📡 Edge Computing Architecture for Smart Apiaries

About this simulation

A smart apiary scatters battery-powered sensors across its hives, each logging weight, temperature, humidity and acoustic data many times a minute. Sending every raw reading straight to the cloud is simple but costly: it burns radio and cellular bandwidth and every alert has to wait for a full round trip over the internet. An edge gateway sitting in the apiary changes that trade-off — it collects readings locally, makes time-critical decisions (like a swarm-risk alert) on the spot, and only uploads compact, aggregated summaries to the cloud.

How it works

• Each glowing hive node emits a data packet at the chosen sample rate, which travels down to the edge gateway.
• With edge processing ON, the gateway buffers and compresses arriving packets over the aggregation window, then sends one small batched packet to the cloud — decisions that only need local context happen instantly at the gateway.
• With edge processing OFF, every raw packet is relayed straight through to the cloud, so decisions must wait for the full cloud link latency round trip.
• Decision latency and cloud bandwidth update live from these settings so you can see the trade-off directly.

Controls

• Edge processing — toggle local (edge) processing at the gateway on or off.
• Hive sensor nodes — how many hives are reporting data (3–10).
• Sample rate — how often each sensor reports a reading.
• Cloud link latency — simulated internet round-trip time to the cloud backend.
• Edge aggregation window — how long the gateway buffers readings before summarising and uploading (edge mode only). Drag to rotate the scene, scroll to zoom.

Did you know?

Real precision-beekeeping deployments report that pushing anomaly detection to a local gateway can cut cellular data usage by an order of magnitude compared with streaming raw sensor logs, while still flagging events like sudden weight loss (robbing or swarming) within seconds instead of minutes.