| More

Toward the widespread application of low-cost technologies in coastal ocean observing (Internet of Things for the Ocean)

Views: 404 Downloads: 234
MARCO MARCELLI (http://orcid.org/0000-0003-2619-8521), VIVIANA PIERMATTEI (http://orcid.org/0000-0002-9000-9070), RICCARDO GERIN (https://orcid.org/0000-0002-9788-0803), FABIO BRUNETTI, ERMANNO PIETROSEMOLI, SAM ADDO, LOBDA BOUDAYA, RICHARD COLEMAN (https://orcid.org/0000-0002-9731-7498), OLULUNMI AYOOLA NUBI, RICK JOJANNES, SUBRATA SARKER (https://orcid.org/0000-0001-9672-1889), ZACHARIE SOHOU (http://orcid.org/0000-0003-3314-3481), MARCO ZENNARO, KAREN WHILTSHIRE, ALESSANDRO CRISE (https://orcid.org/0000-0002-5183-3921)


The ability to access user-friendly, low-cost instrumentation remains a limiting factor in coastal ocean observing. The majority of currently available marine observation equipment is difficult to deploy, costly to operate, and requires specific technical skills. Moreover, a harmonized observation program for the world’s coastal waters has not yet been established despite the efforts of the global ocean organizations. Global observational systems are mainly focused on open ocean waters and do not include coastal and shelf areas, where models and satellites require large data sets for their calibration and validation. Fortunately, recent technological advances have created opportunities to improve sensors, platforms, and communications that will enable a step-change in coastal ocean observing, which will be driven by a decreasing cost of the components, the availability of cheap housing, low-cost controller/data loggers based on embedded systems, and low/no subscription costs for LPWAN communication systems. Considering the above necessities and opportunities, POGO’s OpenMODs project identified a series of general needs/requirements to be met in an Open science development framework. In order to satisfy monitoring and research necessities, the sensors to be implemented must be easily interfaced with the data acquisition and transmission system, as well as compliant with accuracy and stability requirements. Here we propose an approach to co-design a cost-effective observing modular instrument architecture based on available low-cost measurement and data transmission technologies, able to be mounted/operated on various platforms. This instrument can fit the needs of a large community that includes scientific research (including those in developing countries), non-scientific stakeholders, and educators.


internet of things; low-cost technologies; ocean observations

Full Text:



Abdillah, A.F., Berlian, M.H., Panduman, Y.Y.F., Akbar, M.A.W., Afifah, M.A. et al., 2017. Design and development of low-cost coral monitoring system for shallow water based on internet of underwater things. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9 (2-5), 97-101.

Albaladejo, C., Sánchez, P., Iborra, A., Soto, F., López, J.A. et al., 2010. Wireless sensor networks for oceanographic monitoring: A systematic review. Sensors, 10, 6948-6968.

Albaladejo, C., Soto, F., Torres, R., Sanchez, P., Lopez, J., 2012. A low-Cost sensor buoy system for monitoring shallow marine environments. Sensors, 12, 9612-9634.

Beddows, P.A., Mallon, E.K., 2018. Cave pearl data logger: A flexible Arduino-based logging platform for long-Term monitoring in harsh environments. Sensors, 18 (2), 530.

Bermudez, L., Delory, E., O’Reilly, T., del Rio Fernandez, J., 2009. Ocean observing systems demystified. In: Proceedings of the OCEANS 2009, MTS/IEEE Biloxi-Marine Technology for Our Future: Global and Local Challenges, (Piscataway, NJ: IEEE).

Boehme, L., Lovell, P., Biuw, M., Roquet, F., Nicholson, J. et al., (2009). Animal-borne CTD-Satellite Relay Data Loggers for real-time oceanographic data collection. Ocean Science 5, 685-695.

Boero, F., Putti, M., Trainito, E., Prontera, E., Piraino, S. et al., 2009. First records of Mnemiopsis leidyi (Ctenophora) from the Ligurian, Thyrrhenian and Ionian Seas (Western Mediterranean) and first record of Phyllorhiza punctata (Cnidaria) from the Western Mediterranean. Aquatic Invasions, 4 (4), 675-680.

Bresnahan, P.J., Cyronak, T., Martz, T., Andersson, A., Waters, S. et al., 2017. Engineering a Smartfin for surf-zone oceanography. In: OCEANS 2017-Anchorage (pp. 1-4). IEEE.

Brewin, R.J., Hyder, K., Andersson, A.J., Billson, O., Bresnahan, P.J. et al., 2017. Expanding aquatic observations through recreation. Frontiers in Marine Science, 4, 351.

Carminati, M., Stefanelli, V., Luzzatto-Fegiz, P., 2016. Micro- USB Connector Pins as Low-Cost, Robust Electrodes for Microscale Water Conductivity Sensing in Oceanographic Research. Procedia Engineering, 168, 407-410.

CMFRI, K., 2018. Nano News NF-POGO Alumni Network for Oceans-NF-POGO Alumni E-Newsletter.

Crise, A.M., Ribera D’Alcalà, M., Mariani, P., Petihakis, G., Robidart, J. et al., 2018. A conceptual framework for developing the next generation of Marine OBservatories (MOBs) for science and society. Frontiers in Marine Science, 5, 318.

Daniel, A., Laes Huon, A., Barus, C., Beaton, A.D., Blandfort, D. et al., 2019. Towards a harmonization for using in situ nutrient sensors in the marine environment. Frontiers in Marine Science, 6, 773.

Danovaro, R., Carugati, L., Berzano, M., Cahill, A.E., Carvalho, S., et al. 2016. Implementing and innovating marine monitoring approaches for assessing marine environmental status. Frontiers in Marine Science, 3, 213.

Davis, J., 2016. A Novel Aquatic Sensor and Network. Doctoral dissertation, Rensselaer Polytechnic Institute, New York, United States of America.

Delory, E., Castro, A., Waldmann, C., Rolin, J.F., Woerther, P. et al., 2014. Objectives of the NeXOS project in developing next generation ocean sensor systems for a more cost-efficient assessment of ocean waters and ecosystems, and fisheries management. In: OCEANS 2014-Taipei (pp. 1-6). IEEE.

Demetillo, A.T., Japitana, M.V., Taboada, E.B., 2019. A system for monitoring water quality in a large aquatic area using wireless sensor network technology. Sustainable Environment Research, 29( 1), 1-9.

DFRobot, Dissolved oxygen sensor manual [online] available: https://www.dfrobot.com.

Digiacomo, P., Muelbert, J., Malone, T., Parslow, J., Sweijd, N. et al., 2012. Requirements for Global Implementation of the Strategic Plan for Coastal GOOS.

Falco, P., Belardinelli, A., Santojanni, A., Cingolani, N., Russo, A. et al., 2007. An observing system for the collection of fishery and oceanographic data. Ocean Science, 3 (2), 189-203.

Faustine, A., Mvuma, A.N., Mongi, H.J., Gabriel, M.C., Tenge, et al., 2014. Wireless Sensor Networks for Water Quality Monitoring and Control within Lake Victoria Basin.

Friedrichs, A., Busch, J.A., Van der Woerd, H.J., Zielinski, O., 2017. SmartFluo: A method and affordable adapter to measure chlorophyll a fluorescence with smartphones. Sensors, 17 (4), 678.

Garcia-Soto, C., van der Meeren, G.I., 2017. Advancing citizen science for coastal and ocean research. European Marine Board IVZW.

Gerin, R., Zennaro, M., Rainone, M., Pietrosemoli, E., Poulain, P.M. et al., 2018. On the design of a sustainable ocean drifter for developing countries. EAI Endorsed Transactions on Internet of Things, 4 (13), 155483.

Glud, R.N., Gundersen, J.K., Ramsing, N.B., 2000. Electrochemical and optical oxygen microsensors for in situ measurements. In Situ Monitoring of Aquatic Systems: Chemical Analysis and Speciation, Wiley, Chichester, pp. 19-73.

Hodgson, G., 2001. Reef Check: The first step in community- based management. Bulletin of Marine Science, 69 (2), 861-868. Hodson, D., 1988. Experiments in science and science teaching. Educational philosophy and theory, 20 (2), 53-66.

Hussain, I., Das, M., Ahamad, K.U., Nath, P., 2017. Water salinity detection using a smartphone. Sensors and Actuators B: Chemical, 239, 1042-1050.

Jiang, P., Xia, H., He, Z., Wang, Z., 2009. Design of a water environment monitoring system based on wireless sensor networks. Sensors, 9, 6411-6434.

Johnson, K.S., Jannasch, H.W., Coletti, L.J., Elrod, V.A., Martz, T.R. et al., 2016. Deep-sea DuraFET: a pressure tolerant pH sensor designed for global sensor networks. Analytical chemistry, 88 (6), 3249-3256.

Kao, C.C., Lin, Y.S., Wu, G.D., Huang, C.J., 2017. A comprehensive study on the internet of underwater things: applications, challenges, and channel models. Sensors, 17 (7), 1477.

Kirton, J., 2018). A G7 Summit of Significant Success at Charlevoix 2018, G7 Research Group, July 13. Available http://www.g7.utoronto.ca/evaluations/2018charlevoix/ kirton-performance-full.html.

Kumari, C. U., Samiappan, D., Rao, T.R., Sudhakar, T., 2016. Mach-Zehnder Interferometer based high sensitive water salinity sensor for oceanographic applications. In: 2016 IEEE Annual India Conference (INDICON) (pp. 1-4). IEEE.

Kumari, C.U., Samiappan, D., Kumar, R., Sudhakar, T., 2019. Fiber optic sensors in ocean observation: A comprehensive review. Optik, 179, 351-360.

Lauro, F.M., Senstius, S.J., Cullen, J., Neches, R., Jensen, R.M. et al., 2014. The common oceanographer: crowdsourcing the collection of oceanographic data. PLoS biology, 12(9), e1001947.

Lockridge, G., Dzwonkowski, B., Nelson, R., Powers, S., 2016. Development of a low-cost arduino-based sonde for coastal applications. Sensors, 16 (4), 528.

Lumpkin, R., Özgökmen, T., Centurioni, L., 2017. Advances in the application of surface drifters. Annual Review of Marine Science, 9, 59-81.

Marcelli, M., Piermattei, V., Madonia, A., Mainardi, U., 2014. Design and Application of New Low-Cost Instruments for Marine Environmental Research. Sensors, 14, 23348- 23364.

Martín, F.J.F., Rodriguez, J.C.C., Anton, J.A., Perez, J.V., Sánchez- Barragán, I. et al., 2006. Design of a low-cost optical instrument for pH fluorescence measurements. IEEE Transactions on Instrumentation and Measurement, 55 (4), 1215- 1221.

Méndez-Barroso, L.A., Rivas-Márquez, J.A., Sosa-Tinoco, I., Robles-Morúa, A., 2020. Design and implementation of a low-cost multiparameter probe to evaluate the temporal variations of water quality conditions on an estuarine lagoon system. Environmental Monitoring and Assessment, 192 (11), 1-18.

Mills, G., Fones, G., 2012. A review of in situ methods and sensors for monitoring the marine environment. Sensor Review, 32 (1), 17-28.

Koss, R.S., Miller, K., Wescott, G., Bellgrove, A., Boxshall, A. et al., 2009. An evaluation of Sea Search as a citizen science programme in Marine Protected Areas. Pacific Conservation Biology, 15 (2), 116-127.

Miloslavich, P., Bax, N.J., Simmons, S.E., Klein, E., Appeltans, W., et al., 2018. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Global Change Biology, 24, 2416-2433.

Morton, S., Gano, S., 2015. Phytoplankton Monitoring Network: Using. Available: https://coastalscience.noaa.gov/ research/stressor-impacts-mitigation/pmn/.

Muller-Karger, F.E., Miloslavich, P., Bax, N.J., Simmons, S., Costello, M.J. et al., 2018. Advancing marine biological observations and data requirements of the complementary essential ocean variables (EOVs) and essential biodiversity variables (EBVs) frameworks. Frontiers in Marine Science, 5, 211.

Murphy, K., Heery, B., Sullivan, T., Zhang, D., Paludetti, L. et al., 2015. A low-cost autonomous optical sensor for water quality monitoring. Talanta, 132, 520-527. Organisation for Economic Co-Operation and Development (OECD). 2017. The Ocean Economy in 2030. IWA Publishing.

Parra, L., Rocher, J., Escrivá, J., Lloret, J., 2018. Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms. Aquacultural Engineering, 81, 10-18.

Pham, T.T., Burnett, D., Handugan, L., Frashure, D., Chen, C.J. et al., 2007. A Low-Cost, Data-Logging Salinity Sensor. In: OCEANS 2007 (pp. 1-5). IEEE.

Piermattei, V., Madonia, A., Bonamano, S., Martellucci, R., et al., 2018. Cost-Effective Technologies to Study the Arctic Ocean Environment. Sensors, 18 (7), 2257.

Pinto, V.C., Araújo, C.F., Sousa, P.J., Gonçalves, L.M., Minas, G., 2019. A low-cost lab-on-a-chip device for marine pH quantification by colorimetry. Sensors and Actuators B: Chemical, 290, 285-292.

Reyers, B., Stafford-Smith, M., Erb, K.H., Scholes, R.J., Selomane, O., 2017. Essential variables help to focus sustainable development goals monitoring. Current Opinion in Environmental Sustainability, 26, 97-105.

Roemmich, D., Johnson, G.C., Riser, S., Davis, R., Gilson, J. et. al. 2009. The Argo Program: Observing the global ocean with profiling floats. Oceanography, 22 (2), 34-43.

Rudnick, D., Costa, D., Johnson, K., Lee, C., Timmermans, M.L., 2018 (January). ALPS II-Autonomous Platforms and Sensors. A Report of the ALPS II Workshop. In: ALPS II Workshop.

Ryabinin, V., Barbière, J., Haugan, P., Kullenberg, G., Smith, N. et al. 2019. The UN Decade of Ocean Science for Sustainable Development. Frontiers in Marine Science, 6, 470.

Sanchez-Iborra, R., Cano, M.D., 2016. State of the art in LP-WAN solutions for industrial IoT services. Sensors, 16 (5), 708.

Solomon, J., 1980. Teaching children in the laboratory. Taylor & Francis.

Tanhua, T., McCurdy, A., Fischer, A., Appeltans, W., Bax, N. et al., 2019. What we have learned from the framework for ocean observing: Evolution of the global ocean observing system. Frontiers in Marine Science, 6, 471.

Thaler, A., Sturdivant, K., 2013. Oceanography for Everyone - The OpenCTD. Retrieved from http://www.rockethub.com/ Medit. Mar. Sci., 22/2 2021, 255-269 267 projects/26388-oceanography-for-everyone-the-openctd#- description-tab.

Trevathan, J., Johnstone, R., Chiffings, T., Atkinson, I., Bergmann, N. et al., 2012. SEMAT -the next generation of inexpensive marine environmental monitoring and measurement systems. Sensors, 12 (7), 9711-9748.

UNEP, F., 2016. Investment portfolios in a carbon constrained world: the second annual progress report of the portfolio decarbonization coalition.

Wang, Z.A., Moustahfid, H.A., Mueller, A., Mowlem, M.C., Michel, M. et al., 2019. Advancing observation of ocean biogeochemistry, biology, and ecosystems with cost-effective in situ sensing technologies. Frontiers in Marine Science, 6, 519.

Wright, N.G., Chan, H.K., 2016. Low-cost Internet of Things ocean observation. In: OCEANS 2016 MTS/IEEE Monterey (pp. 1-5). IEEE.

Yang, Q., Yoo, S.J., 2018. Optimal UAV path planning: Sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IEEE Access, 6, 13671- 13684.

http://www.nexosproject.eu/ (Accessed 11 December 2020)

http://www.schema-ocean.eu/ (Accessed 11 December 2020)

https://www.commonsenseproject.eu/ (Accessed 12 December 2020)

http://www.senseocean.eu/ (Accessed 12 December 2020)

https://pogo-ocean.org/innovation-in-ocean-observing/activities/ openmods-open-access-marine-observation-devices/ (Accessed October 2020)

https://create.arduino.cc/projecthub (Accessed September 2020


  • There are currently no refbacks.