The　Received　Signal　Strength　(RSS)　fingerprint-based　indoor　localization　is　an　important　research　topic　in　wireless　network　communications.　Most　current　RSS　fingerprint-based　indoor　localization　methods　do　not　explore　and　utilize　the　spatial　or　temporal　correlation　existing　in　fingerprint　data　and　measurement　data,　which　is　helpful　for　improving　localization　accuracy.　In　this　paper,　we　propose　an　RSS　fingerprint-based　indoor　localization　method　by　integrating　the　spatio-temporal　constraints　into　the　sparse　representation　model.　The　proposed　model　utilizes　the　inherent　spatial　correlation　of　fingerprint　data　in　the　fingerprint　matching　and　uses　the　temporal　continuity　of　the　RSS　measurement　data　in　the　localization　phase.　Experiments　on　the　simulated　data　and　the　localization　tests　in　the　real　scenes　show　that　the　proposed　method　improves　the　localization　accuracy　and　stability　effectively　compared　with　state-of-the-art　indoor　localization　methods.