In　the　era　of　Internet　of　Things,　WiFi　fingerprinting　based　indoor　positioning　system　(IPS)　has　been　recognized　as　the　most　promising　IPS　for　indoor　location-based　service.　Fingerprinting-based　algorithms　critically　rely　on　a　fingerprint　database　built　from　machine　learning　methods,　and　extreme　machine　learning　(ELM)　is　preferred　for　its　fast　training　speed.　However,　traditional　WiFi　based　IPS　usually　requires　a　central　server　to　collect　and　process　data,　which　is　tremendously　vulnerable　to　server　breakdown　and　communication　link　failure.　To　address　this　issue,　we　propose　Consensus-based　Parallel　ELM　(CPELM)　to　enhance　the　robustness　by　distributing　the　data　on　different　computation　nodes.　Specifically,　each　node　keeps　updating　the　corresponding　terms　in　the　ELM　regression　equation　as　a　weighted　average　of　those　from　neighboring　nodes　based　on　the　distributed　consensus　iterative　scheme.　Upon　the　agreement　of　the　regression　equation　within　the　network,　the　output　weight　of　ELM　can　be　calculated　on　some　nodes　and　propagated　to　other　nodes.　Extensive　simulation　with　real　data　has　demonstrated　that　CPELM　is　able　to　produce　same　level　of　localization　accuracy　as　centralized　ELM　without　incurring　additional　computational　cost,　and　in　the　meanwhile　provides　more　robustness　to　the　entire　IPS　in　case　of　server　breakdown　and　link　failures.　©　2016　IEEE.