Internet of Things for Water Innovative Networks
About the project
Ensuring the sustainability, security resilience of water supplies in the face of climate change is a global challenge which demands increasingly intelligent approaches to the management and transport of finite water resources. The design and operation of smarter water networks a currently unmet demand for highly-skilled and multi-disciplinary engineers and scientists who apply advanced sensors, ICT and the Internet of Things (IoT) to enable smarter water networks in an increasingly complex regulated environment.
Internet of Things for Water Innovative Networks (IoT4Win) is a European Industrial Doctorate (EID) training network which will train Early Stage Researchers (ESR) to become the highly skilled multi-disciplinary professionals needed to design and implement the smart water networks of the future. The programme will explore and develop – both as a field of study and focus of scientific –, advanced sensors and IoT technology for water and environment monitoring and control. This will build capacity within the EU and nationally to develop and deliver novel IoT solutions in the urban science domain for global application. IoT4Win will exploit the synergies between ICT, intelligent data processing, water domain knowledge and social sciences through multilateral relationships between academia, technology providers and water suppliers in the public and private sectors.
Professor Wenyan Wu (BCU) will coordinate IoT4Win and work with project partners in the consortium, including ICT / IoT technology company, Singular Logic from Romania and Greece, water innovation company - from Spain and United Utilities plc (UU) in the UK.
IOT4WIN has received funding from the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 765921
Funded by the Horizon 2020 Framework Programme of the European Union
- Y Fu and Wenyan Wu, Behavioural Informatics for Improving Water Hygiene Practice based on IoT Environment Journal of Biomedical Informatics 78 (2018) 156-166, https://doi.org/10.1016/j.jbi.2017.11.006
- Y Fu and Wenyan Predicting Household Water Use Behavior for Improved Hygiene Practices in of Things Environment via Dynamic Behavior Intervention Model, IET Networks, Vol 5 Issue 5 143-151, Oct 2016 10.1049/iet-net.2015.0111
- P. Yang, Wenyan Wu (2014), Efficient Particle Filter Localisation Algorithm in Dense Passive RFID Tags Environment, IEEE Transactions on Industry Electronics, Vol 61, Issue 10, pp. 5641 – 5651. ISSN:0278-0046
- A Hussain, Wenyan Wu, Sustainable Interoperability and Data Integration for the IoT-Based Information Systems, the 10th IEEE conference on of Things ( i-Things 2017) Exeter UK June 2017