{"title":"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","authors":"","doi":"10.1016/j.adhoc.2024.103628","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-enhanced multi-stage learning-to-learning approach for secure smart cities load management in IoT networks\",\"authors\":\"\",\"doi\":\"10.1016/j.adhoc.2024.103628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":55555,\"journal\":{\"name\":\"Ad Hoc Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ad Hoc Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1570870524002397\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870524002397","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.