Analytics & ML · Study deck

Edge Processing: Filtering Patterns

A vibration node can send every sample or remove noise near the machine.

Data Dora is your guide for this deck.

edgepatternsprocessing

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

After studying this chapter

Learning objectives

You will be able to:

Apply Four Edge Processing Patterns: Implement Filter, Aggregate, Infer, and Store-Forward patterns for different IoT scenarios
Select Optimal Patterns: Match processing patterns to application requirements based on latency, bandwidth, and reliability needs
Evaluate Trade-offs: Compare edge ML inference versus cloud ML, and batch processing versus real-time streaming
Avoid Common Mistakes: Understand when edge processing saves costs versus when it adds unnecessary complexity

Major section

Start With the Story

The site needs a fast alarm, but the central service needs only a short trend and a record of unusual events.

Sending every raw value may waste power and make an outage harder to manage.
Edge processing means changing data on or near the device before it travels farther.
The edge is not always cheaper or safer.

Major section

Edge Processing Patterns

Edge computing employs four primary data processing patterns, but choosing among them requires seeing what each one changes in the data path.

The first three alter the information or its representation, while store-forward preserves the payload and delays delivery.

Four Edge Processing Patterns: Filter, Aggregate, Infer, Store-Forward

Major section

Latency-Based Processing Location

Pattern selection starts with a placement bound.

The 10ms-1s branch reaches: Fog Processing with 1-4GB RAM for pattern detection and regional aggregation, while >1s OK allows: Cloud Processing to use global correlation, historical analysis, and ML training.
The final: Hybrid Approach combines tiers rather than erasing their limits.

Edge, fog, and cloud processing decision tree

Major section

Edge Processing Timeline

Once placement is chosen, the next question is how much information each local step removes.

The annotation summarises a 90% bandwidth saving, but each reduction also discards or changes representation.

Deck summary

Key takeaways

The site needs a fast alarm, but the central service needs only a short trend and a record of unusual events.

Edge computing employs four primary data processing patterns, but choosing among them requires seeing what each one changes in the data path.
Pattern selection starts with a placement bound.
Once placement is chosen, the next question is how much information each local step removes.

Retrieval practice

Recall check 1 of 2

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

Q1A greenhouse gateway receives 200 humidity readings per second but only needs to act when humidity crosses 40% or 80%. Which edge pattern minimizes bandwidth?

AThe Filter pattern, transmitting only the readings that cross the 40% or 80% thresholds

BThe Store-and-Forward pattern, buffering every reading for later bulk upload

CThe Infer pattern, running an ML model on every reading before sending it

DThe Aggregate pattern, averaging all readings into a single daily value

Show answer

Answer: A When only threshold crossings matter, the Filter pattern transmits just those events and minimizes bandwidth.

Retrieval practice

Recall check 2 of 2

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

Q2Complete the edge data filtering before cloud upload:

Aif self.last_sent is None:

Bif not self.last_sent:

Cif self.last_sent == 0:

Dif self.last_sent is False:

Show answer

Answer: A Dead-band filtering at the edge only transmits when the value changes by more than a threshold.

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Answers

Answer key.

A · When only threshold crossings matter, the Filter pattern transmits just those events and minimizes bandwidth.
A · Dead-band filtering at the edge only transmits when the value changes by more than a threshold.