Edge-to-Cloud IoT Architecture
Edge-to-cloud IoT architecture places sensing and immediate control near devices, then sends selected data to cloud services for fleet management, storage, and analysis. It helps you decide which work must stay local and which work benefits from central scale.
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Don't Panic
Don't Panic — Edge-to-Cloud IoT Architecture
Edge-to-cloud IoT architecture is the arrangement that lets physical equipment do the work that must happen nearby while a central service manages the wider collection. The equipment is in a factory, building, vehicle, or field site, which is inconveniently far from the tidy assumptions made by most diagrams.
The three places have different jobs. A device senses or acts on the physical world. The edge is nearby compute that translates local protocols, makes local decisions, and holds data when the connection goes away. The cloud keeps the fleet's shared record, deployment intent, long-term data, and cross-site analysis. This is not a contest between edge and cloud. It is a division of labor, like a very distributed office where the nearest person is also holding the valve.
Start with a physical outcome. Stopping a pump when its temperature becomes unsafe must have a local path. Comparing battery health across thousands of assets belongs in the cloud. That one distinction sorts much of the architecture: local latency, local protocols, and disconnected operation push work toward the device or edge; fleet-wide management and retention pull it toward the cloud.
Telemetry says what happened. A command asks for something to happen. An acknowledgment says whether the receiver accepted, applied, or rejected it. Keep those three separate, because a network connection is not proof that an action took place. Similarly, desired state is the configuration somebody requested; observed state is the evidence reported by the target after trying. Intent is useful. Evidence is better.
The awkward part is that the edge can store only a finite amount of unsent data. A store-and-forward queue holds messages until connectivity returns, but eventually the disk fills and the architecture must decide what to drop. That is not an edge case, despite the name's best efforts. It is one of the main cases.
Read the Intro for the full device-edge-cloud flow and the failure questions. Use Slides when the placement boundaries need a quick map. Keep the Cheatsheet nearby for contracts, command paths, and fleet signals. The Practice tab turns one representative site path into a reviewable design before a product choice makes the decision look permanent.
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Sources
- https://learn.microsoft.com/en-us/azure/iot/iot-introduction
Supports
- IoT solutions combine devices, edge components, cloud services, and SDKs.
- Cloud-connected devices send messages directly to cloud services, while edge-connected devices use a local environment that can process messages before forwarding data.
- Edge-connected patterns fit local protocols, low-latency processing, and environments that restrict direct internet connectivity.
- Cloud services and edge runtimes support device connection, monitoring, and control, while cloud services can provide storage, analysis, and visualization.
- https://docs.aws.amazon.com/greengrass/v2/developerguide/how-it-works.html
Supports
- Greengrass core software supports local data processing, filtering, aggregation, and local execution of functions, containers, processes, and custom runtimes.
- Core devices can process data from MQTT client devices and relay messages between local devices, components, and AWS IoT Core.
- Components are deployable software modules with recipes, artifacts, and dependencies.
- Deployments apply component configuration to targeted devices or groups, and platform support can vary.
- https://learn.microsoft.com/en-us/azure/iot-edge/iot-edge-modules
Supports
- Azure IoT Edge deploys and manages business logic on edge devices as modules.
- Module images, instances, identities, and twins distinguish deployable software, running units, security scope, and configuration state.
- A module identity scopes local and cloud communication, and edge modules can operate offline after initial synchronization.
- https://learn.microsoft.com/en-us/azure/iot-edge/module-development
Supports
- Edge modules can process local message routes and send device-to-cloud messages through the local edge hub.
- Azure IoT Edge support varies by operating system, device architecture, and language.
- Module security includes runtime permissions and container capability controls.
- https://docs.aws.amazon.com/greengrass/v2/developerguide/deployments.html
Supports
- AWS IoT Greengrass deployments manage component versions and configurations for core devices and groups.
- https://github.com/qijianpeng/awesome-edge-computing
Supports
- The curated list includes K3s as a lightweight Kubernetes option for edge and IoT use cases.
- The curated list describes FogFlow as an IoT edge framework for dynamic data processing across cloud and edge.
- The curated list includes gem5 as a modular computer-system architecture research simulator.
- https://web.mit.edu/autoid/index.html
Supports
- The MIT Auto-ID Center was founded in 1999 and coined the term Internet of Things.
- https://opcfoundation.org/about/opc-technologies/opc-ua/
Supports
- OPC Unified Architecture was released in 2008 as a platform-independent service-oriented architecture.
- https://docs.edgexfoundry.org/3.2/about/
Supports
- Project Fuse began in July 2015 and became EdgeX Foundry.
- EdgeX entered open source through the Linux Foundation in April 2017 and was announced at Hannover Messe.
- https://aws.amazon.com/about-aws/whats-new/2016/11/announcing-aws-greengrass-now-in-limited-preview/
Supports
- AWS announced Greengrass preview in November 2016 for local compute, messaging, caching, and intermittent connectivity.
- https://news.microsoft.com/de-de/azure-iot-edge-verfugbar/
Supports
- Azure IoT Edge reached general availability in June 2018 for local analytics on devices and machines.
- https://lfedge.org/the-linux-foundation-launches-new-lf-edge-to-establish-a-unified-open-source-framework-for-the-edge/
Supports
- The Linux Foundation launched LF Edge in January 2019 as an interoperable open-source framework for edge computing.
- https://aws.amazon.com/blogs/aws/announcing-aws-iot-greengrass-2-0-with-an-open-source-edge-runtime-and-new-developer-capabilities/
Supports
- AWS announced Greengrass version 2 in December 2020 with an open-source runtime, components, and fleet deployment controls.
- https://docs.edgexfoundry.org/2.1/
Supports
- EdgeX Foundry version 2.0 was released in June 2021 with revised service APIs.
- https://aws.amazon.com/greengrass/
Supports
- AWS IoT Greengrass is an open-source edge runtime and cloud service for building, deploying, and managing device software.
- https://learn.microsoft.com/en-us/azure/iot-operations/
Supports
- Azure IoT Operations provides a unified edge data plane with MQTT and data-flow capabilities.
- https://www.edgexfoundry.org/
Supports
- EdgeX Foundry is an open-source edge platform for IoT gateway software.
- https://kubeedge.io/
Supports
- KubeEdge extends cloud-native orchestration to edge environments.
- https://eclipse.dev/kura/
Supports
- Eclipse Kura is an open-source Java and OSGi framework for building and managing IoT edge applications.
