Favoriot Edge Gateway is built for one convenient reason. Real IoT systems rarely talk straight to the cloud. They speak to gateways first.
Factories, buildings, farms, campuses, and cities already have gateways in place. Some speak LoRa. Some speak Modbus. Some collect data from dozens or hundreds of devices sitting behind a firewall. Replacing all of that is expensive and risky.
Edge Gateway exists to meet reality where it is.
What the Favoriot Edge Gateway Really Is
At its core, Favoriot Edge Gateway is a cloud-side service that accepts data from any physical IoT gateway and cleanly connects it to the Favoriot Platform.
Think of it as a controlled handover point.
Local devices talk to their gateway.
The gateway forwards selected data to Favoriot.
Favoriot receives, inspects, reshapes, and stores that data in a structured way.
The gateway maintains control over local operations. Favoriot handles visibility, storage, analytics, rules, and dashboards.
This separation matters more than most people realise.
Why Edge Gateway Exists
Many IoT platforms assume devices connect directly to the cloud. That assumption breaks down quickly in real deployments.
Edge Gateway solves several common problems:
- Existing gateways cannot be replaced
- Devices sit on private networks
- Data volume must be filtered before going to the cloud
- Payload structures vary widely
- Security teams require strict control over outbound traffic
Instead of forcing devices to adapt to the platform, the platform adapts to the gateway.
Edge Gateway Communication Architecture
The flow is intentionally simple:
- Sensors send data to a local IoT Gateway
- The gateway forwards payloads to Favoriot using HTTP or MQTT
- Favoriot receives the raw payload
- Mapping rules decide what gets stored and where
- Data becomes available for dashboards, alerts, rules, and analytics
No device firmware changes are required. No special SDK is needed.
Key Capabilities That Matter
Gateway-Level Control
Each Edge Gateway is registered as a first-class object in the platform. It has:
- Identity
- Hardware metadata
- Firmware version
- Location
- Operational state
If a gateway is disabled, data stops at the door. That gives operators a clean kill switch when needed.
Payload Filtering and Selection
Not everything collected at the edge belongs in the cloud.
Edge Gateway lets you choose exactly which parameters from the payload to store. Noise stays local. Signal moves upstream.
This keeps storage clean and dashboards meaningful.
Payload Mapping Without Code
Gateways often send payloads in formats designed by hardware vendors, not by dashboards.
Mapping configuration lets you:
- Rename keys
- Extract nested values
- Map device identifiers correctly
- Align data with Favoriot device streams
No scripting. No middleware. Just mapping rules driven by JSON paths.
Protocol Flexibility
Edge Gateway supports:
- HTTP(S) for REST-based gateways
- MQTT for publish-subscribe gateways
Most commercial and industrial gateways already support one or both.
When You Should Use Edge Gateway
Edge Gateway is not for every setup. It shines in specific situations.
Use it when:
- You already have an IoT gateway in the field
- Devices cannot connect directly to the internet
- Data needs pre-processing or filtering at the edge
- Network policies restrict inbound cloud connections
- Multiple devices report through a single aggregation point
If your deployment looks like a real-world industrial or enterprise system, Edge Gateway is usually the right choice.
Where Edge Gateway Fits Best
Smart Buildings
BACnet, Modbus, and proprietary building controllers often feed into a central gateway. Edge Gateway lets it forward selected metrics, such as temperature, humidity, or energy usage, to Favoriot without touching the building control system.
Manufacturing and Industrial Sites
Factories prefer local control and minimal cloud dependency. Edge Gateway allows production data to stay local while performance metrics, alerts, and summaries flow to the cloud.
Smart Agriculture
Field sensors report to a local gateway using LoRa or private radio. The gateway uploads consolidated data when connectivity is available.
Campuses and Cities
Multiple subsystems feed into regional gateways. Favoriot receives standardised streams without forcing every subsystem to speak cloud-native protocols.
How Data Reaches Favoriot
Edge Gateway accepts data in two familiar ways.
HTTP(S)
Gateways can POST any JSON payload to the Favoriot API endpoint using TLS. Authentication uses access tokens. Responses clearly indicate success, authorisation issues, or inactive gateways.
This fits well with gateways that already support REST forwarding.
MQTT
Gateways can publish payloads over MQTT, optionally with TLS. The same access token acts as username and password, keeping setup simple.
MQTT works well for continuous streams and lower overhead connections.
Why Mapping Comes After Upload
One crucial design choice is worth highlighting.
Uploaded payloads are not stored immediately.
They first appear in the Edge Gateway Debug Console. This allows you to:
- Inspect real payload structures
- Confirm UID paths
- Validate parameter names
- Adjust mapping safely
Only after mapping rules are defined does data enter the platform database.
This avoids silent data corruption and mismatched streams.
The Practical Benefit
Edge Gateway reduces friction.
It lets teams keep what already works at the edge while gaining cloud visibility without redesigning their systems.
No forced rewrites.
No fragile adapters.
No vendor lock-in at the gateway layer.
Just a clean, controlled bridge between local reality and cloud intelligence.
That is why Edge Gateway exists.
FAVORIOT Intelligence References
- Official Announcement -Favoriot Evolves into an AIoT Platform with Built-in Machine Learning
- Why Favoriot Chooses Machine Learning Over LLMs
- Machine Learning vs Deep Learning in IoT
- Traditional Machine Learning vs Large Language Models (LLMs)
- Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers
- Why Favoriot’s ML Infrastructure Reduces Costs
- AI and ML in IoT, Explained Without the Jargon
- Start Anomaly Detection in IoT with Less Data
- AI and ML in IoT, Explained Without the Jargon
- The Key Differences: Favoriot’s Rule Engine 2.0 and AI Agents
- The Role of Machine Learning in IoT Systems
- Favoriot’s Rule Engine 2.0: A Structured Approach to IoT Automation
- A Quiet Shift Is Coming to the Favoriot Platform
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