- -

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

Mostrar el registro completo del ítem

García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2017). ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish. Sensors. 17(11):1-19. https://doi.org/10.3390/s17112606

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/148910

Ficheros en el ítem

Thumbnail
Nombre: García-Magariño;L ...
Tamaño: 1.145Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish
Autor: García-Magariño, Iván Lacuesta Gilabert, Raquel Lloret, Jaime
Entidad UPV: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Fecha difusión:
Resumen:
[EN] Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet's area. Simulators can be helpful to test and discover some possible ...[+]
Palabras clave: Agent-based simulation , Agent-based social simulation , Multi-agent system , Agent-oriented software engineering , Underwater sensor , Underwater sensor network , Simulator software , Fish measurement
Derechos de uso: Reconocimiento (by)
Fuente:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s17112606
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/s17112606
Código del Proyecto:
info:eu-repo/grantAgreement/UNIZAR//JIUZ-2017-TEC-03/
...[+]
info:eu-repo/grantAgreement/UNIZAR//JIUZ-2017-TEC-03/
info:eu-repo/grantAgreement/MECD//CAS17%2F00005/
info:eu-repo/grantAgreement/OAPEE//2013-1-CZ1-GRU06-14277/
info:eu-repo/grantAgreement/Gobierno de Aragón//Ref-T81/
info:eu-repo/grantAgreement/MINECO//TIN2014-57028-R/ES/DESARROLLLO COLABORATIVO DE SOLUCIONES AAL/
info:eu-repo/grantAgreement/Fundación Bancaria Ibercaja//IT24%2F16/
info:eu-repo/grantAgreement/UNIZAR//UZ2017-TEC-02/
[-]
Agradecimientos:
This work acknowledges the research project Desarrollo Colaborativo de Soluciones AAL with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. This work has been supported by the ...[+]
Tipo: Artículo

References

Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors, 13(9), 11782-11796. doi:10.3390/s130911782

Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: research challenges. Ad Hoc Networks, 3(3), 257-279. doi:10.1016/j.adhoc.2005.01.004

Santos, R., Orozco, J., Micheletto, M., Ochoa, S., Meseguer, R., Millan, P., & Molina, C. (2017). Real-Time Communication Support for Underwater Acoustic Sensor Networks. Sensors, 17(7), 1629. doi:10.3390/s17071629 [+]
Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors, 13(9), 11782-11796. doi:10.3390/s130911782

Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: research challenges. Ad Hoc Networks, 3(3), 257-279. doi:10.1016/j.adhoc.2005.01.004

Santos, R., Orozco, J., Micheletto, M., Ochoa, S., Meseguer, R., Millan, P., & Molina, C. (2017). Real-Time Communication Support for Underwater Acoustic Sensor Networks. Sensors, 17(7), 1629. doi:10.3390/s17071629

Das, A. P., & Thampi, S. M. (2017). Simulation Tools for Underwater Sensor Networks: A Survey. Network Protocols and Algorithms, 8(4), 41. doi:10.5296/npa.v8i4.10471

Kawahara, R., Nobuhara, S., & Matsuyama, T. (2016). Dynamic 3D capture of swimming fish by underwater active stereo. Methods in Oceanography, 17, 118-137. doi:10.1016/j.mio.2016.08.002

Schaner, T., Fox, M. G., & Taraborelli, A. C. (2009). An inexpensive system for underwater video surveys of demersal fishes. Journal of Great Lakes Research, 35(2), 317-319. doi:10.1016/j.jglr.2008.12.003

Shinoda, R., Wu, H., Murata, M., Ohnuki, H., Yoshiura, Y., & Endo, H. (2017). Development of an optical communication type biosensor for real-time monitoring of fish stress. Sensors and Actuators B: Chemical, 247, 765-773. doi:10.1016/j.snb.2017.03.034

Chen, Z., Zhang, Z., Dai, F., Bu, Y., & Wang, H. (2017). Monocular Vision-Based Underwater Object Detection. Sensors, 17(8), 1784. doi:10.3390/s17081784

Saberioon, M. M., & Cisar, P. (2016). Automated multiple fish tracking in three-Dimension using a Structured Light Sensor. Computers and Electronics in Agriculture, 121, 215-221. doi:10.1016/j.compag.2015.12.014

Pais, M. P., & Cabral, H. N. (2017). Fish behaviour effects on the accuracy and precision of underwater visual census surveys. A virtual ecologist approach using an individual-based model. Ecological Modelling, 346, 58-69. doi:10.1016/j.ecolmodel.2016.12.011

Burget, P., & Pachner, D. (2005). FISH FARM AUTOMATION. IFAC Proceedings Volumes, 38(1), 137-142. doi:10.3182/20050703-6-cz-1902.02113

Simon, Y., Levavi-Sivan, B., Cahaner, A., Hulata, G., Antler, A., Rozenfeld, L., & Halachmi, I. (2017). A behavioural sensor for fish stress. Aquacultural Engineering, 77, 107-111. doi:10.1016/j.aquaeng.2017.04.001

Petreman, I. C., Jones, N. E., & Milne, S. W. (2014). Observer bias and subsampling efficiencies for estimating the number of migrating fish in rivers using Dual-frequency IDentification SONar (DIDSON). Fisheries Research, 155, 160-167. doi:10.1016/j.fishres.2014.03.001

Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654

Lloret, J., Garcia, M., Sendra, S., & Lloret, G. (2014). An underwater wireless group-based sensor network for marine fish farms sustainability monitoring. Telecommunication Systems, 60(1), 67-84. doi:10.1007/s11235-014-9922-3

Bharamagoudra, M. R., Manvi, S. S., & Gonen, B. (2017). Event driven energy depth and channel aware routing for underwater acoustic sensor networks: Agent oriented clustering based approach. Computers & Electrical Engineering, 58, 1-19. doi:10.1016/j.compeleceng.2017.01.004

Gallehdari, Z., Meskin, N., & Khorasani, K. (2017). Distributed reconfigurable control strategies for switching topology networked multi-agent systems. ISA Transactions, 71, 51-67. doi:10.1016/j.isatra.2017.06.008

Jurdak, R., Elfes, A., Kusy, B., Tews, A., Hu, W., Hernandez, E., … Sikka, P. (2015). Autonomous surveillance for biosecurity. Trends in Biotechnology, 33(4), 201-207. doi:10.1016/j.tibtech.2015.01.003

García-Magariño, I., & Plaza, I. (2015). FTS-SOCI: An agent-based framework for simulating teaching strategies with evolutions of sociograms. Simulation Modelling Practice and Theory, 57, 161-178. doi:10.1016/j.simpat.2015.07.003

Cooke, S. J., Brownscombe, J. W., Raby, G. D., Broell, F., Hinch, S. G., Clark, T. D., & Semmens, J. M. (2016). Remote bioenergetics measurements in wild fish: Opportunities and challenges. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 202, 23-37. doi:10.1016/j.cbpa.2016.03.022

García, M. R., Cabo, M. L., Herrera, J. R., Ramilo-Fernández, G., Alonso, A. A., & Balsa-Canto, E. (2017). Smart sensor to predict retail fresh fish quality under ice storage. Journal of Food Engineering, 197, 87-97. doi:10.1016/j.jfoodeng.2016.11.006

Tušer, M., Frouzová, J., Balk, H., Muška, M., Mrkvička, T., & Kubečka, J. (2014). Evaluation of potential bias in observing fish with a DIDSON acoustic camera. Fisheries Research, 155, 114-121. doi:10.1016/j.fishres.2014.02.031

Rakowitz, G., Tušer, M., Říha, M., Jůza, T., Balk, H., & Kubečka, J. (2012). Use of high-frequency imaging sonar (DIDSON) to observe fish behaviour towards a surface trawl. Fisheries Research, 123-124, 37-48. doi:10.1016/j.fishres.2011.11.018

Cenek, M., & Franklin, M. (2017). An adaptable agent-based model for guiding multi-species Pacific salmon fisheries management within a SES framework. Ecological Modelling, 360, 132-149. doi:10.1016/j.ecolmodel.2017.06.024

Gao, L., & Hailu, A. (2011). Evaluating the effects of area closure for recreational fishing in a coral reef ecosystem: The benefits of an integrated economic and biophysical modeling. Ecological Economics, 70(10), 1735-1745. doi:10.1016/j.ecolecon.2011.04.014

Helbing, D., & Balietti, S. (2011). From social simulation to integrative system design. The European Physical Journal Special Topics, 195(1), 69-100. doi:10.1140/epjst/e2011-01402-7

Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), 25-34. doi:10.1145/37402.37406

Beltran, R. S., Testa, J. W., & Burns, J. M. (2017). An agent-based bioenergetics model for predicting impacts of environmental change on a top marine predator, the Weddell seal. Ecological Modelling, 351, 36-50. doi:10.1016/j.ecolmodel.2017.02.002

Berman, M., Nicolson, C., Kofinas, G., Tetlichi, J., & Martin, S. (2004). Adaptation and Sustainability in a Small Arctic Community : Results of an Agent-based Simulation Model. ARCTIC, 57(4). doi:10.14430/arctic517

Kadir, H. A., & Arshad, M. R. (2015). Cooperative Multi Agent System for Ocean Observation System based on Consensus Algorithm. Procedia Computer Science, 76, 203-208. doi:10.1016/j.procs.2015.12.343

Trygonis, V., Georgakarakos, S., Dagorn, L., & Brehmer, P. (2016). Spatiotemporal distribution of fish schools around drifting fish aggregating devices. Fisheries Research, 177, 39-49. doi:10.1016/j.fishres.2016.01.013

De Kerckhove, D. T., Milne, S., & Shuter, B. J. (2015). Measuring fish school swimming speeds with two acoustic beams and determining the angle of the school detection. Fisheries Research, 172, 432-439. doi:10.1016/j.fishres.2015.08.001

Source Code of the Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fishes Called ABS-FishCounthttp://dx.doi.org/10.17632/yzmt73x8j8.1

Cossentino, M., Gaud, N., Hilaire, V., Galland, S., & Koukam, A. (2009). ASPECS: an agent-oriented software process for engineering complex systems. Autonomous Agents and Multi-Agent Systems, 20(2), 260-304. doi:10.1007/s10458-009-9099-4

García-Magariño, I., Palacios-Navarro, G., & Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107. doi:10.1016/j.simpat.2017.05.006

García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., & Palacios-Navarro, G. (2015). PEABS: A Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112. doi:10.1016/j.engappai.2015.09.003

Rosenthal, J. A. (1996). Qualitative Descriptors of Strength of Association and Effect Size. Journal of Social Service Research, 21(4), 37-59. doi:10.1300/j079v21n04_02

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem