Existing　WiFi　fingerprinting-based　Indoor　Positioning　System　(IPS)　suffers　from　the　vulnerability　of　environmental　dynamics.　To　address　this　issue,　we　propose　TKL-WinSMS　as　a　systematic　strategy,　which　is　able　to　realize　robust　and　adaptive　localization　in　dynamic　indoor　environments.　We　developed　a　WiFi-based　Non-intrusive　Sensing　and　Monitoring　System　(WinSMS)　that　enables　COTS　WiFi　routers　as　online　reference　points　by　extracting　real-time　RSS　readings　among　them.　With　these　online　data　and　labeled　source　data　from　the　offline　calibrated　radio　map,　we　further　combine　the　RSS　readings　from　target　mobile　devices　as　unlabeled　target　data,　to　design　a　robust　localization　model　using　an　emerging　transfer　learning　algorithm,　namely　transfer　kernel　learning　(TKL).　It　can　learn　a　domain-invariant　kernel　by　directly　matching　the　source　and　target　distributions　in　the　reproducing　kernel　Hilbert　space　instead　of　the　raw　noisy　signal　space.　By　leveraging　the　resultant　kernel　as　input　for　the　SVR　training,　the　trained　localization　model　can　inherit　the　information　from　online　phase　to　adaptively　enhance　the　offline　calibrated　radio　map.　Extensive　experimental　results　verify　the　superiority　of　TKL-WinSMS　in　terms　of　localization　accuracy　compared　with　existing　solutions　in　dynamic　indoor　environments.