Introduction — IoT PC Monitoring

🔍 Background

Modern operating systems provide basic visibility into system resources such as CPU, memory, and disk usage. However, hardware-level telemetry—including temperature, fan speed, and power consumption—is often hidden behind vendor-specific tools.

These tools are typically:

• UI-based and not programmatically accessible
• Not designed for long-term data storage
• Difficult to integrate into analytics pipelines

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❗ Problem Statement

There is no unified system that can:

1. Collect both system metrics and hardware sensor data
2. Handle different data collection intervals (seconds vs minutes)
3. Store time-series data efficiently
4. Trigger real-time alerts
5. Provide analytical insights over time

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🎯 Objective

This project aims to build a modular IoT-based monitoring platform that:

• Collects:
  • CPU, Memory, Disk
  • Temperature, Fan Speed, Power
• Processes:
  • High-frequency sensor data (3 seconds)
  • Low-frequency system data (60 seconds)
• Delivers:
  • Real-time alerting (Slack)
  • Long-term analytics (Metabase)

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🧠 Key Idea

Monitoring is not the goal. Insight is the goal.

Instead of just visualizing data, the system focuses on:

• Detecting anomalies
• Identifying trends
• Enabling proactive responses

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🏗️ System Overview

The architecture separates responsibilities into:

• Data Collection
• Processing Layer
• Time-Series Storage
• Alert System
• Analytics Layer

This ensures flexibility and scalability.

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🚀 What’s Next

In the next post, we will define the system requirements and data collection strategy, including how to handle multi-rate data ingestion and hardware limitations.

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