In　this　paper,　we　study　data　gathering　with　compressive　sensing　from　the　perspective　of　in-network　computation　in　random　networks,　in　which　n　nodes　are　uniformly　and　independently　deployed　in　a　unit　square　area.　We　formulate　the　problem　of　data　gathering　to　compute　multiround　random　linear　function.　We　study　the　performance　of　in-network　computation　with　compressive　sensing　in　terms　of　energy　consumption　and　latency　in　centralized　and　distributed　fashions.　For　the　centralized　approach,　we　propose　a　tree-based　protocol　for　computing　multiround　random　linear　function.　The　complexity　of　computation　shows　that　the　proposed　protocol　can　save　energy　and　reduce　latency　by　a　factor　of　Theta　(root　n/log　n)　for　data　gathering　comparing　with　the　traditional　approach,　respectively.　For　the　distributed　approach,　we　propose　a　gossip-based　approach　and　study　the　performance　of　energy　and　latency　through　theoretical　analysis.　We　show　that　our　approach　needs　fewer　transmissions　than　the　scheme　using　randomized　gossip.