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dc.contributor.authorL. Quintero, Vanessa
dc.contributor.authorEstevez, Carlos
dc.contributor.authorOrchard, Marcos
dc.date.accessioned2019-07-02T18:13:55Z
dc.date.accessioned2019-07-02T18:13:55Z
dc.date.available2019-07-02T18:13:55Z
dc.date.available2019-07-02T18:13:55Z
dc.date.issued07/27/2017
dc.date.issued07/27/2017
dc.identifierhttps://ieeexplore.ieee.org/document/7993766/
dc.identifier.issn2165-8536
dc.identifier.other10.1109/ICUFN.2017.7993766
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6159
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6159
dc.descriptionWireless sensor networks are pervasive systems that continuously demonstrate increase in growth by branching into diverse applications. The state of charge is an indicator that conveys the amount of energy available in the battery, information that contributes to better decision-making and energy-efficient protocols by creating smart cross-layer designs. WSN research trends portray the importance of energy-efficient systems by prioritizing energy efficiency over other arguably equally important aspects as throughput, channel utilization, latency, etc. This demonstrates the impact of improving the energy conservation techniques and extending the battery life of the sensor nodes. By using Bayesian inference, more specifically particle filtering, it is shown that the state of charge can be accurately estimated within the linear region of the voltage-SOC curve. Battery discharge experiments are compared to simulations of the voltage-SOC evolution behavior using a state-space representation model, which showed good agreement between the results. The SOC estimation obtained by the particle filter yields essential information that can, and should, be incorporated into MAC protocols.en_US
dc.description.abstractWireless sensor networks are pervasive systems that continuously demonstrate increase in growth by branching into diverse applications. The state of charge is an indicator that conveys the amount of energy available in the battery, information that contributes to better decision-making and energy-efficient protocols by creating smart cross-layer designs. WSN research trends portray the importance of energy-efficient systems by prioritizing energy efficiency over other arguably equally important aspects as throughput, channel utilization, latency, etc. This demonstrates the impact of improving the energy conservation techniques and extending the battery life of the sensor nodes. By using Bayesian inference, more specifically particle filtering, it is shown that the state of charge can be accurately estimated within the linear region of the voltage-SOC curve. Battery discharge experiments are compared to simulations of the voltage-SOC evolution behavior using a state-space representation model, which showed good agreement between the results. The SOC estimation obtained by the particle filter yields essential information that can, and should, be incorporated into MAC protocols.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.publisher2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)en_US
dc.publisher2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectwireless sensor networksen_US
dc.subjecttelecommunication power managementen_US
dc.subjectsecondary cellsen_US
dc.subjectparticle filtering (numerical methods)en_US
dc.subjectenergy conservationen_US
dc.subjectBayes methodsen_US
dc.subjectaccess protocolsen_US
dc.subjectwireless sensor networks
dc.subjecttelecommunication power management
dc.subjectsecondary cells
dc.subjectparticle filtering (numerical methods)
dc.subjectenergy conservation
dc.subjectBayes methods
dc.subjectaccess protocols
dc.titleState-of-charge estimation to improve energy conservation and extend battery life of wireless sensor network nodesen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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