302 research outputs found

    Systems and algorithms for wireless sensor networks based on animal and natural behavior

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    In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. Finally, this paper will try to analyze the applications of each bioinspired mechanism as a function of the imitated animal and the deployed application. Although this research area is considered an area with highly theoretical content, we intend to show the great impact that it is generating from the practical perspective.Sendra, S.; Parra Boronat, L.; Lloret, J.; Khan, S. (2015). Systems and algorithms for wireless sensor networks based on animal and natural behavior. International Journal of Distributed Sensor Networks. 2015:1-19. doi:10.1155/2015/625972S1192015Iram, R., Sheikh, M. I., Jabbar, S., & Minhas, A. A. (2011). Computational intelligence based optimization in wireless sensor network. 2011 International Conference on Information and Communication Technologies. doi:10.1109/icict.2011.5983561Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Dasgupta, P. (2008). A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38(3), 549-563. doi:10.1109/tsmca.2008.918619Quwaider, 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.v4i3.2054Sendra, 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.6.6.439-459Liu, M., & Song, C. (2012). Ant-Based Transmission Range Assignment Scheme for Energy Hole Problem in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 8(12), 290717. doi:10.1155/2012/290717Riva, G., & Finochietto, J. M. (2012). Pheromone-based In-Network Processing for Wireless Sensor Network Monitoring Systems. Network Protocols and Algorithms, 4(4). doi:10.5296/npa.v4i4.2206Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Kim, J.-Y., Sharma, T., Kumar, B., Tomar, G. S., Berry, K., & Lee, W.-H. (2014). Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment. International Journal of Distributed Sensor Networks, 10(4), 457402. doi:10.1155/2014/457402Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks, 54(6), 881-900. doi:10.1016/j.comnet.2009.10.024Atakan, B., & Akan, O. B. (2006). Immune System Based Distributed Node and Rate Selection in Wireless Sensor Networks. 2006 1st Bio-Inspired Models of Network, Information and Computing Systems. doi:10.1109/bimnics.2006.361806Di Pietro, R., & Verde, N. V. (2011). Introducing epidemic models for data survivability in Unattended Wireless Sensor Networks. 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. doi:10.1109/wowmom.2011.5986165Marwaha, S., Indulska, J., & Portmann, M. (2009). Biologically Inspired Ant-Based Routing in Mobile Ad hoc Networks (MANET): A Survey. 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing. doi:10.1109/uic-atc.2009.95Jha, V., Khetarpal, K., & Sharma, M. (2011). A survey of nature inspired routing algorithms for MANETs. 2011 3rd International Conference on Electronics Computer Technology. doi:10.1109/icectech.2011.5942042Fernandez-Marquez, J. L., Di Marzo Serugendo, G., Montagna, S., Viroli, M., & Arcos, J. L. (2012). Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, 12(1), 43-67. doi:10.1007/s11047-012-9324-yCamilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. Lecture Notes in Computer Science, 49-59. doi:10.1007/11839088_5Selvakennedy, S., Sinnappan, S., & Shang, Y. (2006). T-ANT: A Nature-Inspired Data Gathering Protocol for Wireless Sensor Networks. Journal of Communications, 1(2). doi:10.4304/jcm.1.2.22-29Almshreqi, A. M. S., Ali, B. M., Rasid, M. F. A., Ismail, A., & Varahram, P. (2012). An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks. 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Journal of Zhejiang University-SCIENCE A, 9(4), 531-538. doi:10.1631/jzus.a071382Liao, W.-H., Kao, Y., & Wu, R.-T. (2011). Ant colony optimization based sensor deployment protocol for wireless sensor networks. Expert Systems with Applications, 38(6), 6599-6605. doi:10.1016/j.eswa.2010.11.079Pavai, K., Sivagami, A., & Sridharan, D. (2009). Study of Routing Protocols in Wireless Sensor Networks. 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. doi:10.1109/act.2009.133Juan, L., Chen, S., & Chao, Z. (2007). Ant System Based Anycast Routing in Wireless Sensor Networks. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. doi:10.1109/wicom.2007.603Wang, C., & Lin, Q. (2008). Swarm intelligence optimization based routing algorithm for Wireless Sensor Networks. 2008 International Conference on Neural Networks and Signal Processing. doi:10.1109/icnnsp.2008.4590326Jiang, H., Wang, M., Liu, M., & Yan, J. (2012). A quantum-inspired ant-based routing algorithm for WSNs. Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD). doi:10.1109/cscwd.2012.6221881Okazaki, A. M., & Frohlich, A. A. (2011). Ant-based Dynamic Hop Optimization Protocol: A routing algorithm for Mobile Wireless Sensor Networks. 2011 IEEE GLOBECOM Workshops (GC Wkshps). doi:10.1109/glocomw.2011.6162356Hui, X., Zhigang, Z., & Xueguang, Z. (2009). A Novel Routing Protocol in Wireless Sensor Networks Based on Ant Colony Optimization. 2009 International Conference on Environmental Science and Information Application Technology. doi:10.1109/esiat.2009.460AbdelSalam, H. S., & Olariu, S. (2012). BEES: BioinspirEd backbonE Selection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 23(1), 44-51. doi:10.1109/tpds.2011.100Da Silva Rego, A., Celestino, J., dos Santos, A., Cerqueira, E. C., Patel, A., & Taghavi, M. (2012). BEE-C: A bio-inspired energy efficient cluster-based algorithm for data continuous dissemination in Wireless Sensor Networks. 2012 18th IEEE International Conference on Networks (ICON). doi:10.1109/icon.2012.6506592Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2012). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997. doi:10.1007/s10462-012-9342-2Antoniou, P., Pitsillides, A., Blackwell, T., & Engelbrecht, A. (2009). Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. 2009 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2009.4983153Ruihua, Z., Zhiping, J., Xin, L., & Dongxue, H. (2011). Double cluster-heads clustering algorithm for wireless sensor networks using PSO. 2011 6th IEEE Conference on Industrial Electronics and Applications. doi:10.1109/iciea.2011.5975688Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. 2009 IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2009.5346107Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 663-675. doi:10.1109/tsmcc.2010.2049649Xin Song, Cuirong Wang, Wang, J., & Bin Zhang. (2010). A hierarchical routing protocol based on AFSO algorithm for WSN. 2010 International Conference On Computer Design and Applications. doi:10.1109/iccda.2010.5541265Gao, X. Z., Wu, Y., Zenger, K., & Huang, X. (2010). A Knowledge-Based Artificial Fish-Swarm Algorithm. 2010 13th IEEE International Conference on Computational Science and Engineering. doi:10.1109/cse.2010.49Wang, L., & Ma, L. (2011). A hybrid artificial fish swarm algorithm for Bin-packing problem. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6022829Yiyue, W., Hongmei, L., & Hengyang, H. (2012). Wireless Sensor Network Deployment Using an Optimized Artificial Fish Swarm Algorithm. 2012 International Conference on Computer Science and Electronics Engineering. doi:10.1109/iccsee.2012.453Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence, 65-74. doi:10.1007/978-3-642-12538-6_6Goyal, S., & Patterh, M. S. (2013). Performance of BAT Algorithm on Localization of Wireless Sensor Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 6(3), 351-358. doi:10.24297/ijct.v6i3.4481Krishnanand, K. N., & Ghose, D. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems, 2(3), 209-222. doi:10.3233/mgs-2006-2301Apostolopoulos, T., & Vlachos, A. (2011). Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem. International Journal of Combinatorics, 2011, 1-23. doi:10.1155/2011/523806Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Sun, Y., Jiang, Q., & Zhang, K. (2012). A clustering scheme for Reachback Firefly Synchronicity in wireless sensor networks. 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content. doi:10.1109/icnidc.2012.6418705Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Termite-Hill. International Journal of Swarm Intelligence Research, 3(4), 1-22. doi:10.4018/jsir.2012100101KumarE, S., S. M., K., & Kumar B. P., V. (2014). Clustering Protocol for Wireless Sensor Networks based on Rhesus Macaque (Macaca mulatta) Animal's Social Behavior. International Journal of Computer Applications, 87(8), 20-27. doi:10.5120/15229-3754Breza, M., & McCann, J. A. (2008). Lessons in Implementing Bio-inspired Algorithms on Wireless Sensor Networks. 2008 NASA/ESA Conference on Adaptive Hardware and Systems. doi:10.1109/ahs.2008.72Aziz, N. A. B. A., Mohemmed, A. W., & Sagar, B. S. D. (2007). Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization. 2007 International Conference on Intelligent and Advanced Systems. doi:10.1109/icias.2007.4658528Falcon, R., Li, X., Nayak, A., & Stojmenovic, I. (2012). A harmony-seeking firefly swarm to the periodic replacement of damaged sensors by a team of mobile robots. 2012 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2012.6363859Antoniou, P., & Pitsillides, A. (2010). A bio-inspired approach for streaming applications in wireless sensor networks based on the Lotka–Volterra competition model. 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Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 51(4), 1031-1051. doi:10.1016/j.comnet.2006.06.013Jia, J., Chen, J., Chang, G., & Tan, Z. (2009). Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computers & Mathematics with Applications, 57(11-12), 1756-1766. doi:10.1016/j.camwa.2008.10.036Nan, G.-F., Li, M.-Q., & Li, J. (2007). Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs. 2007 International Conference on Machine Learning and Cybernetics. doi:10.1109/icmlc.2007.4370265Saleem, K., Fisal, N., Abdullah, M. S., Zulkarmwan, A. B., Hafizah, S., & Kamilah, S. (2009). Proposed Nature Inspired Self-Organized Secure Autonomous Mechanism for WSNs. 2009 First Asian Conference on Intelligent Information and Database Systems. doi:10.1109/aciids.2009.75Jabbari, A., & Lang, W. (2010). Advanced Bio-inspired Plausibility Checking in a Wireless Sensor Network Using Neuro-immune Systems: Autonomous Fault Diagnosis in an Intelligent Transportation System. 2010 Fourth International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2010.24Ponnusamy, V., & Abdullah, A. (2010). Biologically Inspired (Botany) Mobile Agent Based Self-Healing Wireless Sensor Network. 2010 Sixth International Conference on Intelligent Environments. doi:10.1109/ie.2010.46Li, J., Cui, Z., & Shi, Z. (2012). An Improved Artificial Plant Optimization Algorithm for Coverage Problem in WSN. Sensor Letters, 10(8), 1874-1878. doi:10.1166/sl.2012.2627Sendra, S., Llario, F., Parra, L., & Lloret, J. (2014). Smart Wireless Sensor Network to Detect and Protect Sheep and Goats to Wolf Attacks. Recent Advances in Communications and Networking Technology, 2(2), 91-101. doi:10.2174/22117407112016660012Sendra, S., Granell, E., Lloret, J., & Rodrigues, J. J. P. C. (2013). 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    An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

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    In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

    Edge Computing for Internet of Things

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    The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

    Multi-Agent Systems

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    A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

    Smart Sensor Technologies for IoT

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    The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

    On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

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    Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

    Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

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    Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

    μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

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    [ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)
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