In　this　paper,　the　target　tracking　problem　is　investigated　with　mobile　edge　computing　(MEC)　mechanism　in　internet　of　things　(IoT),　where　the　challenge　of　energy　efficiency　is　a　significant　issue　when　the　target　tracking　event　is　driven.　In　order　to　prolong　the　lifetime　of　IoT,　we　adopt　dynamic　clustering　methods　to　improve　energy　efficiency　while　guaranteeing　target　tracking　effects.　We　deign　the　sensor　selection　scheme　for　carrying　out　the　tracking　tasks　according　to　energy　distribution　of　the　sensor　node.　Concretely,　by　considering　the　reality　of　random　deployment　and　introducing　the　definition　of　node　density　for　IoT,　we　develop　a　Pareto　optimality　for　sensor　nodes　selection　in　terms　of　energy　efficiency　without　reducing　the　accuracy　of　target　tracking.　Furthermore,　we　recruit　voluntary　mobile　devices　as　mobile　edge　computing　servers　to　offload　the　data　from　selected　sensor　nodes　in　the　cluster　and　process　them　to　the　estimate　the　target　state.　Simulations　demonstrate　the　efficiency　for　tracking　performance　on　energy　balance　in　terms　of　efficiently　prolonging　the　IoT　lifetime.　©　2021,　Springer　Nature　Switzerland　AG.