Project
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
Overview
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.
Objectives
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.
Collaboration
Advisory board:
Prof Rob Morris (NTU) - Research focused on ultrasound imaging.
Prof Rachel Stubbington (NTU) - Research is focused on River Ecology
Prof Andrew Hirst (NTU) - Research is focused on ecology, physiology, and impacts of climate warming, with special emphasis on aquatic organisms.
Dr Erika Whiteford (NTU) - Her research is focused on terrestrial and freshwater ecology, in particular investigating biogeochemical cycling and nutrient stoichiometry of low nutrient systems in the Arctic.
Mr Matt Easter (CEO of the Trent River Trust) - The TRT has several projects across the river Trent.
Mr Flemming Christensen (CEO of Sundance Multiprocessor Technology Ltd.) - Expertise on Heterogeneous Computing and Vision Systems.
Related staff
Dr Pedro Machado (PI)
Dr Isibor Kennedy Ihianle (CoI)
Dr Farhad Fassihi-Tash (CoI)
Dr Abdallah Naser (CoI)
Dr David Adama (CoI),
Dr Kayode Owa (CoI)
Jack Larkin - Research Assistant
Areas of Research
Pedro’s expertise includes Neuromorphic Engineering, Edge Computer Vision, Bio-inspired Computing, robotics and Intelligent Sensors. Furthermore, Pedro was the PI of the Field Companion project, Grant agreement 600359 funded by InnovateUK that aimed at developing a smart pulveriser by combining Computer Vision, Edge-Computing and state-of-the-art Deep Learning algorithms. Pedro will focus on the project management and the development of intelligent IoT devices (edge devices) to reduce the impact of low visibility underwater, and interoperability between edge devices and the cloud.
Kennedy’s research interest includes applications of computational intelligence techniques and expertise in machine and deep learning techniques for object detection and tracking, human activity recognition, data management, ontology, data analysis and analytics. He will be responsible for data governance and adhere to data standards to facilitate data fusion.
Farhad’s Research interests include systematic innovation and sustainability. Farhard has extensive experience in developing digital information systems and will be responsible to look at sustainability and factors concerning the short to mid-term issues which will have an impact on the outcomes and potential future development/deployment.
Abdallah has industrial experience in leading and developing cloud-based services for different sectors. He also teaches a crash course in Microsoft Cloud AI Services at NTU. Abdallah will concentrate on the integration of Cloud Data Processing tools.
David’s expertise is in the applications of artificial intelligence techniques including transfer learning in developing systems for human behaviour monitoring and recognition, and human-robot interaction, object detection and tracking using machine learning algorithms.
Kayode comes with expertise that can support this project in the aspect of cloud data access control and firewalls as there will be requirements for networking, storage, and computing power for access to resources. Kayode will be responsible for the deployment of cloud data infrastructures and security.
Jordan’s research interests involve Human-Robot Interaction, Artificial Intelligence, Machine and Deep Learning, Transfer Learning, and Data Augmentation. His main goal is to apply technology to aid in everyday work and help to improve real-world situations. Jordan will focus on how machine learning can be used to automate underwater observations.
Salisu Yahaya is a Lecturer with the Department of Computer Science at Nottingham Trent University, where he is also a member of the Computational Intelligence and Applications (CIA) research group. His research interest is in the application of computational intelligence for human activity recognition, behaviour modelling, abnormality detection and pattern recognition.
Advisory Board
Dr Damaris Monari - Technical University of Mombasa
Dr Mathew Egessa - Technical University of Mombasa
Dr Cosmas Munda - Technical University of Mombasa
Dr Kevin Tole - Technical University of Mombasa
Publications
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.