Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach

Chen, Bin; Zhang, Weihua; Shi, Yanbin; Lv, Di; Yang, Zilan

doi:10.1016/j.comcom.2023.05.005

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Chen B and others have further investigated how these issues lead to resource wastage and delays in response in prac-tical operations. Blockchain technology is considered a potential solution to these problems [13][14][15]. Liu D and others have conducted in-depth discussions, particularly ana-lyzing how the decentralized and immutable nature of blockchain can enhance data security in the overall supply chain [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain

Wang,

Guo,

Shi

et al. 2024

PLoS ONE

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In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

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Section: Introductionmentioning

confidence: 99%

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain

Wang,

Guo,

Shi

et al. 2024

PLoS ONE

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Enhancing agricultural wireless sensor network security through integrated machine learning approaches

Sharma,

Bhardwaj,

Kaushik

2024

Security and Privacy

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Wireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.

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Matching and Integration of Hybrid Powertrain and Onboard System for Emergency Rescue Vehicles Using an Integrated Model-Based Methodology

Agyeman,

Fayzullayevich,

Liu

et al. 2024

Preprint

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Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach

Cited by 5 publications

References 32 publications

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain

Enhancing agricultural wireless sensor network security through integrated machine learning approaches

Matching and Integration of Hybrid Powertrain and Onboard System for Emergency Rescue Vehicles Using an Integrated Model-Based Methodology

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