With　the　rapid　development　of　the　Internet　of　Things　(IoT),　improving　the　lifetime　of　nodes　and　networks　has　become　increasingly　important.　Most　existing　medium　access　control　protocols　are　based　on　scheduling　the　standby　and　active　periods　of　nodes　and　do　not　consider　the　alarm　state.　This　paper　proposes　a　Q-learning　and　efficient　low-quantity　charge　(QL-ELQC)　method　for　the　smoke　alarm　unit　of　a　power　system　to　reduce　the　average　current　and　to　improve　the　lifetime　of　the　wireless　sensor　network　(WSN)　nodes.　Quantity　charge　models　were　set　up,　and　the　QL-ELQC　method　is　based　on　the　duty　cycle　of　the　standby　and　active　times　for　the　nodes　and　considers　the　relationship　between　the　sensor　data　condition　and　the　RF　module　that　can　be　activated　and　deactivated　only　at　a　certain　time.　The　QL-ELQC　method　effectively　overcomes　the　continuous　state-action　space　limitation　of　Q-learning　using　the　state　classification　method.　The　simulation　results　reveal　that　the　proposed　scheme　significantly　improves　the　latency　and　energy　efficiency　compared　with　the　existing　QL-Load　scheme.　Moreover,　the　experimental　results　are　consistent　with　the　theoretical　results.　The　proposed　QL-ELQC　approach　can　be　applied　in　various　scenarios　where　batteries　cannot　be　replaced　or　recharged　under　harsh　environmental　conditions.