518  Sensor Applications: Hardware Selection Guide

518.1 Hardware Selection Guide by Application Domain

⏱️ ~20 min | ⭐⭐⭐ Advanced | πŸ“‹ P06.C03.U16

Selecting the right sensors and hardware for your specific IoT application domain requires understanding the unique requirements, constraints, and priorities of each use case. This guide provides practical recommendations for sensor selection across different application domains.

518.1.1 Smart Cities Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Smart Parking Magnetic field sensor (PNI RM3100) ESP32 LoRaWAN/NB-IoT Battery (5-10 yr) $80-120
Traffic Monitoring Magnetic field + Camera Raspberry Pi 4 Wi-Fi/4G Mains $150-300
Smart Lighting LDR/BH1750 + PIR motion ESP32 Wi-Fi/Zigbee Mains $25-40
Waste Management HC-SR04 ultrasonic ESP32 LoRaWAN Solar + battery $60-90
Air Quality MQ-135, MH-Z19 (CO2), PMS5003 ESP32 Wi-Fi/4G Solar + battery $80-150
Noise Monitoring MEMS microphone (SPH0645) ESP32 Wi-Fi/4G Solar + battery $50-80

Key Considerations: - Coverage: Smart parking needs 1 sensor per space; air quality 1 per kmΒ² in urban areas - Battery Life: Parking sensors need 5-10 years; waste management 3-5 years - Connectivity: LoRaWAN for wide area (10 km range); Wi-Fi for dense urban - Cost: City-wide deployments require cost under $100/node for viability

Example BOM: Smart Parking Sensor Node - Magnetic sensor PNI RM3100: $25 - ESP32 (low-power variant): $5 - LoRaWAN module (RFM95W): $8 - Lithium battery 3.6V 19Ah: $30 - Enclosure IP67: $15 - Installation hardware: $10 - Total: ~$93 per parking space

518.1.2 Smart Agriculture Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Soil Moisture Capacitive sensor (STEMMA) ESP32 LoRaWAN Solar + battery $40-60
Weather Station BME280, anemometer, rain gauge ESP32 Wi-Fi/4G Solar + battery $100-200
Irrigation Control Soil moisture + flow sensor ESP32 Wi-Fi Solar + battery $80-120
Greenhouse Monitoring DHT22, CO2 (MH-Z19), light ESP32 Wi-Fi Mains $60-100
Livestock Tracking GPS + accelerometer ESP32 LoRaWAN/GSM Battery (6 mo) $50-80

Key Considerations: - Outdoor Rating: IP65+ enclosures required for field deployment - Power: Solar panels (5-10W) with 10-20 Ah batteries for year-round operation - Wireless Range: Farms need 1-5 km range (LoRaWAN ideal) - Durability: Sensors exposed to UV, rain, temperature extremes (-20 to 50Β°C)

Example BOM: Soil Moisture Monitoring Station - 4Γ— Capacitive soil moisture sensors: $20 - ESP32 Dev Board: $5 - LoRaWAN RFM95: $8 - Solar panel 6V 3.5W: $12 - 18650 Li-ion battery 3000mAh: $6 - Waterproof box IP65: $8 - Total: ~$59 per monitoring station

518.1.3 Smart Home Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Security System PIR + door/window sensors ESP32 Wi-Fi Battery (2-3 yr) $30-50
Energy Monitoring CT clamps (SCT-013) ESP32 Wi-Fi Mains $40-60
HVAC Automation DHT22 + occupancy (PIR) ESP32 Wi-Fi/Zigbee Mains $25-40
Water Leak Detection Water detection sensor ESP8266 Wi-Fi Battery (1-2 yr) $15-25
Indoor Air Quality BME680 + MH-Z19 (CO2) ESP32 Wi-Fi Mains/USB $50-80

Key Considerations: - Wi-Fi Coverage: Ensure 2.4 GHz Wi-Fi reaches all sensors (range extenders may be needed) - Battery vs. Mains: Battery sensors for doors/windows; mains for energy monitors - Integration: Choose ESP32/Zigbee for Home Assistant, MQTT compatibility - User-Friendly: Simple setup, OTA updates, mobile app control

Example BOM: Complete Smart Home Sensor Kit - 1Γ— ESP32 hub: $5 - 3Γ— PIR motion sensors: $6 - 2Γ— Door/window sensors: $4 - 1Γ— DHT22 temp/humidity: $5 - 1Γ— MQ-2 gas sensor: $3 - 1Γ— Water leak sensor: $2 - Enclosures, wiring: $5 - Total: ~$30 for basic 8-sensor home system

518.1.4 Smart Health Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Heart Rate Monitor MAX30102 (pulse oximeter) nRF52840 BLE Battery (7-14 days) $30-50
Fall Detection MPU6050 (accelerometer/gyro) ESP32 Wi-Fi/BLE Battery (6-12 mo) $25-40
Body Temperature MLX90614 (IR non-contact) ESP32 Wi-Fi/BLE Battery/USB $35-55
Medication Adherence Load cell + RFID ESP32 Wi-Fi Mains/USB $40-60
Sleep Monitoring Pressure mat + MPU6050 ESP32 Wi-Fi Mains $50-80

Key Considerations: - Medical Grade vs. Wellness: FDA approval needed for medical claims (costly); wellness ok for consumer - Privacy: HIPAA compliance if storing health data (encryption, secure transmission) - Battery Life: Wearables need 7+ days; bedside monitors can use mains power - Accuracy: Heart rate Β±2 bpm; temperature Β±0.2Β°C for medical use

Example BOM: Wearable Heart Rate + Activity Monitor - MAX30102 pulse oximeter: $8 - MPU6050 accelerometer/gyro: $4 - nRF52840 BLE module: $12 - 3.7V 250mAh LiPo battery: $5 - TP4056 charging module: $2 - Custom 3D printed case: $3 - Total: ~$34 for wearable device

518.1.5 Smart Industrial Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Vibration Monitoring ADXL345 (3-axis accelerometer) STM32F103 Modbus/Ethernet Mains/24V DC $80-150
Temperature Monitoring Multiple DS18B20 (1-Wire) ESP32 Wi-Fi/Ethernet Mains $50-80
Current Monitoring SCT-013 CT clamps ESP32 Wi-Fi/Modbus Mains $60-100
Machine Vision Camera (ESP32-CAM) ESP32 Wi-Fi Mains $40-80
Gas Detection MQ-4 (methane), MQ-7 (CO) ESP32 Wi-Fi/4G Mains $50-90

Key Considerations: - Industrial Protocols: Support Modbus RTU/TCP, OPC UA, MQTT for integration - Harsh Environments: IP65-67 rated enclosures, -40 to +85Β°C operating range - Reliability: Industrial-grade components, redundancy, UPS backup - Real-time: Sub-second response for safety-critical applications

Example BOM: Predictive Maintenance Vibration Monitor - ADXL345 accelerometer: $8 - STM32F103 microcontroller: $3 - RS485 to TTL module: $5 - 24V to 5V DC-DC converter: $6 - Industrial DIN rail enclosure: $25 - Mounting bracket: $8 - Total: ~$55 per machine monitor

518.1.6 Smart Environment Hardware Recommendations

Application Primary Sensors Microcontroller Connectivity Power Estimated Cost/Node
Air Quality PMS5003, MH-Z19, BME680 ESP32 Wi-Fi/LoRaWAN Solar + battery $100-180
Water Quality pH, DO, turbidity probes ESP32 4G/LoRaWAN Solar + battery $300-600
Seismic Monitoring ADXL355 (high-g accelerometer) Raspberry Pi 4G Mains/battery $150-300
Forest Fire Detection MQ-2, DHT22, smoke detector ESP32 LoRaWAN Solar + battery $60-100
Noise Pollution SPH0645 MEMS mic ESP32 Wi-Fi/4G Solar + battery $50-90

Key Considerations: - Remote Locations: Solar power mandatory; 4G/satellite for connectivity - Calibration: Water quality sensors need monthly calibration - Weather Resistance: IP67+ rating, conformal coating on PCBs - Data Frequency: Seismic 100+ Hz; air quality 0.1 Hz

Example BOM: Environmental Air Quality Station - PMS5003 PM2.5/PM10: $25 - MH-Z19C CO2 sensor: $20 - BME680 (temp/hum/VOC): $15 - ESP32: $5 - LoRaWAN RFM95: $8 - Solar panel 10W + controller: $25 - 12V 7Ah sealed lead-acid battery: $18 - Weatherproof enclosure: $30 - Total: ~$146 per monitoring station

518.1.7 Microcontroller Selection by Domain

Domain Recommended MCU Why? Alternative
Smart Cities ESP32 + LoRaWAN Long range, Wi-Fi fallback, low power nRF52840 (BLE mesh)
Agriculture ESP32 Wi-Fi + BLE, 18 ADC channels for sensors STM32 (industrial)
Home Automation ESP8266/ESP32 Low cost, Wi-Fi, huge community Zigbee modules
Healthcare nRF52840 Ultra-low power BLE, wearable-friendly ESP32 (if Wi-Fi needed)
Industrial STM32F103/F4 Real-time, industrial protocols, rugged ESP32 (with RS485)
Environment ESP32 Multi-sensor, Wi-Fi/LoRaWAN options Raspberry Pi (edge AI)

518.1.8 Connectivity Selection Matrix

Range Needed Data Rate Power Budget Recommended Protocol Hardware Module
< 100m Low-Med Very Low Zigbee CC2530, XBee
< 100m Med-High Low Wi-Fi 2.4GHz ESP32, ESP8266
< 100m Very High Medium Wi-Fi 5GHz Raspberry Pi 4
1-10 km Very Low Ultra Low LoRaWAN RFM95W, SX1276
1-10 km Low Low NB-IoT SIM7020, BC95
Unlimited Med-High Medium-High 4G LTE SIM7600, EC25

518.1.9 Power Budget Planning

Battery Life Estimation Formula:

Battery Life (hours) = Battery Capacity (mAh) / Average Current (mA)

Example: 2500 mAh battery, ESP32 sensor node
- Active (1 sec): 80 mA
- Sleep (59 sec): 10 Β΅A = 0.01 mA
- Average = (80Γ—1 + 0.01Γ—59) / 60 = 1.34 mA
- Life = 2500 / 1.34 = 1865 hours = 78 days

With solar (10W panel):
- Daily generation: 10W Γ— 4 hours (effective sunlight) = 40 Wh
- Daily consumption: 1.34 mA Γ— 3.7V Γ— 24h = 0.12 Wh
- Result: Solar provides 333Γ— needed power β†’ indefinite operation

Power Budgeting Tips: 1. Deep Sleep is Critical: ESP32 active (80 mA) vs. deep sleep (10 Β΅A) = 8000Γ— difference 2. Sensor Selection: Digital sensors (I2C) use less power than analog sensors requiring continuous ADC 3. Transmission Cost: Wi-Fi transmission (170 mA) vs. LoRaWAN (40 mA) = 4Γ— difference 4. Solar Sizing: Minimum 5W panel for ESP32 with daily transmission in temperate climates

518.1.10 Cost Optimization Strategies

Budget-Conscious Choices: - Prototyping: Buy from Adafruit/SparkFun for reliability and documentation ($30-50 kit) - Small Production (10-50): Amazon for balance of cost/speed/support ($15-25 kit) - Mass Production (100+): AliExpress bulk orders for 50-70% cost reduction ($8-12 kit)

Cost Comparison Example (10-node temperature monitoring) | Source | Per-Node Cost | Total Cost | Shipping | Lead Time | Quality | |——–|————–|———–|β€”β€”β€”-|———–|β€”β€”β€”| | Adafruit | $35 | $350 | $15 | 3-5 days | Excellent | | Amazon | $22 | $220 | Free | 2-3 days | Good | | AliExpress | $12 | $120 | $20 | 3-4 weeks | Variable |

Hidden Costs to Budget: - Enclosures: $5-30 per node depending on IP rating - Installation Labor: $20-100 per node for mounting, wiring - Gateway/Hub: $50-200 (1 per site for LoRaWAN) - Annual Maintenance: 10-15% of hardware cost - Cloud/Data: $5-50/month depending on data volume

NoteQuick Application-to-Hardware Guide

β€œI need to monitor…”

β†’ Parking spaces (city-wide) β†’ Hardware: ESP32 + PNI RM3100 magnetic + LoRaWAN RFM95 + 5-year battery β†’ Cost: ~$90/space | Range: 10 km | Life: 5-10 years

β†’ Soil moisture (farm, 50 locations) β†’ Hardware: ESP32 + 4Γ— capacitive sensors + LoRaWAN + solar β†’ Cost: ~$60/node | Range: 5 km | Life: 5+ years (solar)

β†’ Home energy usage β†’ Hardware: ESP32 + 3Γ— SCT-013 CT clamps + Wi-Fi β†’ Cost: ~$45 total | Range: 50m (Wi-Fi) | Life: Mains powered

β†’ Heart rate (wearable) β†’ Hardware: nRF52840 + MAX30102 + 250mAh battery + BLE β†’ Cost: ~$35 | Range: 10m (BLE) | Life: 7-14 days/charge

β†’ Machine vibration (factory, 20 machines) β†’ Hardware: STM32 + ADXL345 + Modbus RS485 + 24V DC β†’ Cost: ~$55/machine | Range: 1 km (wired) | Life: Industrial-grade

β†’ Air quality (neighborhood, 10 locations) β†’ Hardware: ESP32 + PMS5003 + MH-Z19 + BME680 + solar β†’ Cost: ~$150/node | Range: Wi-Fi/LoRaWAN | Life: 5+ years

Not sure? Use the Sensor Selection Wizard below to get personalized recommendations!

518.2 Mermaid Diagrams

518.2.1 Sensor Application Domain Hierarchy

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mindmap
  root((IoT Sensor<br/>Applications))
    Smart Cities
      Parking systems
      Traffic monitoring
      Street lighting
      Waste management
      Air quality
    Healthcare
      Patient monitoring
      Wearable devices
      Medication tracking
      Emergency response
    Agriculture
      Soil moisture
      Weather stations
      Livestock tracking
      Irrigation control
      Crop monitoring
    Industrial
      Asset tracking
      Predictive maintenance
      Quality control
      Energy monitoring
      Safety systems
    Home Automation
      HVAC control
      Security systems
      Energy management
      Lighting control
      Appliances
    Environmental
      Weather monitoring
      Pollution tracking
      Water quality
      Wildlife monitoring
      Disaster prediction

Figure 518.1: IoT Application Domains: Healthcare, Agriculture, Industrial, and Smart Home

This layered view categorizes sensors by criticality level - from safety-critical to convenience - helping you understand different reliability and accuracy requirements.

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flowchart TB
    subgraph L1["SAFETY-CRITICAL (Failure = Harm)"]
        S1["Medical: SpO2, ECG, Blood Glucose"]
        S2["Industrial: Gas leak, Radiation"]
        S3["Automotive: Collision, Tire pressure"]
        REQ1["Requirements: Redundancy, Certification, Real-time"]
    end

    subgraph L2["OPERATIONAL (Failure = Downtime)"]
        O1["Industrial: Vibration, Temperature"]
        O2["Agriculture: Soil moisture, pH"]
        O3["Building: HVAC, Occupancy"]
        REQ2["Requirements: Accuracy, Reliability, 99%+ uptime"]
    end

    subgraph L3["INFORMATIONAL (Failure = Inconvenience)"]
        I1["Smart Home: Motion, Light levels"]
        I2["Wearables: Step count, Sleep"]
        I3["Environment: Weather, Air quality"]
        REQ3["Requirements: Cost-effective, Good-enough accuracy"]
    end

    L1 --> L2
    L2 --> L3

    style L1 fill:#E67E22,stroke:#2C3E50
    style L2 fill:#16A085,stroke:#2C3E50
    style L3 fill:#7F8C8D,stroke:#2C3E50

Figure 518.2: Criticality-based sensor selection: Safety-critical applications (medical, gas detection) demand certified sensors with redundancy. Operational applications (industrial monitoring) need high reliability. Informational applications (smart home) can use lower-cost consumer sensors. Match sensor grade to application criticality. 

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flowchart TB
    subgraph smartcity["SMART CITY"]
        SC1["Ultrasonic β†’ Parking spots"]
        SC2["Camera β†’ Traffic counting"]
        SC3["PM2.5 β†’ Air quality"]
        SC4["LDR β†’ Street light control"]
    end

    subgraph health["HEALTHCARE"]
        H1["PPG β†’ Heart rate"]
        H2["Accelerometer β†’ Fall detection"]
        H3["Temperature β†’ Fever monitoring"]
        H4["SpO2 β†’ Oxygen saturation"]
    end

    subgraph agri["AGRICULTURE"]
        A1["Soil moisture β†’ Irrigation"]
        A2["DHT22 β†’ Greenhouse climate"]
        A3["pH sensor β†’ Soil health"]
        A4["GPS β†’ Livestock tracking"]
    end

    subgraph industry["INDUSTRIAL"]
        I1["Vibration β†’ Predictive maintenance"]
        I2["Current clamp β†’ Energy monitoring"]
        I3["Thermocouple β†’ High-temp process"]
        I4["Flow meter β†’ Production rate"]
    end

    style smartcity fill:#E3F2FD,stroke:#2C3E50
    style health fill:#FCE4EC,stroke:#E91E63
    style agri fill:#E8F5E9,stroke:#16A085
    style industry fill:#FFF3E0,stroke:#E67E22

Figure 518.3: Sensor-to-domain mapping: Each IoT domain has characteristic sensors. Smart cities use ultrasonic (parking), cameras (traffic), and PM2.5 (air quality). Healthcare relies on PPG (heart rate), accelerometer (falls), and SpO2 (oxygen). Agriculture needs soil moisture, climate sensors, and GPS. Industrial IoT uses vibration, current, and flow sensors. Know your domain to select appropriate sensors.

{fig-alt=β€œIoT application diagram showing key components and relationships illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.2.2 Sensor Selection Decision Tree

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flowchart TD
    Start[Sensor Selection<br/>for Application]

    Q1{Indoor or<br/>Outdoor?}
    Q2{Battery<br/>powered?}
    Q3{Real-time<br/>data needed?}
    Q4{Budget per<br/>sensor?}

    Indoor[Indoor Deployment]
    Outdoor[Outdoor Deployment]

    Wi-Fi[Wi-Fi Sensors<br/>ESP32-based]
    BLE[BLE Beacons<br/>Ultra low power]
    LoRa[LoRaWAN Sensors<br/>Long range]
    Cellular[Cellular IoT<br/>NB-IoT/LTE-M]
    Wired[Wired Sensors<br/>Modbus/RS485]

    Start --> Q1
    Q1 -->|Indoor| Q2
    Q1 -->|Outdoor| Q3
    Q2 -->|Yes| BLE
    Q2 -->|No| Wi-Fi
    Q3 -->|Yes| Cellular
    Q3 -->|No| Q4
    Q4 -->|Low <$50| LoRa
    Q4 -->|High >$50| Wired

    style Start fill:#2C3E50,stroke:#16A085,color:#fff
    style Q1 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Q2 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Q3 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Q4 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Wi-Fi fill:#16A085,stroke:#2C3E50,color:#fff
    style BLE fill:#16A085,stroke:#2C3E50,color:#fff
    style LoRa fill:#16A085,stroke:#2C3E50,color:#fff
    style Cellular fill:#16A085,stroke:#2C3E50,color:#fff
    style Wired fill:#16A085,stroke:#2C3E50,color:#fff

Figure 518.4: Sensor Selection Decision Tree: Indoor vs Outdoor Deployment Paths

{fig-alt=β€œIoT application diagram showing Sensor Selection for Application, Indoor Deployment, Outdoor Deployment illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.2.3 Data Flow Architecture

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flowchart LR
    Sensors[Sensor Layer<br/>DHT22, BMP280<br/>PIR, LDR, etc.]

    Gateway[Gateway/Edge<br/>ESP32/Raspberry Pi<br/>Data aggregation]

    Network[Network Layer<br/>Wi-Fi/LoRaWAN<br/>Cellular/Ethernet]

    Cloud[Cloud Platform<br/>AWS IoT/Azure IoT<br/>ThingSpeak/Blynk]

    Analytics[Analytics Layer<br/>Data processing<br/>ML/AI inference]

    App[Application Layer<br/>Web dashboard<br/>Mobile app<br/>Alerts]

    Sensors -->|Raw data| Gateway
    Gateway -->|Filtered data| Network
    Network -->|MQTT/HTTP| Cloud
    Cloud -->|Batch/Stream| Analytics
    Analytics -->|Insights| App
    App -.->|Control commands| Gateway

    style Sensors fill:#2C3E50,stroke:#16A085,color:#fff
    style Gateway fill:#E67E22,stroke:#2C3E50,color:#fff
    style Network fill:#E67E22,stroke:#2C3E50,color:#fff
    style Cloud fill:#16A085,stroke:#2C3E50,color:#fff
    style Analytics fill:#16A085,stroke:#2C3E50,color:#fff
    style App fill:#27ae60,stroke:#2C3E50,color:#fff

Figure 518.5: End-to-End IoT Data Flow: Sensors to Cloud to Application

{fig-alt=β€œIoT application diagram showing Sensor Layer DHT22, BMP280 PIR, LDR, etc., Gateway/Edge ESP32/Raspberry Pi Data aggregation, Network Layer Wi-Fi/LoRaWAN Cellular/Ethernet illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.2.4 Sensor Usage Statistics

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pie title Most Common IoT Sensors by Application Volume
    "Temperature" : 28
    "Motion/Proximity" : 22
    "Light/Color" : 15
    "Humidity" : 12
    "Pressure/Altitude" : 10
    "Acceleration/Gyro" : 8
    "Gas/Air Quality" : 5

Figure 518.6: IoT Sensor Market Distribution: Temperature Leads at 28%

{fig-alt=β€œIoT application diagram showing key components and relationships illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.2.5 Sensor Type Comparison by Application

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graph LR
    subgraph Smart_Cities["πŸ™οΈ Smart Cities"]
        SC1[Smart Parking<br/>Magnetic Field]
        SC2[Traffic<br/>Magnetic + Camera]
        SC3[Air Quality<br/>Gas Sensors]
        SC4[Lighting<br/>LDR + PIR]
    end

    subgraph Agriculture["🌾 Agriculture"]
        AG1[Soil Moisture<br/>Capacitive]
        AG2[Weather<br/>Temp/Humid/Press]
        AG3[Livestock<br/>GPS + RFID]
        AG4[Irrigation<br/>Flow + Soil]
    end

    subgraph Healthcare["❀️ Healthcare"]
        HC1[Heart Rate<br/>Optical PPG]
        HC2[Fall Detection<br/>Accelerometer]
        HC3[Temperature<br/>IR Thermometer]
        HC4[Activity<br/>IMU Fusion]
    end

    subgraph Industrial["🏭 Industrial"]
        IN1[Vibration<br/>Accelerometer]
        IN2[Temperature<br/>Thermocouple]
        IN3[Current<br/>CT Clamps]
        IN4[Position<br/>Encoder/Hall]
    end

    subgraph Environment["🌍 Environment"]
        EN1[Air Quality<br/>PM2.5/CO2/VOC]
        EN2[Water Quality<br/>pH/DO/Turbidity]
        EN3[Noise<br/>MEMS Microphone]
        EN4[Seismic<br/>High-g Accel]
    end

    style Smart_Cities fill:#2C3E50,stroke:#16A085,color:#fff
    style Agriculture fill:#E67E22,stroke:#2C3E50,color:#fff
    style Healthcare fill:#e74c3c,stroke:#2C3E50,color:#fff
    style Industrial fill:#7F8C8D,stroke:#2C3E50,color:#fff
    style Environment fill:#16A085,stroke:#2C3E50,color:#fff

    style SC1 fill:#ECF0F1,stroke:#2C3E50,color:#000
    style SC2 fill:#ECF0F1,stroke:#2C3E50,color:#000
    style SC3 fill:#ECF0F1,stroke:#2C3E50,color:#000
    style SC4 fill:#ECF0F1,stroke:#2C3E50,color:#000
    style AG1 fill:#ECF0F1,stroke:#E67E22,color:#000
    style AG2 fill:#ECF0F1,stroke:#E67E22,color:#000
    style AG3 fill:#ECF0F1,stroke:#E67E22,color:#000
    style AG4 fill:#ECF0F1,stroke:#E67E22,color:#000
    style HC1 fill:#ECF0F1,stroke:#e74c3c,color:#000
    style HC2 fill:#ECF0F1,stroke:#e74c3c,color:#000
    style HC3 fill:#ECF0F1,stroke:#e74c3c,color:#000
    style HC4 fill:#ECF0F1,stroke:#e74c3c,color:#000
    style IN1 fill:#ECF0F1,stroke:#7F8C8D,color:#000
    style IN2 fill:#ECF0F1,stroke:#7F8C8D,color:#000
    style IN3 fill:#ECF0F1,stroke:#7F8C8D,color:#000
    style IN4 fill:#ECF0F1,stroke:#7F8C8D,color:#000
    style EN1 fill:#ECF0F1,stroke:#16A085,color:#000
    style EN2 fill:#ECF0F1,stroke:#16A085,color:#000
    style EN3 fill:#ECF0F1,stroke:#16A085,color:#000
    style EN4 fill:#ECF0F1,stroke:#16A085,color:#000

Figure 518.7: Sensor Types by Application Domain: Smart Cities, Agriculture, Healthcare, Industrial

{fig-alt=β€œIoT application diagram showingβ€πŸ™οΈ Smart Cities”, Smart Parking Magnetic Field, Traffic Magnetic + Camera illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.2.6 Sensor Deployment Lifecycle

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flowchart TD
    Start([Sensor Application<br/>Project Start])

    Requirements[Requirements Analysis<br/>- Application domain<br/>- Coverage area<br/>- Accuracy needs<br/>- Budget constraints]

    Selection[Sensor Selection<br/>- Type matching<br/>- Power budget<br/>- Cost analysis<br/>- Protocol choice]

    Design[System Design<br/>- Network topology<br/>- Gateway placement<br/>- Data flow<br/>- Edge processing]

    Prototype[Prototype & Test<br/>- Small deployment<br/>- Validate coverage<br/>- Test connectivity<br/>- Measure performance]

    Optimize{Performance<br/>Acceptable?}

    Deploy[Full Deployment<br/>- Install sensors<br/>- Configure network<br/>- Calibrate sensors<br/>- Test end-to-end]

    Monitor[Operations & Monitor<br/>- Data collection<br/>- Alert management<br/>- Performance tracking<br/>- Battery monitoring]

    Maintain[Maintenance<br/>- Calibration<br/>- Battery replacement<br/>- Firmware updates<br/>- Issue resolution]

    Evaluate{System<br/>Health OK?}

    Scale[Scale & Expand<br/>- Add sensors<br/>- New locations<br/>- Additional types]

    End([Continuous<br/>Operation])

    Start --> Requirements
    Requirements --> Selection
    Selection --> Design
    Design --> Prototype
    Prototype --> Optimize
    Optimize -->|No| Selection
    Optimize -->|Yes| Deploy
    Deploy --> Monitor
    Monitor --> Maintain
    Maintain --> Evaluate
    Evaluate -->|Issues| Maintain
    Evaluate -->|Good| Monitor
    Monitor -.->|Expansion needed| Scale
    Scale --> Deploy
    Monitor --> End

    style Start fill:#2C3E50,stroke:#16A085,color:#fff
    style End fill:#27ae60,stroke:#16A085,color:#fff
    style Requirements fill:#E67E22,stroke:#2C3E50,color:#fff
    style Selection fill:#E67E22,stroke:#2C3E50,color:#fff
    style Design fill:#E67E22,stroke:#2C3E50,color:#fff
    style Prototype fill:#16A085,stroke:#2C3E50,color:#fff
    style Deploy fill:#16A085,stroke:#2C3E50,color:#fff
    style Monitor fill:#3498db,stroke:#2C3E50,color:#fff
    style Maintain fill:#3498db,stroke:#2C3E50,color:#fff
    style Optimize fill:#f39c12,stroke:#2C3E50,color:#000
    style Evaluate fill:#f39c12,stroke:#2C3E50,color:#000
    style Scale fill:#9b59b6,stroke:#2C3E50,color:#fff

Figure 518.8: Sensor Deployment Lifecycle: From Requirements to Scale and Optimization

{fig-alt=β€œIoT application diagram showing Sensor Application Project Start, Requirements Analysis - Application domain - Coverage area - Accuracy needs - Budget constraints, Sensor Selection - Type matching - Power budget - Cost analysis - Protocol choice illustrating sensor-application mapping, industry use cases, deployment scenarios, or system architecture for real-world IoT solutions across different application domains.”}

518.3 Hands-On Labs

518.4 What’s Next

Apply your hardware selection knowledge in hands-on labs with real-world deployment scenarios, or return to sensor application domains to review use cases.