The　Internet　of　Things　(IoT)　eHealth　systems　composed　by　Wireless　Body　Area　Network　(WBAN)　has　emerged　recently.　Sensor　nodes　are　placed　around　or　in　the　human　body　to　collect　physiological　data.　WBAN　has　many　different　applications,　for　instance　health　monitoring.　Since　the　limitation　of　the　size　of　the　battery,　besides　speed,　reliability,　and　accuracy;　design　of　WBAN　protocols　should　consider　the　energy　efficiency　and　time　delay.　To　solve　these　problems,　this　paper　adopt　the　end-edge-cloud　orchestrated　network　architecture　and　propose　a　transmission　based　on　reinforcement　algorithm.　The　priority　of　sensing　data　is　classified　according　to　certain　application.　System　utility　function　is　modeled　according　to　the　channel　factors,　the　energy　utility,　and　successful　transmission　conditions.　The　optimization　problem　is　mapped　to　Q-learning　model.　Following　this　online　power　control　protocol,　the　energy　level　of　both　the　senor　to　coordinator,　and　coordinator　to　edge　server　can　be　modified　according　to　the　current　channel　condition.　The　network　performance　is　evaluated　by　simulation.　The　results　show　that　the　proposed　power　control　protocol　has　higher　system　energy　efficiency,　delivery　ratio,　and　throughput.