Cell　selection　and　data　offloading　are　the　keys　to　obtaining　MCS　services　with　low　sensing　cost　and　low　data　processing　delay.　Due　to　the　spatiotemporal　correlation　between　data　and　the　local-area　coverage　of　edge　servers,　cell　selection　and　data　offloading　will　affect　each　other　and　require　co-optimization.　To　achieve　the　co-optimization,　we　design　the　method　OptInter　based　on　the　hierarchical　reinforcement　learning.　OptInter　can　realize　the　interactive　training　between　cell　selection　model　and　data　offloading　model.　Finally,　we　evaluate　our　proposed　method　based　on　four　datasets,　each　of　which　composited　by　real-world　(e.g.,　NO$_{2}$2　concentration,　AQI　value,　Didi　order,　and　Didi　trajectory)　data　and　simulated　data.　Compared　with　the　four　baseline　methods　(e.g.,　OptMOEA/D,　OptStageCD,　OptStageDC,　and　OptWeight),　the　comprehensive　performance　of　our　proposed　method　can　be　improved　by　11.83%,　20.48%,　10.14%,　and　42.27%　on　average,　respectively.