With　the　popularization　of　wireless　networks　and　mobile　intelligent　terminals,　mobile　crowd　sensing　is　becoming　a　promising　sensing　paradigm.　Tasks　are　assigned　to　users　with　mobile　devices,　which　then　collect　and　submit　ambient　information　to　the　server.　The　composition　of　participants　greatly　determines　the　quality　and　cost　of　the　collected　information.　This　paper　aims　to　select　fewest　participants　to　achieve　the　quality　required　by　a　sensing　task.　The　requirement　namely　＂t-sweep　k-coverage＂　means　for　a　target　location,　every　t　time　interval　should　at　least　k　participants　sense.　The　participant　selection　problem　for　＂t-sweep　k-coverage＂　crowd　sensing　tasks　is　NP-hard.　Through　delicate　matrix　stacking,　linear　programming　can　be　adopted　to　solve　the　problem　when　it　is　in　small　size.　We　further　propose　a　participant　selection　method　based　on　greedy　strategy.　The　two　methods　are　evaluated　through　simulated　experiments　using　users＇　call　detail　records.　The　results　show　that　for　small　problems,　both　the　two　methods　can　find　a　participant　set　meeting　the　requirement.　The　number　of　participants　picked　by　the　greedy　based　method　is　roughly　twice　of　the　linear　programming　based　method.　However,　when　problems　become　larger,　the　linear　programming　based　method　performs　unstably,　while　the　greedy　based　method　can　still　output　a　reasonable　solution.