Blockchain-based Secure Delivery of Medical Supplies Using Drones

Cheema, Asaad, Ansari, Rafay, Ashraf, Nouman, Hassan, Syed Ali, Qureshi, Hassaan Khaliq and Bashir, Ali Kashif (2022) Blockchain-based Secure Delivery of Medical Supplies Using Drones. Computer Networks, 204. p. 108706. ISSN 1389-1286

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Official URL: https://doi.org/10.1016/j.comnet.2021.108706

Abstract

The advantages provided by the drones with regards to three dimensional mobility and ease of deployment makes them a viable candidate for 5G and beyond (B5G) networks. Significant amount of research has been conducted on the aspect of networking for using drones as base stations to provide different services. In this work, we deviate from the traditional use of drones to provide connectivity and explore the delivery of products through drones in the context of maintaining social distancing. However, drone delivery process for critical applications such as delivering medical supplies is vulnerable to attacks such as impersonation attacks and eavesdropping. The security of drone operation is important to save the users from any breaches that can lead to financial and physical losses. To cope with these security issues and to make the delivery process transparent, we propose a blockchain-based drone delivery system that registers and authenticates the participating entities including products (medical supplies), warehouse (medical centers) and drones. To this end, we utilize Ethereum platform for implementation of blockchain and smart contract and we present an analysis of different factors that influence the authentication process in terms of time and the number of transactions. Furthermore, to make the communication of a drone with command and control center more secure and robust, we use machine learning (ML)-based intrusion prevention system to detect any impersonation attacks with an accuracy of 97%.

Item Type: Article
Uncontrolled Keywords: Unmanned aerial vehicles, Blockchain, Machine learning, Fifth generation (5G) and beyond
Subjects: B800 Medical Technology
G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 21 Dec 2021 14:34
Last Modified: 18 Jan 2022 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48033

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