Energy Availability Forecasting for Harvesting-aware Wireless Sensor Networks: Analysis of Energy Demand of a Predictor Based on Evolutionary Fuzzy Rules

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096788" target="_blank" >RIV/61989100:27240/15:86096788 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/INCoS.2015.54" target="_blank" >http://dx.doi.org/10.1109/INCoS.2015.54</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2015.54" target="_blank" >10.1109/INCoS.2015.54</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy Availability Forecasting for Harvesting-aware Wireless Sensor Networks: Analysis of Energy Demand of a Predictor Based on Evolutionary Fuzzy Rules

Popis výsledku v původním jazyce

Environmental monitoring sensor networks often operate in remote locations and thus must be designed for energy-efficiency and reliability. The first goal of energy efficiency can be achieved through low-power design of the monitoring hardware, often supplemented by energy management schemes of varying complexity and sophistication. In case of sensor nodes endowed with energy-harvesting capabilities, the energy management systems can take advantage of harvesting outlook that can take the form of prediction of energy available for harvest in the near future. When designing such sophisticated prediction and management schemes, it is important to consider the energy cost of gathering the predictor data and executing the forecasting algorithm. This contribution describes the results of experiments designed to assess the energy required to run a recently introduced energy-availability forecasting algorithm based on evolutionary fuzzy rules. In particular, it presents an analysis of experime

Název v anglickém jazyce

Energy Availability Forecasting for Harvesting-aware Wireless Sensor Networks: Analysis of Energy Demand of a Predictor Based on Evolutionary Fuzzy Rules

Popis výsledku anglicky

Environmental monitoring sensor networks often operate in remote locations and thus must be designed for energy-efficiency and reliability. The first goal of energy efficiency can be achieved through low-power design of the monitoring hardware, often supplemented by energy management schemes of varying complexity and sophistication. In case of sensor nodes endowed with energy-harvesting capabilities, the energy management systems can take advantage of harvesting outlook that can take the form of prediction of energy available for harvest in the near future. When designing such sophisticated prediction and management schemes, it is important to consider the energy cost of gathering the predictor data and executing the forecasting algorithm. This contribution describes the results of experiments designed to assess the energy required to run a recently introduced energy-availability forecasting algorithm based on evolutionary fuzzy rules. In particular, it presents an analysis of experime

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Networking and Collaborative Systems INCoS-2015 : 7th International Conference : proceedings : September 2-4, 2015, Taipei, Tchaj-wan

ISBN

978-1-4673-7694-5

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

200-203

Název nakladatele

IEEE

Místo vydání

Danvers

Místo konání akce

Taipei

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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