37 Sensing with Existing Infrastructure

37.2 Prerequisites

This section: Prerequisites

• Sensor Classification: Types of sensors
• Braitenberg Model: Sensor-behavior relationships

37.3 Use Existing Infrastructure

This section: Use Existing Infrastructure

37.4 Indirect Infrastructure Sensing

This section: Indirect Infrastructure Sensing — Key Insight: Every piece of infrastructure emits signals.

Instead of deploying new sensors, extract sensing information from infrastructure already in place:

Key Insight: Every piece of infrastructure emits signals. We can sense the world by observing how the environment disturbs these signals.

37.5 Comparison: Deploy vs Leverage

This section: Comparison: Deploy vs Leverage

When to Choose What:

• Deploy sensors when: Accuracy critical, point measurement needed, safety-critical
• Leverage infrastructure when: Large-scale deployment, cost-sensitive, retrofit scenario, quick pilot

37.6 Wi-Fi Breathing/Gestures

This section: Wi-Fi Breathing/Gestures — Sensing Capability: Channel State Information (CSI) captures how Wi-Fi signals propagate through space.

Infrastructure: Standard Wi-Fi router (802.11n/ac/ax)

Sensing Capability: Channel State Information (CSI) captures how Wi-Fi signals propagate through space. Human movement, breathing, even heartbeat disturbs these signals.

What It Measures:

• Occupancy estimation: Device count gives zone-level headcount estimates when checked against a local devices-per-person divisor.
• Presence detection: CSI-based binary detection (occupied vs. empty room, no device needed).
• Gesture recognition: Hand waves and swipes can be classified from Wi-Fi signal changes, with accuracy depending on the environment and test set.
• Breathing rate: Chest movement modulates signal (+/-2 breaths/min under controlled single-occupant, line-of-sight conditions; accuracy degrades with multiple occupants or movement).
• Fall detection: Sudden signal disruption pattern.

Real Deployment:

• University dorms: Detect occupancy without cameras (privacy-preserving).
• Elderly care: Fall detection without wearables.
• Smart homes: Gesture control of lights and appliances.

37.7 Non-Intrusive Load Monitoring

This section: Non-Intrusive Load Monitoring — Sensing Capability: Analyze current and voltage waveforms to identify unique “signatures” of individual appliances.

Infrastructure: Smart electricity meter (already installed for billing)

Sensing Capability: Analyze current and voltage waveforms to identify unique “signatures” of individual appliances.

What It Measures:

• Appliance state: On/off, standby, active.
• Energy breakdown: Per-appliance consumption.
• Usage patterns: When devices are used.

How It Works:

A NILM algorithm watches changes in total home power:

• 150 W baseline: Only always-on devices are active.
• 1,650 W: A +1,500 W jump strongly suggests a hair dryer or heater.
• 1,750 W: A further +100 W step suggests a refrigerator compressor.
• 250 W: A -1,500 W drop suggests the hair dryer turned off.

Each appliance has unique electrical signatures:

• Resistive loads (heaters, toasters): Clean on/off transitions.
• Motors (refrigerators, fans): Inrush current spike, steady-state hum.
• Switched-mode power supplies (computers, chargers): High-frequency harmonics.

37.8 Example 3: Cellular Signal Analysis

This section: Example 3: Cellular Signal Analysis — Sensing Capability: Analyze signal strength patterns and handoff events to infer crowd density and movement.

Infrastructure: Cell towers (already everywhere)

Sensing Capability: Analyze signal strength patterns and handoff events to infer crowd density and movement.

What It Measures:

• Crowd density: Number of active devices in area
• Traffic flow: Movement patterns between cell zones
• Event detection: Large gatherings, unusual patterns

37.9 The Three-Layer Model

This section: The Three-Layer Model

• Infrastructure Layer: Existing devices emitting signals (Wi-Fi, power, cellular).
• Disturbance Layer: Environment modulates these signals (human movement, appliance usage).
• Analytics Layer: Software extracts sensing information from signal perturbations.

37.10 Design Guidelines

This section: Design Guidelines

37.11 Campus Occupancy Monitoring

This section: Campus Occupancy Monitoring

37.12 Wi-Fi Occupancy Estimator

This section: Wi-Fi Occupancy Estimator — Use this calculator to estimate room occupancy from Wi-Fi access point device counts.

Use this calculator to estimate room occupancy from Wi-Fi access point device counts. Adjust the number of connected devices and the average devices per person to see the estimated headcount range.

37.13 Infrastructure Sensing ROI

This section: Infrastructure Sensing ROI — Estimate the payback period and cost savings when using infrastructure-based sensing instead of dedicated sensors for building occupancy monitoring.

Estimate the payback period and cost savings when using infrastructure-based sensing instead of dedicated sensors for building occupancy monitoring.

37.14 Activity: Wi-Fi CSI Presence Detection

This section: Activity: Wi-Fi CSI Presence Detection — This compact example demonstrates the core idea behind Wi-Fi Channel State Information (CSI) occupancy sensing.

This compact example demonstrates the core idea behind Wi-Fi Channel State Information (CSI) occupancy sensing. When a person moves through a Wi-Fi signal path, the CSI amplitude fluctuates more than in an empty room. Real deployments process many subcarriers over time, but the student-facing decision is simple:

def variance(values):
    mean = sum(values) / len(values)
    errors = [
        (x - mean) ** 2
        for x in values
    ]
    return sum(errors) / len(values)

empty_room = [
    0.50, 0.51,
    0.49, 0.50,
]

person_moving = [
    0.20, 0.82,
    0.35, 0.75,
]

baseline = variance(empty_room)
current = variance(person_moving)
ratio = current / baseline

occupied = ratio > 2.0
print(f"variance ratio = {ratio:.1f}")
print(f"occupied = {occupied}")

Key design decisions:

37.15 Concept Relationships

This section: Concept Relationships

37.16 Label the Diagram

This section: Label the Diagram

37.17 Code Challenge

This section: Code Challenge

37.18 Proxy Sensing and Loop Limits

This section: Proxy Sensing and Loop Limits — The body above explains how existing Wi-Fi, power, cellular, and building infrastructure can act as indirect sensors.

The body above explains how existing Wi-Fi, power, cellular, and building infrastructure can act as indirect sensors. Continue to Infrastructure Proxy Sensing and Inductive Loop Limits when you need the deeper physics and evidence boundaries behind those proxies. That page separates the L2 material: Wi-Fi fingerprint reuse, inductive-loop oscillator shifts, eddy-current blind spots, threshold trade-offs, auto-baselining, and proxy drift.

37.19 Summary

This section: Summary — Infrastructure-leveraging sensing turns existing equipment into sensors through software analysis, providing area-wide coverage at near-zero marginal cost while trading some accuracy for convenience.

Infrastructure-leveraging sensing turns existing equipment into sensors through software analysis, providing area-wide coverage at near-zero marginal cost while trading some accuracy for convenience. Key takeaways:

• Existing infrastructure can serve as sensors — Wi-Fi routers (CSI for presence/gestures), power meters (NILM for appliance identification), and cell towers (crowd density estimation).
• Software deployment replaces hardware installation — Faster, cheaper, and easier to maintain.
• Area-wide vs point measurements — In the illustrative campus model, ±10–15% zone error is an assumed HVAC-design target.
• Privacy-preserving options — RF sensing avoids cameras while still detecting presence and activity.
• Combine approaches for best results — Hybrid deployments (infrastructure + targeted dedicated sensors) deliver the best cost-accuracy tradeoff.
• Reserve dedicated sensors for safety-critical, high-precision, or real-time applications.

37.20 See Also

This section: See Also

• Sensor Classification - Active vs passive sensing principles apply to infrastructure.
• Signal Processing - CSI data requires filtering and feature extraction.
• Common Mistakes - Infrastructure sensing still needs validation logic.
• Selection Guide - When to leverage infrastructure vs deploy dedicated sensors.

37.21 Try It Yourself

This section: Try It Yourself

09:00 → 2 devices
09:30 → 8 devices
10:00 → 8 devices
10:30 → 12 devices
11:00 → 3 devices

37.22 What’s Next

This section: What’s Next