Cellular Communications in Ocean Waves for Maritime Internet of Things

Huo, Yiming; Dong, Xiaodai; Beatty, Scott

doi:10.1109/jiot.2020.2988634

Cited by 82 publications

(41 citation statements)

References 35 publications

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“…In addition, the height and angle of ship-borne antennas can change rapidly with the fluctuation of the sea surface. A representative insight on this issue can be found in [73], where the impact of sea waves to radio propagation and the communications link quality has been comprehensively discussed. Hence, maritime channel fading is particularly sensitive to parameters such as antenna height and angle, which may cause frequent link interruption.…”

Section: Enhancing Maritime Transmission Efficiencymentioning

confidence: 99%

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

Wei

Feng

Chen

et al. 2021

IEEE Internet Things J.

206

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Section: Enhancing Maritime Transmission Efficiencymentioning

confidence: 99%

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

Wei

Feng

Chen

et al. 2021

IEEE Internet Things J.

206

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“…Therefore, where and when to apply DL should be carefully investigated. This is especially the case for many deep learning frameworks that utilize specialized processing cores in the form of Graphics processing units (GPUs) and for some special scenarios, like DL on maritime IoT [105] and terrestrial satellite systems [106]. Moreover, not only the DL architecture but also the implementation approach (i.e., centralized or distributed deployment) would affect the balance between the cost saving and consuming, where the distributed method can be supported by end user devices equipped with DL processing chip [107].…”

Section: The Cost Of Deep Learningmentioning

confidence: 99%

A Survey of Deep Learning for Data Caching in Edge Network

Wang

Friderikos

2020

Informatics

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The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.

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“…With digitization of oil and gas platforms over the Sea, IoT devices are installed to provide smart production operations. IoT improves energy efficiency, remote monitoring, security, and control of physical assets on offshore platforms [8,9]. However, the conventional over the sea communication techniques are not appropriate for such sophisticated actions on remote offshore platforms.…”

Section: Introductionmentioning

confidence: 99%

Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

et al. 2021

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The prevalent use of the Internet of Things (IoT) devices over the Sea, such as, on oil and gas platforms, cargo, and cruise ships, requires high-speed connectivity of these devices. Although satellite based backhaul links provide vast coverage, but they are inherently constrained by low data rates and expensive bandwidth. If a signal propagated over the sea is trapped between the sea surface and the Evaporation Duct (ED) layer, it can propagate beyond the horizon, achieving long-range backhaul connectivity with minimal attenuation. This paper presents experimental measurements and simulations conducted in the Industrial, Scientific, and Medical (ISM) Band Wi-Fi frequencies, such as 5.8 GHz to provide hassle-free offshore wireless backhaul connectivity for IoT devices over the South China Sea in the Malaysian region. Real-time experimental measurements are recorded for 10 km to 80 km path lengths to determine average path loss values. The fade margin calculation for ED must accommodate additional slow fading on top of average path loss with respect to time and climate-induced ED height variations to ensure reliable communication links for IoT devices. Experimental results confirm that 99% link availability of is achievable with minimum 50 Mbps data rate and up to 60 km distance over the Sea to connect offshore IoT devices.

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Cellular Communications in Ocean Waves for Maritime Internet of Things

Cited by 82 publications

References 35 publications

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

A Survey of Deep Learning for Data Caching in Edge Network

Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

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