Publication Type : Conference Paper
Publisher : IEEE Xplore
Source : 2021 IEEE 46th Conference on Local Computer Networks (LCN) (pp. 621-628). IEEE. (2021) DOI: 10.1109/LCN52139.2021.9525021
Url : https://ieeexplore.ieee.org/document/9525021
Campus : Amritapuri
School : School of Computing
Center : AI (Artificial Intelligence) and Distributed Systems
Year : 2021
Abstract : One of the primary difficulties of fishermen engaged in deep-sea fishing is the lack of effective communication systems to the shore. The Offshore Communication Network(OCN) resolves this problem by providing Internet over the ocean through a fishing vessel network. OCN is a multi-layered architecture with heterogeneous connectivity ranges, directionality, resources, and mobility patterns. Connectivity maintenance is challenging due to the lack of infrastructure, expanded mobility, network sparsity, and sea-wave-induced movements. This paper discusses a framework to improve OCN connectivity with a multi-level optimization strategy. We propose a predictive model to generate real-time forecasts of link status. At the physical level, node position re-orientations to higher connectivity locations are suggested. The transmission queue management and prioritized scheduling in the link-layer minimize the queuing delay. A reinforcement routing strategy in the network layer determines the best next-hop for message dissemination. The proposed three-level optimization approach facilitates communication capability enhancement in OCN.
Cite this Research Publication : Surendran, S., Ramesh, M. V., & Montresor, A. (2021). Predictive analytics integrated multi-level optimization of offshore connectivity in ocean network. In 2021 IEEE 46th Conference on Local Computer Networks (LCN) (pp. 621-628). IEEE. (2021) DOI: 10.1109/LCN52139.2021.9525021