Mostrar el registro sencillo del ítem

dc.creatorGonzalez-Palacio M.
dc.creatorSepulveda-Cano L.
dc.creatorValencia J.
dc.creatorD'Amato J.
dc.creatorQuiza-Montealegre J.
dc.creatorPalacio L.G.
dc.date2020
dc.date.accessioned2021-02-05T14:58:52Z
dc.date.available2021-02-05T14:58:52Z
dc.identifier.isbn9789895465903
dc.identifier.issn21660727
dc.identifier.urihttp://hdl.handle.net/11407/6024
dc.descriptionWireless Sensor Networks (WSN) are dedicated networks used in applications where environmental information must be collected, such as temperature, humidity, level, flow, pressure, rain, radiation, among others. These kinds of networks are constrained regarding power, bandwidth, number of nodes per area unit, etc. It is desirable that they operate without supervision and can work steadily in time, because they are normally located in difficult or far places. Nonetheless, some of these metrics are conflicting with others, so if one improves, some of the others get worse. So, it is mandatory to know what is the best combination of metrics that in conjunction can fit an application the best. Literature reports works where \neg optimization is used as a mathematical scheme to solve this problem, and two scenarios are provided: First, where a single objective function is proposed regarding one metric, and the other metrics are restricted via constraints, and second, where multi-objective optimization (MOOP) approaches are proposed, but without considering the whole set of significant metrics involved in WSN, so there is not a definitive solution that finds a real optimal set of metrics. System Dynamics (SD) is a computer-aided approach to design and analyze (mostly) social, economic and enterprise systems, that allows proposing a mathematical framework to analyze such complex systems, by using relationships of interdependence, mutual interaction, feedback and causality. This work aims to show a first dynamic hypothesis of a model that considers important metrics ofWSN, in order to find a set of equations that serve as objective functions in a MOOP context. By applying this methodology is possible to find some difficult relations between metrics, that are not clearly reported by previous work so far. © 2020 AISTI.
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089035502&doi=10.23919%2fCISTI49556.2020.9140915&partnerID=40&md5=5d5b5a55dec2e7d9a2c6e66ff050c533
dc.sourceIberian Conference on Information Systems and Technologies, CISTI
dc.subjectMulti-Objective optimizationspa
dc.subjectSystem Dynamicsspa
dc.subjectWireless Sensor Networksspa
dc.titleSystem dynamics baseline model for determining a multivariable objective function optimization in Wireless Sensor Networks
dc.typeConference Papereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.publisher.programIngeniería de Sistemasspa
dc.identifier.doi10.23919/CISTI49556.2020.9140915
dc.subject.keywordFunctionseng
dc.subject.keywordInformation systemseng
dc.subject.keywordInformation useeng
dc.subject.keywordMultiobjective optimizationeng
dc.subject.keywordSystem theoryeng
dc.subject.keywordComputer aided-approacheng
dc.subject.keywordDedicated networkseng
dc.subject.keywordEnterprise systemeng
dc.subject.keywordEnvironmental informationeng
dc.subject.keywordMathematical frameworkseng
dc.subject.keywordMutual interactioneng
dc.subject.keywordObjective functionseng
dc.subject.keywordSingle objectiveeng
dc.subject.keywordWireless sensor networkseng
dc.relation.citationvolume2020-June
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationGonzalez-Palacio, M., University of Medellín, Telecommunications Enginnering Department, Colombia
dc.affiliationSepulveda-Cano, L., Pladema Institute, University of Medellín, Computer Science Engineering Department, Colombia
dc.affiliationValencia, J., Pladema Institute, University of Medellín, Computer Science Engineering Department, Colombia
dc.affiliationD'Amato, J., Universidad Nacional Del Centro de la Provincia de Buenos Aires, Argentina
dc.affiliationQuiza-Montealegre, J., University of Medellín, Telecommunications Enginnering Department, Colombia
dc.affiliationPalacio, L.G., University of Medellín, Telecommunications Enginnering Department, Colombia
dc.relation.referencesGungor, V.C., Hancke, G.P., Industrial wireless sensor networks: Challenges, design principles, and technical approaches (2009) Industrial Electronics, IEEE Transactions on, 56, pp. 4258-4265
dc.relation.referencesHabib, S.J., Marimuthu, P.N., Energy optimization in data communications through cluster evolution (2014) 2014 International Conference on Information and Communication Technology Convergence (ICTC, pp. 161-166
dc.relation.referencesHeinzelman, W.R., Chandrakasan, A., Balakrishnan, H., Energy-efficient communication protocol for wireless microsensor networks (2000) Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2, p. 10
dc.relation.referencesNikolidakis, S., Kandris, D., Vergados, D., Douligeris, C., Energy efficient routing in wireless sensor networks through balanced clustering (2013) Algorithms, 6, pp. 29-42
dc.relation.referencesSchiele, G., Becker, C., Rothermel, K., Energy-efficient cluster-based service discovery for Ubiquitous Computing (2004) Proceedings of the 11th Workshop on ACM SIGOPS European Workshop, p. 14
dc.relation.referencesFei, Z., Li, B., Yang, S., Xing, C., Chen, H., Hanzo, L., A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems (2016) IEEE Communications Surveys & Tutorials, 19, pp. 550-586
dc.relation.referencesHanes, D., Salgueiro, G., Grossetete, P., Barton, R., Henry, J., (2017) IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things: Cisco Press
dc.relation.referencesImon, S.K.A., Khan, A., Di Francesco, M., Das, S.K., Energy-efficient randomized switching for maximizing lifetime in tree-based wireless sensor networks (2015) IEEE/ACM Transactions on Networking (TON), 23, pp. 1401-1415
dc.relation.referencesLu, J., Wang, X., Zhang, L., Zhao, X., Fuzzy random multi-objective optimization based routing for wireless sensor networks (2014) Soft Computing, 18, pp. 981-994
dc.relation.referencesForrester, J.W., System dynamics, systems thinking, and soft or (1994) System Dynamics Review, 10, pp. 245-256
dc.relation.referencesKonstantinidis, A., Yang, K., Zhang, Q., Zeinalipour-Yazti, D., A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks (2010) Computer Networks, 54, pp. 960-976
dc.relation.referencesRani, K.S.S., Devarajan, N., Multiobjective sensor node deployment in wireless sensor networks (2012) International Journal of Engineering Science and Technology, 4, pp. 1262-1266
dc.relation.referencesChen, J., He, S., Sun, Y., Thulasiraman, P., Shen, X.S., Optimal flow control for utility-lifetime tradeoff in wireless sensor networks (2009) Computer Networks, 53, pp. 3031-3041
dc.relation.referencesAmmari, H.M., Das, S.K., Trade-off between energy savings and source-to-sink delay in data dissemination for wireless sensor networks (2005) Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 126-133
dc.relation.referencesChoi, W., Das, S.K., A novel framework for energyconserving data gathering in wireless sensor networks (2005) Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1985-1996
dc.relation.referencesMolina, G., Alba, E., Talbi, E.-G., Optimal sensor network layout using multi-objective metaheuristics (2008) J. UCS, 14, pp. 2549-2565
dc.relation.referencesZhu, J., Hung, K.-L., Bensaou, B., Nait-Abdesselam, F., Rate-lifetime tradeoff for reliable communication in wireless sensor networks (2008) Computer Networks, 52, pp. 25-43
dc.relation.referencesArya, R., Sharma, S., Optimization approach for energy minimization and bandwidth estimation of WSN for data centric protocols (2018) International Journal of System Assurance Engineering and Management, 9, pp. 2-11
dc.relation.referencesJafari, H., Nazari, M., Shamshirband, S., Optimization of energy consumption in wireless sensor networks using density-based clustering algorithm (2018) International Journal of Computers and Applications, pp. 1-10
dc.relation.referencesJiang, A., Zheng, L., An effective hybrid routing algorithm in wsn: Ant colony optimization in combination with hop count minimization (2018) Sensors, 18, p. 1020
dc.relation.referencesSingh, K., Singh, K., Aziz, A., Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm (2018) Computer Networks, 138, pp. 90-107
dc.relation.referencesMysorewala, M., Time and energy savings in leak detection in wsn-based water pipelines: A novel parametric optimization-based approach (2019) Water Resources Management, 33, pp. 2057-2071
dc.relation.referencesLatiff, N.A., Tsimenidis, C.C., Sharif, B.S., Performance comparison of optimization algorithms for clustering in wireless sensor networks (2007) 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems, pp. 1-4
dc.relation.referencesAbo-Zahhad, M., Sabor, N., Sasaki, S., Ahmed, S.M., A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks (2016) Information Fusion, 30, pp. 36-51
dc.relation.referencesJourdan, D.B., De Weck, O.L., Layout optimization for a wireless sensor network using a multi-objective genetic algorithm (2004) 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat No. 04CH37514, pp. 2466-2470
dc.relation.referencesYang, J., Zhang, H., Ling, Y., Pan, C., Sun, W., Task allocation for wireless sensor network using modified binary particle swarm optimization (2013) IEEE Sensors Journal, 14, pp. 882-892
dc.relation.referencesNama, H., Chiang, M., Mandayam, N., Utility-lifetime trade-off in self-regulating wireless sensor networks: A cross-layer design approach (2006) 2006 IEEE International Conference on Communications, pp. 3511-3516
dc.relation.referencesSchurgers, C., Tsiatsis, V., Ganeriwal, S., Srivastava, M., Optimizing sensor networks in the energy-latency-density design space (2002) IEEE Transactions on Mobile Computing, 1, pp. 70-80
dc.relation.referencesGarcía, J.M., Teoría y Ejercicios Prácticos de la Dinámica de Sistemas, edición-2010
dc.relation.referencesBarcelona-España, , http://www.dinamica-de-sistemas.com, Disponible: Compras en
dc.relation.referencesRajagopalan, R., Mohan, C.K., Varshney, P., Mehrotra, K., Multi-objective mobile agent routing in wireless sensor networks (2005) 2005 IEEE Congress on Evolutionary Computation, pp. 1730-1737
dc.relation.referencesMitrou, N., Kontovasilis, K., Rouskas, G., Iliadis, I., Merakos, L., (2004) Networking 2004: Networking Technologies, Services, and Protocols
dc.relation.referencesPerformance of Computer and Communications Networks
dc.relation.referencesMobile and Wireless Communications
dc.relation.referencesThird International IFIP-TC6 Networking Conference, , Athens, Greece, May 9-14, 2004
dc.relation.referencesProceedings 3042: Springer Science & Business Media
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/other


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem