Sensor Data Replay Tool for IoT Testing

Testing IoT firmware against live sensors is frustratingly non-deterministic: a freezer fault happens once, at 3 a.m., and you miss it. Regression-testing a firmware fix against the same real-world conditions is nearly impossible, and CI pipelines cannot keep hardware on the bench for every commit. This project takes the standard industry approach — record once, replay forever. A replay console streams recorded sensor datasets (CSV with timestamps and numeric channels) exactly as recorded, with sample-accurate timing, so the device under test cannot tell replay from live sensors. Faults are injectable on demand: a +6° temperature spike, a 5-minute sensor dropout, a slow humidity drift. Automated test scenarios script a replay slice with a fault, then check the expected device or cloud response (cloud alert within 5 minutes, nulls instead of stale values, anomaly flags), reporting PASS, QUEUED or FAIL. Three realistic datasets ship with it: a 24-hour cold-storage log (ESP32 + SHT31), a motor vibration run (ADXL345) and a week-long greenhouse log (DHT22 + soil).

1. Recorded datasets (CSV: timestamp + numeric channels) are loaded; the engine maps columns automatically and preserves original timestamps and gaps.
2. The replay console streams samples with sample-accurate timing at 1×, or 0.5×–8× for review; loop replay repeats the recording seamlessly.
3. The live chart window shows the last 120 minutes of temperature, humidity and vibration while current values update in the sensor cards.
4. Fault injection buttons splice a fault into the stream at the current position: a +6° temp spike, a 5-minute dropout (publishes nulls), or a slow humidity drift.
5. Every streamed sample is published to MQTT and written to the publish log, so the firmware's and cloud's responses can be observed in real time.
6. Automated scenarios replay a scripted slice with a fault injected, then evaluate the expected response — cloud alert within 5 minutes of a threshold breach, monotonic sequence checks after a clock jump — and mark PASS or FAIL.

Tech stack:

• HTML5 + vanilla JavaScript (single-file replay console)
• Canvas 2D live charts
• MQTT (test.mosquitto.org:1883 public broker)
• CSV datasets (1,440 / 480 / 2,016 samples)
• Seeded pseudo-random generators (deterministic replay)
• Git

• [Deterministic replay engine] CSVs stream sample-by-sample with timestamps and gaps preserved; the device under test cannot distinguish replay from live sensors.
• [Transport controls] Play, pause, restart, ±1-minute seek, and 0.5×–8× speed so long recordings can be reviewed fast.
• [Fault injection] One-click temp spike (+6°), 5-minute sensor dropout, humidity drift — edge cases on demand without waiting for real failures.
• [Automated test scenarios] Five scripted checks (freezer door open, sensor dropout, bearing fault, clock jump, humidity drift calibration), each with pass criteria and PASS/FAIL/QUEUED status.
• [Three realistic datasets] Cold-storage 24 h (1,440 samples, temp/hum/vib), motor vibration 8 h (480 samples), greenhouse 7 d (2,016 samples) — with anomaly tags.
• [MQTT publish log] Live log of every published sample; broker and topic shown (test.mosquitto.org:1883, replaylab/sensors) so cloud ingestion can be verified.
• [CSV auto-mapping] Import any CSV with a timestamp column and numeric channels — the engine maps columns automatically.

• Complete replay console web app (transport, live charts, publish log)
• Deterministic replay engine with 0.5×–8× speed and fault injection
• Three realistic recorded datasets with anomaly tags (CSV)
• Five automated test scenarios with pass/fail criteria
• MQTT publishing integration (broker + topic configuration)
• User guide (dataset format, scenario authoring, CI integration notes)
• Project report PDF (background, replay theory, methodology, results)
• PPT presentation for final review
• Viva Q&A preparation document (deterministic testing, MQTT, sensor faults)