add device anomaly detection demo

[HaojieZhang6848]

What type of PR is this?

/kind feature

What this PR does / why we need it:

This PR adds a comprehensive Device Anomaly Detection Demo to showcase the device anomaly detection capabilities introduced in KubeEdge PR #6543. The demo provides a complete, runnable example that demonstrates:

1. IoT Device Simulation: A FastAPI-based room simulator with controllable lights and brightness sensors
2. KubeEdge Mapper Integration: A RESTful mapper that manages device lifecycle and communicates with both edge devices and cloud services
3. ML-based Anomaly Detection: A gRPC service that uses machine learning models to detect device anomalies and updates Kubernetes DeviceStatus CRDs
4. End-to-End Workflow: Complete demonstration of edge-cloud collaboration for real-time device monitoring and anomaly detection

This example serves as a reference implementation for developers who want to:

• Build custom mappers with external anomaly detection capabilities
• Integrate machine learning models with KubeEdge device management
• Understand the device anomaly detection workflow in KubeEdge

Which issue(s) this PR fixes:

Relates to kubeedge/kubeedge#6543

Special notes for your reviewer:

• All three components (room-simulator, external-anomaly-detector, me-restful-mapper) can be started with a single docker-compose up command
• Docker images are pre-built and available on Docker Hub for easy testing
• The demo includes fault injection capabilities to easily trigger and observe anomaly detection
• Comprehensive documentation is provided in the README with architecture diagrams and step-by-step instructions
• Uses modern Python tooling (uv) for dependency management
• Follows KubeEdge mapper framework conventions and DMI protocol standards

[kubeedge-bot]

Welcome @HaojieZhang6848! It looks like this is your first PR to kubeedge/examples 🎉

[gemini-code-assist]

Summary of Changes

Hello @HaojieZhang6848, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a fully functional demonstration of device anomaly detection integrated with KubeEdge. It provides a practical example for developers to understand and implement ML-driven anomaly detection for IoT devices, leveraging KubeEdge's device management and communication protocols to update device statuses in Kubernetes.

Highlights

• New Device Anomaly Detection Demo: Introduces a comprehensive demonstration for device anomaly detection within the KubeEdge ecosystem, showcasing an end-to-end solution.
• IoT Device Simulation: Includes a FastAPI-based room simulator with controllable lights and a brightness sensor, featuring fault injection capabilities for testing.
• KubeEdge Mapper Integration: Provides a Go-based RESTful mapper that manages device lifecycle, communicates with simulated edge devices, and integrates with cloud services.
• ML-based Anomaly Detection: Features a Python gRPC service that uses machine learning models (Bayesian Networks) to detect device anomalies and update Kubernetes DeviceStatus CRDs.
• End-to-End Workflow: Demonstrates the complete edge-cloud collaboration for real-time device monitoring and anomaly detection, runnable with a single docker-compose up command.

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[gemini-code-assist]

Code Review

This pull request introduces a KubeEdge-based device anomaly detection demo. The changes include a new external-anomaly-detector Python gRPC service that uses a Bayesian Network model to detect anomalies in IoT device data, a room-simulator FastAPI application to simulate light devices and a brightness sensor with fault injection capabilities, and modifications to the me-restful-mapper Go application to integrate with the anomaly detection service via gRPC. The mapper now collects device property updates and sends them to the external anomaly detector, which then updates Kubernetes DeviceStatus CRDs with anomaly information. New Protobuf definitions, Docker Compose configurations, and detailed READMEs (English and Chinese) are added to support the demo. Review comments highlight several issues: a critical logical error where the anomaly status is updated for all devices instead of the specific one, masked errors in the Go mapper's SetDeviceData and GetDeviceData functions, inconsistencies in port numbers between the architecture diagram and implementation, incorrect plain text vs. JSON body expectations in fault injection examples across documentation and code, an improper string formatting in SetDeviceValue, and a security concern regarding the use of grpc.WithInsecure() in the demo. Additionally, there are suggestions to replace print statements with proper logging and to handle Kubernetes config loading errors more gracefully.

[gemini-code-assist]

The update_anomaly_status function currently updates the anomaly status for all three devices (light-1, light-2, brightness-sensor-1) regardless of which device the anomaly was detected for. This is a critical logical error. The anomaly status should only be updated for the specific device where the anomaly was observed (e.g., brightness-sensor-1 if its predicted brightness doesn't match the actual value).

        api.patch_namespaced_custom_object(
            group="devices.kubeedge.io",
            version="v1beta1",
            namespace="default",
            plural="devicestatuses",
            name="brightness-sensor-1", # Only update the sensor that reports brightness
            body=patch
        )

[gemini-code-assist]

When an error occurs in SetDeviceData, it returns nil. This masks the error, making it difficult for callers to know if the operation failed. It should return err to propagate the error.

Suggested change

	return nil
	}
	return err

[gemini-code-assist]

When an error occurs in GetDeviceData, it returns nil, nil. This masks the error, making it difficult for callers to distinguish between a successful call with no data and a failed call. It should return nil, err to propagate the error.

Suggested change

	klog.Errorf("GetDeviceData failed for http://%s:%d%s, error: %v", c.ProtocolConfig.ConfigData.Ip, c.ProtocolConfig.ConfigData.Port, c.ProtocolConfig.ConfigData.Url, err)
	return nil, nil
	return nil, err

[gemini-code-assist]

The architecture diagram indicates the External Anomaly Detector runs on Port 5000. However, the docker-compose.yaml and external-anomaly-detector/main.py configure it to run on Port 6804. This discrepancy should be corrected in the diagram for accuracy.

        Detector[External Anomaly Detector<br/>gRPC Service<br/>Port 6804]

[gemini-code-assist]

The Python requests example for injecting faults uses json={"fault": True}. This is inconsistent with the Python main.py implementation which expects a plain text body ('0' or '1'). Please correct the example to match the expected plain text input.

Suggested change

	response = requests.post(
	f"{base_url}/lights/light1/fault",
	json={"fault": True}
	response = requests.post(
	f"{base_url}/lights/light1/fault",
	data="1",
	headers={"Content-Type": "text/plain"}
	)

[gemini-code-assist]

The comment Fetch current value from k8s apiserver and store in memory is misleading. The propertyValue is received directly from the gRPC request, not fetched from the Kubernetes API server at this point.

Suggested change

	# Fetch current value from k8s apiserver and store in memory
	self._store_device_data(request.deviceName, request.propertyName, request.propertyValue)
	# Store received device data in memory

[gemini-code-assist]

The comment Fetch current value from k8s apiserver and store in memory is misleading. The propertyValue is received directly from the gRPC request, not fetched from the Kubernetes API server at this point.

Suggested change

	# Fetch current value from k8s apiserver and store in memory
	self._store_device_data(request.deviceName, request.propertyName, request.propertyValue)
	# Store received device data in memory

[gemini-code-assist]

Mounting ~/.kube/config directly into the container can be a security risk as it exposes the host's Kubernetes credentials. While acceptable for a demo, consider more secure methods for production deployments, such as using Kubernetes Secrets or service accounts, or limiting the scope of the mounted config.

[gemini-code-assist]

Typo: "mappper" should be "mapper".

Suggested change

	098a371611f7 huajuan6848/me-restful-mappper:1.0.0 "/kubeedge/main --co…" About an hour ago Up About an hour me-restful-mapper
	098a371611f7 huajuan6848/me-restful-mapper:1.0.0 "/kubeedge/main --co…" About an hour ago Up About an hour me-restful-mapper

[gemini-code-assist]

The log file path room_simulator.log is hardcoded. For better flexibility and manageability in a containerized environment, consider making the log file path configurable (e.g., via an environment variable) so it can be mounted to a persistent volume.

[kubeedge-bot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: HaojieZhang6848
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