Scholarlink

This chapter looks at machine learning (ML) techniques—which include supervised learning and deep learning—with the aim of efficiently developing sustainable cities through IoT sensor networks. It talks about how real-time IoT data collection fits into the framework of predictive models that target the fine-tuning of energy consumption, traffic management, and resource allocation for smart cities. The mathematical concepts defining various supervised models, ranging from regression and regularization methods to deep learning architectures such as convolutional and recurrent neural networks for very high-dimensional and streaming data, are discussed herein. Case studies from Barcelona, Amsterdam, and Singapore document successful urban applications of ML solutions, focusing heavily on TensorFlow-based models. A Python example execution also showcases deploying ML models for energy forecasting. The discourse heads into the challenges of noise filtering; scalability; truly real-time inference; and newer focus areas such as edge computing and privacy-preserving computing. In effect, the chapter stands as a highly technical reference endorsing the use of ML/IoT toward the development of more efficient, resilient, and sustainable urban landscapes.

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

Machine Learning Approaches for Sustainable Cities Using Internet of Things: Supervised and Deep Learning Techniques

  • 章节

  • 发表日期：

    2026年

查看全文
  • Rajesh Dey,
  • Rupali Atul Mahajan,
  • Neha Gupta

摘要

This chapter looks at machine learning (ML) techniques—which include supervised learning and deep learning—with the aim of efficiently developing sustainable cities through IoT sensor networks. It talks about how real-time IoT data collection fits into the framework of predictive models that target the fine-tuning of energy consumption, traffic management, and resource allocation for smart cities. The mathematical concepts defining various supervised models, ranging from regression and regularization methods to deep learning architectures such as convolutional and recurrent neural networks for very high-dimensional and streaming data, are discussed herein. Case studies from Barcelona, Amsterdam, and Singapore document successful urban applications of ML solutions, focusing heavily on TensorFlow-based models. A Python example execution also showcases deploying ML models for energy forecasting. The discourse heads into the challenges of noise filtering; scalability; truly real-time inference; and newer focus areas such as edge computing and privacy-preserving computing. In effect, the chapter stands as a highly technical reference endorsing the use of ML/IoT toward the development of more efficient, resilient, and sustainable urban landscapes.