Mobile　Crowdsensing　(MCs)　is　a　widely　applicable　and　inexpensive　data-obtaining　method　that　leverages　mobile　devices　to　sense　and　report　data　without　deploying　sensors.　The　rapid　development　of　the　Integration　of　IoT　systems,　as　well　as　the　widespread　use　of　communication　satellites,　make　MCs　further　develop　and　receive　extensive　attention　from　researchers.　One　key　issue　is　how　to　ensure　the　reliability　of　the　reported　data　to　avoid　the　presence　of　malicious　participants　leading　to　poor　IoT　service　quality.　In　this　paper,　we　proposed　an　Active　and　Trace　Back　based　Trust　Management　(ATBTM)　algorithm　to　evaluate　the　trust　of　participants　and　handle　malicious　participants.　The　main　innovations　are:　(1)　An　active　trust　evaluation　approach　for　MCs　is　proposed,　which　uses　an　Unmanned　Aerial　Vehicle　(UAV)　to　collect　data　as　a　baseline　to　verify　the　reliability　of　the　data　reported　by　participants.　Then,　the　data　reported　by　high　reputation　participants　can　also　be　used　as　a　sub-baseline　to　verify　the　trust　of　some　participants.　(2)　A　traceback　based　trust　evaluation　method　is　also　proposed.　In　this　approach,　some　reliable　devices　provide　historical　sensing　data　that　has　been　collected　but　not　reported.　Then　the　system　compares　it　to　real　data　with　corresponding　timestamps　to　evaluate　the　trust　of　other　participants.　Sufficient　theoretical　analysis　and　experimental　results　demonstrate　that　the　proposed　ATBTM　framework　can　effectively　identify　the　malicious　workers,　and　conquer　the　drawbacks　of　lacking　trust　evaluation　method　in　existed　MCs.