用于物联网网络中安全智慧城市负载管理的人工智能增强型多阶段 "从学习到学习 "方法

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0

摘要

在依赖物联网网络的快速城市化智能城市中,高效的负载管理对于可持续能源利用至关重要。本文提出了一种人工智能增强型多阶段学习(Multi-Stage Learning-to-Learning,MSLL)方法,专门用于物联网网络中的安全负载管理。所提出的方法利用了 MMStransformer,这是一种基于变压器的模型,旨在处理多变量相关数据,并捕捉负荷预测中固有的长距离依赖关系。MMStransformer 采用多任务学习对学习策略,在不影响预测准确性的前提下优化了计算效率。该研究通过整合各种环境和运行变量,解决了智慧城市数据的动态性和复杂性问题。研究还解决了物联网网络固有的安全和隐私问题,确保了数据处理和通信的安全性。实验结果证明了所提方法的有效性,与传统方法和基线模型相比,该方法取得了极具竞争力的性能。研究结果凸显了人工智能驱动的解决方案在提高负荷预测准确性方面的潜力,同时确保了智能城市基础设施的稳健安全措施。这项研究有助于推动人工智能在可持续城市发展和能源管理中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-enhanced multi-stage learning-to-learning approach for secure smart cities load management in IoT networks

In the context of rapidly urbanizing smart cities reliant on IoT networks, efficient load management is critical for sustainable energy use. This paper proposes an AI-enhanced Multi-Stage Learning-to-Learning (MSLL) approach tailored for secure load management in IoT networks. The proposed approach leverages MMStransformer, a transformer-based model designed to handle multivariate, correlated data, and to capture long-range dependencies inherent in load forecasting. MMStransformer employs a multi-mask learning-to-learning strategy, optimizing computational efficiency without compromising prediction accuracy. The study addresses the dynamic and complex nature of smart city data by integrating diverse environmental and operational variables. Security and privacy concerns inherent in IoT networks are also addressed, ensuring secure data handling and communication. Experimental results demonstrate the efficacy of the proposed approach, achieving competitive performance compared to traditional methods and baseline models. The findings highlight the potential of AI-driven solutions in enhancing load forecasting accuracy while ensuring robust security measures in smart city infrastructures. This research contributes to advancing the state-of-the-art in AI applications for sustainable urban development and energy management.

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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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