Semantic-Relevance Based Sensor Selection for Edge-AI Empowered Sensing Systems

2025-03-17

AI Summary

In 6G integrated sensing and edge AI (ISEA), massive high-dimensional sensor deployments cause severe communication bottlenecks. Method: We propose a semantics-aware sensor selection framework that, unlike conventional task-agnostic approaches, explicitly models the semantic relevance between sensor observations and downstream tasks; it jointly incorporates channel state information to construct a fusion priority metric optimized for end-to-end sensing accuracy. The problem is formulated as an integer program, solved via a tight objective approximation and a low-complexity heuristic algorithm. Contribution/Results: Experiments on synthetic and real-world datasets demonstrate substantial improvements in task accuracy over baseline methods, validating that semantics-driven sensor selection delivers critical performance gains for ISEA systems—enhancing both communication efficiency and perception fidelity.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The sixth-generation (6G) mobile network is envisioned to incorporate sensing and edge artificial intelligence (AI) as two key functions. Their natural convergence leads to the emergence of Integrated Sensing and Edge AI (ISEA), a novel paradigm enabling real-time acquisition and understanding of sensory information at the network edge. However, ISEA faces a communication bottleneck due to the large number of sensors and the high dimensionality of sensory features. Traditional approaches to communication-efficient ISEA lack awareness of semantic relevance, i.e., the level of relevance between sensor observations and the downstream task. To fill this gap, this paper presents a novel framework for semantic-relevance-aware sensor selection to achieve optimal end-to-end (E2E) task performance under heterogeneous sensor relevance and channel states. E2E sensing accuracy analysis is provided to characterize the sensing task performance in terms of selected sensors' relevance scores and channel states. Building on the results, the sensor-selection problem for accuracy maximization is formulated as an integer program and solved through a tight approximation of the objective. The optimal solution exhibits a priority-based structure, which ranks sensors based on a priority indicator combining relevance scores and channel states and selects top-ranked sensors. Low-complexity algorithms are then developed to determine the optimal numbers of selected sensors and features. Experimental results on both synthetic and real datasets show substantial accuracy gain achieved by the proposed selection scheme compared to existing benchmarks.

Problem

• Optimizes sensor selection for edge-AI sensing systems
• Addresses communication bottleneck in integrated sensing and edge AI
• Maximizes sensing accuracy using semantic relevance and channel states

Innovation

• Semantic-relevance-aware sensor selection framework
• Priority-based sensor ranking using relevance and channel states
• Low-complexity algorithms for optimal sensor and feature selection