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      • Dept. of Electrical and Electronics Engineering - Master's degree
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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      Energy management in energy harvesting wireless sensor nodes with lifetime constraints

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      Author(s)
      Tunç, Çağlar
      Advisor
      Akar, Nail
      Date
      2016-06
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      Advancements in the \Internet of Things (IoT)" concept enables large numbers of low-power wireless sensors and electronic devices to be connected to the Internet and outside world over a wide area wireless network without a need for human interaction. Using rechargeable batteries with energy harvesting to power these wireless sensors has been shown to preserve the self-sustainability and selfsu fficiency of a sensor node and prolong its lifetime, hence the whole network it belongs to. However, it brings the question of how to intelligently manage the energy in the battery so that the node maintains its functionalities by keeping the battery level over zero for an extended duration of time, known as the lifehorizon. We propose a risk-theoretic Markov uid queue model to compute the battery outage probability of a wireless sensor node for a given finite life-horizon. The proposed method enables the performance evaluation of a wide spectrum of energy management policies including those with adaptive sensing rate (or duty cycling). In this model, the node gathers data from the environment according to a Poisson process whose rate is to depend on the instantaneous battery level and/or the state of the energy harvesting process (EHP) which is characterized by a Continuous time Markov Chain (CTMC). Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing rate policies are obtained that maximize the information sensing rate of the sensor node while meeting lifetime constraints given in terms of battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using two-state CTMC-based EHPs.
      Keywords
      Wireless sensor nodes
      Internet of Things
      Energy harvesting
      Markov uid queues
      Risk theory
      Battery outage probability
      Adaptive duty cycling
      Permalink
      http://hdl.handle.net/11693/30144
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      • Dept. of Electrical and Electronics Engineering - Master's degree 655
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