Mobile　crowdsensing　(MCS)　has　gained　much　attention　due　to　the　proliferation　of　smart　devices　equipped　with　powerful　sensors.　Large-scale　users　are　the　foundation　of　MCS,　so　designing　incentive　mechanisms　to　motivate　users　to　participate　in　MCS　is　necessary.　Existing　works　on　incentive　mechanisms　usually　assume　a　scenario　where　a　group　of　tasks　arrive　at　the　platform　at　the　same　time　and　are　immediately　assigned　to　users.　We　argue　that　a　more　realistic　MCS　scenario　can　delay　a　task,　which　is　called　the　assignment　duration　time,　to　wait　for　appropriate　users.　In　this　scenario,　we　focus　on　proposing　a　truthful　incentive　mechanism　to　reduce　the　overall　social　cost.　Due　to　the　uncertainty　of　coming　users,　the　problems　of　selecting　the　appropriate　users　and　calculating　the　payment　for　each　recruited　user　(winner)　are　more　complicated.　To　overcome　these　challenges,　we　design　a　dynamic　truthful　incentive　mechanism　(DTIM)　including　winner　selection　and　payment　decision　processes.　The　former　uniformly　recruits　users　before　the　assignment　deadline　of　tasks　and　dynamically　readjusts　the　recruiting　frequency　of　other　tasks　to　select　winners　iteratively,　which　achieves　an　approximation　ratio.　Furthermore,　the　latter　determines　truthful　payment　for　each　winner　to　encourage　user　participation　as　well　as　avoid　being　deceived,　which　achieves　truthfulness,　individual　rationality,　and　computational　efficiency.　Finally,　massive　simulations　based　on　a　real　dataset　roma/taxi　validate　the　DTIM,　which　can　effectively　reduce　the　overall　social　cost　and　make　a　truthful　payment　for　each　winner.