Markov fluid queue model of an energy harvesting IoT device with adaptive sensing

Simple item page
dc.citation.epage16en_US
dc.citation.spage1en_US
dc.citation.volumeNumber111en_US
dc.contributor.authorTunc C.en_US
dc.contributor.authorAkar, N.en_US
dc.date.accessioned2018-04-12T11:10:45Z
dc.date.available2018-04-12T11:10:45Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractEnergy 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.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:10:45Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.embargo.release2019-05-01en_US
dc.identifier.doi10.1016/j.peva.2017.03.004en_US
dc.identifier.issn0166-5316
dc.identifier.urihttp://hdl.handle.net/11693/37345
dc.language.isoEnglishen_US
dc.publisherElsevier B.V.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.peva.2017.03.004en_US
dc.source.titlePerformance Evaluationen_US
dc.subjectAdaptive sensingen_US
dc.subjectEnergy harvestingen_US
dc.subjectInternet of thingsen_US
dc.subjectMarkov fluid queuesen_US
dc.subjectRisk theoryen_US
dc.subjectWireless sensor networksen_US
dc.titleMarkov fluid queue model of an energy harvesting IoT device with adaptive sensingen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Markov fluid queue model of an energy harvesting IoT device with adaptive sensing.pdf
Size:
1.27 MB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download

Collections

Scholarly Publications - Electrical and Electronics Engineering