In recent decades, multi-domain (air-water) optical wireless communications have garnered significant attention from both scientific and civil communities due to their diverse impacts and applications. However, in addition to security challenges, these types of communications face limitations regarding direct cross-medium communication, transmission capacity, transmission range, and energy consumption due to water properties and reflections occurring at the interface between the two mediums. While various solutions have been proposed to address these challenges, the majority of them are either not energy-efficient or fail to guarantee communication security in specific applications. Therefore, in this research, we propose a secure covert communication protocol with energy efficiency for multi-domain communication applications to address the aforementioned challenges. In this protocol, to enhance security and reduce bandwidth consumption, data is sampled based on its entropy and then simultaneously compressed and encrypted according to its sparsity level. Next, the resulting output is modulated onto amplified spontaneous emission (ASE) noise, hidden, and spread out over time using a chirped fiber Bragg grating (CFBG). The signal is then transmitted through a wide-field optical system. Here, we utilize an array of ultrasonic sensors and a prediction algorithm to calculate the optimal water surface impact point. We also, implement laser diode switching to optimize energy efficiency and enhance data transmission capacity, while utilizing On-Off Keying (OOK) pulse-based modulation to minimize implementation complexity.
Esmaeili, S. and Ghasemi, J. (2025). A Covert, Secure and Energy-Efficient Communication Protocol Based on Statistical Machine Learning in Multi-Domain Communication Applications. Scientia Iranica, (), -. doi: 10.24200/sci.2025.65894.9722
MLA
Esmaeili, S. , and Ghasemi, J. . "A Covert, Secure and Energy-Efficient Communication Protocol Based on Statistical Machine Learning in Multi-Domain Communication Applications", Scientia Iranica, , , 2025, -. doi: 10.24200/sci.2025.65894.9722
HARVARD
Esmaeili, S., Ghasemi, J. (2025). 'A Covert, Secure and Energy-Efficient Communication Protocol Based on Statistical Machine Learning in Multi-Domain Communication Applications', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2025.65894.9722
CHICAGO
S. Esmaeili and J. Ghasemi, "A Covert, Secure and Energy-Efficient Communication Protocol Based on Statistical Machine Learning in Multi-Domain Communication Applications," Scientia Iranica, (2025): -, doi: 10.24200/sci.2025.65894.9722
VANCOUVER
Esmaeili, S., Ghasemi, J. A Covert, Secure and Energy-Efficient Communication Protocol Based on Statistical Machine Learning in Multi-Domain Communication Applications. Scientia Iranica, 2025; (): -. doi: 10.24200/sci.2025.65894.9722
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