AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey
2026-06-22 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain how using energy-harvesting technology allows small devices to work without batteries, by reflecting signals they receive, called backscatter communication. However, this method has problems like weak signals and reliance on outside sources. They describe how drones (UAVs) can help by moving around to send, receive, and manage these signals better. The article also covers integrating sensing and communication, using AI to optimize the system, and reviews current research while pointing out challenges and future research needs.
Backscatter CommunicationZero-energy IoTUnmanned Aerial Vehicles (UAVs)Integrated Sensing and Communication (ISAC)Energy HarvestingArtificial Intelligence (AI)LocalizationRF SignalsEdge Intelligence6G Technologies
Authors
Ruhul Amin Khalil
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.