# Enhancing Observatories With Fog Computing

Ammar Kazem, Guillaume Pierre, Laurent Longuevergne

► To cite this version:

Ammar Kazem, Guillaume Pierre, Laurent Longuevergne. Enhancing Observatories With Fog Computing. SSDBM 2024 - 36th International Conference on Scientific and Statistical Database Management, Jul 2024, Rennes, France. pp.1-1, 2024. [hal-04634470](https://hal-04634470v1)

HAL Id: hal-04634470

https://inria.hal.science/hal-04634470v1

Submitted on 4 Jul 2024

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# ENHANCING OBSERVATORIES WITH FOG COMPUTING

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## FORMA

CONCEVOIR ET TESTER LES
OBSERVATOIRES DE
L'ANTHROPOCENE

Ammar KAZEM

Guillaume PIERRE

Laurent LONGUEVERGNE

IRISA

IRISA

OSUR

ammar.kazem@irisa.fr

guillaume.pierre@irisa.fr

laurent.longuevergne@univ-rennes.fr

## OBJECTIVES

* Define the current state of practice in environmental monitoring systems used by observatories.

* Underline the challenges and common requirements for future systems.

* Highlight the potential of fog computing in observatories.

## METHODOLOGY

We conducted a survey with observatories affiliated with :

* OZCAR

* Réseau Zone Atelier

25
OZCAR

5
Countries

5
Réseau Zone Atelier

63%
Full responses

4
TERENO, ANAEE, ...

6
Interviews

## CURRENT PRACTICES

The survey identified two types of observatories:

* Event-oriented observatories representing **54%** of the surveyed observatories

* Long-term oriented observatories representing **46%** of the surveyed observatories

And the usage of two types of environmental monitoring systems:

Data logging

Environmental sensor network

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## ADVANTAGES OF CURRENT SYSTEMS

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Adapted to power
constraints

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Durable and tested
hardware

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Widely recognized in
the community

## DRAWBACKS OF CURRENT SYSTEMS

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Insufficient processing
power

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Local view of data

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Limited visibility into
hardware status

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Site internet de
TERRA FORMA

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anr

### Power sources in observatories

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### Different delays in observatories

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### Number of sensors per site

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### Usage of wireless transmission

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## COMMON SYSTEM CHARACTERISTICS

### Event Detection and Prediction

Utilizing both in situ and external data to detect specific events and forecast them in advance.

### Event Response

Updating the environmental monitoring system based on the prediction or detection of an event.

### System Health Monitoring

Ensuring the earliest possible detection of any hardware failures within the system.

### Real Time data Transmission

Enabling the transmission of data in near real-time as needed.

## POTENTIAL OF FOG IN OBSERVATORIES

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1. Detection of rain

2. Collection of data from the rainfall sensor

3. A model combines the data from the sensor with external data to detect a rain event

4. Another model is alerted of the event and orders a response

<table><thead><tr><th></th><th>Global View Over Data</th><th>Remote Control Of Hardware</th><th>Integration of External Data</th></tr></thead><tbody><tr><td>With Fog Computing</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Without Fog Computing</td><td>x</td><td>x</td><td>x</td></tr></tbody></table>

## CONCLUSION

Observatories' data are strategic. Integrating fog computing into environmental monitoring systems could significantly enhance processing capabilities and provide a comprehensive view of in situ data globally. These key improvements would support the development of essential functionalities. Special attention must be given to the unique challenges in observatories, such as limited energy and connectivity.