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

A Javadpour, FS AliPour, AK Sangaiah… - Journal of Innovation & …, 2023 - Elsevier
A Javadpour, FS AliPour, AK Sangaiah, W Zhang, F Ja'far, A Singh
Journal of Innovation & Knowledge, 2023Elsevier
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 …
Abstract
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.
Elsevier
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