Wi-Fi　uploading　is　considered　an　effective　method　for　offloading　the　traffic　of　cellular　networks　generated　by　the　data　uploading　process　of　mobile　crowd　sensing　applications.　However,　previously　proposed　Wi-Fi　uploading　schemes　mainly　focus　on　optimizing　one　performance　objective:　the　offloaded　cellular　traffic　or　the　reduced　uploading　cost.　In　this　paper,　we　propose　an　Intelligent　Data　Uploading　Selection　Mechanism　(IDUSM)　to　realize　a　trade-off　between　the　offloaded　traffic　of　cellular　networks　and　participants＇　uploading　cost　considering　the　differences　among　participants＇　data　plans　and　direct　and　indirect　opportunistic　transmissions.　The　mechanism　first　helps　the　source　participant　choose　an　appropriate　data　uploading　manner　based　on　the　proposed　probability　prediction　model,　and　then　optimizes　its　performance　objective　for　the　chosen　data　uploading　manner.　In　IDUSM,　our　proposed　probability　prediction　model　precisely　predicts　a　participant＇s　mobility　from　spatial　and　temporal　aspects,　and　we　decrease　data　redundancy　produced　in　the　Wi-Fi　offloading　process　to　reduce　waste　of　participants＇　limited　resources　(e.g.,　storage,　battery).　Simulation　results　show　that　the　offloading　efficiency　of　our　proposed　IDUSM　is　(56.54x10(-7)),　and　the　value　is　the　highest　among　the　other　three　Wi-Fi　offloading　mechanisms.　Meanwhile,　the　offloading　ratio　and　uploading　cost　of　IDUSM　are　respectively　52.1%　and　(6.79x10(3)).　Compared　with　other　three　Wi-Fi　offloading　mechanisms,　it　realized　a　trade-off　between　the　offloading　ratio　and　the　uploading　cost.