With　the　continuous　improvement　of　the　sensing,　transmission,　storage,　and　computing　capabilities　of　mobile　devices,　they　have　become　important　tools　for　perceiving　the　physical　environment　and　social　phenomena.　Mobile　crowdsensing　(MCS)　is　a　data　sensing　paradigm　that　utilizes　a　large　number　of　mobile　devices　to　collect　various　types　of　sensing　data,　ultimately　accomplishing　large-scale　and　complex　tasks.　Effective　incentive　mechanisms　can　motivate　users　to　actively　participate　in　data　collection　tasks　and　provide　high-quality　data,　making　it　one　of　the　key　issues　in　MCS.　This　article　reviews　the　state-of-the-art　incentive　mechanisms　in　MCS　systems.　This　article　begins　with　an　introduction　to　the　concept　of　the　MCS　incentive　mechanism,　categorizing　incentive　mechanisms　based　on　different　standards.　Subsequently,　it　addresses　the　primary　research　issues　concerning　incentive　mechanisms,　including　data　quality,　online　scenarios,　and　privacy　protection.　Then,　from　the　perspective　of　incentive　mechanism　technology,　it　reviews　the　research　progress　of　incentive　mechanisms　in　recent　years,　mainly　including　four　types　of　incentive　mechanisms:　1)　game　theory-based　incentive　mechanisms;　2)　auction　theory-based　incentive　mechanisms;　3)　reward　allocation-based　incentive　mechanisms;　and　4)　learning-based　incentive　mechanisms,　and　provides　a　brief　evaluation　of　each　mechanism.　Finally,　we　propose　future　research　directions　for　MCS　incentive　mechanisms.