Energy Efficiency in Cooperative Wireless Sensor Networks

[EN] The transport of sensitive products is very important because their deterioration may cause the value lost and even the product rejection by the buyer. In addition, it is important to choose the optimal way to achieve this end. In a data network, the task of calculating the best routes is performed by routers. We can consider the optimal path as the one that provides a shortest route. However, if a real transport network is considered the shortest path can sometimes be affected by incidents and traffic jams that would make it inadvisable. On the other hand, when we need to come back, due to features that symmetry provides, it would be interesting to follow the same path in reverse sense. For this reason, in this paper we present a symmetric routing mechanism for cooperative monitoring system for the delivery of fresh products. The systems is based on a combination of fixed nodes and a mobile node that stores the path followed to be able of coming back following the same route in reverse sense. If this path is no longer available, the system will try to maintain the symmetry principle searching the route that provide the shortest time to the used in the initial trip. The paper shows the algorithm used by the systems to calculate the symmetric routes. Finally, the system is tested in a real scenario which combines different kind of roads. As the results shows, the energy consumption of this kind of nodes is highly influenced by the activity of sensors.This work has been supported by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D -Programa Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", (project TIN2014-57991-C3-1- P) and the "programa para la Formacion de Personal Investigador - (FPI-2015-S2-884)" by the "Universitat Politecnica de Valencia".Sendra, S.; Lloret, J.; Lacuesta, R.; Jimenez, JM. (2019). Energy Efficiency in Cooperative Wireless Sensor Networks. Mobile Networks and Applications. 24(2):678-687. https://doi.org/10.1007/s11036-016-0788-3S678687242Derks HG, Buehler WS, Hall MB (2013) Real-time method and system for locating a mobile object or person in a tracking environment. US Patent 8514071 B2. Aug 20, 2013Witmond R, Dutta R, Charroppin P (2006) Method for tracking a mail piece. US Patent 7003376 B2, Feb 21, 2006Lu L, Liu Y, Han J (2015) ACTION: breaking the privacy barrier for RFID systems. 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A framework for obesity control using a wireless body sensor network

Low-cost low-power consumption small wireless sensor devices have empowered the development of wireless body area networks (WBANs). In WBANs many sensors are attached to human body for sensing particular health related information to improve healthcare and quality of life. Obesity is one of the most common problems all over the world, which is amongst main causes of cardiovascular diseases. In this research, we explore hardware and software architecture of WBAN for obesity monitoring. The proposed framework consists of few sensor nodes that monitor body motion, calories calculator, and a personal server running on a personal smart phone or a personal computer. The focus of this research is to make obesity patients easier to get rid of this disease.The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this Research Group no. RG-1435-037.Alrajeh, NA.; Lloret, J.; Cánovas Solbes, A. (2014). A framework for obesity control using a wireless body sensor network. International Journal of Distributed Sensor Networks. 2014:1-6. https://doi.org/10.1155/2014/534760S162014Schmidt, R., Norgall, T., Mörsdorf, J., Bernhard, J., & von der Grün, T. (2002). Body Area Network BAN – a Key Infrastructure Element for Patient-Centered Medical Applications. Biomedizinische Technik/Biomedical Engineering, 47(s1a), 365-368. doi:10.1515/bmte.2002.47.s1a.365Garcia, M., Catala, A., Lloret, J., & Rodrigues, J. J. P. C. (2011). A wireless sensor network for soccer team monitoring. 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS). doi:10.1109/dcoss.2011.5982204Sun, G., Qiao, G., & Xu, B. (2012). Link Characteristics Measuring in 2.4 GHz Body Area Sensor Networks. International Journal of Distributed Sensor Networks, 8(10), 519792. doi:10.1155/2012/519792Tomas, J., Lloret, J., Bri, D., & Sendra, S. (2011). Sensors and their Application for Disabled and Elderly People. Handbook of Research on Personal Autonomy Technologies and Disability Informatics, 311-330. doi:10.4018/978-1-60566-206-0.ch020Latré, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2010). A survey on wireless body area networks. Wireless Networks, 17(1), 1-18. doi:10.1007/s11276-010-0252-4Zasowski, T., Meyer, G., Althaus, F., & Wittneben, A. (2006). UWB signal propagation at the human head. IEEE Transactions on Microwave Theory and Techniques, 54(4), 1836-1845. doi:10.1109/tmtt.2006.871989Bri, D., Lloret, J., Turro, C., & Garcia, M. (s. f.). Measuring Specific Absorption Rate by using Standard Communications Equipment. Telemedicine and E-Health Services, Policies, and Applications, 81-111. doi:10.4018/978-1-4666-0888-7.ch004Di Renzo, M., Buehrer, R. M., & Torres, J. (2007). Pulse Shape Distortion and Ranging Accuracy in UWB-Based Body Area Networks for Full-Body Motion Capture and Gait Analysis. IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. doi:10.1109/glocom.2007.717Neirynck D.Channel characterisation and physical layer analysis for body and personal area network development [Ph.D. thesis]2006Bristol, UKUniversity of BristolSendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Tang, Q., Tummala, N., Gupta, S. K. S., & Schwiebert, L. (2005). Communication Scheduling to Minimize Thermal Effects of Implanted Biosensor Networks in Homogeneous Tissue. IEEE Transactions on Biomedical Engineering, 52(7), 1285-1294. doi:10.1109/tbme.2005.847527Bag, A., & Bassiouni, M. (2006). Energy Efficient Thermal Aware Routing Algorithms for Embedded Biomedical Sensor Networks. 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Sysetems. doi:10.1109/mobhoc.2006.278619Quwaider, M., & Biswas, S. (2012). Delay Tolerant Routing Protocol Modeling for Low Power Wearable Wireless Sensor Networks. Network Protocols and Algorithms, 4(3). doi:10.5296/npa.v4i3.2054Machado, T. M. F., Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., & Lloret, J. (2012). Performance evaluation of cooperation mechanisms for m-health applications. 2012 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2012.6503353Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Macias, E., Suarez, A., & Lloret, J. (2013). Mobile Sensing Systems. Sensors, 13(12), 17292-17321. doi:10.3390/s131217292Meghanathan, N., & Mumford, P. (2013). Centralized and Distributed Algorithms for Stability-based Data Gathering in Mobile Sensor Networks. Network Protocols and Algorithms, 84. doi:10.5296/npa.v5i4.4208Hanson, M. A., Powell, H. C., Barth, A. T., Ringgenberg, K., Calhoun, B. H., Aylor, J. H., & Lach, J. (2009). Body Area Sensor Networks: Challenges and Opportunities. Computer, 42(1), 58-65. doi:10.1109/mc.2009.5Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., Lloret, J., & Proenca, M. L. (2011). A mobile health monitoring solution for weight control. 2011 International Conference on Wireless Communications and Signal Processing (WCSP). doi:10.1109/wcsp.2011.6096926Nachman, L., Huang, J., Shahabdeen, J., Adler, R., & Kling, R. (2008). IMOTE2: Serious Computation at the Edge. 2008 International Wireless Communications and Mobile Computing Conference. doi:10.1109/iwcmc.2008.19

A QoS-Based Wireless Multimedia Sensor Cluster Protocol

Wireless Sensor Networks (WSNs) provide a wireless network infrastructure for sensed data transport in environments where wired or satellite technologies cannot be used. Because the embedded hardware of the sensor nodes has been improved very much in the last years and the number of real deployments is increasing considerably, they have become a reliable option for the transmission of any type of sensed data, from few sensed measures to multimedia data. This paper proposes a new protocol that uses an ad hoc cluster based architecture which is able to adapt the logical sensor network topology to the delivered multimedia stream features, guaranteeing the quality of the communications. The proposed protocol uses the quality of service (QoS) parameters, such as bandwidth, delay, jitter, and packet loss, of each type of multimedia stream as a basis for the sensor clusters creation and organization inside the WSN, providing end-to-end QoS for each multimedia stream. We present real experiments that show the performance of the protocol for several video and audio cases when it is runningThis work has been partially supported by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," Project TEC2011-27516. This work has also been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Government of Russian Federation, Grant 074-U01, and by National Funding from the Fundacao para a Ciencia e a Tecnologia (FCT) through the PEst-OE/EEI/LA0008/2013 Project.Díaz Santos, JR.; Lloret, J.; Jimenez, JM.; Rodrigues, JJPC. (2014). A QoS-Based Wireless Multimedia Sensor Cluster Protocol. International Journal of Distributed Sensor Networks. 2014:1-17. https://doi.org/10.1155/2014/480372S1172014Bri, D., Garcia, M., Lloret, J., & Dini, P. (2009). Real Deployments of Wireless Sensor Networks. 2009 Third International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2009.69Karim, L., Anpalagan, A., Nasser, N., & Almhana, J. (2013). Sensor-based M2M Agriculture Monitoring Systems for Developing Countries: State and Challenges. Network Protocols and Algorithms, 5(3), 68. doi:10.5296/npa.v5i3.3787Edo, M., Canovas, A., Garcia, M., & Lloret, J. (s. f.). Providing VoIP and IPTV Services in WLANs. Handbook of Research on Mobility and Computing, 426-444. doi:10.4018/978-1-60960-042-6.ch028Diab, R., Chalhoub, G., & Misson, M. (2013). Overview on Multi-Channel Communications in Wireless Sensor Networks. Network Protocols and Algorithms, 5(3), 112. doi:10.5296/npa.v5i3.3811Khoukhi, L., & Cherkaoui, S. (2010). Intelligent QoS management for multimedia services support in wireless mobile ad hoc networks. Computer Networks, 54(10), 1692-1706. doi:10.1016/j.comnet.2010.01.014Abbas, C. J. B., Orozco, A. L. S., & Villalba, L. J. G. (2012). A distributed QoS mechanism for ad hoc network. International Journal of Ad Hoc and Ubiquitous Computing, 11(1), 25. doi:10.1504/ijahuc.2012.049282Çevik, T., & Zaim, A. H. (2013). A Multichannel Cross-Layer Architecture for Multimedia Sensor Networks. International Journal of Distributed Sensor Networks, 9(3), 457045. doi:10.1155/2013/457045Li, Z., Bi, J., & Chen, S. (2013). Traffic Prediction-Based Fast Rerouting Algorithm for Wireless Multimedia Sensor Networks. International Journal of Distributed Sensor Networks, 9(5), 176293. doi:10.1155/2013/176293Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Computer Communications, 31(14), 3438-3450. doi:10.1016/j.comcom.2008.05.030Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lehsaini, M., Guyennet, H., & Feham, M. (2010). An efficient cluster-based self-organisation algorithm for wireless sensor networks. International Journal of Sensor Networks, 7(1/2), 85. doi:10.1504/ijsnet.2010.031852Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Diaz, J. R., Lloret, J., Jimenez, J. M., & Sendra, S. (2014). MWAHCA: A Multimedia Wireless Ad Hoc Cluster Architecture. The Scientific World Journal, 2014, 1-14. doi:10.1155/2014/913046Wei, D., & Chan, H. (2006). Clustering Ad Hoc Networks: Schemes and Classifications. 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. doi:10.1109/sahcn.2006.288583Yu, J. Y., & Chong, P. H. J. (2005). A survey of clustering schemes for mobile ad hoc networks. IEEE Communications Surveys & Tutorials, 7(1), 32-48. doi:10.1109/comst.2005.1423333Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14-15), 2826-2841. doi:10.1016/j.comcom.2007.05.024Boyinbode, O., Le, H., & Takizawa, M. (2011). A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing, 1(2/3), 130. doi:10.1504/ijssc.2011.040339Ramachandran, L., Kapoor, M., Sarkar, A., & Aggarwal, A. (2000). Clustering algorithms for wireless ad hoc networks. Proceedings of the 4th international workshop on Discrete algorithms and methods for mobile computing and communications - DIALM ’00. doi:10.1145/345848.345860Chatterjee, M., Das, S. K., & Turgut, D. (2002). Cluster Computing, 5(2), 193-204. doi:10.1023/a:1013941929408Huang, Y.-M., Hsieh, M.-Y., & Wang, M.-S. (2007). Reliable transmission of multimedia streaming using a connection prediction scheme in cluster-based ad hoc networks. Computer Communications, 30(2), 440-452. doi:10.1016/j.comcom.2006.09.012Tang, S., & Li, W. (2006). QoS supporting and optimal energy allocation for a cluster based wireless sensor network. Computer Communications, 29(13-14), 2569-2577. doi:10.1016/j.comcom.2006.02.007Rosário, D., Costa, R., Paraense, H., Machado, K., Cerqueira, E., Braun, T., & Zhao, Z. (2012). A Hierarchical Multi-hop Multimedia Routing Protocol for Wireless Multimedia Sensor Networks. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2121Diaz, J. R., Lloret, J., Jiménez, J. M., & Hammoumi, M. (2014). A new multimedia-oriented architecture and protocol for wireless ad hoc networks. International Journal of Ad Hoc and Ubiquitous Computing, 16(1), 14. doi:10.1504/ijahuc.2014.062486Meghanathan, N., & Mumford, P. (2013). Centralized and Distributed Algorithms for Stability-based Data Gathering in Mobile Sensor Networks. Network Protocols and Algorithms, 84. doi:10.5296/npa.v5i4.420

Study of Requirements and Design of Sensors for Monitoring Water Quality and Feeding Process in Fish Farms and Other Environments

Se están realizando muchos esfuerzos en la acuicultura para alcanzar la sostenibilidad, sin embargo aún está lejos de ser sostenible. Sus impactos sobre el medio ambiente pueden prevenirse y corregirse mediante el uso de sensores, desarrollando la conocida como acuicultura de precisión. La calidad del agua afecta el rendimiento de los peces. La temperatura y la salinidad son algunos factores que afectan al crecimiento de los peces. Sin embargo, otros factores como la turbidez, el fotoperíodo y el oxígeno disuelto entre otros pueden afectar a las necesidades nutritivas de los peces. Ajustar la cantidad de alimento necesario es crucial para garantizar la sostenibilidad de la acuicultura y para aumentar el beneficio económico de las instalaciones. Al monitorear la calidad del agua, es posible estimar las necesidades de alimentación. Sin embargo, no es suficiente. El monitoreo del comportamiento de los peces, especialmente durante el período de alimentación, puede ayudar a adaptar el alimento proporcionado. Entonces, si está tan claro que el monitoreo puede ayudar a la producción acuícola, ¿por qué no vemos este sistema de monitoreo en las instalaciones acuícolas? ¿Por qué en la mayoría de las instalaciones la alimentación se da manualmente sin considerar el comportamiento de alimentación de los peces? El precio de los sensores para monitorizar las piscifactorías es extremadamente alto. Los sensores empleados son, en la mayoría de los casos, los mismos que se utilizan para la oceanografía. Los sistemas propuestos en la literatura cubren pocos parámetros de calidad del agua y generalmente no consideran el comportamiento de alimentación de los peces. Son necesarios sensores de bajo costo adecuados para la monitorización de la acuicultura. Esos sensores deben ser de bajo costo, bajo consumo de energía, fáciles de usar y con la posibilidad de incluirlos en una red para enviar los datos. Por lo tanto, podremos utilizar esta red de sensores y sensores para controlar la actividad, enviar alarmas si es necesario y automatizar los procesos. Además, si incluimos Internet, los datos se pueden ver de forma remota. El uso de esos sensores puede ayudar a la producción acuícola. En esta tesis mostramos el estudio de los requisitos y el diseño de sensores para monitorizar la calidad del agua y el proceso de alimentación en piscifactorías y otros entornos. Primero estudiamos en detalle los requisitos de los sensores en acuicultura. Luego mostramos el estado del arte de los sensores actuales para el monitoreo de la calidad del agua y para el monitoreo de la acuicultura. A continuación, presentamos el diseño y desarrollo de nuestros propios sensores de bajo costo y su aplicación en instalaciones de piscifactorías con sistema abierto y sistema de recirculación. Además, mostramos la posibilidad de monitorizar hasta 10 parámetros incluyendo calidad del agua (temperatura, salinidad, turbidez y presencia de hidrocarburo / capa de aceite), ambiente del tanque (nivel de agua, iluminación, presencia de trabajadores) y comportamiento de alimentación de peces (profundidad de natación de bajura, estimación de los cambios en la velocidad de nado de bajíos y la caída de alimento). El sistema propuesto, capaz de monitorear todos estos parámetros, tiene un bajo coste y bajo consumo de energía. El precio estimado es inferior a 100 € por tanque. Además, mostramos el uso de algunos de los sensores antes mencionados para el ajuste automático del proceso de alimentación de peces. Finalmente, mostramos como algunos de los sensores desarrollados se utilizan en otras áreas acuáticas naturales como manglares y estuarios. Además, se presenta un sistema inteligente para monitorear y rastrear la contaminación en los cuerpos de agua.There are many efforts done in the aquaculture to reach its sustainability, although in reality, it is far from being sustainable. Its negative impacts on the environment can be prevented and corrected by the use of sensors, developing precision aquaculture. The water quality affects to the fish performance. The temperature and salinity are some factors that affect to the fish growth. Nevertheless, other factors such as turbidity, photoperiod and dissolved oxygen among other can affect to the fish feeding needs. To adjust the amount of feed needed is crucial to ensure the sustainability of the aquaculture and to increase the economic profit of the facilities. Monitoring the water quality allows estimating the feed needs. However, it is not enough. To monitor the fish behavior, especially during the feeding period can help to adapt the provided feed. Then, if it is so clear that the monitoring can help to the aquaculture production, why we do not see this monitoring systems in the aquaculture facilities? Why in most of the facilities the feed is given manually without considering the fish feeding behavior? Nevertheless, the current price of the sensors for monitoring the fish farms is extremely high. The employed sensors are in most of the cases, the same that are used for oceanography. The proposed systems in the literature only cover some water quality parameters and usually do not consider fish feeding behavior. It is need low-cost sensors suitable for aquaculture monitoring. Those sensors must also be low-energy consumption, easy to use and with the option to include them in a network in order to send the data. Thus, we can use these sensors and sensors network to monitor the activity, to send alarms if it is necessary and to automatize processes. Moreover, including Internet, the data can be seen remotely. The use of those sensors can help to the aquaculture production. In this thesis, we show the study of requirements and design of sensors for monitoring water quality and feeding process in fish farms and other environments. First, we study in detail the requirements of sensors in aquaculture. Then, we show the state of the art of the current sensors for water quality monitoring and for aquaculture monitoring. Following, we present the design and development of some low-cost sensors and their applications in fish farm facilities with open system and recirculating system. Moreover we show a complete system which monitors up to 10 parameters including water quality (temperature, salinity, turbidity and presence of hydrocarbon/oil layer), tank environment (water level, illumination, presence of workers), and fish feeding behavior (shoal swimming depth, estimation of changes on shoal swimming velocity and feed falling). Moreover, it accomplishes the features of low-cost and low energy consumption. The estimated price for proposed system is less than 100€ per tank. In addition, we show the use of some of the aforementioned sensors for automatic adjustment of fish feeding process. Finally, some of the developed sensors are plied in other natural aquatic areas such as mangroves, and estuaries. Moreover, an intelligent system for pollution monitoring and tracking in water bodies are presented.S'estan realitzant molts esforços en l'aqüicultura per assolir la sostenibilitat, malgrat això, encara està lluny de ser sostenible. Els seus impactes sobre el medi ambient es poden prevenir i corregir mitjançant l'ús de sensors, desenvolupant la coneguda com a aqüicultura de precisió. La qualitat de l'aigua afecta el rendiment dels peixos. La temperatura i la salinitat són alguns factors que afecten el creixement dels peixos. A més a més, altres factors com la terbolesa, el fotoperíode i l'oli dissolt entre uns altres poden afectar a les necessitats nutritives dels peixos. Ajustar la quantitat d'aliment necessari és crucial per garantir la sostenibilitat de l'aqüicultura i per augmentar el benefici econòmic de les instal·lacions. Al monitoritzar la qualitat de l'aigua, és possible estimar les necessitats d'alimentació. No obstant això, no és suficient. Monitoritzar el comportament dels peixos, especialment durant el període d'alimentació, pot ajudar a adaptar el subministrament alimentari. Aleshores, si es tan clar que el monitoratge pot ajudar a la producció aqüícola, per què no veiem aquest sistema de monitoratge en les instal·lacions aquàtiques? Per què a la majoria de les instal·lacions la alimentació es dóna manualment sense considerar el comportament alimentari dels peixos? El preu dels sensors per controlar les piscifactories és extremadament alt. Els sensors empleats són, en la majoria dels casos, els mateixos que es fan servir per a l'oceanografia. Els sistemes proposats en la literatura monitoritzen pocs paràmetres de qualitat de l'aigua i generalment no consideren el comportament dels peixos durant l'alimentació. Són necessaris sensors de baix cost adequats per a la monitorització de l'aqüicultura. Aquests sensors han de ser de baix cost, baix consum d'energia, senzills d'usar i amb la possibilitat d'incloure'ls en una xarxa per enviar-los. Per tant, podrem utilitzar aquesta xarxa de sensors i sensors per controlar l'activitat, enviar alarmes si és necessari i automatitzar els processos. A més, si incloem Internet, les dades es podran veure de forma remota. L'ús d'aquests sensors pot ajudar a la producció aqüícola. En aquesta tesi es mostra l'estudi dels requisits i el disseny de sensors per a monitoritzar la qualitat de l'aigua i el procés d'alimentació en piscifactories i altres entorns. Primer, estudiem en detall els requisits dels sensors en aqüicultura. A continuació, es mostra el estat de l'art dels sensors actuals per al monitoratge de la qualitat de l'aigua i per al monitoratge de l'aqüicultura. A continuació, presentem el disseny i desenvolupament dels nostres propis sensors de baix cost i la seva aplicació en instal·lacions d'aqüicultura amb sistema obert i sistema de recirculació. A més, mostrem la possibilitat de monitoritzar fins a 10 paràmetres, incloent-hi la qualitat de l'aigua (temperatura, salinitat, terbolesa i presència d'hidrocarburs / capa d'oli), ambient del tanc (nivell d'aigua, il·luminació, presència de treballadors) i alimentació del consum de peces (profunditat de natació de baix, estimació dels canvis en la velocitat de naixement de baixos i la caiguda d'aliment). El sistema proposat, capaç de controlar tots aquests paràmetres, té un baix cost i baix consum d'energia. El preu estimat és inferior a 100 € per tanc. A més, mostrem l'ús d'alguns dels sensors abans esmentats per a l'ajust automàtic del procés d'alimentació de peces. Finalment, mostrem com alguns dels sensors desenvolupats es fan servir en altres àrees aquàtiques naturals com manglars i estuaris. A més, es presenta un sistema intel·ligent per monitoritzar i rastrejar la contaminació en els cossos d'aigua.Parra Boronat, L. (2018). Study of Requirements and Design of Sensors for Monitoring Water Quality and Feeding Process in Fish Farms and Other Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106369TESI

Cooperative Monitoring of the Delivery of Fresh Products

12th International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2015; Mallorca; Spain; 20 September 2015 through 23 September 2015The proper transport of hit sensitive products, such as fish and fruit, is very important because their deterioration may cause the value lost and even the product rejection by the buyer. For this reason, in this paper we present a cooperative monitoring system for the delivery of fresh products. The system consists of fixed wireless nodes and a mobile wireless node that is installed in the packet. This mobile node is able to take data of the internal temperature, external temperature and the 3 axis movement. With the information stored in the network, a vendor can know the optimal conditions of the transport. Finally, we test the maximum distance to the fixed nodes, as well as the data collected by the sensor.Sendra, S.; Lloret, J.; Lacuesta, R.; Jimenez, JM. (2015). Cooperative Monitoring of the Delivery of Fresh Products. Lecture Notes in Computer Science. 9320:76-86. doi:10.1007/978-3-319-24132-6_10S76869320Derks, H.G., Buehler, W.S., Hall, M.B.: Real-time method and system for locating a mobile object or person in a tracking environment. US Patent 8514071 B2, 20 August 2013Witmond, R., Dutta, R., Charroppin, P.: Method for tracking a mail piece. US Patent 7003376 B2, 21 February 2006Lu, L., Liu, Y., Han, J.: ACTION: breaking the privacy barrier for RFID systems. Ad Hoc Sens. Wirel. Netw. 24(1–2), 135–159 (2015)Dhakal, S., Shin, S.: Precise time system efficiency of a frame slotted aloha based anti-collision algorithm in a RFID system. Netw. Protoc. Algorithms 5(2), 16–27 (2013)Garcia, M., Bri, D., Sendra, S., Lloret, J.: Practical deployments of wireless sensor networks: a survey. Int. J. Adv. Netw. Serv. 3(1&2), 163–178 (2010)Bri, D., Garcia, M., Lloret, J., Dini, P.: Real deployments of wireless sensor networks. In: Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009), Athens, Greece, 18–23 June 2009, pp. 415–423Karim, L., Anpalagan, A., Nasser, N., Almhana, J.: Sensor-based M2M agriculture monitoring systems for developing countries: state and challenges. Netw. Protoc. Algorithms 5(3), 68–86 (2013)Garcia, M., Lloret, J., Sendra, S., Rodrigues, J.J.: Taking cooperative decisions in group-based wireless sensor networks. In: Luo, Y. (ed.) CDVE 2011. LNCS, vol. 6874, pp. 61–65. Springer, Heidelberg (2011)Garcia-Sabater, J.P., Lloret, J., Marin-Garcia, J.A., Puig-Bernabeu, X.: Coordinating a cooperative automotive manufacturing network – an agent-based model. In: Luo, Y. (ed.) CDVE 2010. LNCS, vol. 6240, pp. 231–238. Springer, Heidelberg (2010)Li, J., Cao, J.: Survey of object tracking in wireless sensor networks. Netw. Protoc. Algorithms 25(1–2), 89–120 (2015)Vock, C.A., Larkin, A.F., Amsbury, B.W., Youngs, P.: Device for monitoring movement of shipped goods. U.S. Patent 8,280,682, 2 October 2012Jedermann, R., Schouten, R., Sklorz, A., Lang, W., Van Kooten, O.: Linking keeping quality models and sensor systems to an autonomous transport supervision system. In: The 2nd International Workshop Cold Chain Management, Bonn, Germany, 8–9 May 2006, pp. 3–18Ko, D., Kwak, Y., Song, S.: Real time traceability and monitoring system for agricultural products based on wireless sensor network. Int. J. Distrib. Sens. Netw. 2014, 1–7 (2014). Article ID 832510Ruiz-Garcia, L., Barreiro, P., Robla, J.I.: Performance of ZigBee-based wireless sensor nodes for real-time monitoring of fruit logistics. J. Food Eng. 87(3), 405–415 (2008)Shamsuzzoha, A., Addo-Tenkorang, R., Phuong, D., Helo, P.: Logistics tracking: an implementation issue for delivery network. In: PICMET 2011Conference Technology Management in the Energy Smart World, Portland, Oregon, USA, July 31–August 4 2011, pp. 1–10Torres, R.V., Sanchez, J.C., Galan, L.M.: Unmarked point and adjacency vertex, mobility models for the generation of emergency and rescue scenarios in urban areas. Ad Hoc Sens. Wirel. Netw. 23(3–4), 211–233 (2014)Waspmote features. In Digi Web Site. http://www.digi.com/products/wireless-wired-embedded-solutions/zigbee-rf-modules/point-multipoint-rfmodules/xbee-series1-module#specs . Accessed 18 April 2015Meghanathan, N., Mumford, P.: Centralized and distributed algorithms for stability-based data gathering in mobile sensor networks. Netw. Protoc. Algorithms 5(4), 84–116 (2013)Alrajeh, N.A., Khan, S., Lloret, J., Loo, J.: Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting. Ad Hoc Sens. Wirel. Netw. 22(3–4), 109–133 (2014)Garcia, M., Sendra, S., Lloret, J., Canovas, A.: Saving energy and improving communications using cooperative group-based wireless sensor networks. Telecommun. Syst. 52(4), 2489–2502 (2013