A method for modeling the battery state of charge in wireless sensor networks

(c) 2015 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.In this paper we propose a method for obtaining an analytic model of the battery State-of-Charge (SoC) in wireless sensor nodes. The objective is to find simple models that can be used to estimate accurately the real battery state and consequently the node lifetime. Running the model in the network nodes, we can provide the motes with the required information to implement applications that can be considered as battery-aware. The proposed methodology reduces the computational complexity of the model avoiding complicated electrochemical simulations and treating the battery as an unknown system with an output that can be predicted using simple mathematical models. At a first stage, during a setup period, the method starts with the measurement of several battery parameters under different environmental and operational conditions. After that, the method uses the previous collected data for building mathematical models, considering the linear regression or the multilayer perceptron as the most appropriated. Finally, the models are validated experimentally with new measures. Results show the suitability of the method that produces accurate and simple models, capable of being implemented even in low-cost and very constrained real motesThis work was supported by the I+D+i Program, Generalitat Valenciana, through the Research and Development under Project GV05/043 and in part by the Vicerrectorado of Investigation, Development and Innovation, Universidad Politecnica de Valencia, under Grant PAID-06-06-002-61.Lajara Vizcaíno, JR.; Pérez Solano, JJ.; Pelegrí Sebastiá, J. (2015). A method for modeling the battery state of charge in wireless sensor networks. IEEE Sensors Journal. 15(2):1186-1197. https://doi.org/10.1109/JSEN.2014.2361151S1186119715

E-Nose Application to Food Industry Production

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic nose (e-nose) can be a useful tool to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of the e-nose in the food industry.Chilo, J.; Pelegrí Sebastiá, J.; Cupane, M.; Sogorb Devesa, TC. (2016). E-Nose Application to Food Industry Production. IEEE Instrumentation and Measurement Magazine. 19(1):27-33. doi:10.1109/MIM.2016.7384957S273319

Predicting the Batteries' State of Health in Wireless Sensor Networks Applications

[EN] The lifetime of wireless sensor networks deployments depends strongly on the nodes battery state of health (SoH). It is important to detect promptly those motes whose batteries are affected and degraded by ageing, environmental conditions, failures, etc. There are several parameters that can provide significant information of the battery SoH, such as the number of charge/discharge cycles, the internal resistance, voltage, drained current, temperature, etc. The combination of these parameters can be used to generate analytical models capable of predicting the battery SoH. The generation of these models needs a previous process to collect dense data traces with sampled values of the battery parameters during a large number of discharge cycles under different operating conditions. The collected data allow the development of mathematical models that can predict the battery SoH. These models are required to be simple because they must be executed in motes with low computational capabilities. The paper shows the complete process of acquiring the training data, the models generation and its experimental validation using rechargeable batteries connected to Telosb motes. The obtained results provide significant insight of the battery SoH at different temperatures and charge/discharge cycles.This work was supported in part by the Spanish MINECO under Grant BIA2016-76957-C3-1-R and in part by the I+D+i Program of the Generalitat Valenciana, Spain, under Grant AICO/2016/046.Lajara Vizcaino, JR.; Perez Solano, JJ.; Pelegrí Sebastiá, J. (2018). Predicting the Batteries' State of Health in Wireless Sensor Networks Applications. IEEE Transactions on Industrial Electronics. 65(11):8936-8945. https://doi.org/10.1109/TIE.2018.2808925S89368945651

Distribución Linux para electrónicos

En el presente artículo nos encontramos con un binomio, que integra las variables: uso de una distribución Linux y aplicaciones para la electrónica. Tras la resolución de la ecuación nos encontramos con la distribución FEL (Fedora Electronic Lab). Dicha distribución presenta la ventaja de ser utilizada para la formación de electrónica sin la necesidad de entrar en detalle en los procesos de instalación de las mismas, además de la posibilidad de disponer de dicha distribución en una memoria portátil para aumentar la portabilidad del trabajo realizado, ya que se dispone de todos los programas necesarios para crear o editar los proyectos relacionados con la electrónica que puedan surgir.Pareja Aparicio, M.; Lajara Vizcaino, JR.; Pelegrí Sebastiá, J. (2012). Distribución Linux para electrónicos. Revista Española de Electrónica. (686):44-48. http://hdl.handle.net/10251/48085S444868

Design and simulation of dual-band RF energy harvesting antenna for WSNs

[EN] Radio frequency energy harvesting is attracting an increasing deal of attention in order to provide power to the electronic devices, including Wireless Sensor Networks. In this context, we focus on ambient radiofrequency energy available in the ambient environment. The receiving antenna is the main element of radiofrequency harvester, as it has to capture the RF energy from the radiating sources. Our work provides a new design of the receiving antenna to harness RF energy more effectively. Several simulations and optimizations are performed in order to maximize the antenna gain around the Wi-Fi bandwidth (2.45 GHz and 5 GHz). Variation of the return loss passes below -29 dB for 5 GHz frequencies and the radiation shape shows a quasi-omnidirectional pattern in Wi-Fi bands. The dual band antenna proposed is very interesting for the RF harvesting energy system, meeting the desired criteria to maximize the ambient harvested RF energy.This research was supported in part by EMMAG Program, 2014, funded by the European Commission.Bakkali, A.; Pelegrí Sebastiá, J.; Sogorb Devesa, T.; Bou Escrivà, A.; Lyhyaoui, A. (2017). Design and simulation of dual-band RF energy harvesting antenna for WSNs. Energy Procedia. 139:55-60. doi:10.1016/j.egypro.2017.11.172S556013

Monitorización de instrumentación avanzada para docencia en red

[EN] Due to university internationalization, learning’s concept is not linked to a specific geographical place. This document describes the methods used to visualize the laboratory’s instruments (oscilloscope Tektronix TDS 210 and the generator Agilent 33120A) monitored remotely. Being able to generalize to any instrument that has an interface for remote connection regardless of the type of bus technology.[ES] Debido a la internacionalización universitaria, el concepto aprendizaje no está vinculado a un lugar geográfico específico. Este documento describe los métodos utilizados para visualizar en el laboratorio los instrumentos (osciloscopio Tektronix TDS 210 y el generador Agilent 33120A) monitorizados de forma remota. Pudiéndose generalizar a cualquier instrumento que tenga interfaz para conexión remota independientemente del tipo de tecnología bus.Talens Felis, JB.; Pelegrí Sebastiá, J.; Ibáñez Sabater, FJ.; Sogorb, T. (2018). Monitorización de instrumentación avanzada para docencia en red. En IN-RED 2018. IV Congreso Nacional de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 256-269. https://doi.org/10.4995/INRED2018.2018.8731OCS25626

Modeling of Photovoltaic Cell Using Free Software Application for Training and Design Circuit in Photovoltaic Solar Energy

There are numerous studies that develop the mathematical modeling of photovoltaic cells and verified by software. The model presented is based on an equivalent circuit implemented in free software. Free software used is Quite Universal Circuit Simulator (QUCS). QUCS uses a generic diode for adjust the current and voltage curve (IVcurve) at photovoltaic cell. Additionally, you can use equations to define the model of photovoltaic cell and represent the characteristic curves on the same page. QUCS is a multiplatform application that runs on Windows and Linux, this software is available in Linux distributions for electronics. Using QUCS to model a PV cell allows subcircuit and a real representation to a attractive presentation for teaching. In section 5 show examples of practices used on formation, further can be used on: courses of photovoltaic, online formation or distance learning, because only need download QUCS application, and is a good complement to a previous works on labo¿ ratory or concepts review for theory. Advantage to used QUCS is that allow several PV cells with a few mouse click, also does not needs buy additional PV cells to used on laboratory because can be modelled the PV cell available on laboratory. Further, is not a problem the availability material on laboratory, because the material of PV system can be expensive, then is best provide a good photovoltaic devices that a devices for all student in a class.Pareja Aparicio, M.; Pelegrí Sebastiá, J.; Sogorb Devesa, TC.; Llario Sanjuan, JV. (2013). Modeling of Photovoltaic Cell Using Free Software Application for Training and Design Circuit in Photovoltaic Solar Energy. En New Developments in Renewable Energy. InTech. 121-139. doi:10.5772/51925S12113

Power Consumption Analysis of Operating Systems for Wireless Sensor Networks

In this paper four wireless sensor network operating systems are compared in terms of power consumption. The analysis takes into account the most common operating systems—TinyOS v1.0, TinyOS v2.0, Mantis and Contiki—running on Tmote Sky and MICAz devices. With the objective of ensuring a fair evaluation, a benchmark composed of four applications has been developed, covering the most typical tasks that a Wireless Sensor Network performs. The results show the instant and average current consumption of the devices during the execution of these applications. The experimental measurements provide a good insight into the power mode in which the device components are running at every moment, and they can be used to compare the performance of different operating systems executing the same tasks

A Solar Energy Powered Autonomous Wireless Actuator Node for Irrigation Systems

The design of a fully autonomous and wireless actuator node ("wEcoValve mote") based on the IEEE 802.15.4 standard is presented. The system allows remote control (open/close) of a 3-lead magnetic latch solenoid, commonly used in drip irrigation systems in applications such as agricultural areas, greenhouses, gardens, etc. The very low power consumption of the system in conjunction with the low power consumption of the valve, only when switching positions, allows the system to be solar powered, thus eliminating the need of wires and facilitating its deployment. By using supercapacitors recharged from a specifically designed solar power module, the need to replace batteries is also eliminated and the system is completely autonomous and maintenance free. The "wEcoValve mote" firmware is based on a synchronous protocol that allows a bidirectional communication with a latency optimized for real-time work, with a synchronization time between nodes of 4 s, thus achieving a power consumption average of 2.9 mW. © 2011 by the authors.This work was supported by the I + D + i program of the Generalitat Valenciana, R&D Project GV05/043, and the Vicerecorate of Investigation, Development and Innovation of Universidad Politecnica de Valencia PAID-06-06-002-61 and PAID-10-11.Lajara Vizcaino, JR.; Alberola, J.; Pelegrí Sebastiá, J. (2011). A Solar Energy Powered Autonomous Wireless Actuator Node for Irrigation Systems. Sensors. 11:329-340. doi:10.3390/s110100329S3293401

A Dual-Band Antenna for RF Energy Harvesting Systems in Wireless Sensor Networks

In this paper, we focus on ambient radio frequency energy available from commercial broadcasting stations in order to provide a system based on RF energy harvesting using a new design of receiving antenna. Several antenna designs have been proposed for use in RF energy harvesting systems, as a pertinent receiving antenna design is highly required since the antenna features can affect the amount of energy harvested. The proposed antenna is aimed at greatly increasing the energy harvesting efficiency over Wi-Fi bands: 2.45GHz and 5GHz. This provides a promising alternative energy source in order to power sensors located in harsh environments or remote places, where other energy sources are impracticable.The dual-band antenna can be easily integrated with RF energy harvesting system on the same circuit board. Simulations and measurements were carried out to evaluate the antenna performances and investigate the effects of different design parameters on the antenna performance.The receiving antenna meets the required bandwidth specification and provides peak gain of more than 4 dBi across the operating band.This work was supported in part by EMMAG Program 2014. The tests have been performed under the collaboration with the Electromagnetic Radiation Laboratory (GRE Lab) of the UPV.Bakkali, A.; Pelegrí Sebastiá, J.; Sogorb Devesa, TC.; Llario Sanjuan, JV.; Bou Escrivà, A. (2016). A Dual-Band Antenna for RF Energy Harvesting Systems in Wireless Sensor Networks. Journal of Sensors. 2016:1-8. doi:10.1155/2016/5725836S182016Sudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443-461. doi:10.1109/surv.2011.060710.00094Bottner, H., Nurnus, J., Gavrikov, A., Kuhner, G., Jagle, M., Kunzel, C., … Schlereth, K.-H. (2004). New thermoelectric components using microsystem technologies. Journal of Microelectromechanical Systems, 13(3), 414-420. doi:10.1109/jmems.2004.828740Hande, A., Polk, T., Walker, W., & Bhatia, D. (2007). Indoor solar energy harvesting for sensor network router nodes. Microprocessors and Microsystems, 31(6), 420-432. doi:10.1016/j.micpro.2007.02.006Alippi, C., & Galperti, C. (2008). An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(6), 1742-1750. doi:10.1109/tcsi.2008.922023Mikeka, C., & Arai, H. (2011). Design Issues in Radio Frequency Energy Harvesting System. Sustainable Energy Harvesting Technologies - Past, Present and Future. doi:10.5772/25348Nintanavongsa, P., Muncuk, U., Lewis, D. R., & Chowdhury, K. R. (2012). Design Optimization and Implementation for RF Energy Harvesting Circuits. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2(1), 24-33. doi:10.1109/jetcas.2012.2187106Vyas, R. J., Cook, B. B., Kawahara, Y., & Tentzeris, M. M. (2013). E-WEHP: A Batteryless Embedded Sensor-Platform Wirelessly Powered From Ambient Digital-TV Signals. IEEE Transactions on Microwave Theory and Techniques, 61(6), 2491-2505. doi:10.1109/tmtt.2013.2258168Farinholt, K. M., Park, G., & Farrar, C. R. (2009). RF Energy Transmission for a Low-Power Wireless Impedance Sensor Node. IEEE Sensors Journal, 9(7), 793-800. doi:10.1109/jsen.2009.2022536Md. Din, N., Chakrabarty, C. K., Bin Ismail, A., Devi, K. K. A., & Chen, W.-Y. (2012). DESIGN OF RF ENERGY HARVESTING SYSTEM FOR ENERGIZING LOW POWER DEVICES. Progress In Electromagnetics Research, 132, 49-69. doi:10.2528/pier1207200