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A Cost-Benefit Analysis of Edge Computing vs. Cloud Computing for Latency-Sensitive Industrial IoT Applications

IoT Sensor Data

Edge Computing vs. Cloud Computing for Latency-Sensitive Industrial IoT Applications

  • Use Case : Industrial IoT (IIoT) applications such as robotic automation, predictive maintenance, and real-time process control require low-latency decision-making. Companies often face a dilemma: process data at the edge for low latency, or send it to the cloud for complex analytics and storage. Understanding the trade-offs in latency, cost, bandwidth, and scalability helps in designing efficient IIoT architectures.

Objective

  • Evaluate the cost, performance, and latency trade-offs between edge computing and cloud computing for IIoT.

    Quantify response time, bandwidth usage, compute costs, and operational efficiency for latency-sensitive workloads.

    Provide recommendations for hybrid architectures that balance edge and cloud computing benefits.

    Simulate and benchmark real-time industrial IoT workloads using Google Cloud services and edge devices.

Project Description

  • This project implements a comparative analysis framework for edge vs cloud computing:

    IoT Device Simulation: Use IoT sensors to simulate industrial data streams (e.g., vibration, temperature, pressure).

    Edge Processing Setup: Deploy lightweight ML inference and analytics on edge devices (Edge TPU or IoT Edge) for low-latency decision-making.

    Cloud Processing Setup: Send the same IoT data to Google Cloud ML / Dataflow pipelines for complex analytics and model inference.

    Data Collection: Measure latency, bandwidth consumption, compute cost, energy usage, and prediction accuracy in both setups.

    Cost-Benefit Analysis: Compare results to understand trade-offs, scalability, and ROI.

    Reporting & Recommendations: Visualize findings and provide guidance for hybrid architectures (edge + cloud).

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud IoT Core Connects and manages IoT devices, streams sensor telemetry to edge or cloud pipelines.
    Edge TPU / IoT Edge Devices Runs ML inference locally on edge devices to reduce latency.
    Pub/Sub Streams real-time sensor data from edge or devices to cloud pipelines.
    Dataflow Processes streaming or batch IoT data in the cloud for analytics and ML inference.
    Vertex AI / Cloud ML Trains and deploys ML models in the cloud; supports predictive analytics for industrial IoT.
    BigQuery Stores historical IoT and processed data for analytics and benchmarking.
    Cloud Functions Automates alerts, triggers workflows, or sends results from edge/cloud pipelines.
    Cloud Monitoring / Logging Monitors latency, throughput, resource utilization, and operational performance.
    Cloud Storage Stores raw sensor data, processed datasets, and model checkpoints.
    Looker / Data Studio Visualizes latency, cost, bandwidth, and performance comparisons between edge and cloud.
    Cloud Key Management Service (KMS) Secures sensitive IoT data and ML model communications.