Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G
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Abstract (arXiv)
Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowered applications rely on powerful AI models to extract features and understand semantics from ubiquitous wireless sensors. On the other hand, the massive amount of sensory data serves as the fuel to continuously refine edge AI models. This deep integration of sensing and edge AI has given rise to a new task-oriented paradigm known as integrated sensing and edge AI (ISEA), which features a holistic design approach to communication, AI computation, and sensing for optimal sensing-task performance. In this article, we present a comprehensive survey for ISEA. We first provide technical preliminaries for sensing, edge AI, and new communication paradigms in ISEA. Then, we study several use cases of ISEA to demonstrate its practical relevance and introduce current standardization and industrial progress. Next, the design principles, metrics, tradeoffs, and architectures of ISEA are established, followed by a thorough overview of ISEA techniques, including digital air interface, over-the-air computation, and advanced signal processing. Its interplay with various 6G advancements, e.g., new physical-layer and networking techniques, are presented. Finally, we present future research opportunities in ISEA, including the integration of foundation models, convergence of ISEA and integrated sensing and communications (ISAC), ultra-low-latency ISEA, and practicality issues.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jan 2025.
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Citing papers per year: 2025: 3; 2026: 3.
6 papers.
- 2025Revisiting Outage for Edge Inference Systemscited by 5
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
- 2026Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmissioncited by 0
- 2026Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel Statescited by 0
- 2026When Agentic AI Meets Integrated Sensing and Communicationcited by 0
- 2026When Agentic AI Meets Integrated Sensing and Communication6 Aug 2026
- 2026Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States3 Mar 2026
- 2026Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission13 Jan 2026
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyond20 May 2025
- 2025Revisiting Outage for Edge Inference Systems22 Mar 2025