An Analytical Study of Low Energy Monitoring Networks for Large-Scale Data Centers

Jafarizadeh, Mehdi; Zheng, Rong

doi:10.1109/globecom42002.2020.9322228

Cited by 2 publications

(1 citation statement)

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“…A similar analytical approach that is used to evaluate LEMoNet in [14], can be utilized to estimate the PRR and the mean power consumption of sensors in SoftBLE as well. As listed in Table 2, the analytical model of SoftBLE takes almost the same input parameters as that of LEMoNet.…”

Section: Estimating Prr and Power Consumptionmentioning

confidence: 99%

SoftBLE

Jafarizadeh

Liu

Zheng

2021

Proceedings of the International Conference on Internet-of-Things Design and Implementation

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Today's Industrial IoT (IIoT) applications often employ large-scale and dense sensor deployments for environmental monitoring. A hierarchical Bluetooth Low Energy (BLE) based architecture can facilitate power efficiency and reliability for data collection in dense networks. But if the network is static with fixed parameter settings, it can not be adaptable to dynamic application requirements. Although BLE is a parametric protocol, it does not provide any builtin feature for parameter tuning. To achieve network adaptability, we introduce and design SoftBLE, a Software Defined Networking (SDN) framework that provides controllability for BLE based 2-tier networks. It takes advantages of advanced control knobs recently available in BLE protocol stacks. SoftBLE is complemented by two orchestration algorithms to optimize gateway and sensor parameters. Evaluation results from both an experimental testbed and a large-scale simulation study show that almost all the SoftBLE sensors can save around 70% of transmission power while keeping Packet Reception Rate (PRR) above 99.9%. CCS CONCEPTS• Networks → Network protocol design; Ad hoc networks.

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Section: Estimating Prr and Power Consumptionmentioning

confidence: 99%