With　the　advancement　of　Internet　of　Things　(IoT)　technology,　both　the　methods　of　data　collection　by　citizen　data　collectors　and　the　willingness　of　citizens　to　share　data　are　evolving.　To　analyze　the　long-term　impact　of　government　reward-penalty　mechanisms　on　citizen　data　collection,　this　paper　constructed　three　evolutionary　game　models　under　scenarios　of　no　reward-penalty,　static　reward-penalty,　and　dynamic　reward-penalty　mechanisms.　The　focus　is　on　comparing　and　analyzing　evolutionarily　stable　strategies　of　a　collector　and　a　citizen,　followed　by　computational　simulations.　Key　findings　include:　1)　Under　no　reward-penalty　and　static　reward-penalty　mechanisms,　evolutionarily　stable　strategies　typically　result　in　either　consistently　active　or　consistently　passive　behaviors　by　both　the　collector　and　the　citizen.　Mixed　strategies,　which　are　evolutionarily　stable,　emerge　only　under　dynamic　reward-penalty　mechanisms.　2)　The　balance　between　the　risk　associated　with　data　security　for　the　citizen　and　the　benefits　they　gain　from　public　services　significantly　influences　evolutionary　outcomes.　3)　Policy　directions　may　be　influenced　by　initial　conditions　in　the　pre-collection　stage,　which　can　be　mitigated　by　improving　the　benefit　to　citizen　or　reducing　the　cost　during　passive　data　collection.　4)　While　reward-penalty　mechanisms　may　not　directly　enhance　data　collection　success　rates,　they　do　accelerate　the　evolutionary　process　of　data　collection.