A Benchmarking Framework for Latency and Bandwidth in IoT

Use Case:

In IoT ecosystems (smart cities, industrial IoT, healthcare IoT), real-time responsiveness is crucial. Applications like autonomous vehicles, predictive maintenance, and patient monitoring require low latency and optimized bandwidth usage.

Cloud-Centric IoT Models rely on centralized data processing, which may cause higher latency due to long round trips.

Edge-Centric IoT Models process data locally on edge devices, reducing latency but requiring additional computation resources at the edge.

This benchmarking framework helps organizations evaluate trade-offs between latency and bandwidth when choosing between cloud vs. edge architectures on Azure.

To design and implement a benchmarking framework that measures latency, bandwidth usage, and throughput across cloud-centric and edge-centric IoT models.

To provide insights into when cloud is suitable (e.g., for storage-heavy workloads) vs. when edge is better (e.g., for low-latency, real-time applications).

To help IoT solution architects optimize hybrid architectures using Azure services.

IoT Scenario Setup :

Simulate IoT devices (e.g., sensors, wearables, smart meters).

Collect telemetry (temperature, vibration, video stream, etc.).

Data from IoT devices is sent directly to Azure IoT Hub.

Data is processed in the Azure Cloud (Azure Stream Analytics, Azure Machine Learning).

Metrics: network bandwidth consumption, average round-trip latency.

Deploy Azure IoT Edge runtime on edge devices.

Pre-process/filter data locally before sending to the cloud.

Metrics: reduced latency, lower bandwidth usage.

Implement workload generators to simulate different IoT traffic loads.

Measure and log latency, bandwidth, and throughput for both architectures.

Compare performance across different workloads (low, medium, high data rate).

Identify scenarios where cloud-centric is more cost-efficient (batch analytics, archival).
Identify scenarios where edge-centric is essential (real-time decision making).
Propose hybrid cloud-edge strategies for optimized IoT solutions.

Azure Service	Purpose
Azure IoT Hub	Central IoT messaging hub for connecting devices to cloud
Azure IoT Edge	Running AI/analytics workloads at the edge, closer to IoT devices
Azure Stream Analytics	Real-time data processing from IoT streams
Azure Machine Learning	AI model training for IoT analytics (anomaly detection, prediction)
Azure Blob Storage / Data Lake	Storage of IoT telemetry data for benchmarking and analysis
Azure Functions / Logic Apps	Event-driven serverless automation during experiments
Azure Monitor & Application Insights	Collecting latency, bandwidth, and throughput metrics for benchmarking

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