A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks
نویسندگان
چکیده
The localizationofsensornode isanessentialproblemformanyeconomic forecastingapplications inwireless sensor networks.Consideringthat themobilesensorschangetheir locations frequentlyover time,MonteCarlo localization algorithmutilizes themovingcharacteristicsofnodesandemploys theprobabilitydistributionfunction(PDF) in the previous timeslot toestimatethecurrent locationbyusingaweightedparticle filter.However, it alsohastheproblemof insufficientnumberofvalidsamples,which furtheraffects thenode’s localizationaccuracy. In thispaper,differential evolutionmethod is introduced intotheMonteCarlo localizationalgorithm.Thesampleweight is takenastheobjective function,anddifferential evolutionalgorithmis implemented insamplestage.Finally, thenodeposition isestimatedby makingthesampleclosetotheactual locationof thenode insteadofbeingfilteredout. Thesimulationresults demonstrate that theproposedalgorithmprovidesabetterpositionestimationwith less localizationerror.
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