Underwater wireless communications face significant challenges due to high attenuation, turbulence, and water turbidity. Traditional methods like acoustic and radio frequency (RF) communication suffer from low data rates (1 s), and limited transmission distances (<10 km).Visible Light Communication (VLC) emerges as a promising alternative, offering high-speed data transmission (up to 5 Gbps), low latency (<1 ms), and immunity to electromagnetic interference. This paper provides an in-depth review of underwater VLC, covering fundamental principles, environmental factors (scattering, absorption), and dynamic water properties. We analyze modulation techniques, including adaptive and hybrid schemes (QAM-OFDM achieving 4.92 Gbps over 1.5 m), and demonstrate their superiority over conventional methods. Practical applications—underwater exploration, autonomous vehicle control, and environmental monitoring—are discussed alongside security challenges. Key findings highlight UVLC’s ability to overcome traditional limitations, with experimental results showing 500 Mbps over 150 m using PAM4 modulation. Future research directions include integrating quantum communication and Reconfigurable Intelligent Surfaces (RISs) to further enhance performance, with simulations projecting 40% improved spectral efficiency in turbulent conditions.

The hybridization of Near-Field Communication (NFC) with Visible Light Communication (VLC) presents a promising framework for robust, secure, and efficient wireless transmission. By combining proximity-based authentication of NFC with high-speed and interference-resistant data transfer of VLC, this approach mitigates the inherent limitations of each technology, such as the restricted range of NFC and authentication challenges of VLC. The resulting hybrid system leverages NFC for secure handshaking and VLC for high-throughput communication, enabling scalable, real-time applications across diverse domains. This study examines integration strategies, technical enablers, and potential use cases, including smart street poles for secure citizen engagement, patient authentication and record access systems in healthcare, personalized retail advertising, and automated attendance tracking in education. Additionally, this paper addresses key challenges in hybridization and explores future research directions, such as the integration of Artificial Intelligence and 6G networks.

Monitoring electrical appliances in residential buildings is essential for minimizing energy waste and enhancing safety through the early detection of abnormal conditions. While researchers have investigated both intrusive and nonintrusive load monitoring approaches, the non-intrusive approach has emerged as preferred due to its cost-effectiveness and noninvasive implementation. Despite considerable progress in appliance monitoring and fault detection systems over the past two decades, critical challenges and limitations persist. This paper proposes a low-complexity appliance identification and monitoring solution to overcome those issues. Furthermore, the proposed solution is integrated with an abnormal condition detection mechanism for critical appliances, aiming to save energy and ensure the safety of the power system. Furthermore, the solution incorporates user feedback via a dedicated mobile application, enhancing adaptability and performance. The proposed solution has been validated in real-time environments using both custom and publicly available datasets, demonstrating improved accuracy in energy monitoring and increased consumer safety.