The　internet　of　things　(IoT)　attracts　great　interest　in　many　application　domains　concerned　with　monitoring　and　control　of　physical　phenomena.　IoT　applications　try　to　provide　more　and　more　functionality　and　then　they　inevitably　become　so　complex　as　to　make　the　limits　of　devices　worse,　which　may　lead　to　poor　performance　of　applications.　Computation　offloading　is　a　promising　way　to　improve　the　performance　of　an　IoT　application　by　executing　some　parts　of　the　application　on　remote　devices　or　servers.　However,　supporting　such　capability　is　not　easy　for　application　developers　due　to　(1)　adaptability:　IoT　applications　often　face　changes　of　runtime　environments　so　that　the　adaptation　on　offloading　is　needed.　(2)　effectiveness:　when　the　device　context　changes,　it　needs　to　dynamically　decide　the　deployment　plan　of　computation　tasks,　and　the　reduced　execution　time　must　be　greater　than　the　network　delay　and　extra　overheads　caused　by　offloading.　This　paper　proposes　a　framework　which　supports　IoT　applications　with　adaptive　computation　offloading　capability.　First,　a　design　pattern　is　proposed　to　enable　an　application　to　be　computation　offloaded　on-demand.　Second,　an　estimation　model　is　presented　to　automatically　decide　the　deployment　plan　for　offloading.　Third,　a　framework　is　implemented　to　support　the　design　pattern　and　the　estimation　model.　A　thorough　zxevaluation　on　the　real-world　application　is　proposed,　and　the　results　show　that　our　approach　can　help　reduce　execution　time　by　over　45%　in　most　scenarios.