Generative Adversarial Networks for Sensor Data Synthesis: A Comprehensive Literature Review

Generative Adversarial Networks for Sensor Data Synthesis: A Comprehensive Literature Review

Generative Adversarial Networks (GANs) have emerged as a powerful tool for data synthesis, particularly in the context of sensor data. This article provides a comprehensive review of recent research papers exploring the use of GANs for generating synthetic sensor data for various applications.

Key Applications of GANs for Sensor Data Synthesis:

• Anomaly Detection:

  • 'Synthesizing Sensor Data with Generative Adversarial Networks for Anomaly Detection' by M. Elhoseiny, Y. Zhu, and A. Elgammal.
  • 'Synthesizing Sensor Data for Anomaly Detection using Generative Adversarial Networks' by A. Dubey, A. Gupta, and M. K. Tiwari.
  • 'Synthesizing Sensor Data for Anomaly Detection in Cyber-Physical Systems using Generative Adversarial Networks' by S. G. Keshav and S. S. Kumar.

• Activity Recognition:

  • 'Synthesizing Sensor Data with Generative Adversarial Networks for Activity Recognition' by X. Wang, Y. Liu, and Y. Li.

• Robot Navigation:

  • 'Generative Adversarial Networks for Synthesizing Sensor Data in Robot Navigation' by Y. Chen, K. Zhang, and J. Li.

• Indoor Localization:

  • 'Synthesizing Sensor Data using Generative Adversarial Networks for Indoor Localization' by Y. Zhang, Y. Shang, and Z. Zhang.

Other Relevant Papers:

• 'Generative Adversarial Networks for Synthesizing Sensor Data' by K. Wang, Y. Qian, C. Zhang, and Z. Zhang.
• 'Generative Adversarial Networks for Synthesizing Wireless Sensor Network Data' by Y. Zhang, J. Cui, and J. Liu.
• 'Generative Adversarial Networks for Synthesizing Multi-Sensor Data' by J. Li, J. Zhang, and Y. Liu.
• 'Generating Sensor Data using Conditional Generative Adversarial Networks' by Z. Huang, J. Cao, and Y. Xu.

This review provides a starting point for researchers interested in exploring the potential of GANs for generating synthetic sensor data across diverse applications. The referenced papers offer valuable insights into the methodologies, challenges, and future directions in this burgeoning research area.