IoT isn’t just smart homes and toasters with Wi-Fi.
The Internet of Things is about connecting physical objects to digital systems - collecting data, automating decisions, and creating real-world feedback loops. From sensors in industrial factories to electric scooters on city streets, IoT is everywhere. But behind the buzzword is a stack of systems that only works when the fundamentals are strong.
Real IoT is about constraints - and how you solve them.
Unlike traditional apps, IoT deals with devices that have limited power, unreliable connectivity, and weird edge-case behavior. You need to think in protocols like LoRaWAN, NB-IoT, or BLE, and choose platforms like ChirpStack or The Things Stack for managing your gateways and data routing. For messaging, MQTT is the gold standard - lightweight, real-time, and perfect for devices that wake up, speak, and sleep.
The stack behind the signal matters.
Once the data hits your backend, things get interesting. You’ll need to store time-series data (hello, InfluxDB), process telemetry in near real-time, and trigger actions based on thresholds or anomalies. Platforms like AWS IoT Core or Google Cloud IoT help manage device identities, security policies, and stream processing at scale. And let’s not forget the firmware layer - often written in C or MicroPython - where bugs can mean bricked hardware or dead batteries.
Great IoT systems feel invisible - but they’re anything but simple.
You’re coordinating sensors, databases, APIs, dashboards, and human behavior. That means solid architecture, bulletproof observability, and ruthless focus on uptime and reliability. I’ve built systems where a 3-minute delay in sensor data meant operational chaos - and systems where thousands of devices spoke to the cloud like clockwork. The difference was never the tool. It was the thinking.
