Page 356 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

5) it is critical that the solution is highly scalable, by applying AI and 5G-A ISAC, applying
beyond coastal areas to rural and forest regions where human-wildlife interactions occur,
providing robust support for global conservation efforts.
6) it is expected that by enabling remote monitoring from shore-based systems, it eliminates
the need for frequent offshore enforcement trips, thereby allows protectors to operate in
a safer and more efficient environment.
7) it is expected that by implementing a precise perception and early warning system to track
ship trajectories, the solution effectively prevents unauthorized intrusions into protected
areas.

4 Sequence Diagram

• SRC (source): This node is the source of data that can be used as input to the ML pipeline.
• C (collector): This node is responsible for collecting data from one or more SRC nodes.
• PP (preprocessor): This node is responsible for cleaning data, aggregating data or
performing any other preprocessing needed.
• M (model): This is a machine learning model
• P (policy): This node enables the application of policies to the output of the model node.
• D (distributor): This node is responsible for identifying the SINK(s) and distributing the
output of the M node.
• SINK: This node is the target of the ML output on which it takes action.

5 References

[1] TM Forum, "Wildlife Guardian: AI + 5G Advanced Sustainable Application," Catalysts Project
C24.5.747, accessed June 24, 2025, https:// www .tmforum .org/ catalysts/ projects/ C24 .5 .747/
wildlife -guardian -ai -5g -advanced -sustainable -application.

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