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Internet of Water (IoW)

Unit(s) of assessment: Computer Science and Informatics

Research theme: Computational Intelligence and Applications Research Group (CIA)

School: School of Science and Technology


The Internet of Water (IoW) aims to lead the Transformation of the UK aquatic species monitoring, using digital technologies. The latter will be built upon the work of the UPD project, which enhances detection/visualisation of pollution agents, as well as real-time monitoring of endangered and invasive aquatic species; plants/animals. This project aims at extending the UPD project for monitoring underwater invasive and endangered species. It will use a human-in-the-loop model that constantly focuses on mixing human knowledge and expertise with digital information. Furthermore, the research will use the research outcomes to be successful in future applications for external funding and development of impact case studies which will contribute to the REF2028.


1. Create new knowledge to extend the scope to further support aquatic ecosystem.

2. Develop 2 x intelligent underwater sensor prototypes for compensating the low visibility underwater by combining Stereo Vision Cameras/LiDAR/acoustic systems and edge computing platforms.

3. Creation of a proof of concept of an IoW ecosystem that will cover the interoperability across data, services, machine-to-machine communication.

4. Develop a solution to transform using an array of digital technologies including Internet of Things, Big Data and Artificial Intelligence.

5. Disseminate project outcomes in two articles to be submitted to high-impact journals [IEEE transactions on Internet of things and Elsevier Applied Soft Computing Journal].

6. Develop and integrate existing complementary tools to be utilised for enhancing the understanding of underwater ecosystems.


External collaboration:

Mr Flemming Christensen – Sundance Multiprocessor Technology

Dr Raquel Alfama Lopes Dos Santos – British Geological Survey


P. Machado, A. Oikonomou, J. F. Ferreira and T. M. Mcginnity, "HSMD: An Object Motion Detection Algorithm Using a Hybrid Spiking Neural Network Architecture," in IEEE Access, vol. 9, pp. 125258-125268, 2021, doi: 10.1109/ACCESS.2021.3111005.

Bottcher, W.; Machado, P.; Lama, N.; and McGinnity, T.M.; 2021. Object recognition for robotics from tactile time series data utilising different neural network architectures. In: Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN 2021), Virtual Event, 18-22 July 2021.

BRANDENBURG, S.,MACHADO, P., SHINDE, P., FERREIRA, J.F. and MCGINNITY, T.M., 2019. Object classification for robotic platforms. In: ROBOT 2019: Fourth Iberian Robotics Conference, Porto, Portugal, 20-22 November 2019.