Radio　frequency　(RF)　source　reconstruction　is　useful　for　RF　radiation　analysis　and　interference　diagnosis　in　Internet　of　Things.　Near　field　(NF)　sampling　is　a　critical　step　of　RF　source　reconstruction.　Accurate　RF　source　reconstruction　usually　requires　a　large　number　of　NF　samples,　which　results　in　tremendous　effort　of　NF　sampling.　This　article　presents　an　adaptive　NF　sampling　method　based　on　the　skeletonization　scheme.　First,　sources　to　be　reconstructed　are　related　to　NF　samples　through　integral　equation　(IE).　Second,　the　IE　is　discretized　with　the　method　of　moments,　and　thus　the　interaction　matrix　between　source　and　field　points　is　found.　Third,　strong　rank-revealing　QR　factorization　is　applied　to　the　interaction　matrix,　which　results　in　a　permutation　matrix,　a　row　skeleton　matrix,　and　a　transformation　matrix.　Finally,　a　small　number　of　skeleton　sampling　points　are　selected　by　analyzing　the　permutation　matrix.　The　fields　at　skeleton　sampling　points　can　be　used　to　calculate　the　fields　at　other　sampling　points　through　the　transformation　matrix.　Hence,　one　only　needs　to　perform　NF　sampling　at　a　small　number　of　skeleton　sampling　points,　which　significantly　reduces　the　expenditure　of　NF　sampling.　Simulations　using　synthetic　and　measurement　data　are　presented　to　show　the　effectiveness　and　advantages　of　the　proposed　sampling　method.