Indoor　localization　has　attracted　much　attention　recently,　due　to　its　wide　applications　in　location-based　services　(LBSs).　Localization　accuracy　and　system　costs　are　the　key　issues　while　designing　indoor　localization　schemes.　In　this　paper,　an　edge　big　data-enabled　indoor　localization　scheme　is　proposed.　We　use　the　radio　signal　strength　(RSS)　information　that　is　always　available　wherever　WiFi　coverage　is　available,　to　avoid　the　costs　on　deploying　and　maintaining　specific　devices　for　indoor　localization.　Bayesian　theory　and　edge　computing　are　adopted　in　our　system,　so　that　big　localization　data　is　collected　and　utilized　to　update　the　prior　location　probabilities.　A　testbed,　BJUTLocate,　is　built　to　evaluate　the　performance　of　the　proposed　scheme,　and　the　evaluation　results　show　its　significant　performance　improvement.