Internet　of　Things　(IoT)　can　be　used　to　promote　many　advanced　applications　by　utilizing　the　sensed　data　collected　from　various　settings.　To　reduce　the　energy　consumption　of　IoT　devices,　and　to　extend　the　lifetime　of　network,　the　sensed　data　are　usually　compressed　before　their　transmission　through　compressed　sensing　theory.　By　reconstructing　the　sensed　data　at　the　edge　of　network　with　more　resourceful　devices,　such　as　laptops　and　servers,　the　intensive　computation　and　energy　consumption　of　the　IoT　nodes　could　be　effectively　offloaded.　However,　most　of　the　existing　data　collection　schemes　are　limited　in　their　sealability,　because　the　unified　data　reconstruction　models　of　them　are　not　suitable　for　large-scale　surveillance　scenarios.　In　our　proposed　scheme,　the　whole　network　is　first　partitioned　into　a　number　of　data　correlated　clusters　based　on　spatial　correlation.　Then,　a　data　collection　tree　is　built　to　collect　the　compressed　data　in　a　hybrid　mode.　Finally,　the　data　reconstruction　problem　is　modelled　as　a　group　sparse　problem　and　solved　through　using　an　alternating　direction　method　of　multiplier-based　algorithm.　The　performance　of　data　communication　and　reconstruction　of　the　proposed　scheme　is　evaluated　through　experiments　with　real　data　set.　The　experimental　results　show　that　the　pro　posed　scheme　can　indeed　lower　the　amount　of　data　transmission,　prolong　the　network　life,　and　achieve　a　higher　level　of　accuracy　in　data　collection　compared　to　existing　data　collection　schemes.