Optimizing UAV delivery for pervasive systems through blockchain integration and adversarial machine learning

Abstract

Unmanned Aerial Vehicles (UAVs), play a significant role in the advancement of pervasive systems by providing efficient, scalable, and innovative solutions in various sectors, such as smart cities or location-based services. However, the current UAV delivery scenario presents various challenges for recipients, including lengthy identity verification processes, privacy concerns, and risks of fraud and theft. In response to these issues, this paper proposes an innovative system that leverages Blockchain technology and Adversarial Machine Learning (AML) to tackle these problems effectively. The proposed system streamlines the verification process, enhances privacy safeguards, and reduces fraud risks. The integration of AML is crucial as it enables users to have greater control over their personal data, boosting privacy and security. AML also plays a critical role in this system by creating test scenarios that reinforce the machine learning model against adversarial threats, ensuring its precision and dependability in the face of malicious manipulations. The paper also provides details on the practical implementation and evaluation of this system in real-life adversarial situations. The evaluation results demonstrate superior performance on selected metrics, highlighting the potential of this system as an effective solution for verifying recipients in UAV delivery.