Scalable and Interoperable AIoT Ecosystems: Market-Driven Adoption of Large-Scale AI Models for Context-Aware Intelligent Infrastructure

Description

The convergence of Artificial Intelligence of Things (AIoT) and large-scale artificial intelligence models is reshaping intelligent infrastructure across industries. While advances in deep learning, foundation models, and edge computing have expanded the technical capabilities of AIoT systems, large-scale adoption remains constrained by scalability, interoperability, and market readiness. This study investigates how scalable and interoperable AIoT ecosystems enable the market-driven adoption of large-scale AI models for context-aware intelligent infrastructure. A conceptual architecture is proposed that integrates cloud–edge–device intelligence, interoperable data standards, and modular AI services to support real-time contextual reasoning. Using a market-oriented analytical framework, the study examines adoption drivers, including cost efficiency, operational resilience, regulatory compliance, and ecosystem maturity. Synthetic scenario-based simulations are used to demonstrate how large-scale AI models enhance situational awareness, predictive decision-making, and adaptive automation in smart infrastructure environments. The findings highlight that interoperability and scalability are not merely technical requirements but strategic enablers that determine economic viability and long-term sustainability. This research contributes a systems-level perspective that bridges technical design with market dynamics, offering practical insights for policymakers, system architects, and industry stakeholders seeking to deploy large-scale, context-aware AIoT infrastructures.

Steps to reproduce

This study investigates AIoT scalability from a system-level and market-oriented perspective, arguing that intelligence performance alone is insufficient to guarantee adoption. Using a structured synthetic dataset representing heterogeneous AIoT deployments, the research empirically evaluates relationships among interoperability, AI model complexity, inference latency, energy consumption, carbon emissions, and overall system efficiency. The results indicate that interoperability and architectural orchestration exert a stronger influence on system efficiency than AI model size alone, while sustainability metrics are directly linked to long-term operational viability. The paper contributes three key outcomes: (1) empirical evidence supporting interoperability-driven AIoT efficiency, (2) a reproducible analytical framework for evaluating large-scale AIoT systems, and (3) a future-oriented blueprint for scalable, sustainable, and market-aligned AIoT infrastructure.

Institutions

• North South University

  Dhaka Division, Dhaka
• Independent University

  Dhaka Division, Dhaka

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