It sounds easy to make a water quality monitoring system – gather the data, store it, display charts, and raise alarms. It isn’t. Creating reliable environmental monitoring software requires solving quite a number of problems.
Environmental data is dirty.
Environmental sensors have their own problems – drifting in readings, connectivity issues, unreliable readings due to environmental factors, and hardware that occasionally delivers incorrect results. Water monitoring software developers soon understand that nothing is as stable as in test environments when they have to deal with real-life infrastructure.
The first problem to be solved is inconsistency in data streams. Sometimes it is difficult or even impossible to get continuous communication from a remote monitoring location, which requires software that is able to buffer data and restore the data stream without losing timestamp information or compromising analysis.
The second big problem is accuracy. Unlike in other industries, wrong data in environmental monitoring systems may cost lives. This means that software should be ready to detect anomalies, calibrate itself, validate its own findings, etc.
Numerous environmental technology companies in today's world are addressing such challenges through edge computing and assisted diagnoses powered by AI. Rather than transmitting all raw readings of sensors to the cloud server, the devices can filter out unusual data.
Software engineering for environmental purposes lies at the crossroads between healthcare, infrastructure development, and data analytics. Software engineering is among the handful of coding domains where a programmer can have a tangible impact on public safety.
For more info: https://envirotesters.com/
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