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

2026-06-22

AI Summary

This work addresses key challenges in backscatter communication for zero-power Internet of Things (IoT), including weak reflection, double-path loss, limited coverage, direct-link interference, and dependence on external RF sources, by proposing an artificial intelligence (AI)-enabled unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) framework. Leveraging the PRISMA methodology, the study establishes a structured review system that, for the first time, systematically integrates UAVs, backscatter communication, ISAC, and AI into a cohesive paradigm. It introduces a unified taxonomy encompassing network architecture, UAV roles, backscatter modes, sensing functionalities, and AI-driven optimization strategies. Through quantitative analysis, comparative tables, and case studies, the paper clarifies the current state of the art and outlines critical future directions—such as realistic channel modeling, scalable and trustworthy AI, hardware validation, and integration with 6G—to advance zero-power IoT systems.

Abstract

Zero-energy Internet of Things (IoT) enables passive or near-passive devices to operate on harvested energy rather than batteries. Backscatter communication (BackCom) supports this vision by enabling tags to transmit data via reflection and modulation of incident RF signals, but it suffers from weak reflections, double-path loss, limited coverage, direct-link interference, and dependence on external RF sources. Unmanned aerial vehicles (UAVs) can mitigate these limitations by acting as mobile carrier emitters, data collectors, relays, aerial receivers, mobile anchors, sensing platforms, and edge-intelligence nodes. Integrated sensing and communication (ISAC) further enables the sharing of wireless resources for data transmission, localization, target sensing, and environmental awareness. This article surveys RF-based AI-empowered UAV-assisted backscatter localization and ISAC for zero-energy IoT. It reviews enabling technologies, presents a structured PRISMA-informed methodology, and develops a unified taxonomy covering network architectures, UAV roles, backscatter modes, RF sources, localization and sensing functions, AI techniques, and performance metrics. It also discusses UAV-assisted BackCom, passive localization, ISAC-enabled UAV-backscatter systems, and AI-driven optimization through comparative tables, quantitative trend analysis, coverage evaluation, and tutorial-style numerical illustrations. Finally, it identifies open challenges and future directions in realistic channel modeling, energy-neutral operation, benchmarking, reproducibility, scalable and trustworthy AI, security, privacy, hardware validation, and integration with RIS, MEC, digital twins, and 6G technologies.

Problem

• Backscatter communication
• Zero-energy IoT
• UAV-assisted localization
• Integrated sensing and communication
• Passive devices

Innovation

• UAV-assisted backscatter
• Integrated Sensing and Communication (ISAC)
• Zero-energy IoT
• AI-empowered localization
• Passive backscatter communication