Small C SDK for constrained environments.
ZNano
Payload-level compression for constrained devices.
ZNano helps connected devices reduce structured payload size without losing information.
Built for IoT, telemetry, metering and edge systems, it compresses structured payloads before transmission and reconstructs the original payload deterministically on decode.
Built for repeated sensor, meter and telemetry frames.
Deterministic reconstruction of the original payload.
Works at payload level before LoRaWAN, NB-IoT, MQTT and other transport layers.
Why ZNano exists
Small payloads become large systems.
Across utilities, logistics, agriculture, smart cities and industrial operations, connected devices often send compact messages repeatedly across large fleets. Reducing bytes at payload level can lower airtime, bandwidth usage and operating pressure across the system.
ZNano is designed for this practical layer: compress the structured payload, keep the existing transport, and recover the original message when needed.
- Reduce repeated telemetry payloads before transmission.
- Keep existing transport and application logic.
- Fit device, gateway or backend workflows.
- Verify the round trip in the live demo.
How it works
Compress the payload. Keep the protocol.
ZNano sits between payload preparation and transmission. It works on structured payload data, not protocol headers, CRCs, MACs or application semantics.
Structured DataDeterministic SerializationZNano CompressionOptional Entropy ShapingTransport / StorageDeterministic DecodeOriginal Payload Recovery
ZNano works on structured payload data, not headers, CRCs, MACs or protocol semantics.
It detects vertical redundancy across aligned payload rows, making it suitable for repeated telemetry and metering records.
Transport rules, validation, access control and system-level safeguards remain outside the compression engine.
Deployment contexts
Device, gateway or backend.
ZNano can be placed where it makes the most architectural sense: close to the device, at the gateway, or in backend systems responsible for decode, replay and verification.
Compress before transmission.
For MCU-class and embedded environments where memory, bandwidth and power are constrained.
Batch and forward smarter.
For batching, store-and-forward workflows and local preprocessing before transmission.
Decode, replay and verify.
For deterministic decode, verification, replay and integration with existing data pipelines.
Compatible payload contexts:
LoRaWANNB-IoTMQTTDLMS/COSEMwM-BusModbusOPC-UABACnetCustom binary protocols
Use cases
Where ZNano applies.
ZNano is designed for environments where connected assets repeatedly send structured telemetry, sensor readings, metering data or operational logs across constrained networks.
Electricity, water and gas meters sending frequent consumption readings.
Equipment telemetry, vibration, temperature, pressure and maintenance logs.
Soil sensors, irrigation systems, livestock wearables and field telemetry.
GPS, engine diagnostics, cold-chain monitoring and container sensors.
Air quality, traffic, waste, parking and public infrastructure sensors.
Maritime, mining, oil & gas, UAV and offshore environments where bandwidth is expensive.
Use-case suitability depends on payload structure, batching strategy and integration model. ZNano is strongest where payloads are structured, repeated and stable over time.
Observed performance
Strongest where payloads repeat.
ZNano is designed for structured telemetry workloads where fields repeat over time. Public evidence reports verified structured reductions from 23.46% to 90.85%, with a 56.32% mean and 58.14% median on the current verified evidence set.
23.46-90.85%
Structured reduction range in the public verified evidence set.
56.32%
Mean structured reduction across the current verified rows.
58.14%
Median structured reduction across the current verified rows.
64 KB
Measured host RSS delta in the fresh public rerun.
Verified structured rerun10 / 10 passSHA-256 checked
Incompressible controls4 / 4 passSHA-256 checked
Fresh host timing1.407 ms encode mean1.537 ms decode mean
Actual performance depends on payload structure, batching strategy, target CPU, compiler configuration and integration model. Public benchmark figures should be read with their documented methodology and scope.
Live ZNano simulation
Compress, decode and verify a structured device stream.
Select a simulated device stream, generate fixed-width payloads, compress them in ZNano batches, decode the output and verify reconstruction with SHA256 checks.
Loading stream devices...
DeviceSensor
Standard stream0 B / 0 payloads
ZNano stream0 B / 0 payloads
ReceiverDecode & verify
Reference stream--
ZNano stream--
Bandwidth reduction--
SHA256 integrity--
Record size--
Compared / pending--
View technical log
Custom payload test
Test your own structured payload.
Use the payload tester to validate ZNano behavior on custom hexadecimal payloads. In production, this workflow will sit behind email verification and controlled access.
SDK integration
From firmware to wrapper services.
ZNano can be integrated through native libraries, command-line workflows or thin wrapper services depending on the deployment context.
Direct SDK integration for embedded and native applications.
Embed ZNano into offline or firmware-level systems.
Automate compression workflows from scripts or tools.
Wrap ZNano behind a lightweight local service.
Prototype quickly with wrappers and local process calls.
Boundaries
Clear boundaries by design.
ZNano is a compression engine, not a transport protocol or application framework. It keeps payload transformation separate from protocol validation, access control and system-level safeguards.
That separation keeps ZNano small, predictable and suitable for controlled embedded deployments.
Does not parse or modify protocol semantics.
Does not replace transport or application safeguards.
Optimized for structured, repeated payloads.
Deployment
Bring ZNano to your device, gateway or backend pipeline.
Discuss embedded compression, structured telemetry, metering payloads or private integration requirements with Zetako.