Intelligent　occupancy　sensing　is　becoming　a　vital　underpinning　for　various　emerging　applications　in　smart　homes,　such　as　security　surveillance　and　human　behavior　analysis.　However,　prevailing　approaches　mainly　rely　on　video　camera,　ambient　sensors,　or　wearable　devices,　which　either　requires　arduous　deployment　or　arouses　privacy　concerns.　In　this　paper,　we　present　a　novel　real-time,　device-free,　and　privacy-preserving　WiFi-enabled　Internet　of　Things　platform　for　occupancy　sensing,　which　can　promote　a　myriad　of　emerging　applications.　It　is　designed　to　achieve　an　optimal　tradeoff　between　performance　and　scalability.　Our　system　empowers　commercial　off-the-shelf　WiFi　routers　to　collect　channel　state　information　(CSI)　measurements　and　provides　an　efficient　cloud　server　for　computing　via　a　lightweight　communication　protocol.　To　demonstrate　the　usefulness　of　our　platform,　an　occupancy　detection　system　is　developed　by　exploiting　the　CSI　curve　of　human　presence.　Furthermore,　we　also　design　an　innovative　activity　recognition　system　based　on　our　platform　and　machine　learning　techniques　with　high　availability　and　extensibility.　In　the　evaluation,　the　experimental　results　show　that　our　platform　enables　these　applications　efficiently,　with　the　accuracy　of　96.8%　and　90.6%　in　terms　of　occupancy　detection　and　recognition,　respectively.