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优化农业物联网部署中的QoS和安全性:一种生物灵感的Q学习模型,具有定制的分片

Sonali Mahendra Sonavane¹, G R Prashantha², Pranjali Deepak Nikam³

¹G H Raisoni College of Engineering and Management, Pune, Maharashtra, India.

|January 31, 2024

概括

本研究介绍了一种高效的Q-Learning生物启发模型,以提高农业物联网 (AIoT) 部署的服务质量 (QoS) 和安全性. 这种新的方法提高了采矿速度,降低了能源消耗,确保了稳定的网络性能.

科学领域:

计算机科学计算机科学
人工智能的人工智能
网络安全网络安全

背景情况:

农业物联网 (AIoT) 部署面临着基于区块链的现有安全和服务质量 (QoS) 模型的挑战,这些模型因复杂性,高延迟和显著的能源消耗而受到影响.
当前模型的可扩展性受到共识效率和矿工效率的限制,这阻碍了大规模AIoT网络的实时性能.

研究的目的:

设计一个高效的Q-Learning生物灵感模型,以在AIoT部署中使用定制分片来增强QoS.
通过提高网络性能,安全性和能源效率来解决现有的区块链模型的局限性.

主要方法:

一个Q-学习过程利用AIoT节点的持续更新的信任指标来识别合适的矿工进行区块添加.
一个基于矿工节点性能的动态共识函数的新型性能证明 (PoP) 共识模型促进了区块的添加.
使用Mayfly优化 (MO) 和细菌食优化 (BFO) 模型配置的定制分片提高了效率和可扩展性.

主要成果:

拟议的模型实现了4.5%的采矿速度改善和10.4%的采矿能源消耗降低.
与现有模型相比,AIoT通信中的吞吐量增加了8.3%,数据包交付一致性提高了2.5%.
该模型即使在大规模攻击下也表现出一致的性能.

结论:

具有定制分片的Q-Learning生物启发模型为AIoT部署提供了显著的效率和性能改进.
新的PoP共识机制和优化技术有效地解决了AIoT网络中的可扩展性和能源消耗问题.

关键词:

艾洛特 (AloteT) 的时间攻击攻击攻击细菌是一种细菌.区块链区块链区块链区块链客户化客户化客户化客户化延迟延迟延迟的时间寻找食物寻找食物五月可能会飞.这就是PoP PoP.在 QoS 系统中,QoS 是 QoS.安全的安全的安全的安全的安全.碎片化碎片化破碎通过量吞吐量吞吐量

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