Accurate　Location　Based　Service　(LBS)　is　one　of　the　fundamental　but　crucial　services　in　the　era　of　Internet　of　Things　(IoT).　WiFi　fingerprinting-based　Indoor　Positioning　System　(IPS)　has　become　the　most　promising　solution　for　indoor　LBS.　However,　the　offline　calibrated　received　signal　strength　(RSS)　radio　map　is　unable　to　provide　consistent　LBS　with　high　localization　accuracy　under　various　environmental　dynamics.　To　address　this　issue,　we　propose　TKL-WinSMS　as　a　systematic　strategy,　which　is　able　to　realize　robust　and　adaptive　indoor　localization　in　dynamic　indoor　environments.　We　developed　a　WiFi-based　Non-intrusive　Sensing　and　Monitoring　System　(WinSMS)　that　enables　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　is　able　to　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.　The　resultant　kernel　can　be　used　as　input　for　the　SVR　training　procedure.　In　this　manner,　the　trained　localization　model　can　inherit　the　information　from　online　phase　to　adaptively　enhance　the　offline　calibrated　radio　map.　Extensive　experiments　were　conducted　and　demonstrated　that　the　proposed　TKL-　WinSMS　is　able　to　improve　the　localization　accuracy　by　at　least　26%　compared　with　existing　solutions　under　various　environmental　interferences.　©　2017　IEEE.