AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey

A comprehensive survey examines AI-powered UAV-assisted backscatter communication and integrated sensing for zero-energy IoT devices that harvest energy from ambient RF signals. The research addresses fundamental limitations in backscatter systems—including weak signal reflection, double-path loss, and coverage constraints—by leveraging unmanned aerial vehicles as mobile emitters, relays, and edge processors combined with AI optimization techniques.

This survey represents a significant convergence of three enabling technologies: backscatter communication for energy-harvesting IoT, unmanned aerial vehicles for enhanced coverage and mobility, and AI-driven optimization. The work addresses a fundamental challenge in IoT deployment: enabling billions of passive devices to operate indefinitely without battery replacement through ambient energy harvesting. Backscatter communication achieves this by reflecting existing RF signals rather than generating new transmissions, but suffers from severe propagation limitations and signal degradation. UAVs serve as intelligent solutions by acting as mobile RF sources, improving line-of-sight paths, and providing real-time sensing capabilities. The integration of ISAC (Integrated Sensing and Communication) enables simultaneous data transmission and environmental awareness, maximizing spectral efficiency. AI techniques optimize network performance through dynamic path planning, signal detection algorithms, and localization improvements. The survey identifies critical gaps in current research, including realistic channel modeling, energy-neutral operations, hardware validation, and integration pathways with emerging technologies like reconfigurable intelligent surfaces (RIS) and 6G networks. For practitioners and researchers, this comprehensive taxonomy provides a structured framework for understanding interdependencies between backscatter modes, localization functions, and AI methodologies. The identified challenges around trustworthy AI, security, and privacy indicate that practical deployment requires not only technical advances but also robust validation frameworks and security architectures. The convergence toward 6G integration suggests this technology domain will increasingly influence future wireless infrastructure.

• →UAV-assisted backscatter systems address fundamental propagation limitations by providing mobile RF sources and improved signal paths for zero-energy IoT devices.
• →Integrated sensing and communication enables simultaneous data transmission and target detection, improving resource efficiency in constrained wireless networks.
• →AI optimization techniques are essential for dynamic UAV path planning, signal detection, and localization in backscatter systems.
• →Critical research gaps remain in realistic channel modeling, hardware validation, and integration with emerging technologies like RIS and 6G.
• →Security, privacy, and trustworthy AI frameworks are prerequisite for practical deployment of AI-driven UAV-backscatter systems.