158  Technology Selection and Energy Management

158.1 Learning Objectives

By the end of this chapter, you will be able to:

  • Apply Selection Frameworks: Use decision trees to select appropriate communication technologies based on requirements
  • Calculate Power Budgets: Analyze energy consumption across different IoT operational modes
  • Design Energy Systems: Plan energy harvesting and battery systems for autonomous IoT devices
  • Understand Miniaturization Trends: Trace the historical progression of hardware miniaturization and its impact on IoT
NoteKey Concepts
  • Technology Selection Framework: Systematic approach to choosing communication protocols based on battery constraints, range requirements, and data rate needs
  • Duty Cycling: Energy optimization technique where devices sleep most of the time, waking briefly for sensing and transmission
  • Energy Harvesting: Collecting power from environmental sources (solar, thermal, vibration, RF) to extend or eliminate battery replacement
  • Power Budget: Analysis of energy consumption across sleep, sensing, and transmission modes to predict battery lifetime
  • Miniaturization: Progressive shrinking of electronic components enabling smaller, cheaper, more efficient IoT devices

158.2 Prerequisites

Before diving into this chapter, you should be familiar with:

NoteChapter Position in Series

This is the third chapter in the Architectural Enablers series:

  1. IoT Evolution and Enablers Overview - History and convergence
  2. IoT Communications Technology - Protocols and network types
  3. Technology Selection and Energy Management (this chapter) - Decision frameworks
  4. Labs and Assessment - Hands-on practice

158.3 Technology Selection Decision Framework

~12 min | Advanced | P04.C08.U10

Use this flowchart to select appropriate communication technologies:

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graph TD
    Start{Define<br/>Requirements}
    Battery{Battery<br/>Powered?}
    Range{Range<br/>Required?}
    DataRate{Data<br/>Rate?}

    Start --> Battery
    Battery -->|Yes, >1 year| LongBattery[Long Battery<br/>Life Priority]
    Battery -->|Mains power| Range

    LongBattery --> Range
    Range -->|less than 100m| BLE[Bluetooth LE<br/>Zigbee<br/>Thread]
    Range -->|100m-10km| LPWAN[LoRaWAN<br/>Sigfox<br/>NB-IoT]
    Range -->|greater than 10km| Cellular[Cellular<br/>LTE-M]

    Battery -->|Mains power| DataRate
    DataRate -->|less than 1 Mbps| SubGHz[Sub-GHz<br/>Mesh Networks]
    DataRate -->|1-100 Mbps| Wi-Fi[Wi-Fi 6/7<br/>Ethernet]
    DataRate -->|greater than 100 Mbps| Fiber[Fiber<br/>5G]

    style Start fill:#E67E22,stroke:#16A085,color:#fff
    style BLE fill:#16A085,stroke:#2C3E50,color:#fff
    style LPWAN fill:#2C3E50,stroke:#16A085,color:#fff
    style Wi-Fi fill:#16A085,stroke:#2C3E50,color:#fff

Figure 158.1: Decision tree for selecting IoT communication technology based on power source, range requirements, and data rate needs

158.4 Energy Management Guidelines

158.4.1 Power Consumption Tiers

Typical IoT device power consumption:

Tier Power Range Battery Life (2000 mAh) Example Applications
Ultra-Low 1-100 uW 10-20 years Soil sensors, leak detectors
Low 100 uW - 10 mW 1-5 years Weather stations, parking sensors
Medium 10-100 mW Weeks-months Wearables, smart locks
High 100 mW - 1 W Days-weeks Cameras, Wi-Fi devices

158.4.2 Power Budget Calculation

Formula for average current:

Average Current = (Sleep% x Sleep_I) + (Active% x Active_I) + (TX% x TX_I)

Example: LoRa soil sensor - Sleep: 99.9% at 5 uA = 0.005 mA - Sensing: 0.05% at 5 mA = 0.0025 mA - Transmit: 0.05% at 100 mA = 0.05 mA - Average: 0.058 mA - Battery life (2000 mAh): 34,483 hours = 3.9 years

158.5 IoT Evolution Timeline

~8 min | Foundational | P04.C08.U11

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timeline
    title IoT Evolution Timeline
    1999 : RFID Technology : Auto-ID Labs
    2008 : First IoT Wave : Smart devices emerge
    2012 : IPv6 Adoption : Unlimited device addressing
    2015 : LPWAN Networks : LoRa, Sigfox deployed
    2018 : 5G & Edge Computing : Ultra-low latency
    2020 : AI at Edge : TinyML, inference on devices
    2025 : Massive IoT Deployments : Billions of connected devices

Figure 158.2: Timeline showing key milestones in IoT evolution from RFID technology in 1999 to massive IoT deployments in 2025

158.6 Communication Technology Selection Flowchart

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graph TD
    Start{Range<br/>Requirement?}
    Short[Short: less than 100m]
    Medium[Medium: 100m-1km]
    Long[Long: greater than 1km]

    Start -->|0-100m| Short
    Start -->|100m-1km| Medium
    Start -->|greater than 1km| Long

    Short --> BLE[Bluetooth LE]
    Short --> Zigbee[Zigbee]

    Medium --> Wi-Fi[Wi-Fi]
    Medium --> Thread[Thread]

    Long --> LoRa[LoRaWAN]
    Long --> NB[NB-IoT]
    Long --> Cell[Cellular]

    style Start fill:#E67E22,stroke:#16A085,color:#fff
    style BLE fill:#16A085,stroke:#2C3E50,color:#fff
    style Wi-Fi fill:#16A085,stroke:#2C3E50,color:#fff
    style LoRa fill:#2C3E50,stroke:#16A085,color:#fff

Figure 158.3: Flowchart showing communication technology selection based on range requirements with short-range, medium-range, and long-range options

158.7 Energy Harvesting Architecture

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graph LR
    Solar[Solar Panel]
    Thermal[Thermoelectric]
    Vibration[Piezoelectric]
    RF[RF Harvesting]

    Solar --> PMU[Power Management<br/>Unit]
    Thermal --> PMU
    Vibration --> PMU
    RF --> PMU

    PMU --> Battery[Energy Storage<br/>Battery/Capacitor]
    Battery --> MCU[Microcontroller<br/>& Sensors]

    style Solar fill:#E67E22,stroke:#16A085,color:#fff
    style PMU fill:#16A085,stroke:#2C3E50,color:#fff
    style MCU fill:#2C3E50,stroke:#16A085,color:#fff

Figure 158.4: Energy harvesting architecture showing multiple energy sources (solar, thermal, vibration, RF) feeding through power management to battery storage and microcontroller

158.7.1 Energy Harvesting Power Density

Source Typical Power Best Application
Outdoor Solar 10-100 mW/cm2 Environmental sensors, agricultural
Indoor Solar 10-100 uW/cm2 Building sensors, retail tags
Vibration 10-500 uW Industrial machinery, bridges
Thermal 10-50 uW/cm2 Body heat, industrial processes
RF 1-100 uW Near wireless power transmitters

158.8 Miniaturization Trend

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graph LR
    subgraph 1970s[1970s-1980s]
        A[Large ICs<br/>100+ pins]
    end
    subgraph 2000s[2000s]
        B[System-on-Chip<br/>SoC Integration]
    end
    subgraph 2010s[2010s]
        C[Wearable MCUs<br/>5mm x 5mm]
    end
    subgraph 2020s[2020s+]
        D[Sub-mm Sensors<br/>Dust-sized IoT]
    end

    A -->|Moore's Law| B
    B -->|Integration| C
    C -->|Nano-scale| D

    style 1970s fill:#7F8C8D,stroke:#16A085,color:#fff
    style 2000s fill:#16A085,stroke:#2C3E50,color:#fff
    style 2010s fill:#2C3E50,stroke:#16A085,color:#fff
    style 2020s fill:#E67E22,stroke:#16A085,color:#fff

Figure 158.5: Historical progression of hardware miniaturization from large integrated circuits in the 1970s to dust-sized IoT sensors in 2020s driven by Moore’s Law

158.9 Architecture Enablers Ecosystem

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mindmap
  root((Architecture<br/>Enablers))
    Miniaturization
      SoC Integration
      MEMS Sensors
      Reduced Cost
    Computing Power
      Edge Processing
      Low-power MCUs
      AI Acceleration
    Energy
      Batteries
      Harvesting
      Power Management
    Communications
      Short-range: BLE
      Long-range: LoRa
      Cellular: NB-IoT
    Development
      Platforms
      Tools & IDEs
      Cloud Services

Figure 158.6: Mind map showing the five key architectural enablers (miniaturization, computing power, energy, communications, development) and their sub-components 

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graph TB
    subgraph L5[" IoT Application Layer "]
        App[Smart Applications<br/>Analytics & Intelligence]
    end

    subgraph L4[" Development Enablers "]
        Dev[Platforms & Tools<br/>Arduino, Raspberry Pi, Cloud SDKs]
    end

    subgraph L3[" Communications Enablers "]
        Comm[Protocols & Networks<br/>BLE - Zigbee - LoRa - NB-IoT - 5G]
    end

    subgraph L2[" Computing & Energy Enablers "]
        Compute[Processing Power<br/>Edge MCUs, AI Accelerators]
        Energy[Power Management<br/>Batteries, Harvesting, Low-Power Design]
    end

    subgraph L1[" Physical Foundation "]
        Mini[Miniaturization<br/>SoC, MEMS, Nano-sensors]
    end

    App --> Dev
    Dev --> Comm
    Comm --> Compute
    Comm --> Energy
    Compute --> Mini
    Energy --> Mini

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    style L4 fill:#16A085,stroke:#2C3E50,color:#fff
    style L3 fill:#2C3E50,stroke:#16A085,color:#fff
    style L2 fill:#16A085,stroke:#2C3E50,color:#fff
    style L1 fill:#7F8C8D,stroke:#16A085,color:#fff
    style App fill:#E67E22,stroke:#16A085,color:#fff
    style Dev fill:#16A085,stroke:#2C3E50,color:#fff
    style Comm fill:#2C3E50,stroke:#16A085,color:#fff
    style Compute fill:#16A085,stroke:#2C3E50,color:#fff
    style Energy fill:#16A085,stroke:#2C3E50,color:#fff
    style Mini fill:#7F8C8D,stroke:#16A085,color:#fff

Figure 158.7: Alternative View: Layered dependency diagram showing how architectural enablers stack to support IoT applications. Foundation layer (miniaturization) enables computing and energy management, which support communications infrastructure, accessible through development platforms that power smart applications.

158.10 Power Consumption Comparison

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graph TB
    subgraph High[High Power: 1-10W]
        H1[Wi-Fi Active: 500mW]
        H2[LTE Cat-M: 800mW]
    end
    subgraph Medium[Medium Power: 10-100mW]
        M1[Bluetooth LE: 15mW]
        M2[Zigbee: 30mW]
    end
    subgraph Low[Low Power: less than 10mW]
        L1[LoRa TX: 5mW]
        L2[Sleep Mode: 1uW]
    end

    style High fill:#E67E22,stroke:#16A085,color:#fff
    style Medium fill:#16A085,stroke:#2C3E50,color:#fff
    style Low fill:#2C3E50,stroke:#16A085,color:#fff

Figure 158.8: Power consumption comparison across IoT communication technologies showing high power (Wi-Fi, cellular), medium power (BLE, Zigbee), and low power (LoRa, sleep mode) tiers

158.11 Knowledge Check

Question: For a wearable health monitor requiring 7-day battery life between charges, which factor is MOST critical in the design?

Explanation: Power budget dominates wearable design: Battery constraint: Typical wearable = 200-300mAh. For 7 days (168 hours): Maximum average current = 200mAh / 168h = 1.19mA. Power optimization strategies: 1) Deep sleep 98% of time (10uA sleep vs. 20mA active = 200x reduction), 2) Optimize sensor duty cycle: Sample every 10s instead of continuously, 3) Efficient transmission: BLE batch-send vs. continuous streaming, 4) Low-power MCU: <5mA active current. Why not others: Fast transmission and high processing increase power consumption. Large memory is static cost (mW), less critical than dynamic consumption (tens of mW).

Question: An outdoor environmental monitoring station has a 10cm2 solar panel. Under bright sunlight (100mW/cm2) with 20% panel efficiency, how much power can be harvested?

Explanation: Calculation: 10cm2 x 100mW/cm2 x 20% efficiency = 200mW (50mA at 4V). What this powers: 1) Continuous sensing: 10-20mA sensors easily powered, 2) Periodic transmission: LoRa (100mA for 1s every minute) = 1.67mA average - well within budget, 3) Microcontroller: 10-50mA depending on processing, 4) Leftover: Charge battery for nighttime operation. Practical deployment: 200mW allows fully autonomous operation with no battery replacement. Contrast: Indoor lighting (0.2mW/cm2) gives only 0.4mW total - 500x less!

WarningCommon Misconception: “More Power = Better IoT Device”

Myth: Higher processing power and data rates always result in better IoT systems.

Reality: IoT design is all about optimization for constraints, not maximizing specifications. A Wi-Fi-enabled device might have 100x the data rate of a LoRa device, but it’s useless for a remote agricultural sensor that needs 5-year battery life.

Why this matters: - Battery life trumps performance: A soil sensor transmitting 20 bytes/hour needs years of battery life, not megabit speeds - Cost at scale: Deploying 10,000 sensors? A $5 difference per device = $50,000 total cost difference - Network effects: Choosing cellular ($2/month/device) vs. LoRaWAN (private network, $0/month) = $240,000/year for 10,000 devices

The right approach: Start with constraints (range, battery life, cost), then select the minimum technology that meets requirements.

158.12 Chapter Summary

This chapter presented frameworks for selecting IoT technologies and managing energy:

  • Selection Framework: Use decision trees starting with power constraints, then range, then data rate
  • Power Budget Analysis: Calculate average current from duty cycle percentages to predict battery lifetime
  • Energy Harvesting: Solar, thermal, vibration, and RF sources can extend or eliminate battery replacement
  • Miniaturization Impact: Moore’s Law continues driving smaller, cheaper, more efficient IoT devices

These frameworks enable practical design decisions for real-world IoT deployments.

158.13 What’s Next?

The final chapter in this series provides hands-on labs to practice these concepts and assessment materials for exam preparation.

Continue to Labs and Assessment ->