Abstract
The new technical solution proposed in this paper significantly reduces costs for creating underwater acoustic networks (UWAN) and their exploitation. Such a network has small energy-efficient nodes (modems that function, for example, as network communication devices, nodes of long-baseline bottom antenna, etc.). The UWAN performance parameters are considered with regard to the network application for positioning and navigation of autonomous underwater vehicles. Of particular interest are the results of the study on latencies of small data packet delivery in a network as well as the probability of delivering small data packets from the source to the final recipient (for example, control commands, sensor data, data on the positions of long-baseline nodes, and sync sequences). The solution is based on the development of a dedicated EviNS Framework (Evologics intelligent Networking Software Framework) installed directly on the platform of the underwater acoustic modem (UWAM) as part of its standard software. The main performance parameters of the digital UWAN, which uses part of the EviNS framework protocols, namely, a combination of the protocol with uncoordinated access to the medium and the routing protocol based on the sequence number controlled flooding, are discussed and the results of the experimental case study are given. The values of probabilities and latencies in data delivery from the source to the recipient obtained in the experiments provide the network user with the information necessary to schedule tasks involving group interaction and real-time coordination of the UWAN nodes, in particular, mobile nodes on underwater vehicles (at least, in the UWAN with a configuration and size similar to those given in the paper).
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Original Russian Text © K.G. Kebkal, V.K. Kebkal, A.G. Kebkal, D.D. Minaev, R.V. Leonenkov, A.S. Korytko, 2018, published in Giroskopiya i Navigatsiya, 2018, No. 3 (102), pp. 121–135.
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Kebkal, K.G., Kebkal, V.K., Kebkal, A.G. et al. An Underwater Digital Network on Acoustic Modems with EviNS Framework: A Case Study. Gyroscopy Navig. 9, 325–333 (2018). https://doi.org/10.1134/S2075108718040119
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DOI: https://doi.org/10.1134/S2075108718040119