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      • Department of Electrical and Electronics Engineering
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      Markov fluid queue model of an energy harvesting IoT device with adaptive sensing

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      Embargo Lift Date: 2019-05-01
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      Author(s)
      Tunc C.
      Akar, N.
      Date
      2017
      Source Title
      Performance Evaluation
      Print ISSN
      0166-5316
      Publisher
      Elsevier B.V.
      Volume
      111
      Pages
      1 - 16
      Language
      English
      Type
      Article
      Item Usage Stats
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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.
      Keywords
      Adaptive sensing
      Energy harvesting
      Internet of things
      Markov fluid queues
      Risk theory
      Wireless sensor networks
      Permalink
      http://hdl.handle.net/11693/37345
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.peva.2017.03.004
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      • Department of Electrical and Electronics Engineering 3868
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