electronic Engineering
Abstract
Industrial Internet of Things (IIoT) networks often require minimal data reception latency, which primarily depends on advertising interval in BLE-based networks. Thus, selecting an optimal advertising interval is crucial to get minimal data reception latency. The existing solutions are limited to homogeneous networks (networks using the same set of BLE parameters). However, an IIoT scenario may have numerous devices incorporated with various sensors of different kinds, generating data of different sizes at different periods, which are likely to form a heterogeneous network, thus raising the need to evaluate the heterogeneous networks. To address these issues, we first analytically derived the optimal advertising interval expression that gives minimal latency for a heterogeneous network consisting of n clusters. Based on the analytical expressions, the algorithms are proposed that autonomously optimize the latency for heterogeneous networks. Moreover, an average energy consumption model is also proposed, which will help the service provider to select the suitable parameters, and further help in calculating the expected battery life of the device in the network. Further, the analytical results are verified by a commercial simulator whose simulation results show a good correlation with analytical results thus validates the proposed model.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/34094
DOI
https://doi.org/10.1109/lcomm.2024.3381605
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Article
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SHAN GAOYANG Image
SHAN GAOYANGSHAN, GAOYANG
Department of Software and Computer Engineering
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