Compressive　sensing　(CS)　provides　an　energy-efficient　paradigm　for　data　gathering　in　wireless　sensor　networks　(WSNs).　However,　the　existing　work　on　spatial-temporal　data　gathering　using　compressive　sensing　only　considers　either　multi-hop　relaying　based　or　multiple　random　walks　based　approaches.　In　this　paper,　we　exploit　the　mobility　pattern　for　spatial-temporal　data　collection　and　propose　a　novel　mobile　data　gathering　scheme　by　employing　the　Metropolis-Hastings　algorithm　with　delayed　acceptance,　an　improved　random　walk　algorithm　for　a　mobile　collector　to　collect　data　from　a　sensing　field.　The　proposed　scheme　exploits　Kronecker　compressive　sensing　(KCS)　for　spatial-temporal　correlation　of　sensory　data　by　allowing　the　mobile　collector　to　gather　temporal　compressive　measurements　from　a　small　subset　of　randomly　selected　nodes　along　a　random　routing　path.　More　importantly,　from　the　theoretical　perspective　we　prove　that　the　equivalent　sensing　matrix　constructed　from　the　proposed　scheme　for　spatial-temporal　compressible　signal　can　satisfy　the　property　of　KCS　models.　The　simulation　results　demonstrate　that　the　proposed　scheme　can　not　only　significantly　reduce　communication　cost　but　also　improve　recovery　accuracy　for　mobile　data　gathering　compared　to　the　other　existing　schemes.　In　particular,　we　also　show　that　the　proposed　scheme　is　robust　in　unreliable　wireless　environment　under　various　packet　losses.　All　this　indicates　that　the　proposed　scheme　can　be　an　efficient　alternative　for　data　gathering　application　in　WSNs.