Page 13 - Preliminary Analysis Towards a Standardized Readiness Framework - Interim Report

Preliminary Analysis Towards a Standardized Readiness Framework

study. Visual cameras are deployed 30-50 centimeters (about half the length of a baseball
bat) away from the crop and cover all areas of the plants. Given the field's large surface, such
infrastructure deployment capability is linked to the solution's overall cost. Soft infrastructure
such as hosted algorithms, Graphics Processing Unit (GPU) compute platforms, and network
protocol stacks provide backend computing and communications.
Apart from lab simulations and experimentations, real-world pilots and deployment support
are needed to validate innovative solutions. Peatland Forest use case [48] which aims to predict
the potential fire, provides an exemplar study where the designed algorithm could be applied
and validated in the real world.
4) Stakeholders buy-in enabled by Standards

Interoperability among different solution providers brings the choice of different vendors,
irrespective of open or proprietary solutions, to such primary actors. Standards play an important
role in ensuring compliance and interoperability.

For example, primary actors in the agriculture domain are the farmers who take the initiative in
adopting Internet of Things (IoT)-based sensors for data collection, edge devices for analytics,
and low-power communication systems, which implies that their trust and willingness to
onboard are important.

As an example, an advanced driving assistance system (see clause 4.1.14) involves different
car manufacturers with different implementations who might adopt different parameters, the
divergence in implementation might create lock-in situations for users preventing flexibility
and choice of vendors. Additionally, issues concerning data privacy, data protection, and
responsibilities are to be studied collaboratively in open standards such as ITU, which will
ensure secure, trustable, and interoperable end-to-end solutions.

5) Developer Ecosystem created via Opensource
Cloud-hosted solutions with exposed APIs for subscribing/publishing data from portals [49]
would create value for the overall industry and lead to innovative applications that solve real-
world problems using AI/ML. A prime example is research solutions for satellite data usage in
the fire propagation model [51].
6) Data collection and model validation via Sandbox pilot experimental setups

Implementing continuous improvement of models using feedback and optimizations in the
Sandbox helps to optimize essential tasks within disaster-stricken areas [52]. Unmanned aerial
vehicles (UAVs) can learn and adjust their operations (including route navigation, returning
to charging stations, and data detection and transmission) based on feedback from the
environment.

For example, traffic regulation scenarios using visual cameras and other sensors use AI/ML
feedback loops, which collect data, produce inferences, create action recommendations
and policy applications, and are tested and validated using pre-built traffic plans for specific
occasions.

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