Aiming　at　the　problems　of　low　data　rate　and　limited　battery　power　of　traditional　underwater　wireless　sensor　network　(UWSN)　based　on　acoustic　communication,　a　resource　allocation　strategy　for　magnetic　induction　(MI)　communication-based　UWSN　in　ocean　current　scenario　is　investigated　in　this　paper.　Specifically,　multiple　sensor　nodes　(SNs)　are　distributed　in　seawater　at　different　depths.　First,　an　autonomous　underwater　vehicle　(AUV)　is　introduced　to　charge　the　SNs　by　magnetic　coupling　resonant　wireless　power　transfer　(MCR-WPT)　technology.　Then,　the　SNs　transmit　data　to　the　AUV　through　MI　communication.　The　influence　of　the　ocean　current　on　the　SNs　and　AUV　is　analyzed.　A　genetic　algorithm-based　particle　swarm　optimization　algorithm　is　used　to　optimize　the　AUV　navigation　trajectory.　After　collecting　the　data　from　the　SNs,　the　AUV　moves　to　the　position　under　a　surface　base　station　and　sends　the　collected　data　to　the　surface　base　station　by　MI　communication.　To　minimize　system　energy　consumption,　the　transmitting　power　of　AUV　and　SNs　are　jointly　optimized　under　the　constraints　of　energy　causality　and　transmitting　power.　The　suboptimal　solution　to　the　formulated　optimization　problem　is　obtained　by　adopting　the　improved　sparrow　search　algorithm　(ISSA)　combining　Cauchy　variation　and　reverse　learning.　Simulation　results　show　that　the　ISSA　has　lower　system　energy　consumption　than　other　benchmark　methods.　©　2025　IEEE.