Internet　of　Things　(IoT)　has　been　widely　used　to　facilitate　environmental　perception,　where　detecting　the　boundary　regions　for　continuous　objects　with　energy-efficient　manner　is　a　challenge　to　be　explored　to　support　domain　applications.　This　paper　proposes　a　novel　approach　for　continuous　objects　boundary　prediction　and　detection　in　IoT　sensing　network,　which　is　called　Cloud　Model-IoT　Sensing　Network　Collaborative　(CM-IoTSNC).　Specifically,　when　an　event　is　potentially　occurred,　the　atmospheric　dynamic　diffusion　model　deployed　on　the　cloud　is　adopted　to　predict　gas　diffusion　trend　in　ideal　and　complex　environments,　moreover　prediction　results　are　transmitted　to　the　IoT　devices　in　the　real-time　fashion　for　scheduling　security　plans　in　advance.　One-hop　neighbor　nodes　are　activated　by　abnormal　nodes　to　determine　a　more　accurate　boundary　region.　Compared　our　technique　with　two　traditional　methods,　namely　Wireless　Sensor　Monitoring　and　Activating　One-hop　Neighbor　Nodes,　experimental　results　show　that　our　method　has　a　good　performance　in　reducing　the　energy　consumption　and　prolonging　the　lifetime　of　the　network.