Radio　Frequency　IDentification　(RFID)　system　has　been　wildly　used　in　recent　ten　years.　A　Handhold　reader　and　some　passive　tags　compose　an　efficient　system　for　retail　scenarios,　such　as　storage　management　and　item　inventory.　However,　the　heavily　energy　consumption　of　the　reader　battery,　which　due　to　the　frequent　tag　collisions,　is　a　main　limitation　for　such　a　RFID　system　in　large　scale　item-level　scenario.　To　avoid　the　collisions,　Dynamic　Frame　Slotted　ALOHA　anti-collision　algorithms　(DFSA)　are　popularly　being　used.　But　unreliable　tag　number　estimation　and　non-optimized　frame　size　selection　may　lead　to　more　energy　consumption.　In　this　paper,　we　propose　a　novel　tag　population　estimation　and　frame　length　selection　method　based　on　tag　location-aware　scheme　to　optimize　the　DFSA　tag　anti-collision　algorithm.　In　our　method,　the　handhold　reader　firstly　collects　the　tags＇　location　information　and　divides　them　into　different　clusters　based　on　their　distance　from　the　reader.　Then　by　estimating　the　tag　population　for　each　cluster,　the　reader　utilizes　this　value　to　achieve　an　optimum　frame　size　and　automatically　adjusting　the　transmission　power　to　scan　the　tags　in　corresponding　tag　cluster.　We　also　give　a　quantitative　energy　consumption　model　for　the　passive　RFID　system.　According　to　the　simulation　results,　the　passive　RFID　system　throughput　and　energy　efficiency　can　be　highly　increased　by　using　our　scheme.