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

TLDR

Drones are increasingly deployed across military, surveillance, agriculture, and delivery sectors of the IoT, yet their airborne authentication suffers from latency and security vulnerabilities. This work proposes a low‑latency blockchain‑based authentication model for drones in smart cities to enhance security and reduce latency. The architecture employs a zone‑based network with a custom drone‑based delegated proof‑of‑stake consensus that eliminates reauthentication and is empirically compared to existing IoD models. Experiments show the system achieves lower packet loss, higher throughput, and lower end‑to‑end delay than peers while detecting 97.5 % of malicious drone attacks.

Abstract

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.