Paper Withdrawn Before the Issue Release

This paper presents the architecture and implementation of a set of novel sensor nodes designed to measure ammonium, nitrate and chloride in real time, sending the data, by means of a network, to the base station in order to control the pollution in a lake. The results obtained being compared with those provided by the corresponding reference methods. Recovery analyses with ion selective electrodes and standard methods, study of interferences, and evaluation of major sensor features have also been carried out. The use of a wireless system for monitoring purposes will not only reduce the overall monitoring system cost in term of facilities setup and labor cost, but will also provide flexibility in terms of distance. The major advantages of the proposed in-line analysis compared with the classical off-line procedures are the elimination of contaminants due to sample handling, the minimization of the overall cost of data acquisition, the possibility of real-time analysis, allowing the rapid detection of pollutants, the ability to obtain detailed spatial and temporal data sets of complete environments, obtaining the spatial distribution of the analyzed parameters, as well as its variation with the passing of time, and finally the possibility of performing measurements in locations which are difficult to access (in this case a deep lake)

A New Ammonium Smart Sensor with Interference Rejection

[EN] In many water samples, it is important to determine the ammonium concentration in order to obtain an overall picture of the environmental impact of pollutants and human actions, as well as to detect the stage of eutrophization. Ion selective electrodes (ISEs) have been commonly utilized for this purpose, although the presence of interfering ions (potassium and sodium in the case of NH4+-ISE) represents a handicap in terms of the measurement quality. Furthermore, random malfunctions may give rise to incorrect measurements. Bearing all of that in mind, a smart ammonium sensor with enhanced features has been developed and tested in water samples, as demonstrated and commented on in detail following the presentation of the complete set of experimental measurements that have been successfully carried out. This has been achieved through the implementation of an expert system that supervises a set of ISEs in order to (a) avoid random failures and (b) reject interferences. Our approach may also be suitable for in-line monitoring of the water quality through the implementation of wireless sensor networks.This research was supported by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Capella Hernández, JV.; Bonastre Pina, AM.; Campelo Rivadulla, JC.; Ors Carot, R.; Peris Tortajada, M. (2020). A New Ammonium Smart Sensor with Interference Rejection. Sensors. 20(24):1-17. https://doi.org/10.3390/s20247102S1172024Molins-Legua, C., Meseguer-Lloret, S., Moliner-Martinez, Y., & Campíns-Falcó, P. (2006). A guide for selecting the most appropriate method for ammonium determination in water analysis. TrAC Trends in Analytical Chemistry, 25(3), 282-290. doi:10.1016/j.trac.2005.12.002Zhu, Y., Chen, J., Yuan, D., Yang, Z., Shi, X., Li, H., … Ran, L. (2019). Development of analytical methods for ammonium determination in seawater over the last two decades. TrAC Trends in Analytical Chemistry, 119, 115627. doi:10.1016/j.trac.2019.115627Liu, J. (2020). New directions in sensor technology. TrAC Trends in Analytical Chemistry, 124, 115818. doi:10.1016/j.trac.2020.115818Yaroshenko, I., Kirsanov, D., Marjanovic, M., Lieberzeit, P. A., Korostynska, O., Mason, A., … Legin, A. (2020). Real-Time Water Quality Monitoring with Chemical Sensors. Sensors, 20(12), 3432. doi:10.3390/s20123432Martı́nez-Máñez, R., Soto, J., Garcia-Breijo, E., Gil, L., Ibáñez, J., & Llobet, E. (2005). An «electronic tongue» design for the qualitative analysis of natural waters. Sensors and Actuators B: Chemical, 104(2), 302-307. doi:10.1016/j.snb.2004.05.022Legin, A. ., Rudnitskaya, A. ., Vlasov, Y. ., Di Natale, C., & D’Amico, A. (1999). The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors. Sensors and Actuators B: Chemical, 58(1-3), 464-468. doi:10.1016/s0925-4005(99)00127-6Mueller, A. V., & Hemond, H. F. (2013). Extended artificial neural networks: Incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels. Talanta, 117, 112-118. doi:10.1016/j.talanta.2013.08.045Wen, Y., Mao, Y., Kang, Z., & Luo, Q. (2019). Application of an ammonium ion-selective electrode for the real-time measurement of ammonia nitrogen based on pH and temperature compensation. Measurement, 137, 98-101. doi:10.1016/j.measurement.2019.01.031Handbook of Electrochemistry. (2007). doi:10.1016/b978-0-444-51958-0.x5000-9Umezawa, Y., Bühlmann, P., Umezawa, K., Tohda, K., & Amemiya, S. (2000). Potentiometric Selectivity Coefficients of Ion-Selective Electrodes. Part I. Inorganic Cations (Technical Report). Pure and Applied Chemistry, 72(10), 1851-2082. doi:10.1351/pac200072101851Capella, J. V., Bonastre, A., Ors, R., & Peris, M. (2015). An interference-tolerant nitrate smart sensor for Wireless Sensor Network applications. Sensors and Actuators B: Chemical, 213, 534-540. doi:10.1016/j.snb.2015.02.125Choudhary, J., Balasubramanian, P., Varghese, D., Singh, D., & Maskell, D. (2019). Generalized Majority Voter Design Method for N-Modular Redundant Systems Used in Mission- and Safety-Critical Applications. Computers, 8(1), 10. doi:10.3390/computers8010010Capella, 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.2014.01.039Cuartero, M., Colozza, N., Fernández-Pérez, B. M., & Crespo, G. A. (2020). Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors – an overview of the progress in the last 20 years. The Analyst, 145(9), 3188-3210. doi:10.1039/d0an00327aBembe, M., Abu-Mahfouz, A., Masonta, M., & Ngqondi, T. (2019). A survey on low-power wide area networks for IoT applications. Telecommunication Systems, 71(2), 249-274. doi:10.1007/s11235-019-00557-9Freiser, H. (Ed.). (1980). Ion-Selective Electrodes in Analytical Chemistry. doi:10.1007/978-1-4684-3776-8Peris, M., Bonastre, A., & Ors, R. (1998). Distributed expert system for the monitoring and control of chemical processes. Laboratory Robotics and Automation, 10(3), 163-168. doi:10.1002/(sici)1098-2728(1998)10:33.0.co;2-2Carminati, M., Turolla, A., Mezzera, L., Di Mauro, M., Tizzoni, M., Pani, G., … Antonelli, M. (2020). A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems. Sensors, 20(4), 1125. doi:10.3390/s20041125Nakas, C., Kandris, D., & Visvardis, G. (2020). Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey. Algorithms, 13(3), 72. doi:10.3390/a13030072Capella, J. V., Bonastre, A., Campelo, J. C., Ors, R., & Peris, M. (2020). 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IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water.

[EN] The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods.FundingThis research was funded by Spanish Ministerio de Economia y Competitividad, Gobierno de Espana, grant number DPI2016-80303-C2-1-P.Campelo Rivadulla, JC.; Capella Hernández, JV.; Ors Carot, R.; Peris Tortajada, M.; Bonastre Pina, AM. (2022). IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water. Sensors. 22(3):1-16. https://doi.org/10.3390/s2203084211622

HMP: A Hybrid Monitoring Platform for Wireless Sensor Networks Evaluation

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Wireless sensor networks (WSNs), as an essential part of the deployment of the Internet of Things paradigm, require an adequate debugging and monitoring procedures to avoid errors in their operation. One of the best tools for WSN supervision is the so-called Monitoring Platforms that harvest information about the WSN operation in order to detect errors and evaluate performance. Monitoring platforms for the WSN can be hardware or software implemented, and, additionally, they can work in active or passive mode. Each approach has advantages and drawbacks. To benefit from their advantages and compensate their limitations, hybrid platforms combine different approaches. However, very few hybrid tools, with many restrictions, have been proposed. Most of them are designed for a specific implementation of WSN nodes; many of them are lack of a real implementation, and none of them provides an accurate solution to synchronization issues. This paper presents a hybrid monitoring platform for WSN, called HMP. This platform combines both hardware and software, active and passive monitoring approaches. This hybridization provides many interesting capabilities; HMP harvests the information both actively (directly from the sensor nodes) and passively (by means of messages captured from the WSN), causing a very low intrusion in the observed network. In addition, HMP is reusable; it may be applied to almost any WSN and includes a suitable trace synchronism procedure. Finally, HMP follows an open architecture that allows interoperability and layered development.This work was supported by the Agencia Estatal de Investigacion from the Spanish Ministerio de Economia, Industria y Competitividad, through the project Hacia el hospital inteligente: Investigacion en el diseno de una plataforma basada en Internet de las Cosas y su aplicacion en la mejora del cumplimiento de higiene de manos, under Grant DPI2016-80303-C2-1-P. The project covers the costs of publishing in open access.Navia-Mendoza, MR.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Capella Hernández, JV.; Ors Carot, R. (2019). HMP: A Hybrid Monitoring Platform for Wireless Sensor Networks Evaluation. IEEE Access. 7:87027-87041. https://doi.org/10.1109/ACCESS.2019.2925299S8702787041

Active low intrusion hybrid monitor for wireless sensor networks

Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.This research was supported by the Valencian Regional Government under Research Project GV/2014/012, the Polytechnic University of Valencia under Research Projects VLC/Campus UPV PAID-06-12, financed by the Ministerio de Educacion, Cultura y Deporte as part of the program Campus de excelencia internacional UPV SP20140730 and UPV SP20150050, and the Spanish government under projects CTM2011-29691-C02-01 and TIN2011-28435-C03-0.Navia, M.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Ors Carot, R.; Capella Hernández, JV.; Serrano Martín, JJ. (2015). Active low intrusion hybrid monitor for wireless sensor networks. Sensors. 15(9):23927-23952. https://doi.org/10.3390/s150923927S2392723952159Mahapatro, A., & Khilar, P. M. (2013). Fault Diagnosis in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(4), 2000-2026. doi:10.1109/surv.2013.030713.00062Rodrigues, A., Camilo, T., Silva, J. S., & Boavida, F. (2012). Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey. Journal of Network and Systems Management, 21(3), 408-452. doi:10.1007/s10922-012-9240-6Schoofs, A., O’Hare, G. M. P., & Ruzzelli, A. G. (2012). Debugging Low-Power and Lossy Wireless Networks: A Survey. IEEE Communications Surveys & Tutorials, 14(2), 311-321. doi:10.1109/surv.2011.021111.00098FAQ—TinyOS Wikihttp://tinyos.stanford.edu/tinyos-wiki/index.php/FAQGarcia, F., Andrade, R., Oliveira, C., & de Souza, J. (2014). EPMOSt: An Energy-Efficient Passive Monitoring System for Wireless Sensor Networks. Sensors, 14(6), 10804-10828. doi:10.3390/s140610804Yunhao Liu, Kebin Liu, & Mo Li. (2010). Passive Diagnosis for Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 18(4), 1132-1144. doi:10.1109/tnet.2009.2037497Information Technology—Open Systems Interconnection—Basic Reference Modelhttp://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?-csnumber=20269STM32F051R8 ARM Cortex-M0 MCUhttp://www.st.com/web/catalog/mmc/CMSIS-Cortex Microcontroller Software Interface Standardhttp://www.arm.com/products/processors/cortex-m/cortex-microcontroller-software-interface-standard.phpKeil MDK-ARM Version 5http://www2.keil.com/mdk5/34405A Digital Multimeter, 5½ digit | Keysight (Agilent)http://www.keysight.com/en/pd-686884-pn-34405A/Gharghan, S., Nordin, R., & Ismail, M. (2014). Energy-Efficient ZigBee-Based Wireless Sensor Network for Track Bicycle Performance Monitoring. Sensors, 14(8), 15573-15592. doi:10.3390/s140815573Molina-Garcia, A., Fuentes, J. A., Gomez-Lazaro, E., Bonastre, A., Campelo, J. C., & Serrano, J. J. (2012). Development and Assessment of a Wireless Sensor and Actuator Network for Heating and Cooling Loads. IEEE Transactions on Smart Grid, 3(3), 1192-1202. doi:10.1109/tsg.2012.2187542Lee, D.-S., Liu, Y.-H., & Lin, C.-R. (2012). A Wireless Sensor Enabled by Wireless Power. Sensors, 12(12), 16116-16143. doi:10.3390/s12121611