A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution. Document type: Articl

A new application of Internet of Things and Cloud Services in Analytical Chemistry: Determination of bicarbonate in water

[EN] In a constantly evolving world, new technologies such as Internet of Things (IoT) and cloud-based services offer great opportunities in many fields. In this paper we propose a new approach to the development of smart sensors using IoT and cloud computing, which open new interesting possibilities in analytical chemistry. According to IoT philosophy, these new sensors are able to integrate the generated data on the existing IoT platforms, so that information may be used whenever needed. Furthermore, the utilization of these technologies permits one to obtain sensors with significantly enhanced features using the information available in the cloud. To validate our new approach, a bicarbonate IoT-based smart sensor has been developed. A classical CO2 ion selective electrode (ISE) utilizes the pH information retrieved from the cloud and then provides an indirect measurement of bicarbonate concentration, which is offered to the cloud. The experimental data obtained are compared to those yielded by three other classical ISEs, with satisfactory results being achieved in most instances. Additionally, this methodology leads to lower-consumption, low-cost bicarbonate sensors capable of being employed within an IoT application, for instance in the continuous monitoring of HCO3- in rivers. Most importantly, this innovative application field of IoT and cloud approaches can be clearly perceived as an indicator for future developments over the short-term.This research was funded by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Capella Hernández, JV.; Bonastre Pina, AM.; Ors Carot, R.; Peris Tortajada, M. (2019). A new application of Internet of Things and Cloud Services in Analytical Chemistry: Determination of bicarbonate in water. Sensors. 19(24):1-13. https://doi.org/10.3390/s19245528S1131924Perry, C. T., Salter, M. A., Harborne, A. R., Crowley, S. F., Jelks, H. L., & Wilson, R. W. (2011). Fish as major carbonate mud producers and missing components of the tropical carbonate factory. Proceedings of the National Academy of Sciences, 108(10), 3865-3869. doi:10.1073/pnas.1015895108Pandolfi, J. M., Connolly, S. R., Marshall, D. J., & Cohen, A. L. (2011). Projecting Coral Reef Futures Under Global Warming and Ocean Acidification. Science, 333(6041), 418-422. doi:10.1126/science.1204794Jaquet, J.-M., Nirel, P., & Martignier, A. (2013). Preliminary investigations on picoplankton-related precipitation of alkaline-earth metal carbonates in meso-oligotrophic lake Geneva (Switzerland). Journal of Limnology, 72(3), 50. doi:10.4081/jlimnol.2013.e50Lewis, C. N., Brown, K. A., Edwards, L. A., Cooper, G., & Findlay, H. S. (2013). Sensitivity to ocean acidification parallels natural pCO2 gradients experienced by Arctic copepods under winter sea ice. Proceedings of the National Academy of Sciences, 110(51), E4960-E4967. doi:10.1073/pnas.1315162110Kaloo, M. A., Sunder Raman, R., & Sankar, J. (2016). Novel structurally tuned DAMN receptor for «in situ» diagnosis of bicarbonate in environmental waters. The Analyst, 141(8), 2367-2370. doi:10.1039/c6an00218hBotta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Capella, J. V., Bonastre, A., Ors, R., & Peris, M. (2014). A step forward in the in-line river monitoring of nitrate by means of a wireless sensor network. Sensors and Actuators B: Chemical, 195, 396-403. doi:10.1016/j.snb.2014.01.039Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019). A Survey on Internet of Things and Cloud Computing for Healthcare. Electronics, 8(7), 768. doi:10.3390/electronics8070768Lopez-Barbosa, N., Gamarra, J. D., & Osma, J. F. (2016). The future point-of-care detection of disease and its data capture and handling. Analytical and Bioanalytical Chemistry, 408(11), 2827-2837. doi:10.1007/s00216-015-9249-2Kassal, P., Steinberg, I. M., & Steinberg, M. D. (2013). Wireless smart tag with potentiometric input for ultra low-power chemical sensing. Sensors and Actuators B: Chemical, 184, 254-259. doi:10.1016/j.snb.2013.04.049Piyare, R., & Lee, S. R. (2013). Towards Internet of Things (IOTS): Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing. International journal of Computer Networks & Communications, 5(5), 59-72. doi:10.5121/ijcnc.2013.5505Carminati, M., Mezzera, L., Ferrari, G., Sampietro, M., Turolla, A., Di Mauro, M., & Antonelli, M. (2018). A Smart Sensing Node for Pervasive Water Quality Monitoring with Anti-Fouling Self-Diagnostics. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas.2018.8351833Borrego, C., Ginja, J., Coutinho, M., Ribeiro, C., Karatzas, K., Sioumis, T., … Penza, M. (2018). Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II. Atmospheric Environment, 193, 127-142. doi:10.1016/j.atmosenv.2018.08.028Gervasi, O., Murgante, B., Misra, S., Gavrilova, M. L., Rocha, A. M. A. C., Torre, C., … Apduhan, B. O. (Eds.). (2015). Computational Science and Its Applications -- ICCSA 2015. Lecture Notes in Computer Science. doi:10.1007/978-3-319-21407-8LIU, Y., LIANG, Y., XUE, L., LIU, R., TAO, J., ZHOU, D., … HU, W. (2019). Polystyrene-coated Interdigitated Microelectrode Array to Detect Free Chlorine towards IoT Applications. Analytical Sciences, 35(5), 505-509. doi:10.2116/analsci.18p460Ping, H., Wang, J., Ma, Z., & Du, Y. (2018). Mini-review of application of IoT technology in monitoring agricultural products quality and safety. International Journal of Agricultural and Biological Engineering, 11(5), 35-45. doi:10.25165/j.ijabe.20181105.3092Alreshaid, A. T., Hester, J. G., Su, W., Fang, Y., & Tentzeris, M. M. (2018). Review—Ink-Jet Printed Wireless Liquid and Gas Sensors for IoT, SmartAg and Smart City Applications. Journal of The Electrochemical Society, 165(10), B407-B413. doi:10.1149/2.0341810jesDjelouat, H., Amira, A., & Bensaali, F. (2018). Compressive Sensing-Based IoT Applications: A Review. Journal of Sensor and Actuator Networks, 7(4), 45. doi:10.3390/jsan7040045Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actuators B: Chemical, 266, 228-245. doi:10.1016/j.snb.2018.03.074Alahi, M. E. E., Xie, L., Mukhopadhyay, S., & Burkitt, L. (2017). A Temperature Compensated Smart Nitrate-Sensor for Agricultural Industry. IEEE Transactions on Industrial Electronics, 64(9), 7333-7341. doi:10.1109/tie.2017.2696508FIWARE Foundationhttps://www.fiware.org/Xie, X., & Bakker, E. (2013). Non-Severinghaus Potentiometric Dissolved CO2 Sensor with Improved Characteristics. Analytical Chemistry, 85(3), 1332-1336. doi:10.1021/ac303534

Contactless Sensing of Water Properties for Smart Monitoring of Pipelines

A key milestone for the pervasive diffusion of wireless sensing nodes for smart monitoring of water quality and quantity in distribution networks is the simplification of the installation of sensors. To address this aspect, we demonstrate how two basic contactless sensors, such as piezoelectric transducers and strip electrodes (in a longitudinal interdigitated configuration to sense impedance inside and outside of the pipe with potential for impedimetric leak detection), can be easily clamped on plastic pipes to enable the measurement of multiple parameters without contact with the fluid and, thus, preserving the integrity of the pipe. Here we report the measurement of water flow rate (up to 24 m(3)/s) and temperature with ultrasounds and of the pipe filling fraction (capacitance at 1 MHz with similar to cm(3) resolution) and ionic conductivity (resistance at 20 MHz from 700 to 1400 mu S/cm) by means of impedance. The equivalent impedance model of the sensor is discussed in detail. Numerical finite-element simulations, carried out to optimize the sensing parameters such as the sensing frequency, confirm the lumped models and are matched by experimental results. In fact, a 6 m long, 30 L demonstration hydraulic loop was built to validate the sensors in realistic conditions (water speed of 1 m/s) monitoring a pipe segment of 0.45 m length and 90 mm diameter (one of the largest ever reported in the literature). Tradeoffs in sensors accuracy, deployment, and fabrication, for instance, adopting single-sided flexible PCBs as electrodes protected by Kapton on the external side and experimentally validated, are discussed as well

A multi-modal smart sensing network for marine environmental monitoring

There is an imperative need for long-term, large-scale marine monitoring systems that will allow decisions to be made based on the analysis of collected data to avoid or limit negative impacts on the ecosystem. Modern marine environmental sensing technologies, such as autonomous wireless sensor networks (WSNs), provide the capability to meet the challenges of high spatial and temporal scales. However, the significant amount of data generated from WSNs is a significant challenge for manual analysis. These multitudinous data need to be automatically processed, indexed and catalogued in a smarter way that can be more easily understood, accessed and managed by operators, scientists and policy makers. Moreover, current research works show that WSNs have their own limitations, for example, reliability issues and the fact that they are passive systems and provide context-less data. Thus, it is becoming increasingly clear that in order to adequately monitor marine environments, they need to be characterised from multiple perspectives. Combining multiple technologies and sensing modalities in environmental monitoring programmes can provide not only advantages of reliability and robustness for sensing systems, but also enhanced understanding of environmental processes. In addition, considerable advances can be made if robust sensing technology can be combined with sophisticated methods of data analysis, classification and cataloguing. The aim of this work is to bridge the gap between current aquatic monitoring systems and futuristic ideal large scale multi-modality smart sensing networks for marine environmental monitoring. To illustrate this, a smart sensing system is proposed and two case studies are used to show data processing from in-situ measurements and from camera based visual sensing data automatically using machine learning techniques. Abnormal events detection results from an in-situ sensor and shipping traffic detection results from visual sensor are combined to illustrate the benefit of coupling multiple sensing modalities

Wearable chemo/bio-sensors for sweat sensing in sports applications: combining micro-fluidics and novel materials

In the last decade, we have witnessed an exponential growth in the area of clinical diagnostic but surprisingly little has been done on the development of wearable chemo/bio-sensors in the field of sports science. In particular, the use of wearable wireless sensors capable of analysing sweat during physical exercise can provide access to new information sources that can be used to optimise and manage athletes’ performance. Lab-on-a-Chip technology provides a fascinating opportunity for the development of such wearable sensors. In this thesis two different colorimetric wearable microfluidic devices for real- time pH sensing were developed and used during athlete training activity. In one case a textile-based microfluidic platform employing cotton capillarity to drive sweat toward the pH sensitive area is presented that avoids the use of bulky fluid handling apparatus, i.e. pumps. The second case presents a wearable micro-fluidic device based on the use of pH responsive ionogels to obtain real-time sweat pH measurements through photo analysis of their colour variation. The thesis also presents the first example of sweat lactate sensing using an organic electrochemical transistor incorporating an ionogel as solid-state electrolyte. In this chapter, optimization of the lactate oxidase stability when dissolved in number of hydrated ionic liquids is investigated. Finally, a new fabrication protocol for paper-based microfluidic technology is presented, which may have important implications for future applications such as low-cost diagnostics and chemical sensing technologies

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools