Abstract
In recent years, there has been increasing awareness of the preservation, protection and sustainable use of natural resources. Water resources, being one of the most important, face major threats due to contamination by pollutants of various types and origins. Maintaining the quality of water resources requires more robust, reliable and more frequent monitoring than traditional data collection techniques based on manual sampling methods. This article, which is the result of ongoing research, proposes a practical and cost-effective solution for a surface water monitoring system, using a robotics platform and cloud services. The proposed solution allows for scalability and will accommodate a wide range of end-user specifications. To allow for continuous operation in longer activities, the design of a versatile real-time water quality monitoring system should also take into consideration the question of its energy requirements and self-sufficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pule, M., Yahya, A., Chuma, J.: Wireless sensor networks: a survey on monitoring water quality. J. Appl. Res. Technol. 15, 562–570 (2017)
Ballesteros-Gomez, A., Rubio, S.: Recent advances in environmental analysis. Anal. Chem. 83(12), 4579–4613 (2011)
Dunbabin, M., Marques, L.: Robots for environmental monitoring: significant advancements and applications. IEEE Robot. Autom. Mag. 19, 24–39 (2012)
Xu, G., Shen, W., Wang, X.: Applications of wireless sensor networks in marine environment monitoring: a survey. Sensors 14, 16932–16954 (2014)
Ferreira, H., Almeida, C., Martins, A., Almeida, J., Dias, N., Dias, A.: Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle. In: Proceedings of the OCEANS 2009 IEEE conference, pp. 1–6. IEEE Press, New York (2009)
Raimundo, D., Leite, T., Silva, A.: Projeto de um Veículo Autónomo de Superfície para Monitorização de Águas Fluviais. EECS Project, IPT, Tomar (2016)
ArduPilot Open Source Autopilot. http://ardupilot.org
Bassiliades, N., Antoniades, I., Hatzikos, E., Vlahavas, I., Koutitas, G.: An intelligent system for monitoring and predicting water quality. In: Proceedings of the European Conference Towards Environment, pp. 534–542, Prague (2009)
Bhat, S., Meraj, G., Yaseen, S., Pandit, A.K.: Statistical assessment of water quality parameters for pollution source identification in Sukhnag stream: an inflow stream of lake Wular (Ramsar Site), Kashmir Himalaya. J. Ecosyst. (2014). https://doi.org/10.1155/2014/898054
Thiyagarajan, K., Pappu, S., Vudatha, P., Niharika, A.V.: Intelligent IoT based water quality monitoring system. Int. J. Appl. Eng. Res. 12, 5447 (2017)
Mission Planner Documentation. http://ardupilot.org/planner/index.html
Acknowledgements
This work was supported by Centro 2020, Portugal 2020 and European Union (EU) under the grants, CENTRO-01-0145-FEDER-024052E – Libélula: Mobile robotic surface water quality monitoring system.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Marques, J. et al. (2019). Towards a Practical and Cost-Effective Water Monitoring System. In: Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds) Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-17771-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17770-6
Online ISBN: 978-3-030-17771-3
eBook Packages: Computer ScienceComputer Science (R0)
