An IoE blockchain-based network knowledge management model for resilient disaster frameworks

被引:8
|
作者
Javadpour, Amir [1 ]
Alipour, Farinaz Sabz [2 ]
Sangaiah, Arun Kumar [3 ]
Zhang, Weizhe [1 ]
Ja'far, Forough [4 ]
Singh, Ashish [5 ]
机构
[1] Harbin Inst Technol, Dept Comp Sci & Technol Cyberspace Secur, Shenzhen, Peoples R China
[2] Old Dominion Univ, Dept Engn Managements & Syst Engn, 2101 Hampton Blvd, Norfolk, VA 23529 USA
[3] Natl Yunlin Univ Sci & Technol, Grad Sch Optoelect, Touliu, Yunlin, Taiwan
[4] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran
[5] Kalinga Inst Ind Technol KIIT, Sch Comp Engn, Bhubaneswar 751024, India
来源
JOURNAL OF INNOVATION & KNOWLEDGE | 2023年 / 8卷 / 03期
基金
中国国家自然科学基金;
关键词
Blockchain technology; Decentralized network; Disaster supply network; Internet of things; Network simulation; Resilience; SUPPLY CHAINS; HUMANITARIAN; LOGISTICS; RISK;
D O I
10.1016/j.jik.2023.100400
中图分类号
F [经济];
学科分类号
02 ;
摘要
The disaster area is a constantly changing environment, which can make it challenging to distribute supplies effectively. The lack of accurate information about the required goods and potential bottlenecks in the distribution process can be detrimental. The success of a response network is dependent on collaboration, coordination, sovereignty, and equal distribution of relief resources. To facilitate these interactions and improve knowledge of supply chain operations, a reliable and dynamic logistic system is essential. This study proposes the integration of blockchain technology, the Internet of Things (IoT), and the Internet of Everything (IoE) into the disaster management structure. The proposed disaster response model aims to reduce response times and ensure the secure and timely distribution of goods. The hyper-connected disaster supply network is modeled through a concrete implementation on the Network Simulation (NS2) platform. The simulation results demonstrate that the proposed method yields significant improvements in several key performance metrics. Specifically, it achieved more than a 30% improvement in the successful migration of tasks, a 17% reduction in errors, a 15% reduction in delays, and a 9% reduction in energy consumption.(c) 2023 Published by Elsevier Espana, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页数:10
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