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# Fog Computing Challenges in Natural Environment Observatories

Guillaume Pierre

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## To cite this version:

Guillaume Pierre. Fog Computing Challenges in Natural Environment Observatories. 17th Cloud Control Workshop, Jun 2024, Skåvsjöholm, Sweden. . hal-05008446

HAL Id: hal-05008446

https://inria.hal.science/hal-05008446v1

Submitted on 27 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Fog Computing Challenges in Natural Environment Observatories

Guillaume Pierre
Cloud Control Workshop, June 20th 2024

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Université
de Rennes
Researchers in natural sciences like to create *observatories* where they can measure and observe the ecosystem from multiple points of view

- Define a geographical zone of particular interest

- Collect and archive measurement data

- Derive actionable insight (short-term) and/or long-term analysis

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We surveyed 34 observatories in France, Germany, Laos, Morocco and Nepal [1]

- **Long-term observation observatories:** periodic data collection over long periods of time (several years)

- **Event-oriented observatories:** interested in short- or medium-duration events such as floods, heat waves and earthquakes. Other periods are not interesting.

<figure><img src="image_6.png" /><figcaption>**Long-term**</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Event-oriented**</figcaption></figure>

[1] A. Kazem, G. Pierre and L. Longuevergne. Enhancing Environmental Observatories with Fog Computing. Frontiers in Environmental Science, in press.
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<table><thead><tr><th>Power supply:</th><th>Electrical grid</th><th>Solar</th><th>Internal batteries</th><th>Other</th></tr></thead><tbody><tr><td></td><td>13%</td><td>43%</td><td>39%</td><td>4%</td></tr></tbody></table>
Failure detection delays ~6 days (wireless reporting), up to ~109 days
(manual collection)

■ Crash failures: power sources, sensors, networks, data loggers, etc.

■ Partial failures: incorrect measurements, data corruption, etc.

<figure><img src="image_9.png" /></figure>
Opportunity: introduce fog/edge computing for on-site data processing

- To generate alerts about failures

- To aggregate data before transmission

- To compare produced data with external data sources

- To compare produced data with a model's output

- To adjust sampling frequencies or trigger actuators when an interesting event is about to happen

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# Infrastructure challenges

## Requirements

- Medium computing capacity

- Low energy usage → energy proportionality

- Size

- Cost

## Options

- Solar panel + battery

- Raspberry PIs? Better several old ones than a single recent one?

- Arduino for switching servers on/off?

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## Requirements

- Reliability vs. power loss, network disconnections etc.

- Multitenancy

- Message bus to distribute data to applications

- Ability to reduce energy usage in periods of scarcity

## Options

- ? Orchestration: k3s? k8s?

- ? Power availability predictions

- ? Arbitration between multiple users' requirements

<figure><img src="image_12.png" /></figure>
## Requirements

- Developers are not IT professionals

- Applications may range from very simple functions to full-scale ML models

- Remote system control

- Applications must expose power-saving modes

## Options

- ? Standard libraries (python)?

- ? Dashboard?

- ? Define standard application formats (connect to data sources, power-saving modes)?
Fog for environmental observatories ≠ fog for smart cities or industry

- Often in remote locations

- Often based on poor infrastructure (power, network)

- Multitenancy

- Many new challenges, especially regarding energy management

Cool aspect: getting to work great people who love their work, who can think big yet accept small steps toward better systems.

<figure><img src="image_13.png" /><figcaption>Ammar Kazem</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Laurent Longuevergne</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Christoff Andermann</figcaption></figure>