Scholarlink

Low-power wide-area technology, LoRaWAN, officially recognized as an International Telecommunication Union (ITU) open standard in 2021, has gained widespread adoption due to massive IoT proliferation. This research paper explores the seamless integration of low-power wide-area technology LoRaWAN, IoT with artificial intelligence (AI) and machine learning (ML) to address sustainable development goals (SDGs). Investigating the transformative potential through technical analyses, case studies, and real-world applications, the study emphasizes the synergy between LoRaWAN, IoT, and AI-ML to enhance performance in dense deployments. Additionally, it discusses LoRaWAN radio parameters, performance indicators, and AI-ML algorithms. The paper highlights COP28’s role in climate action, proposes a P2P LoRaIoT solution for cost-effective greenhouse monitoring, and conducts a pilot study on a LoRaWAN-enabled smart city dataset to analyze the behavior of radio parameter so as to carry out future research. This integration propels smart cities toward sustainable urbanization, showcasing technology’s catalytic role in global progress.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Empowering Sustainable Development: Integrating AI, ML, and LoRaIoT for Enhanced LoRaWAN Performance and SDG Achievement

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Shaista Tarannum,
  • S. M. Usha,
  • G. F. Ali Ahammed,
  • Moeen Fathima

摘要

Low-power wide-area technology, LoRaWAN, officially recognized as an International Telecommunication Union (ITU) open standard in 2021, has gained widespread adoption due to massive IoT proliferation. This research paper explores the seamless integration of low-power wide-area technology LoRaWAN, IoT with artificial intelligence (AI) and machine learning (ML) to address sustainable development goals (SDGs). Investigating the transformative potential through technical analyses, case studies, and real-world applications, the study emphasizes the synergy between LoRaWAN, IoT, and AI-ML to enhance performance in dense deployments. Additionally, it discusses LoRaWAN radio parameters, performance indicators, and AI-ML algorithms. The paper highlights COP28’s role in climate action, proposes a P2P LoRaIoT solution for cost-effective greenhouse monitoring, and conducts a pilot study on a LoRaWAN-enabled smart city dataset to analyze the behavior of radio parameter so as to carry out future research. This integration propels smart cities toward sustainable urbanization, showcasing technology’s catalytic role in global progress.