Nanomaterial-Enabled Jamming Detection and Anti-Jamming Mechanisms for Nanoscale UAVs: Towards Resilient Nano-Aerial Systems

Rizwan Raza; Zahoor-ur-Rehman; Farhan Aadil; Salman Liaquat; Qusay Shihab Hamad

Authors

Rizwan Raza COMSATS University Islamabad (CUI), Attock Campus, Pakistan
Zahoor-ur-Rehman Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, Turkey
Farhan Aadil Department of Computer Engineering, Sivas University of Science and Technology, Sivas, Turkey
Salman Liaquat School of Electrical and Electronic Engineering, University Sains Malaysia (USM), Penang, Malaysia
Qusay Shihab Hamad University of Information Technology and Communications (UoITC), Baghdad, Iraq

Keywords:

Nano-UAVs, Jamming, Anti-Jamming, Nano-robotics, Microbotics, Nanotechnology

Abstract

The proliferation of nanoscale Unmanned Aerial Vehicles (nano-UAVs) for surveillance, environmental monitoring, and tactical applications demands a paradigm shift in ensuring secure and uninterrupted wireless communication. Given their size and limited onboard computational and power resources, nano-UAVs are particularly vulnerable to signal interference and intentional jamming attacks. The research paper explores the integration of advanced nanomaterials into the communication and sensing systems of nano-UAVs to enable efficient jamming detection and real-time anti-jamming responses. We investigate functional nanomaterials, such as graphene-based antennas, quantum dots, and spintronic devices, for their superior electromagnetic properties, ultra-low power consumption, and enhanced signal sensitivity characteristics. This work bridges nanotechnology with cybersecurity in aerospace systems, offering a novel direction for resilient communication architectures in nano-drones and paving the way for next-generation intelligent aerial micro robotics. The proposed nano-enabled UAV framework demonstrates a detection accuracy of 94.3%, with a probability of detection (Pd) exceeding 0.92 under moderate jamming conditions. The system achieves an average latency of 12.6 ms and reduces power consumption by approximately 18% compared to conventional UAV architectures. These results validate the effectiveness of nanotechnology-assisted sensing and AI-driven decision-making for robust operation in contested environments.

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