Advances in Wireless Sensing and Cognitive Networks

The field of wireless sensing and cognitive networks is moving towards improving the reliability, security, and efficiency of wireless communication systems. Researchers are exploring innovative solutions to address the challenges of spectrum under-utilization, energy harvesting, and privacy preservation. Notable advancements include the development of new algorithms and protocols for optimizing cognitive radio networks, such as reinforcement learning-based approaches for physical layer security and energy harvesting. Additionally, there is a growing interest in applying machine learning techniques to improve the performance of wireless sensing systems, including device-free tracking and signal processing. Overall, the field is advancing rapidly, with a focus on developing practical and efficient solutions for real-world applications. Noteworthy papers include:

  • Baton, which proposes a novel system for accurate device-free tracking under severe Wi-Fi feature deficiencies.
  • Optimizing Cognitive Networks, which presents a reinforcement learning-based approach to improve physical layer security in underlay cognitive radio networks.
  • RADAR, which introduces a radio-based analytics system for tracking vehicles in VANETs by exploiting multiple radio signals.

Sources

Baton: Compensate for Missing Wi-Fi Features for Practical Device-free Tracking

Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels

Maximizing Reliability in Overlay Radio Networks with Time Switching and Power Splitting Energy Harvesting

Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy

Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning

RADAR: a Radio-based Analytics for Dynamic Association and Recognition of pseudonyms in VANETs

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