Analytics & ML · Study deck

Edge Acquisition: Power Budgets and Pipelines

A fast edge pipeline is useless when its node drains before service day.

Data Dora is your guide for this deck.

edge
Data Dora, the module guide, in a scene from this chapter.
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After studying this chapter

Learning objectives

You will be able to:

  • Explain: Mains power permits continuous acquisition while edge work primarily controls bandwidth; a battery branch makes wake time and radio duty cycle explicit; harvesting adds a variable energy budget that must adapt sampling and transmission.
  • Explain: A single edge gateway serves all three roles, and the dominant cost driver is always the highest-bandwidth device category (cameras, in this case).
  • Explain: Adjust the parameters below to see how edge processing affects connectivity costs for a multi-shed IoT deployment.
  • Explain: If the edge system only shifts cloud cost into unmanaged local maintenance, the architecture is not actually cheaper.
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Major section

Power Budget Decision Tree

Mains power permits continuous acquisition while edge work primarily controls bandwidth; a battery branch makes wake time and radio duty cycle explicit; harvesting adds a variable energy budget that must adapt sampling and transmission.

  • The branches show why the same sensor workload needs different scheduling evidence in different deployments.
Power budget decision tree: mains-powered devices optimize for bandwidth, battery-powered devices duty-cycle sampling and transmission, and energy-harvesting devices adapt to available energy.
Power budget decision tree: mains-powered devices optimize for bandwidth, battery-powered devices duty-cycle sampling and transmission, and energy-harvesting devices adapt to available energy.
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Major section

Edge Processing Savings Calculator

Adjust the parameters below to see how edge processing affects connectivity costs for a multi-shed IoT deployment.

  • The dominant cost driver is typically the highest-bandwidth device (cameras).
  • The 99% data reduction makes rural 4G connectivity economically viable.
  • A single edge gateway serves all three roles, and the dominant cost driver is always the highest-bandwidth device category (cameras, in this case).

Numbers to remember

99%The 99% data reduction makes rural 4G connectivity economically viable.
4-20 mAand Non-IP Things need protocol translation (Modbus/4-20 mA/RFID to MQTT).
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Major section

Edge Acquisition Storage Tiers

Edge acquisition design does not stop at the gateway.

  • The storage plan should be tied to a TCO calculation.
  • If the edge system only shifts cloud cost into unmanaged local maintenance, the architecture is not actually cheaper.
  • Retention policies should be executable, not just documented.
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Major section

Concept Relationships

Data generation patterns vary 1000x across categories (door sensors: bytes/day vs cameras: gigabytes/day).

  • Edge Compute Patterns: Where to process (edge/fog/cloud) depends on device capabilities established here.
  • Edge Fog Computing: Big Things can participate in fog layer; Small/Non-IP Things need edge gateways.
  • Data Quality and Preprocessing: Edge acquisition is where validation catches errors at 1% cost vs cloud fixes.
  • Multi-Sensor Data Fusion: Fusing data from different device categories requires understanding their generation patterns.

Why it matters

Edge Data Acquisition: Sampling and Compression: How to reduce data volume based on device category (cameras need compression, temperature sensors need aggregation).

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Deck summary

Key takeaways

Mains power permits continuous acquisition while edge work primarily controls bandwidth; a battery branch makes wake time and radio duty cycle explicit; harvesting adds a variable energy budget that must adapt sampling and transmission.

  • Adjust the parameters below to see how edge processing affects connectivity costs for a multi-shed IoT deployment.
  • Edge acquisition design does not stop at the gateway.
  • Data generation patterns vary 1000x across categories (door sensors: bytes/day vs cameras: gigabytes/day).
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Retrieval practice

Recall check 1 of 3

Data Dora says: answer from memory, then check your reasoning.

Q1Complete the IMU edge aggregation for data reduction:

Aself.buffer = []
Bself.buffer = {}
Cself.buffer = None
Dself.buffer = 0
Show answer

Answer: A The magnitude of a 3D acceleration vector is sqrt(x^2 + y^2 + z^2), giving a single scalar representing total acceleration regardless of orientation.

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Retrieval practice

Recall check 2 of 3

Data Dora says: answer from memory, then check your reasoning.

Q2A factory has temperature sensors using Modbus RS-485 protocol, IP cameras with Wi-Fi, and a central SCADA server. How would you classify these three device types?

AAll are Big Things because they're in an industrial setting
BTemperature sensors are Small IP Things, cameras are Big Things, SCADA is Non-IP
CTemperature sensors are Non-IP Things, cameras are Small IP Things, SCADA is a Big Thing
DAll are Non-IP Things because they're industrial devices
Show answer

Answer: C see answers page

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Retrieval practice

Recall check 3 of 3

Data Dora says: answer from memory, then check your reasoning.

Q3Why do Non-IP Things typically generate the least data volume despite often being the most numerous devices in IoT deployments?

AThey have slower processors that cannot generate data quickly
BThey typically measure simple physical parameters
CGateways compress their data before counting it
DNetwork protocols limit how much they can transmit
Show answer

Answer: B

Q4Place each acquisition responsibility where it lives so you can keep urgent local decisions separate from remote fleet aggregation.

ASensor and actuator layer
BEdge processing node
CCloud backend
Show answer

Answer: A Separate field observation and action, bounded edge processing, and cloud aggregation so you can choose latency, buffering, and failure behaviour deliberately.

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Print reference

Answers 1 of 3

Answer key.

  1. A · The magnitude of a 3D acceleration vector is sqrt(x^2 + y^2 + z^2), giving a single scalar representing total acceleration regardless of orientation.
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Print reference

Answers 2 of 3

Answer key.

  1. C · The classification depends on connectivity capabilities, not the deployment environment: - Modbus RS-485 temperature sensors = Non-IP Things (no native IP stack, need gateway) - Wi-Fi IP cameras = Small IP Things (embedded device with IP connectivity) - SCADA server = Big Thing (full computer/database system) This classification determines the acquisition architecture: sensors need a gateway for protocol translation, cameras connect directly via Wi-Fi, and the SCADA server can use any IP protocol.
  2. B
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Print reference

Answers 3 of 3

Answer key.

  1. A · Separate field observation and action, bounded edge processing, and cloud aggregation so you can choose latency, buffering, and failure behaviour deliberately.
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