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

被引:112
作者
Yazdinejad, Abbas [1 ]
Parizi, Reza M. [2 ]
Dehghantanha, Ali [1 ]
Karimipour, Hadis [1 ]
Srivastava, Gautam [3 ,4 ]
Aledhari, Mohammed [2 ]
机构
[1] Univ Guelph, Cyber Sci Lab, Guelph, ON 81746, Canada
[2] Kennesaw State Univ, Coll Comp & Software Engn, Kennesaw, GA 30060 USA
[3] Brandon Univ, Dept Math & Comp Sci, Brandon, MB R7A 6A9, Canada
[4] China Med Univ, Res Ctr Interneural Comp, Taichung 404, Taiwan
关键词
Drones; Authentication; Smart cities; Internet of Things; Computer architecture; blockchain; decentralized authentication; drone security; drones; Internet of Things (IoT); CHALLENGES; SYSTEM;
D O I
10.1109/JIOT.2020.3015382
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:6406 / 6415
页数:10
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