Leveraging IoT and Deep Learning for Real-Time Carbon Footprint Monitoring and Optimization in Smart Cities and Industrial Zones

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@article{1704555,
      author = {Jessica Obianuju Ojadi, Ekene Cynthia Onukwulu, Chinekwu Somtochukwu Odionu, Olumide Akindele Owulade},
      title = {Leveraging IoT and Deep Learning for Real-Time Carbon Footprint Monitoring and Optimization in Smart Cities and Industrial Zones},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {946-966},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704555.pdf},
      abstract = {The growing urgency to mitigate climate change has driven the need for advanced technologies to monitor and optimize carbon footprints in smart cities and industrial zones. The integration of the Internet of Things (IoT) and deep learning provides a transformative approach to real-time carbon footprint monitoring and optimization. IoT-enabled sensors and smart meters facilitate continuous data collection on emissions, energy consumption, and environmental parameters. These real-time datasets, when processed through deep learning models, enable predictive analytics, trend forecasting, and adaptive optimization strategies to reduce carbon emissions effectively. This explores how IoT devices enhance environmental monitoring by providing high-resolution, real-time data from urban and industrial infrastructures. Deep learning techniques, including neural networks and reinforcement learning, are leveraged to predict carbon emission trends, identify inefficiencies, and recommend optimal mitigation strategies. The integration of AI-driven optimization techniques with IoT-based monitoring allows for intelligent decision-making in energy management, industrial automation, and smart transportation systems. Case studies highlight successful implementations of IoT and deep learning in carbon management, demonstrating their impact on energy efficiency and emission reduction. Despite the advantages, challenges such as data privacy, scalability, and computational complexity remain critical barriers. The study discusses strategies for overcoming these challenges, including blockchain for secure carbon data management, federated learning for decentralized AI models, and policy frameworks for regulatory compliance. By leveraging IoT and deep learning, cities and industries can transition toward a more sustainable future with data-driven carbon reduction strategies. The findings underscore the potential of AI and IoT in achieving climate goals, emphasizing the need for interdisciplinary collaboration and policy integration. Future research should focus on enhancing model interpretability, real-time optimization, and the integration of carbon credit trading mechanisms for broader adoption.},
      keywords = {Leveraging IoT, Deep learning, Carbon footprint monitoring, Smart cities, Industrial zones},
      month = {May},
  }