Enabling Drones in the Internet of Things With Decentralized Blockchain-Based Security

• Published in: IEEE internet of things journal Vol. 8; no. 8; pp. 6406 - 6415
• Main Authors: Yazdinejad, Abbas, Parizi, Reza M., Dehghantanha, Ali, Karimipour, Hadis, Srivastava, Gautam, Aledhari, Mohammed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Blockchain; Computer architecture; Cryptography; decentralized authentication; drone security; Drones; Empirical analysis; Internet; Internet of Things; Internet of Things (IoT); Network latency; Security; Smart cities
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3015382

There is currently widespread use of drones and drone technology due to their rising applications that have come into fruition in the military, safety surveillance, agriculture, smart transportation, shipping, and delivery of packages in our Internet-of-Things global landscape. However, there are security-specific challenges with the authentication of drones while airborne. The current authentication approaches, in most drone-based applications, are subject to latency issues in real time with security vulnerabilities for attacks. To address such issues, we introduce a secure authentication model with low latency for drones in smart cities that looks to leverage blockchain technology. We apply a zone-based architecture in a network of drones, and use a customized decentralized consensus, known as drone-based delegated proof of stake (DDPOS), for drones among zones in a smart city that does not require reauthentication. The proposed architecture aims for positive impacts on increased security and reduced latency on the Internet of Drones (IoD). Moreover, we provide an empirical analysis of the proposed architecture compared to other peer models previously proposed for IoD to demonstrate its performance and security authentication capability. The experimental results clearly show that not only does the proposed architecture have low packet loss rate, high throughput, and low end-to-end delay in comparison to peer models but also can detect 97.5% of attacks by malicious drones while airborne.