简介:Tofindtheoptimalroutingisalwaysanimportanttopicinwirelesssensornetworks(WSNs).ConsideringaWSNwherethenodeshavelimitedenergy,weproposeanovelEnergy*Delaymodelbasedonantalgorithms('E&DANTS'forshort)tominimizethetimedelayintransferringafixednumberofdatapacketsinanenergy-constrainedmannerinoneround.Ourgoalisnotonlytomaximizethelifetimeofthenetworkbutalsotoprovidereal-timedatatransmissionservices.However,becauseofthetradeoffofenergyanddelayinwirelessnetworksystems,thereinforcementlearning(RL)algorithmisintroducedtotrainthemodel.Inthissurvey,theparadigmofE&DANTSisexplicatedandcomparedtootherant-basedroutingalgorithmslikeAntNetandAntChainabouttheissuesofroutinginformation,routingoverheadandadaptation.SimulationresultsshowthatourmethodperformsaboutseventimesbetterthanAntNetandalsooutperformsAntChainbymorethan150%intermsofenergycostanddelayperround.