For years, the Internet of Things (IoT) conversation has been dominated by sensors, cloud platforms, and the flashier world of AI analytics. While AI remains the current “buzzword,” the reality is that millions of IoT devices are already quietly working in the background, providing the essential sensor data that powers our modern world. However, as these deployments mature and scale, a critical bottleneck has emerged: the cost and physical limits of data transmission.

In my experience with Air Quality Monitoring (AQM) solutions, I’ve seen this play out repeatedly. Projects often aim to transmit high-frequency, continuous air quality measurements over long distances, only to hit a wall. Whether it’s the strict payload size limits of LoRaWAN or the spiraling costs of high-frequency transmissions over LTE/NB-IoT, the “raw data” approach is no longer sustainable.

The Problem with “Raw” Transmission

Most IoT data, especially from air quality sensors, is highly structured and repetitive. Devices often transmit variations of the same environmental measurements over and over. Sending this information raw ignores a simple reality: transmission is expensive, not just in terms of data plans, but in battery life, maintenance, and long-term operational costs.

Read More »