In　most　cases,　the　batteries　of　sensor　nodes　in　the　Internet　of　Things　(IoT)　are　usually　constrained　by　size　and　weight,　and　are　difficult　to　recharge　or　replace.　In　traditional　wireless　sensor　networks,　data　is　transmitted　in　a　multi-hop　manner,　which　may　cause　the　high　data　transmission　delay　and　unbalanced　traffic　load.　In　this　paper,　an　Unmanned　Aerial　Vehicle　(UAV)-assisted　IoT　architecture　is　introduced,　in　which　UAV　is　utilized　to　achieve　low-latency　and　seamless-coverage　acquisition　of　the　sensing　data.　Furthermore,　based　on　the　recent　advances　on　deep　reinforcement　learning　algorithms,　considering　both　data　delay　requirements　and　network　energy　consumption,　a　real-time　flight　path　planning　scheme　of　the　UAV　in　the　dynamic　IoT　sensor　networks　has　been　proposed　based　on　dueling　deep　Q-network　(DQN).　Besides,　the　grid-based　method　is　used　to　handle　the　network　state　modeling,　which　effectively　reduces　the　complexity　of　the　proposed　scheme.　Simulation　results　show　that　the　proposed　scheme　significantly　improves　the　network　performance.