A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks

Area of Science:

• Wireless Communication
• Sensor Networks
• Signal Processing

Background:

• Wireless Sensor Networks (WSNs) growth is driven by Radio Frequency (RF) and Visible Light Communication (VLC) advancements.
• Integrating Cognitive Radio Sensor Networks (CRSNs) with VLC presents performance-complexity trade-offs, especially with more devices and higher data rates.

Purpose of the Study:

• To provide a comprehensive review of state-of-the-art cognitive hybrid RF-VLC systems for WSNs.
• To emphasize the integration challenges and potential solutions for CRSNs and VLC.

Main Methods:

• Literature review of cognitive radio strategies, Machine Learning (ML), and Deep Learning (DL) applications.
• Analysis of fundamental aspects of CRSNs and VLC, including resource allocation, industrial scenarios, and energy harvesting.

Main Results:

• Integration necessitates advanced cognitive radio strategies, potentially using ML/DL, which introduces complexity.
• Synergistic amalgamation of CRSNs and VLC offers enhanced spectrum utilization and network performance.

Conclusions:

• Cognitive hybrid RF-VLC systems represent a promising pathway for innovative wireless communication applications.
• Further research into ML/DL-facilitated strategies is needed to address integration complexities and optimize performance.