Periodic Reporting for period 2 - IoT4Win (Internet of Thing for Smart Water Innovative Networks)
Período documentado: 2020-03-01 hasta 2022-05-31
IoT4Win responds to well-defined and interdisciplinary scientific challenges relating to the Internet of Things for Smart Water Networks (SWN) technological area by recruiting ESRs to undertake research in the context of a joint research training on concepts and methodologies of IoT-enabled SWN. IoT4Win investigated IoT smart framework, integrated and intelligent data management for smart sustainable urban water environments by transforming from the online management based on the internet to real-time dynamic management on the basis of IoT, including investigation of the heterogeneous device connectivity, semantic sensor web in urban water system and integrated and interoperable data management platform, taking account of data intelligence and security for SWN application. The scientific research findings have been applied to and evaluated in real water scenarios with industrial partners acting as the end-users, leading to a specialized technology platform for SWN and a best-practice smart water network demonstrator.
IoT4Win aimed to establish a unique European Industrial Doctorate initiative to meet the current and future demand for highly skilled entrepreneurs in the Internet of Things and water research by developing and advancing European capacity in the design and development of Smart Water Network (SWN). IoT4Win designed three individual research projects to address the challenges and highlight the research gaps in the IoT framework for SWN, covering the core SWN research areas, i.e. smart sensing and trusted communication within energy-limited heterogeneous devices in IoT-enabled urban water environment; dynamic sensor web, and interoperable open platform with Integrated Knowledge Management for smart water networks; data security and intelligence in IoT enabled SWN. Combining these projects should establish an interoperable, secure, and intelligent underlying technology creating an open platform for IoT-enabled SWN applications.
IoT4Win aimed to establish a unique European Industrial Doctorate initiative to meet the current and future demand for highly skilled entrepreneurs in the Internet of Things and water research by developing and advancing European capacity in the design and development of Smart Water Network (SWN). IoT4Win designed three individual research projects to address the challenges and highlight the research gaps in the IoT framework for SWN, covering the core SWN research areas, i.e. smart sensing and trusted communication within energy-limited heterogeneous devices in IoT-enabled urban water environment; dynamic sensor web, and interoperable open platform with Integrated Knowledge Management for smart water networks; data security and intelligence in IoT enabled SWN. Combining these projects should establish an interoperable, secure, and intelligent underlying technology creating an open platform for IoT-enabled SWN applications.
Work package (WP1) –ESR1 (BCU) sought to design IoT context-aware framework in heterogeneous sensors and network devices for smart water networks by bringing the analytics, context-awareness, and decision-making closer to the network edge to achieve nearly real-time water quality monitoring by using smart sensing and IoT technologies. This research focused on optimizing the deployment of water quality sensors to reduce the impact of water contamination on human health; Machine learning (ML) and edging computing were explored to develop scalability, reliability, and a context-aware water quality monitoring framework. WP1 generated 5 publications 4 technical reports and a Ph.D. thesis. ESR1 successfully obtained his Ph.D. degree in May 2022 at BCU. WP1 main achievements:
1. Proposed a sensor deployment method that facilitates near/real-time water quality monitoring by multi-objective optimisation within multi-contamination sources.
2. Analysed the impact on human health in simulating contaminated water networks based on the deployed sensors.
3. Developed machine learning and deep learning models to enhance water contaminant detection.
4. Developed the proposed context-aware framework integrating the results 1-3 for near/real-time monitoring in WDS
Work package 2 (WP2) –ESR2 (SLG-GR) investigated large-scale stream processing solutions to interlink data from IoTs and extract real-time information for SWN, covering the integration of heterogeneous data streams, and large-scale IoT stream processing, and real-time information processing and knowledge extraction. Data Information Interoperability Model (DIIM) can help water utilities to analyse data to help decision making. WP2 generated 3 technical reports and one publication on IEEE ICSC 2022. WP2 main achievements:
1. Reviewed interoperability challenges faced by IoT-enabled SWN.
2. Developed a Data Information Interoperability Model (DIIM) with a focus on the syntactic and semantic interoperability issues to enable and enhance the interoperability of IoT data.
3. Defined a real-world use case of water quality monitoring in the water distribution system with the evaluation of the DIIM.
4. Implement a showcase of DIIM in Python by Natural language processing techniques to find similarities between IoT and domain-specific ontologies.
Work package (WP3) –ESR3 (BCU) developed an architecture using data intelligence for Cyber-physical systems (CPS) in Smart Water Networks (SWN) to improve the security and resilience of water networks.WP3 investigated potential new data intelligent techniques to preserve the security and resilience of CPS in SWN. Attack detection and data validation model and toolkits were developed to detect and mitigate Cyber-Physical attacks in SWN using ML and blockchain. WP3 generated 5 research papers, 4 technical reports, and a Ph.D. thesis. ESR3 successfully obtained his Ph.D. degree in May 2022 at BCU. WP3 main achievements:
1. Reviewed the previous CPS attacks in SWN, their consequences, and learned lessons
2. Developed an attack detection simulation model on MATLAB using machine learning algorithms
3. Developed data validation simulation model in SWN considering the security requirements using consensus algorithms of blockchain technology
4. Developed an integrated attack detection simulation model using hybrid machine-learning and blockchain technology
IOT4Win delivered all S&T WPs; the research outcomes and achievements generated two Ph.D. completions. The research outputs have been published in scientific journals and conferences and published in open access. All ERSs have seconded with industrial partners and academia onsite and virtually. Four research training workshops for ESRs have been delivered by the IOT4Win consortium and other dissemination events were organized for industrial engagement and outreach.
1. Proposed a sensor deployment method that facilitates near/real-time water quality monitoring by multi-objective optimisation within multi-contamination sources.
2. Analysed the impact on human health in simulating contaminated water networks based on the deployed sensors.
3. Developed machine learning and deep learning models to enhance water contaminant detection.
4. Developed the proposed context-aware framework integrating the results 1-3 for near/real-time monitoring in WDS
Work package 2 (WP2) –ESR2 (SLG-GR) investigated large-scale stream processing solutions to interlink data from IoTs and extract real-time information for SWN, covering the integration of heterogeneous data streams, and large-scale IoT stream processing, and real-time information processing and knowledge extraction. Data Information Interoperability Model (DIIM) can help water utilities to analyse data to help decision making. WP2 generated 3 technical reports and one publication on IEEE ICSC 2022. WP2 main achievements:
1. Reviewed interoperability challenges faced by IoT-enabled SWN.
2. Developed a Data Information Interoperability Model (DIIM) with a focus on the syntactic and semantic interoperability issues to enable and enhance the interoperability of IoT data.
3. Defined a real-world use case of water quality monitoring in the water distribution system with the evaluation of the DIIM.
4. Implement a showcase of DIIM in Python by Natural language processing techniques to find similarities between IoT and domain-specific ontologies.
Work package (WP3) –ESR3 (BCU) developed an architecture using data intelligence for Cyber-physical systems (CPS) in Smart Water Networks (SWN) to improve the security and resilience of water networks.WP3 investigated potential new data intelligent techniques to preserve the security and resilience of CPS in SWN. Attack detection and data validation model and toolkits were developed to detect and mitigate Cyber-Physical attacks in SWN using ML and blockchain. WP3 generated 5 research papers, 4 technical reports, and a Ph.D. thesis. ESR3 successfully obtained his Ph.D. degree in May 2022 at BCU. WP3 main achievements:
1. Reviewed the previous CPS attacks in SWN, their consequences, and learned lessons
2. Developed an attack detection simulation model on MATLAB using machine learning algorithms
3. Developed data validation simulation model in SWN considering the security requirements using consensus algorithms of blockchain technology
4. Developed an integrated attack detection simulation model using hybrid machine-learning and blockchain technology
IOT4Win delivered all S&T WPs; the research outcomes and achievements generated two Ph.D. completions. The research outputs have been published in scientific journals and conferences and published in open access. All ERSs have seconded with industrial partners and academia onsite and virtually. Four research training workshops for ESRs have been delivered by the IOT4Win consortium and other dissemination events were organized for industrial engagement and outreach.
IoT4Win focused on the ESRs' training and personal development with a joint supervision team, academic advisers, and professionals from universities and industries. Systematic structured training in IOT4Win significantly impacts on IoT4Win ESRs’ career development. ESRs have undertaken secondment training at ACING R&D company to develop hands-on industrial R &D experience; at water utilities to understand the requirements of end-users and water knowledge; at Singular logic Greece (SLG-GR)-software company to familiarise themselves with IoT research. All ESRs enrolled at BCU in the doctoral training program to strengthen their domain knowledge and academic experiences. Research and industrial experience provided transferrable skills and made progress in their Ph.D. study. ESR1 and ESR3 successfully obtained their doctoral degree at BCU. All ESRs in IOT4Win continued to work at BCU and SLG after IOT4Win completion.
Wider societal implications from IoT4Win are the provision of smart technologies to solve global water issues and enhance efficient and resilient water management for smart water networks under the uncertainty of climate changes, leading to healthy living and a smart society
Wider societal implications from IoT4Win are the provision of smart technologies to solve global water issues and enhance efficient and resilient water management for smart water networks under the uncertainty of climate changes, leading to healthy living and a smart society