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- C. Tunc NYU Tandon School of Engineering, Department of Electrical and Computer Engineering, Brooklyn, NY, USA
NYU Tandon School of Engineering, Department of Electrical and Computer Engineering, Brooklyn, NY, USA
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- N. Akar Electrical and Electronics Engineering Department, Bilkent University, Ankara, Turkey
Electrical and Electronics Engineering Department, Bilkent University, Ankara, Turkey
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Performance EvaluationVolume 111Issue CMay 2017pp 1–16https://doi.org/10.1016/j.peva.2017.03.004
Published:01 May 2017Publication History
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Performance Evaluation
Volume 111, Issue C
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Abstract
Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e.,information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e.,the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP.
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Index Terms
Markov fluid queue model of an energy harvesting IoT device with adaptive sensing
Networks
Network types
Mobile networks
Index terms have been assigned to the content through auto-classification.
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Performance Evaluation Volume 111, Issue C
May 2017
36 pages
ISSN:0166-5316
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Copyright © Elsevier B.V.
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- Published: 1 May 2017
Author Tags
- Adaptive sensing
- Energy harvesting
- Internet of things
- Markov fluid queues
- Risk theory
- Wireless sensor networks
Qualifiers
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