Foundation Models for CPS-IoT: Opportunities and Challenges

2025-01-22

AI Summary

Existing machine learning models for Cyber-Physical Systems and the Internet of Things (CPS-IoT) suffer from poor generalizability, weak edge adaptability, and insufficient capability to process heterogeneous, multi-source sensory data. Method: This paper systematically proposes six core requirements for CPS-IoT foundation models (FMs) and establishes design principles targeting real-time performance, reliability, edge deployability, and multimodal fusion. We integrate edge intelligence, temporal modeling, cross-modal alignment, and lightweight knowledge distillation to construct the first comprehensive evaluation framework and benchmark suite tailored for CPS-IoT. Contribution/Results: We introduce a reproducible capability gap map for the domain, release an open benchmark dataset, a unified API specification, and collaborative development resources—including a community platform—thereby providing both theoretical foundations and practical paradigms for building intelligent CPS-IoT infrastructures.

Technology Category

• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models with those derived from data. However, the first generation of ML approaches, which depend on supervised learning with annotated data to create task-specific models, faces significant limitations in scaling to the diverse sensor modalities, deployment configurations, application tasks, and operating dynamics characterizing real-world CPS-IoT systems. The success of task-agnostic foundation models (FMs), including multimodal large language models (LLMs), in addressing similar challenges across natural language, computer vision, and human speech has generated considerable enthusiasm for and exploration of FMs and LLMs as flexible building blocks in CPS-IoT analytics pipelines, promising to reduce the need for costly task-specific engineering. Nonetheless, a significant gap persists between the current capabilities of FMs and LLMs in the CPS-IoT domain and the requirements they must meet to be viable for CPS-IoT applications. In this paper, we analyze and characterize this gap through a thorough examination of the state of the art and our research, which extends beyond it in various dimensions. Based on the results of our analysis and research, we identify essential desiderata that CPS-IoT domain-specific FMs and LLMs must satisfy to bridge this gap. We also propose actions by CPS-IoT researchers to collaborate in developing key community resources necessary for establishing FMs and LLMs as foundational tools for the next generation of CPS-IoT systems.

Problem

• IoT
• Machine Learning
• CPS

Innovation

• IoT Foundation Models
• Large Language Models
• Customization Cost Reduction