Blockchain-based Secure Delivery of Medical Supplies Using Drones

Asaad Cheema, Rafay Iqbal Ansari, Nouman Ashraf, Syed Ali Hassan, Hassaan Khaliq Qureshi, Ali Kashif Bashir

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
56 Downloads (Pure)

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%.
Original languageEnglish
Article number108706
Number of pages10
JournalComputer Networks
Volume204
Early online date1 Jan 2022
DOIs
Publication statusPublished - 26 Feb 2022

Keywords

  • Unmanned aerial vehicles
  • Blockchain
  • Machine learning
  • Fifth generation (5G) and beyond

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