DOI: 10.26634/jcom.11.2.20051 ISSN: 2347-2227

Deep learning for enhancing internet of things: A comprehensive survey

Eswaran Ushaa, Eswaran Vishal
  • Materials Chemistry
  • Economics and Econometrics
  • Media Technology
  • Forestry
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The Internet of Things (IoT) has emerged as a transformative technology, connecting a wide range of devices and sensors to the internet, enabling data-driven decision-making and automation across various domains. Deep learning, a subset of machine learning, has played a pivotal role in advancing IoT applications by providing the means to process and interpret the massive volumes of data generated by IoT devices. This comprehensive survey provides an extensive review of the integration of deep learning techniques with IoT, aiming to offer a holistic understanding of the state-of-the-art research and practical implementations. The key motivations include handling complex IoT data and enabling smart applications across domains like healthcare, transportation, and industry 4.0. Real-world case studies demonstrate significant improvements on metrics like prediction accuracy, operational efficiency, and energy savings. The adoption of edge computing is further driven by the need to reduce latency in data processing and enhance real-time decisionmaking capabilities in these critical sectors. However, challenges remain around model interpretation, robustness, and computational complexity. Future IoT systems must focus on lightweight deep learning architectures tailored for edge devices, distributed and privacy-preserving analytics, and improved model transparency and fairness. Additionally, these future IoT systems should prioritize robust security measures to protect sensitive data and ensure the reliability and integrity of connected devices and networks. A systematic framework is proposed covering data curation, model development, deployment, and monitoring to guide adoption. The comprehensive nature of this survey aims to bridge the gap between theoretical knowledge and practical deployment, enabling a smoother integration of deep learning into IoT systems. By offering a structured overview of best practices and emerging trends, it empowers professionals to make informed decisions in their IoT projects.

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