Internet　of　Things　has　developed　quickly　to　share　data　from　billions　of　physical　devices.　Completeness　of　data　is　important　especially　in　crowd　sensing　Internet　of　Things.　How　to　recover　the　lost　data　is　a　fundamental　operation　to　utilize　the　coming　of　Internet　of　Things.　Existing　data　recovery　algorithms　depend　heavy　on　the　accuracy　distribution　of　environmental　data　and　result　in　bad　performance　when　reconstructing　the　lost　data.　This　paper　introduces　a　data　recovery　algorithm　based　on　generative　adversarial　networks.　The　convolution　neural　network　is　used　as　the　basic　model　of　this　algorithm.　We　add　a　restore　network　to　reload　the　unlost　data　after　recovery　in　this　algorithm.　The　algorithm　mainly　includes　two　parts:　(1)　training　process,　in　which　all　the　collected　sensory　data　are　used　to　train　the　proposed　generative　adversarial　networks　model　and　(2)　data　recovery　process,　in　which　the　lost　data　is　recovered　by　using　the　trained　generator.　We　use　random　loss　dataset　and　periodic　loss　dataset　to　validate　the　data　recovery　performance.　Finally,　these　two　cases　can　verify　that　the　recovery　algorithm　based　on　generative　adversarial　network　is　more　enhanced　compared　with　the　comparison　experiment　under　the　three　metrics　of　mean　square　error,　mean　absolute　error,　and　R-square.　The　results　show　that　our　proposed　algorithm　can　obtain　data　that　are　reliable　and　thus　improve　the　performance　of　data　recovery.　©　2020,　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature.