Marine Delay and Disruption Tolerant Networks (MaDTN): Application for Artisanal Fisheries
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The artisanal fishing activity carried out on the coasts where the production of fish can be exploited is affected by a lack of communication between the vessels in order to provide relevant information related to multiple marine sensor parameters. It is mainly due to the rugged geographic area that causes highly disruptive communication links and in which traditional IP-based communications with transport protocols such as TCP or UDP do not work properly. This paper presents and evaluates a new communications architecture to provide services to marine sensor networks using a disruption tolerant networking (DTN) based solution. We propose a new architecture that takes into account the different vessels densities. We assume a finite sensor population model and a saturated traffic condition where every sensor always has frames to transmit. The performance was evaluated in terms of delivery probabilities, delay and a DTN scenario indicator (DSI) proposed. Through simulations, this paper reveals that Low Density scenery yield greater latency, and more density of nodes has better results. We achieved a successful delivery rate of 74% and a latency of 2 h approximately. Finally indicators shows that high density of nodes is strongly recommended for fishery scenery models.
How to citeBedón, H., Lopez Pastor, J., Cedeño Herrera, E. & Nieto, C.M. (2021). Marine Delay and Disruption Tolerant Networks (MaDTN): Application for Artisanal Fisheries. In: Botto-Tobar M., Montes León S., Camacho O., Chávez D., Torres-Carrión P., Zambrano Vizuete M. (eds.) Applied Technologies: Second International Conference, ICAT 2020, Quito, Ecuador, December 2–4, 2020, Proceedings, Communications in Computer and Information Science (vol. 1388, pp. 3-17). Springer. https://doi.org/10.1007/978-3-030-71503-8_1
Research area / lineRecursos naturales y medio ambiente / Productos de la biodiversidad
Category / SubcategoryIngeniería de sistemas / Sistemas de información
JournalCommunications in Computer and Information Science
Indexado en Scopus