Page 38 - Preliminary Analysis Towards a Standardized Readiness Framework - Interim Report

Preliminary Analysis Towards a Standardized Readiness Framework

(continued)

Examples Potential AI impacts
Characteristics
1. LoRa-based IoT system for peatland management and detec-
tion was deployed in Raja Musa Forest Reserve (RMFR) in Kuala
Selangor, Malaysia [48]
2. According to the World Economic Forum, the pilot study of agri-
culture-related AI technology on 7000 farmers in the Khammam
district of Telangana (India) showed promising results, where the
net income of the farmers using the AI technology had doubled
($800 per acre) from the average income in 6 months [33]
3. Possible PoC in IMEC (India-Middle East-Europe Economic
Corridor) [50] is considered in the future.
Experimentation,
Deployment capa- 4. [51] has pilots in India, Portugal, and Brazil, currently monitoring
bility more than 117 million hectares of Brazilian wetlands, currently
studying the involvement of communities from Amazon (tribes).
5. Simulation approaches such as (sim2real [53]) - including sim2real
transfer (for leveraging simulated data) and curriculum learning
(for achieving a smoother learning curve from simple to complex
scenarios) are used in combination with drone-based disaster
management [52].
6. Pilot deployments include deployment (https:// asr .idri .edu .kh/ )

https:// hal .science/ hal -03865538/ in Partnership Ministry of Post
and telecommunication and publications for Khmer ASR are avail-
able. [2]

In this table, we take the analysis of use cases related to traffic safety and agriculture domains
as examples. Some of the important use cases within the domain of traffic safety are pedestrian
safety and collision avoidance. While studying these use cases, the different actors (vehicles,
sensors, roadside units, networks, controllers) and characteristics (regulations, infrastructure,
technology, interoperability, human factors, data types, data handling) are listed to find
common patterns, metrics, and evaluation mechanisms for the integration of AI in the domain
of traffic safety. Similarly, some of the important use cases in the domain of agriculture are
plant disease detection, soil moisture management, crop management, etc. The goal of this
multi-domain study is to develop a framework assessing AI readiness to indicate the ability to
reap the benefits of AI integration. Efforts could then be extended to scale this research across
different regions of the world and other domains and use cases.

Standard frameworks may (a) offer clear metrics for measuring readiness levels in terms of
enabling factors derived from the case-based analysis, (b) empower organizations, regions,
and countries to evaluate their preparedness to benefit from AI effectively, with respect to the
characteristics identified in the case-based analysis (c) study the various risk factors in simulated
and experimentation scenarios so as to make informed decisions and (d) apply regional and
domain-specific preferences while deploying AI-based solutions.

So, a methodological bottom-up approach is employed to study various use case scenarios
and to study the corresponding impacts of artificial intelligence (AI).

Two main parts of our analysis are actors and characteristics related to the use case. Some
examples of actors are vehicles and humans, networks, controllers, and traffic management
systems. Actors may be equipped with or utilize sensor technologies that enable AI integration.
AI integration may require network connection with specific requirements such as low latency,

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