Towards Smarter Infrastructure: Integrating CS and AI-Driven Edge IoT Systems

Compressive Sensing (CS) has emerged as a modern and alternative signal processing paradigm that challenges the traditional Nyquist-Shannon sampling theorem by enabling accurate signal reconstruction from significantly fewer measurements. This paradigm proves particularly valuable in edge-enabled IoT systems, where devices operate under strict constraints in terms of energy, bandwidth, and computational capacity. In this article, we explore the application of CS in edge-enabled IoT scenarios, with a focus on smart infrastructure monitoring. We examine various system architectures and frameworks that leverage AI-based solutions to optimize resource utilization while maintaining high levels of accuracy. However, the mere integration of CS or AI models does not inherently ensure optimal performance. Achieving tangible benefits requires careful co-optimization of communication and computation strategies at the edge, in order to fully harness the energy-saving potential of these technologies in real-world IoT deployments. To this end, the article presents a range of strategies designed to address this challenge, highlighting the effectiveness of combining CS and AI in edge IoT systems and paving the way for more efficient and intelligent resource management in AI-empowered IoT environments.