High　precision　and　smart　sensors　make　up　an　indispensable　data　entry　for　the　Internet　of　Things　technology.　Nonetheless,　conventional　calibration　algorithms　mainly　implemented　on　the　software,　such　as　least　squares,　polynomial　fitting,　and　interpolation,　exhibit　limited　calibration　accuracy　that　does　not　reflect　a　real-time　measurement　of　the　sensors.　The　problem　can　be　resolved　with　an　MCU-based　sensor　calibration　system　proposed　herein,　which　mainly　employs　particle　swarm　optimization　(PSO)-back　propagation　(BP)　neural　network.　The　system　firstly　reads　sensor　data　through　l2C　bus　and　then　uses　the　BP　neural　network　and　PSO　algorithm　to　automatically　calibrate　these　data　in　real　time.　Sigmoid　activation　function　was　implemented　via　a　piecewise　polynomial　fitting　to　create　a　trade-off　between　hardware　resource　and　precision.　A　performance　test　conducted　on　temperature　sensors　showed　a　maximum　error　of　0.16　degrees　C　within　the　measurement　range　of　-40-100　degrees　C　with　three　times　the　standard　deviation　(3　sigma)　error　of　+/-　0.23　degrees　C　and　overall　linearity　of　0.1143%　after　the　calibration　system　was　added　as　compared　to　the　significantly　higher　error　of　+/-　0.63　degrees　C　without　the　calibration.　(C)　2019　Elsevier　B.V.　All　rights　reserved.