Missing　data　estimation　(MDE)　for　time　series　is　a　crucial　issue　concerned　with　various　applications　based　on　the　Internet　of　Things　(e.g.　sparse　mobile　crowdsensing).　Although　many　approaches　have　been　proposed　to　address　this　issue,　they　are　either　insufficiently　considered　or　computationally　expensive　for　data　correlations.　Motivated　by　this,　an　echo　state　network　(ESN)　with　bidirectional-feedback　connections　is　first　proposed　to　skillfully　encode　temporal,　cross-domain,　and　lagging　correlations　into　the　high-dimensional　state　of　the　reservoir.　Meanwhile,　the　output　weight　(the　only　unknown　parameter　of　the　network)　can　be　trained　quickly　by　reservoir　computing　to　reduce　computation　overhead.　On　this　basis,　an　improved　version　with　multiple　reservoirs　is　designed　to　further　integrate　data　correlations　from　information　flows　in　different　directions.　Finally,　a　general　process　for　MDE　based　on　ESN　architecture　is　developed　for　popularization　and　application.　Experimental　results　of　various　missing　rates　under　different　missing　mechanisms　show　that　the　proposed　models　perform　better　than　the　current　methods　in　estimation　accuracy.　©　2022,　China　Computer　Federation　(CCF).