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Energy-Aware Resource Scheduling for IoT Workloads in Fog-Cloud Architecture with AWS IoT

Energy-Aware Resource Scheduling

Energy-Aware Resource Scheduling for IoT

  • Use Case: IoT-based smart cities, healthcare monitoring systems, and industrial IoT systems need real-time data processing with optimized energy usage. Efficient scheduling across fog and cloud layers ensures timely task execution without depleting energy resources, especially on edge/fog devices.

Objective

  • To design and implement an energy-aware, latency-optimized scheduling model that dynamically allocates IoT workloads between fog (edge) and cloud layers, leveraging AWS services like AWS IoT Core, Greengrass, and Lambda for real-time, scalable, and intelligent orchestration.

Project Description

  • This project proposes a hybrid fog-cloud architecture using AWS IoT services to handle large-scale IoT workloads efficiently. The system monitors energy consumption and task execution latency to make intelligent decisions on where to process the workload — locally on fog nodes or offloaded to the cloud. By integrating AWS Greengrass V2 for edge processing and AWS Lambda for cloud-side execution, the system ensures reduced latency and energy optimization. Machine Learning models can be deployed using Amazon SageMaker to predict optimal workload placement based on device health, bandwidth, and energy state.
    • Key Technologies & AWS Services :
    Category Technology / AWS Service Purpose
    IoT Connectivity AWS IoT Core Connects IoT devices and routes messages securely.
    Edge Processing AWS IoT Greengrass V2 Deploys lightweight apps and ML models on edge devices.
    ML Model Deployment Amazon SageMaker Trains energy prediction models for workload scheduling.
    Serverless Compute AWS Lambda Processes incoming IoT data and makes scheduling decisions.
    Storage Amazon S3 Stores sensor data, logs, and ML model artifacts.
    Monitoring Amazon CloudWatch Tracks energy metrics and system health across edge and cloud.
    Permissions AWS IAM Provides secure access control across cloud components.
    Messaging Amazon SNS / AWS IoT Device Shadow Delivers alerts or triggers status updates between devices and cloud.
    Local Communication Greengrass Local MQTT Broker Facilitates edge device communication with low latency.
    Scheduling Logic Custom Energy-Aware Scheduler + Lambda or ECS Fargate (Optional) Implements decision logic for task placement based on energy and latency.