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Designing a Self-Healing Wi-Fi Monitor
Published: 2026-01-22 , Last updated: 2026-01-22
A single physical button that controls Spotify playback sounds trivial at first.
In reality, it becomes a compact case study of how hardware, firmware, network reliability, and third-party APIs intersect—and how fragility naturally emerges when a system crosses those boundaries.
This article documents the design of a button-driven Spotify control system and explains why even “simple” real-world systems demand careful thinking.
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Why This Project Exists?
Why This Project Exists?
The original goal was intentionally minimal:
Detect when Wi-Fi connectivity is actually broken (not just slow)
Attempt a single automated recovery action
Leave a traceable signal when recovery fails
Constraints (by design):
No UI, dashboard, or companion app
Only one action mapped to one outcome (recover or record failure)
No persistent cloud state
Why it matters:
Most “self-healing” systems fail quietly because they confuse symptoms with causes. This project forces the system to admit uncertainty instead of masking it.
📋 Key takeaway: If a system can’t explain why it acted, it shouldn’t act automatically.
System Overview: From Connectivity Loss to Recovery
System Overview: From Connectivity Loss to Recovery
At a conceptual level, the system looks simple:
Trigger: Wi-Fi health sensor detects loss of connectivity
Local compute: microcontroller / edge script evaluates state
Network action: controlled reset or reconnection attempt
External effect: Wi-Fi restored—or failure logged
However, each step hides assumptions that stay invisible until something fails.
This is where real-world systems diverge from diagrams.
Prototype snapshot: ESP32-based Wi-Fi monitor used to detect connectivity loss and trigger automated network recovery.
System setup: ESP32 Wi-Fi monitor paired with a smart plug used to reset network equipment when connectivity fails.
Sensing Layer: Detecting “Real” Wi-Fi Failure
Sensing Layer: Detecting “Real” Wi-Fi Failure
(Hardware Layer: why a signal is never “just a signal”)
What we assume:
Packet loss means Wi-Fi is down
Signal strength reflects connectivity quality
How it fails in reality:
DNS failures look like total disconnection
Captive portals and router hiccups create false negatives
What we do about it:
Combine multiple signals (ping + DNS + timeouts)
Require failure persistence over time, not instant triggers
📋 Key takeaway: Connectivity is a state, not a boolean.
Decision Layer: When the System Should Act
Decision Layer: When the System Should Act
(Architecture: where the boundary actually is)
What we assume:
If Wi-Fi is down, recovery is always safe
Rebooting or resetting is harmless
How it fails in reality:
Recovery actions can interrupt valid in-progress traffic
Acting too often creates oscillation loops
What we do about it:
Cool-down timers between actions
Explicit “no-action” states when uncertainty is high
📋 Key takeaway: Not acting is often the most reliable decision.
Recovery Layer: Triggering Self-Healing Safely
Recovery Layer: Triggering Self-Healing Safely
(Symptom vs outcome: what users expect vs what happens)
What we assume:
A reset restores normal operation
Success is immediate and visible
How it fails in reality:
Recovery succeeds but connectivity remains partial
No feedback makes failure indistinguishable from success
What we do about it:
Post-action verification checks
Explicit failure markers when recovery doesn’t work
📋 Key takeaway: A recovery without verification is just another guess.
Prototype device: ESP32 Wi-Fi monitor used to detect connectivity loss in the network.
Why This System Is Fragile by Design?
Why This System Is Fragile by Design?
This system spans multiple domains:
Physical environment (interference, power)
Firmware logic and timing
Network infrastructure and ISP state
Router firmware behavior
Each layer is reasonable on its own. Together, they multiply uncertainty.
This fragility isn’t a mistake — it’s a property of cross-layer systems.
📋 Key takeaway: Assume the layer you can’t see is the one that fails.
Design Takeaways for Real Products
Design Takeaways for Real Products
If this were production, improvements would include:
Feedback mechanisms (LEDs, logs, user-visible signals)
Explicit retry and timeout strategy
Clear offline and degraded-mode handling
State validation before issuing recovery commands
Cross-layer observability (logs, metrics, timestamps)
⚡Most importantly⚡
"A simple interface does not mean a simple system."
Related Reading
Related Reading
➡️ FIELD NOTE｜Automating Router Alerts Inside a Network Boundary
➡️ BUILD LOG｜Vibe Coding with AI: Modifying Embedded Media Players
➡️ SYSTEM REVIEW｜From SMS Trigger to Email Notification Systems
➡️ FIELD NOTE｜Designing Failure-Aware IoT Prototypes
FAQ
FAQ
Q1: Why not just reboot the router immediately?
Q1: Why not just reboot the router immediately?
Because many failures aren’t router failures. Acting too early hides root causes and creates unnecessary disruption.
Q2: Why does nothing sometimes happen when Wi-Fi is “down”?
Q2: Why does nothing sometimes happen when Wi-Fi is “down”?
Because the system detected ambiguous state. Silent failure is safer than confident wrong action.
Q3: What is the most fragile part of the system?
Q3: What is the most fragile part of the system?
The assumptions between sensing and decision logic.
Q4: How would you make this production-ready?
Q4: How would you make this production-ready?
Add user feedback, persistent state logging, and remote observability across all actions.
Q5: Does this apply to non-network systems?
Q5: Does this apply to non-network systems?
Yes. Any system crossing physical and logical boundaries behaves this way.
☕ Talk Through Your System ☕
☕ Talk Through Your System ☕
Shipping real-world systems means designing for failure, not assuming stability.
If you want a second set of eyes on architecture, reliability, or “demo → production” risks, book a session.
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