Abstract:Automatic subsea operations using real-time underwater optical wireless sensor networks are mooted as a new paradigm of underwater Internet of Things in this paper. To this end, we develop an underwater optical wireless sensor network prototype called AquaE-net, which consists of an optical hub and two sensor nodes with temperature, salinity, conductivity, and pH sensing capabilities. Clock synchronization is realized in AquaEnet, which paves the way for future underwater positioning and navigation. Moreover, … Show more
“…Obtain the channel estimation Xtjt using the relation in (24). 8: end if Feedback Stage 9: if τ = 1 then 10:…”
Section: Optical Channel Estimationmentioning
confidence: 99%
“…By networking a swarm of AUVs equipped with both optical and acoustic communication systems, such a hybrid network enables high-bandwidth and low-latency underwater video transmission and real-time underwater operation and control [9,22]. However, implementing such a network is not trivial as the associated challenges should be addressed carefully due to the limitations of the system, the hostile environments, as well as the mobility of AUVs [23,24]. Among them, one critical issue is to maximize the hybrid network throughput by exploiting the complementary communicating technologies in terms of both acoustic and optical, especially the optical waves, as UWOC suffers from limited communication range [23].…”
In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.
“…Obtain the channel estimation Xtjt using the relation in (24). 8: end if Feedback Stage 9: if τ = 1 then 10:…”
Section: Optical Channel Estimationmentioning
confidence: 99%
“…By networking a swarm of AUVs equipped with both optical and acoustic communication systems, such a hybrid network enables high-bandwidth and low-latency underwater video transmission and real-time underwater operation and control [9,22]. However, implementing such a network is not trivial as the associated challenges should be addressed carefully due to the limitations of the system, the hostile environments, as well as the mobility of AUVs [23,24]. Among them, one critical issue is to maximize the hybrid network throughput by exploiting the complementary communicating technologies in terms of both acoustic and optical, especially the optical waves, as UWOC suffers from limited communication range [23].…”
In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.
“…However, there are only a few theoretical and simulation studies on UWOC network implementation [120]- [122]. In [16], the first opticalbased UWSN prototype, named AquaE-net (as shown in Fig. 9(c)), was developed, which consists of an optical hub, two sensor nodes, and a real-time monitoring software platform for shore-based stations.…”
Section: B Toward Real-time and High-speed Ioutmentioning
confidence: 99%
“…(b) A hybrid solar cell system to achieve optimal energy harvesting and signal detection [14]. (c) The first optical based underwater wireless sensor network prototype [16]. (d) Real-time digital video surveillance prototype [17].…”
Section: Toward High Visual-fidelity Ioutmentioning
confidence: 99%
“…In this case, solar cells with dual functions of signal acquisition and energy harvesting have been proposed as detectors in UWOC systems [11], [13]- [15]. To further promote the implementation of IoUT, underwater optical wireless sensor networks, and UWOC-based 2K real digital video surveillance have been studied experimentally [16], [17]. This review is structured as follows: Section 2 presents the comparison between underwater acoustic, RF, and optical communication technologies in IoUT and introduces the research progress of optical wireless communications and channel studies.…”
Our Earth is a "blue planet" that 70% of the surface is covered by the oceans, but most area of oceans remain largely unexplored. Besides supporting the Earth's ecosystem and moderating climate change, oceans are rich in economically relevant natural resources ready for harvesting, such as fishery, oil and gas, and mineral resources. Ocean observation and monitoring are therefore essential for environmental preservation and sea exploration. With the availability of advanced communication techniques, researchers began to look into distributed data acquisition and ocean interconnectivity, which engendered the concepts of intelligent ocean and the Internet-of-Underwater-Things (IoUT) framework. The framework is gaining traction since one could incorporate fiber sensing, acoustic, radio frequency, and optical wireless communication technologies to establish stable, broad-coverage, and massive ocean networks. The development of underwater internet beyond acoustic communication is still in its relative infancy, and therefore more aggregated research efforts from the related communities will be required to eventually achieve breakthroughs in comprehensive IoUT technologies. This review sheds light on the practical considerations and solutions to the challenges and robustness of the optical IoUT network in terms of channel characterization, turbulence studies, mobility, receiver optimization, and the application layer.
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