The JANUS underwater communications standard

Potter, John R.; Alves, João; Green, Dale; Zappa, G.; Nissen, Ivor; McCoy, Kim

doi:10.1109/ucomms.2014.7017134

Cited by 126 publications

(42 citation statements)

References 6 publications

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“…Acoustic communications are known to operate over large distances with respect to carrier frequency, with systems utilising frequencies between 8 kHz and 16 kHz being able to operate in excess of 10 km [11]. More recently, protocols such as JANUS have also been developed to help unify underwater acoustic communications standards [12]. Typically, the carrier frequencies used are in the order of kHz, which leads to limited bandwidth and the relatively high power consumption of acoustic transducers can be a non-trivial drain on batteries [2].…”

Section: Acoustic and Electromagnetic Communicationmentioning

confidence: 99%

“…Another consideration discussed by Anguita et al is the use of simulated hardware for signal processing; to do this, the model should be modified to use only integers and fixed point integers as a numbering system throughout the model, enabling high speed hardware description language (VHDL) to be generated and FPGAs used for parallelised signal processing [27]. In the long term, using principles found in protocols such as JANUS as a base to produce a similar optical equivalent may further increase the system's utility [12]. Funding: All of the funding for equipment and materials used for experiments in this study have been kindly provided by the Faculty of Engineering and Technology at Liverpool John Moores University.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

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Towards Software Based Optical Communication Methods for the Assistance of Docking Autonomous Underwater Vehicles

et al. 2020

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The use of optical communications systems is prevalent in underwater robotics when short-range data transmission is required or preferred. This paper proposes a method of producing and testing an optical communications system for use in the assistance of optical docking for autonomous underwater vehicles (AUVs). It describes how the Simulink modelling environment was used to program and simulate a model of a transmitter, which was then implemented on a microcontroller. The transmitter model implemented on hardware was then used to produce an optical signal, which was sampled, logged and used to design a receiver model in Simulink. For signalling purposes, the experiment used a light-emitting diode (LED) with a driver circuit and photodiode based receiver. This simulated approach using real world data enabled the analysis of the system at every point during the process, allowing for a hardware in the loop style approach to be used in the receiver model design. Consequently, the Simulink Coder was used to produce the receiver model’s equivalent in C++ for later deployment. A benchmark was determined through experimentation to compare within future studies; the system was tested and found to operate effectively at distances between 1 m and 12 m in a controlled in air test environment.

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Section: Acoustic and Electromagnetic Communicationmentioning

confidence: 99%

Section: Conclusion and Further Workmentioning

confidence: 99%

Towards Software Based Optical Communication Methods for the Assistance of Docking Autonomous Underwater Vehicles

et al. 2020

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“…Compared to the wireless networking standard 802.11 developed in the IEEE Standards Association, currently no digital underwater communication standards exist [1]. In standard 802.11, numerous parameters are proposed to obtain higher throughput.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

et al. 2019

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In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

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“…As a matter of fact, acoustic channel models are more complex and acoustic modem devices are often using proprietary protocols, making it dicult to perform simulations. Recently a new standard named JANUS [7] has been proposed for underwater communications, but it is relatively slower than the proprietary protocols used by many acoustic modems.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Constraints and ns-3 Mobility Models

Franchi

Pecorella

Ridolfi

et al. 2017

Proceedings of the Workshop on Ns-3 - 2017 WNS3

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Recently there has been a renewed interest in ns-3 as a tool for Underwater Acoustic communications, with the integration of World Ocean Simulation System (WOSS, [4, 6]) into ns-3. However, the current implementation of ns-3 does not provide specic models suitable for AUVs (Autonomous Underwater Vehicles) mobility. An old proposal is available, made by Andrea Sacco during his Google Summer of Code (GSoC) 2010 project. However, the code has never been integrated into ns-3. In order to simulate the communications of AUVs, it is mandatory to rely also on simple and eective mobility systems, where the kinematic constraints of the node are taken into account. The requirements of a mobility model for AUVs is to be able to take into account the kinematic model of the real device and to set up a feasible path between two (or more) points. This paper presents a new extensible architecture based on kinematic models, which greatly simplies the simulation complexity.

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The JANUS underwater communications standard

Cited by 126 publications

References 6 publications

Towards Software Based Optical Communication Methods for the Assistance of Docking Autonomous Underwater Vehicles

Towards Software Based Optical Communication Methods for the Assistance of Docking Autonomous Underwater Vehicles

Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

Kinematic Constraints and ns-3 Mobility Models

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