If methane, temperature, and humidity sensors are all sufficiently accurate, can their three datasets be analyzed together immediately? Not necessarily. If the methane reading represents the air at 10:00, the temperature reading the conditions at 10:03, and the humidity reading the conditions at 9:58, the three numbers grouped into one row do not describe the same phenomenon. A difference of a few minutes between sensors may seem small, but around fast events such as feeding, fan operation, or a door opening, it can reverse cause and effect.
Time synchronization is not simply a matter of aligning the clocks shown on screens. It means distinguishing when the phenomenon occurred, when the sensor produced the value, when the gateway received it, when the server stored it, and when correction and aggregation were performed, then managing the relationships and uncertainty among them. Without these distinctions, valid data that arrives late can become an outlier, while a communication failure can be interpreted as an actual change in concentration.
Four ways clock errors change an analysis
The first is distorted correlation. Suppose methane concentration declined over 5 minutes after a fan switched on, but the methane sensor clock was 7 minutes fast. The graph would show concentration falling first and the fan switching on afterward. An analytical model might underestimate the ventilation effect or estimate it in the opposite direction.
The second is missing maxima and event windows. If the 30 minutes after feeding are defined as one analytical window but the clock is misaligned by 10 minutes, the rising interval falls outside the window for some sensors. Daily averages may be similar across sensors, yet the cause of the peak and the response time will differ. The third is incorrect interpolation. Treating values from different times as simultaneous and interpolating them linearly creates methane–humidity combinations that never actually existed.
The fourth is duplication and ordering errors. If only receipt time is stored when a device retransmits offline data, a value observed yesterday appears as a sharp change today. If the clock resets after a reboot, past timestamps may repeat; if daylight saving time or local-time conversion is applied incorrectly, one hour may be duplicated or disappear. Even when operating only Korean farms, servers, analytical tools, and partner organizations may use different time zones, so UTC-based storage is safer.
Observation time and receipt time cannot replace each other
For an Observation, the OGC SensorThings API distinguishes phenomenonTime, when the phenomenon occurred, from resultTime, when the result was generated. A field system can add receipt time and ingestion time to these. The benefit of this structure is that network delay and sensor-clock problems can be examined separately.
For example, if the observation time is 10:00, the gateway receipt time is 10:00:03, and the cloud ingestion time is 12:15, the sensor and local communication were probably working normally and the data was retransmitted after an external-network outage. Conversely, if several devices reach the gateway at the same time but one sensor’s observation time remains 4 minutes fast, device clock drift may be the cause.
For a low-specification device without a clock, the gateway may assign the observation time. In that case, mark the data with timestamp_source=gateway and determine the maximum device-to-gateway transmission delay. Quietly copying server receipt time into observation time may appear harmless during normal operations, but it causes major fluctuations in the results on days when communication is delayed.
Principles of time synchronization: consider the reference, error, and delay together
IETF RFC 5905 specifies the NTPv4 architecture and algorithms for synchronizing the system clocks of distributed time servers and clients. However, the fact that a system uses NTP does not mean all its data is accurately aligned. Check when each device last synchronized, what network delay exists to the reference server, and how its clock is maintained during reboots and external-network outages.
When managing time quality as an operational metric, it is useful to examine offset, drift, and uncertainty together. Offset is the difference between the reference time and the device’s current time; drift is the rate at which that error grows over time; and uncertainty is the range around the displayed value within which the actual observation time may lie. If a device is offline for 6 hours and its clock can fall behind by 1 second per hour, its time uncertainty accumulates by the time it reconnects.
Work backward from the purpose of the analysis to determine the required accuracy. For data used to examine monthly trends with 10-minute averages, a difference of a few seconds may have almost no effect on the conclusion. When analyzing a 30-second response after fan control or comparing plume arrival times across sensors, a 1-minute error can be critical. Nanosecond-level accuracy is not necessary for every device, but a synchronization complete status is also meaningless without a defined tolerance.
Field scenario: why humidity appeared to cause a rise in methane
Suppose that during summer, a pattern is found in one barn in which methane concentration rises 3 minutes after every increase in humidity. The analytical team interprets this as humidity changes affecting sensor response or animal activity. Inspection, however, reveals that the humidity sensor used gateway time, while the methane sensor used its internal clock, which was 5 minutes fast. After correction, the methane rise aligned more closely with feeding and the fan stopping, both of which occurred before the humidity change.
The sensor values themselves were not wrong in this case. What was wrong was the order of events. A correlation coefficient and lag-effect model calculated before correcting the time axis may look statistically plausible yet produce an incorrect conclusion. Time synchronization is not a minor preprocessing item; it is a prerequisite for causal interpretation.
The verification method is to create a reference event. Record a controlled event or a change in fan state that multiple devices can detect simultaneously, then compare their timestamp differences. Where possible, compare them with an independent reference clock or gateway log. Do not compare database times alone; trace the same event through the device display, gateway packets, and server logs to locate the interval where delay occurred.
How to design a synchronized analytical pipeline
Preserve the time reported by the device exactly as it appears in the raw data. Use separate fields for the UTC-converted time, corrected observation time, amount of correction, time zone, reference clock, last synchronization time, and quality flags. Overwriting the original timestamp makes it impossible to review the synchronization rules later.
Next, establish resampling rules. If methane is measured every 10 seconds, temperature every 1 minute, and humidity every 5 minutes, use a common time window and aggregation function rather than simply merging rows. Select the mean, median, last observation carried forward, or integration according to the physical meaning. Set the maximum allowable nearest-time difference and treat values beyond that range as missing. It is better to disclose missing data than to force distant observations into the gaps.
Sensor response time must also be distinguished from clock error. Even when exposed to the same air at the same time, methane and humidity readings may respond later because of protective filters, sampling-tube length, pump flow, and sensing principles. This delay does not disappear through time synchronization, so use collocation tests to characterize each device’s response and incorporate it into the analysis.
The version of the time-correction rule must be preserved alongside the calculation formula. For example, if linear drift correction was applied only during a particular period, record its start and end times, reference event, correction coefficient, and approver. Even if better reference-clock data is discovered later, do not overwrite the raw data; create a new correction version so both the earlier report and the revised result can be reproduced. The quality report should also state how much the principal metrics changed before and after correction.
NIST guidance on OT security calls for considering performance, reliability, and safety requirements together in systems that sense and control the physical environment. If a time server or gateway distributes an incorrect time because of an attack or error, the sequence of alerts and the audit trail may also be corrupted. Approved time sources, access control, time-change logs, abnormal-jump detection, and a local timekeeping strategy should be included in operating standards.
Implementation checklist
Define the maximum permissible time error and required sampling interval for each analysis.
Store observation time, result-generation time, receipt time, and ingestion time in separate fields.
Preserve the original time, UTC conversion, corrected value, and reason for the correction.
Collect each device’s time source, last synchronization, offset, drift, and uncertainty.
Test time behavior during reboots, external-network outages, and gateway replacements.
Predefine the common time window, aggregation function, and tolerance for nearest-neighbor matching.
Arrange retransmitted data by the original observation time, not the receipt time.
Use collocation tests to separate sensor response time from clock error.
Link event logs such as fan operation, feeding, and door opening to the same time reference.
Flag intervals where time quality exceeds the limit in the reduction analysis and test sensitivity.
Conclusion
Synchronizing methane, temperature, and humidity data is not a cleanup task intended to create attractive graphs. It is the coordinate system of an analysis that determines which event happened first and how sensors and barns responded to environmental changes. Even when the values are accurate, incorrect timing can change correlations, peaks, response times, and estimated reduction effects.
A good system does not trust every timestamp unconditionally. It records the source and error of time, separates observation from transmission, and applies tolerances suited to the analytical purpose. The first task is to compare what time the same single event is recorded in the methane, temperature, and humidity data and in fan event logs. Only when that difference can be measured and managed can readings from multiple sensors describe one field environment.