Using Magentix2 in Smart-Home Environments

[EN] In this paper, we present the application of a multi-agent platform Magentix2 for the development of MAS in smart-homes. Specificallly, the use of Magentix2 (http://gti-ia.upv.es/sma/tools/magentix2/index.php) platform facilitates the management of the multiple occupancy in smart living spaces. Virtual organizations provide the possibility of defining a set of norms and roles that facilitate the regulation and control of the actions that can be carried out by the internal and external agents depending on their profile. We illustrate the applicability of our proposal with a set of scenarios. © Springer International Publishing Switzerland 2015.This work is supported by the Spanish government grants CONSOLIDER INGENIO 2010 CSD2007-00022, MINECO/FEDER TIN2012-36586-C03-01, TIN2011-27652-C03-01, and SP2014800.Valero Cubas, S.; Del Val Noguera, E.; Alemany Bordera, J.; Botti, V. (2015). Using Magentix2 in Smart-Home Environments. En 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer Verlag. 27-37. https://doi.org/10.1007/978-3-319-19719-7_3S2737Bajo J, Fraile JA, Pérez-Lancho B, Corchado JM (2010) The thomas architecture in home care scenarios: a case study. Expert Syst Appl 37(5):3986–3999Cetina C, Giner P, Fons J, Pelechano V (2009) Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer 42(10):37–43Cook DJ (2009) Multi-agent smart environments. J Ambient Intell Smart Environ 1(1):51–55Crandall AS, Cook DJ (2010) Using a hidden markov model for resident identification. In: 6th international conference on intelligent environments, pp 74–79. IEEECriado N, Argente E, Botti V (2013) THOMAS: an agent platform for supporting normative multi-agent systems. J Logic Comput 23(2):309–333Davidoff S, Lee MK, Zimmerman J, Dey A (2006) Socially-aware requirements for a smart home. In: Proceedings of the international symposium on intelligent, environments, pp 41–44Grupo de Tecnología Informática e Inteligencia Artificial (GTI-IA) (2015). http://www.gti-ia.upv.es/sma/tools/magentix2/archivos/Magentix2UserManualv2.1.0.pdf . Magentix2 User’s Manual v2.0Loseto G, Scioscia F, Ruta M, di Sciascio E (2012) Semantic-based smart homes: a multi-agent approach. In: 13th Workshop on objects and agents (WOA 2012), vol 892, pp 49–55Rodriguez S, Julián V, Bajo J, Carrascosa C, Botti V, Corchado JM (2011) Agent-based virtual organization architecture. Eng Appl Artif Intell 24(5):895–910Rodríguez S, Paz JFD, Villarrubia G, Zato C, Bajo J, Corchado JM (2015) Multi-agent information fusion system to manage data from a WSN in a residential home. Inf Fusion 23:43–57Such JM, Garca-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a Privacy-enhancing Agent Platform. Eng Appl Artif IntellSun Q, Yu W, Kochurov N, Hao Q, Hu F (2013) A multi-agent-based intelligent sensor and actuator network design for smart house and home automation. J Sens Actuator Netw 2(3):557–588Val E, Criado N, Rebollo M, Argente E, Julian V (2009) Service-oriented framework for virtual organizations. 1:108–114Wu C-L, Liao C-F, Fu L-C (2007) Service-oriented smart-home architecture based on osgi and mobile-agent technology. IEEE Trans Syst Man Cybern Part C Appl Rev 37(2):193–205Yin J, Yang Q, Shen D, Li Z-N (2008) Activity recognition via user-trace segmentation. ACM Trans Sens Netw (TOSN) 4(4):1

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112S118201Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. Sensors, 19(18), 4021. doi:10.3390/s19184021González García, C., Meana-Llorián, D., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., & Garcia-Fernandez, N. (2017). Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes. Future Generation Computer Systems, 76, 301-313. doi:10.1016/j.future.2016.12.033Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. doi:10.1016/j.jii.2017.04.005Li, S., Xu, L. D., & Zhao, S. (2014). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. doi:10.1007/s10796-014-9492-7Zdraveski, V., Mishev, K., Trajanov, D., & Kocarev, L. (2017). ISO-Standardized Smart City Platform Architecture and Dashboard. IEEE Pervasive Computing, 16(2), 35-43. doi:10.1109/mprv.2017.31Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112-116. doi:10.1109/mc.2016.245Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Hancke, G., Silva, B., & Hancke, Jr., G. (2012). The Role of Advanced Sensing in Smart Cities. Sensors, 13(1), 393-425. doi:10.3390/s130100393Chen, Y. (2016). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30-64. doi:10.1016/j.jii.2016.04.004Lim, G. H., Suh, I. H., & Suh, H. (2011). Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(3), 492-509. doi:10.1109/tsmca.2010.2076404Zhang, J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1), 5-24. doi:10.1080/19479830903561035Deng, X., Jiang, Y., Yang, L. T., Lin, M., Yi, L., & Wang, M. (2019). Data fusion based coverage optimization in heterogeneous sensor networks: A survey. Information Fusion, 52, 90-105. doi:10.1016/j.inffus.2018.11.020Jain, A. K., Duin, P. W., & Jianchang Mao. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37. doi:10.1109/34.824819Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Adam, M. S., Anisi, M. H., & Ali, I. (2020). Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions. Future Generation Computer Systems, 107, 909-923. doi:10.1016/j.future.2017.12.011Gaur, A., Scotney, B., Parr, G., & McClean, S. (2015). Smart City Architecture and its Applications Based on IoT. Procedia Computer Science, 52, 1089-1094. doi:10.1016/j.procs.2015.05.122Byers, C. C. (2017). Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks. IEEE Communications Magazine, 55(8), 14-20. doi:10.1109/mcom.2017.1600885Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., Puliafito, A., & Buyya, R. (2018). Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms. Software: Practice and Experience, 48(8), 1475-1492. doi:10.1002/spe.2586Rincon, J. A., Poza-Lujan, J.-L., Julian, V., Posadas-Yagüe, J.-L., & Carrascosa, C. (2016). Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments. PLOS ONE, 11(2), e0149665. doi:10.1371/journal.pone.0149665Pérez Tijero, H., & Gutiérrez, J. J. (2018). Desarrollo de Sistemas Distribuidos de Tiempo Real y de Criticidad Mixta a través del Estándar DDS. Revista Iberoamericana de Automática e Informática industrial, 15(4), 439. doi:10.4995/riai.2017.9000Amurrio, A., Azketa, E., Gutiérrez, J. J., Aldea, M., & Parra, J. (2019). Una revisión de técnicas para la optimización del despliegue y planificación de sistemas de tiempo real distribuidos. Revista Iberoamericana de Automática e Informática industrial, 16(3), 249. doi:10.4995/riai.2019.10997Turtlebot http://turtlebot.comChen, L., Wei, H., & Ferryman, J. (2013). A survey of human motion analysis using depth imagery. 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Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. Nature, 412(6846), 543-545. doi:10.1038/35087589United Nations, Department of Economic and Social Affairs, World Population Prospoects 2019 https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdfAtzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140. doi:10.1016/j.adhoc.2016.12.004Cook, D. J., Duncan, G., Sprint, G., & Fritz, R. L. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE, 106(4), 708-722. doi:10.1109/jproc.2017.2787688Cook, D. J., & Krishnan, N. (2014). Mining the home environment. Journal of Intelligent Information Systems, 43(3), 503-519. doi:10.1007/s10844-014-0341-4Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527-539. doi:10.1109/tkde.2010.148Samsung SmartThings http://www.smartthings.com/Apple HomeKit https://www.apple.com/ios/home/Vera3 Advanced Smart Home Controller http://getvera.com/controllers/vera3/AndroidThings https://developer.android.com/things/index.htmlTeleAlarm Assisted Living http://www.telealarm.com/en/products/assisted-livingBirdie—Connected Sensors around the Home https://birdie.care/AllJoyn Framework https://identity.allseenalliance.org/developersCook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2013). CASAS: A Smart Home in a Box. Computer, 46(7), 62-69. doi:10.1109/mc.2012.328Skubic, M., Alexander, G., Popescu, M., Rantz, M., & Keller, J. (2009). A smart home application to eldercare: Current status and lessons learned. Technology and Health Care, 17(3), 183-201. doi:10.3233/thc-2009-0551Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005). The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings. Procedia Computer Science, 19, 669-677. doi:10.1016/j.procs.2013.06.089Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Schmitter-Edgecombe, M., & Cook, D. J. (2009). Assessing the Quality of Activities in a Smart Environment. Methods of Information in Medicine, 48(05), 480-485. doi:10.3414/me0592Alberdi Aramendi, A., Weakley, A., Aztiria Goenaga, A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults based on smart home data. 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Computers & Industrial Engineering, 125, 637-648. doi:10.1016/j.cie.2018.05.00

Utah Farm & Home Science Vol. 25 No. 1, March 1964

Alkaloids and medicines from plants, by F. R. Stermitz 3 Chemical weed control in small fruits, by J. L. Anderson 5 Spring grazing critical to desert ranges, by C. W. Cook and L. A. Stoddart 6 The forecast, fact or fiction? by L. M. Cox, E. A. Richardson, and G. L. Ashcroft 8 Library progress in Utah, by C. Fredrickson, with comments by J. A. Geddes 10 Blocking state owned grazing lands, by N. K. Roberts and E. B. Wennergren 13 Viral polyarthritis of lambs, by J. Storz, J. L. Shupe, R. A. Smart, L. F. James, and W. Binns 16 Retailing Christmas trees in 1963, by J. D. Hunt andW. G. Poulsen 18 Pelleted seed containing insecticides controls insects in sugar beets, by H. E. Dorst 20 Weed control in Utah conifer tree plantings, by W. G. Poulsen 22 Research reports 3

Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy

[EN] A transition to a sustainable energy system is essential. In this context, smart grids represent the future of power systems for efficiently integrating renewable energy sources and active consumer participation. Recently, different studies were performed that defined the conceptual architecture of power systems and their agents. However, these conceptual architectures do not overcome all issues for the development of new electricity markets. Thus, a novel conceptual architecture is proposed. The transactions of energy, operation services, and economic flows among the agents proposed are carefully analysed. In this regard, the results allow setting their activities' boundaries and state their relationships with electricity markets. The suitability of implementing local electricity markets is studied to enforce competition among distributed energy resources by unlocking all the potential that active consumers have. The proposed architecture is designed to offer flexibility and efficiency to the system thanks to a clearly defined way for the exploitation of flexible resources and distributed generation. This upgraded architecture hereby proposed establishes the characteristics of each agent in the forthcoming markets and studies to overcome the barriers to the large deployment of renewable energy sources.This work was supported by the Ministerio de Economia, Industria, y Competitividad (Spanish Government) under research project ENE-2016-78509-C3-1-P, and EU FEDER funds. The authors received funds from these grants for covering the costs to publish in open access. This work was also supported by the Spanish Ministry of Education under the scholarship FPU16/00962.Rodríguez-García, J.; Ribó-Pérez, DG.; Álvarez, C.; Peñalvo-López, E. (2019). Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy. 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IEEE Transactions on Smart Grid, 5(1), 402-410. doi:10.1109/tsg.2013.2278868Ipakchi, A., & Albuyeh, F. (2009). Grid of the future. IEEE Power and Energy Magazine, 7(2), 52-62. doi:10.1109/mpe.2008.931384Coelho, V. N., Weiss Cohen, M., Coelho, I. M., Liu, N., & Guimarães, F. G. (2017). Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids. Applied Energy, 187, 820-832. doi:10.1016/j.apenergy.2016.10.056Logenthiran, T., Srinivasan, D., & Khambadkone, A. M. (2011). Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electric Power Systems Research, 81(1), 138-148. doi:10.1016/j.epsr.2010.07.019Radhakrishnan, B. M., & Srinivasan, D. (2016). A multi-agent based distributed energy management scheme for smart grid applications. Energy, 103, 192-204. doi:10.1016/j.energy.2016.02.117Yoo, C.-H., Chung, I.-Y., Lee, H.-J., & Hong, S.-S. (2013). Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management. Energies, 6(10), 4956-4979. doi:10.3390/en6104956Zhao, B., Xue, M., Zhang, X., Wang, C., & Zhao, J. (2015). An MAS based energy management system for a stand-alone microgrid at high altitude. Applied Energy, 143, 251-261. doi:10.1016/j.apenergy.2015.01.016Ringler, P., Keles, D., & Fichtner, W. (2016). Agent-based modelling and simulation of smart electricity grids and markets – A literature review. Renewable and Sustainable Energy Reviews, 57, 205-215. doi:10.1016/j.rser.2015.12.169Wang, Q., Zhang, C., Ding, Y., Xydis, G., Wang, J., & Østergaard, J. (2015). Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response. Applied Energy, 138, 695-706. doi:10.1016/j.apenergy.2014.10.048Pandžić, H., Kuzle, I., & Capuder, T. (2013). Virtual power plant mid-term dispatch optimization. Applied Energy, 101, 134-141. doi:10.1016/j.apenergy.2012.05.039Pandžić, H., Morales, J. M., Conejo, A. J., & Kuzle, I. (2013). Offering model for a virtual power plant based on stochastic programming. Applied Energy, 105, 282-292. doi:10.1016/j.apenergy.2012.12.077Rahimiyan, M., & Baringo, L. (2016). Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach. IEEE Transactions on Power Systems, 31(4), 2676-2687. doi:10.1109/tpwrs.2015.2483781Mnatsakanyan, A., & Kennedy, S. W. (2015). A Novel Demand Response Model with an Application for a Virtual Power Plant. IEEE Transactions on Smart Grid, 6(1), 230-237. doi:10.1109/tsg.2014.2339213Bartolucci, L., Cordiner, S., Mulone, V., & Santarelli, M. (2019). Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems. 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Everywhere differentiability of viscosity solutions to a class of Aronsson's equations

For any open set $\Omega\subset\mathbb R^n$ and $n\ge 2$, we establish everywhere differentiability of viscosity solutions to the Aronsson equation $$=0 \quad \rm in\ \ \Omega,$$ where $H$ is given by $$H(x,\,p)==\sum_{i,\,j=1}^na^{ij}(x)p_i p_j,\ x\in\Omega, \ p\in\mathbb R^n,$$ and $A=(a^{ij}(x))\in C^{1,1}(\bar\Omega,\mathbb R^{n\times n})$ is uniformly elliptic. This extends an earlier theorem by Evans and Smart \cite{es11a} on infinity harmonic functions.Comment: 24 page

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). 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A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids

[EN] Peer-to-Peer (P2P) overlay communications networks have emerged as a new paradigm for implementing distributed services in microgrids due to their potential benefits: they are robust, scalable, fault-tolerant, and they can route messages even with a large number of nodes which are frequently entering or leaving from the network. However, current P2P systems have been mainly developed for file sharing or cycle sharing applications where the processes of searching and managing resources are not optimized. Locality algorithms have gained a lot of attention due to their potential to provide an optimized path to groups with similar interests for routing messages in order to get better network performance. This paper develops a fully functional decentralized communication architecture with a new P2P locality algorithm and a specific protocol for monitoring and control of microgrids. Experimental results show that the proposed locality algorithm reduces the number of lookup messages and the lookup delay time. Moreover, the proposed communication architecture heavily depends of the lookup used algorithm as well as the placement of the communication layers within the architecture. Experimental results will show that the proposed techniques meet the network requirements of smart microgrids even with a large number of nodes on stream.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2R. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under BES-2013-064539.Marzal-Romeu, S.; González-Medina, R.; Salas-Puente, RA.; Figueres Amorós, E.; Garcerá, G. (2017). A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids. 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A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm. Energies, 10(4), 485. doi:10.3390/en10040485Erol-Kantarci, M., Kantarci, B., & Mouftah, H. (2011). Reliable overlay topology design for the smart microgrid network. IEEE Network, 25(5), 38-43. doi:10.1109/mnet.2011.6033034Hassan Youssef, K. (2016). Optimal management of unbalanced smart microgrids for scheduled and unscheduled multiple transitions between grid-connected and islanded modes. Electric Power Systems Research, 141, 104-113. doi:10.1016/j.epsr.2016.07.015Giotitsas, C., Pazaitis, A., & Kostakis, V. (2015). A peer-to-peer approach to energy production. Technology in Society, 42, 28-38. doi:10.1016/j.techsoc.2015.02.002Kazmi, S. A. A., Shahzad, M. K., Khan, A. Z., & Shin, D. R. (2017). Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective. 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Renewable and Sustainable Energy Reviews, 44, 797-813. doi:10.1016/j.rser.2015.01.008Khatibzadeh, A., Besmi, M., Mahabadi, A., & Reza Haghifam, M. (2017). Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages. Energies, 10(2), 169. doi:10.3390/en10020169Planas, E., Gil-de-Muro, A., Andreu, J., Kortabarria, I., & Martínez de Alegría, I. (2013). General aspects, hierarchical controls and droop methods in microgrids: A review. Renewable and Sustainable Energy Reviews, 17, 147-159. doi:10.1016/j.rser.2012.09.032Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., … Hatziargyriou, N. D. (2014). Trends in Microgrid Control. IEEE Transactions on Smart Grid, 5(4), 1905-1919. doi:10.1109/tsg.2013.2295514Vandoorn, T. L., Vasquez, J. C., De Kooning, J., Guerrero, J. M., & Vandevelde, L. (2013). Microgrids: Hierarchical Control and an Overview of the Control and Reserve Management Strategies. IEEE Industrial Electronics Magazine, 7(4), 42-55. doi:10.1109/mie.2013.2279306Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., & Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61, 30-40. doi:10.1016/j.rser.2016.03.047Ancillotti, E., Bruno, R., & Conti, M. (2013). The role of communication systems in smart grids: Architectures, technical solutions and research challenges. Computer Communications, 36(17-18), 1665-1697. doi:10.1016/j.comcom.2013.09.004Llaria, A., Terrasson, G., Curea, O., & Jiménez, J. (2016). Application of Wireless Sensor and Actuator Networks to Achieve Intelligent Microgrids: A Promising Approach towards a Global Smart Grid Deployment. Applied Sciences, 6(3), 61. doi:10.3390/app6030061Luna, A. C., Diaz, N. L., Graells, M., Vasquez, J. C., & Guerrero, J. M. (2016). Cooperative energy management for a cluster of households prosumers. IEEE Transactions on Consumer Electronics, 62(3), 235-242. doi:10.1109/tce.2016.7613189Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Transactions on Industrial Electronics, 57(10), 3557-3564. doi:10.1109/tie.2009.2039455Zhao, C., He, J., Cheng, P., & Chen, J. (2017). Consensus-Based Energy Management in Smart Grid With Transmission Losses and Directed Communication. IEEE Transactions on Smart Grid, 8(5), 2049-2061. doi:10.1109/tsg.2015.2513772Lo, C.-H., & Ansari, N. (2013). Decentralized Controls and Communications for Autonomous Distribution Networks in Smart Grid. IEEE Transactions on Smart Grid, 4(1), 66-77. doi:10.1109/tsg.2012.2228282Li, C., Savaghebi, M., Guerrero, J., Coelho, E., & Vasquez, J. (2016). Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control. Energies, 9(9), 717. doi:10.3390/en9090717Wu, X., Jiang, P., & Lu, J. (2014). Multiagent-Based Distributed Load Shedding for Islanded Microgrids. Energies, 7(9), 6050-6062. doi:10.3390/en7096050Kantamneni, A., Brown, L. E., Parker, G., & Weaver, W. W. (2015). Survey of multi-agent systems for microgrid control. Engineering Applications of Artificial Intelligence, 45, 192-203. doi:10.1016/j.engappai.2015.07.005Lopes, A. L., & Botelho, L. M. (2008). Improving Multi-Agent Based Resource Coordination in Peer-to-Peer Networks. Journal of Networks, 3(2). doi:10.4304/jnw.3.2.38-47Cameron, A., Stumptner, M., Nandagopal, N., Mayer, W., & Mansell, T. (2015). Rule-based peer-to-peer framework for decentralised real-time service oriented architectures. Science of Computer Programming, 97, 202-234. doi:10.1016/j.scico.2014.06.005Zhang, C., Wu, J., Cheng, M., Zhou, Y., & Long, C. (2016). A Bidding System for Peer-to-Peer Energy Trading in a Grid-connected Microgrid. Energy Procedia, 103, 147-152. doi:10.1016/j.egypro.2016.11.264Malatras, A. (2015). State-of-the-art survey on P2P overlay networks in pervasive computing environments. Journal of Network and Computer Applications, 55, 1-23. doi:10.1016/j.jnca.2015.04.014Eng Keong Lua, Crowcroft, J., Pias, M., Sharma, R., & Lim, S. (2005). A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys & Tutorials, 7(2), 72-93. doi:10.1109/comst.2005.1610546Xu, J., Kumar, A., & Yu, X. (2004). On the Fundamental Tradeoffs Between Routing Table Size and Network Diameter in Peer-to-Peer Networks. IEEE Journal on Selected Areas in Communications, 22(1), 151-163. doi:10.1109/jsac.2003.818805Stoica, I., Morris, R., Karger, D., Kaashoek, M. F., & Balakrishnan, H. (2001). Chord. ACM SIGCOMM Computer Communication Review, 31(4), 149-160. doi:10.1145/964723.383071Rowstron, A., & Druschel, P. (2001). Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems. Lecture Notes in Computer Science, 329-350. doi:10.1007/3-540-45518-3_18Yuh-Jzer Joung, Li-Wei Yang, & Chien-Tse Fang. (2007). Keyword search in DHT-based peer-to-peer networks. IEEE Journal on Selected Areas in Communications, 25(1), 46-61. doi:10.1109/jsac.2007.070106Stoica, I., Morris, R., Liben-Nowell, D., Karger, D. R., Kaashoek, M. F., Dabek, F., & Balakrishnan, H. (2003). Chord: a scalable peer-to-peer lookup protocol for internet applications. IEEE/ACM Transactions on Networking, 11(1), 17-32. doi:10.1109/tnet.2002.808407Gottron, C., König, A., & Steinmetz, R. (2010). A Survey on Security in Mobile Peer-to-Peer Architectures—Overlay-Based vs. Underlay-Based Approaches. Future Internet, 2(4), 505-532. doi:10.3390/fi2040505Seyedi, Y., Karimi, H., & Guerrero, J. M. (2017). Centralized Disturbance Detection in Smart Microgrids With Noisy and Intermittent Synchrophasor Data. IEEE Transactions on Smart Grid, 8(6), 2775-2783. doi:10.1109/tsg.2016.2539947Youssef, T., Elsayed, A., & Mohammed, O. (2016). Data Distribution Service-Based Interoperability Framework for Smart Grid Testbed Infrastructure. Energies, 9(3), 150. doi:10.3390/en9030150Liu, X., Xia, H., & Chien, A. A. (2004). Validating and Scaling the MicroGrid: A Scientific Instrument for Grid Dynamics. Journal of Grid Computing, 2(2), 141-161. doi:10.1007/s10723-004-4200-3Kansal, P., & Bose, A. (2012). Bandwidth and Latency Requirements for Smart Transmission Grid Applications. IEEE Transactions on Smart Grid, 3(3), 1344-1352. doi:10.1109/tsg.2012.2197229Kuo, M.-T., & Lu, S.-D. (2013). Design and Implementation of Real-Time Intelligent Control and Structure Based on Multi-Agent Systems in Microgrids. Energies, 6(11), 6045-6059. doi:10.3390/en6116045Del Val, E., Rebollo, M., & Botti, V. (2012). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Autonomous Agents and Multi-Agent Systems, 28(1), 1-30. doi:10.1007/s10458-012-9210-0Howell, S., Rezgui, Y., Hippolyte, J.-L., Jayan, B., & Li, H. (2017). Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources. Renewable and Sustainable Energy Reviews, 77, 193-214. doi:10.1016/j.rser.2017.03.107Frey, S., Diaconescu, A., Menga, D., & Demeure, I. (2015). A Generic Holonic Control Architecture for Heterogeneous Multiscale and Multiobjective Smart Microgrids. ACM Transactions on Autonomous and Adaptive Systems, 10(2), 1-21. doi:10.1145/2700326Miers, C., Simplicio, M., Gallo, D., Carvalho, T., Bressan, G., Souza, V., … Damola, A. (2010). A Taxonomy for Locality Algorithms on Peer-to-Peer Networks. IEEE Latin America Transactions, 8(4), 323-331. doi:10.1109/tla.2010.5595121Porsinger, T., Janik, P., Leonowicz, Z., & Gono, R. (2017). Modelling and Optimization in Microgrids. Energies, 10(4), 523. doi:10.3390/en10040523Ali, M., Zakariya, M., Asif, M., & Ullah, A. (2012). TCP/IP Based Intelligent Load Management System in Micro-Grids Network Using MATLAB/Simulink. Energy and Power Engineering, 04(04), 283-289. doi:10.4236/epe.2012.44038Shin, I.-J., Song, B.-K., & Eom, D.-S. (2017). International Electronical Committee (IEC) 61850 Mapping with Constrained Application Protocol (CoAP) in Smart Grids Based European Telecommunications Standard Institute Machine-to-Machine (M2M) Environment. Energies, 10(3), 393. doi:10.3390/en10030393Loh, P. C., Li, D., Chai, Y. K., & Blaabjerg, F. (2013). Autonomous Operation of Hybrid Microgrid With AC and DC Subgrids. IEEE Transactions on Power Electronics, 28(5), 2214-2223. doi:10.1109/tpel.2012.2214792Overlay networks for smart gridshttp://users.atlantis.ugent.be/cdvelder/papers/2013/wauters2013sgv.pdfEugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Ali, I. (2012). High-speed Peer-to-peer Communication based Protection Scheme Implementation and Testing in Laboratory. International Journal of Computer Applications, 38(4), 16-24. doi:10.5120/4596-6793Yoo, B.-K., Yang, S.-H., Yang, H.-S., Kim, W.-Y., Jeong, Y.-S., Han, B.-M., & Jang, K.-S. (2011). Communication Architecture of the IEC 61850-based Micro Grid System. Journal of Electrical Engineering and Technology, 6(5), 605-612. doi:10.5370/jeet.2011.6.5.605Dou, X., Quan, X., Wu, Z., Hu, M., Yang, K., Yuan, J., & Wang, M. (2014). Hybrid Multi-Agent Control in Microgrids: Framework, Models and Implementations Based on IEC 61850. Energies, 8(1), 31-58. doi:10.3390/en801003

A Bibliometric Diagnosis and Analysis about Smart Cities

[EN] This article aims to present a bibliometric analysis of Smart Cities. The study analyzes the most important journals during the period between 1991 and 2019. It provides helpful insights into the document types, the distribution of countries/territories, the distribution of institutions, the authors' geographical distribution, the most active authors and their research interests or fields, the relationships between principal authors and more relevant publications, and the most cited articles. This paper also provides important information about the core and historical references and the most cited papers. The analysis used the keywords and thematic noun-phrases in the titles and abstracts of the sample papers to explore the hot research topics in the top journals (e.g., 'Smart Cities', 'Intelligent Cities', 'Sustainable Cities', 'e-Government', 'Digital Transformation', 'Knowledge-Based City', etc.). The main objective is to have a quantitative description of the published literature about Smart Cities; this description will be the basis for the development of a methodology for the diagnosis of the maturity of a Smart City. The results presented here help to define the scientific concept of Smart Cities and to measure the importance that the term has gained through the years. The study has allowed us to know the main indicators of the published literature in depth, from the date of publication of the first articles and the evolution of these indicators to the present day. From the main indicators in the literature, some were selected to be applied: The most influential journals on Smart Cities according to the general citation structure in Smart Cities, Global Impact Factor of Smart Cities, number of publications, publications on Smart Cities around the world, and their correlation.Pérez, LM.; Oltra Badenes, RF.; Oltra Gutiérrez, JV.; Gil Gómez, H. (2020). A Bibliometric Diagnosis and Analysis about Smart Cities. Sustainability. 12(16):1-43. https://doi.org/10.3390/su12166357S1431216Guo, Y.-M., Huang, Z.-L., Guo, J., Li, H., Guo, X.-R., & Nkeli, M. J. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11(13), 3606. doi:10.3390/su11133606Mora, L., Bolici, R., & Deakin, M. (2017). The First Two Decades of Smart-City Research: A Bibliometric Analysis. Journal of Urban Technology, 24(1), 3-27. doi:10.1080/10630732.2017.1285123Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Li, C., Liu, X., Dai, Z., & Zhao, Z. (2019). Smart City: A Shareable Framework and Its Applications in China. Sustainability, 11(16), 4346. doi:10.3390/su11164346Merigó, J. M., & Yang, J.-B. (2016). Accounting Research: A Bibliometric Analysis. Australian Accounting Review, 27(1), 71-100. doi:10.1111/auar.12109Garg, K. C., & Sharma, C. (2017). Bibliometrics of Library and Information Science research in India during 2004-2015. DESIDOC Journal of Library & Information Technology, 37(3), 221-227. doi:10.14429/djlit.37.3.11188Metse, A. P., Wiggers, J. H., Wye, P. M., Wolfenden, L., Prochaska, J. J., Stockings, E. A., … Bowman, J. A. (2016). Smoking and Mental Illness: A Bibliometric Analysis of Research Output Over Time. Nicotine & Tobacco Research, 19(1), 24-31. doi:10.1093/ntr/ntw249Broadus, R. N. (1987). Toward a definition of «bibliometrics». Scientometrics, 12(5-6), 373-379. doi:10.1007/bf02016680Hood, W. W., & Wilson, C. S. (2001). Scientometrics, 52(2), 291-314. doi:10.1023/a:1017919924342Thelwall, M. (2008). Bibliometrics to webometrics. Journal of Information Science, 34(4), 605-621. doi:10.1177/0165551507087238Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. 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Journal of the American Society for Information Science and Technology, 55(14), 1216-1227. doi:10.1002/asi.20077Gupta, B. . M., & Dhawan, S. (2019). Electronic books A scientometric assessment of global literature during 1993 2018. DESIDOC Journal of Library & Information Technology, 39(5), 251-258. doi:10.14429/djlit.39.5.14573Kokol, P., Blažun Vošner, H., & Završnik, J. (2020). Application of bibliometrics in medicine: a historical bibliometrics analysis. Health Information & Libraries Journal, 38(2), 125-138. doi:10.1111/hir.12295Michalopoulos, A., & Falagas, M. E. (2005). A Bibliometric Analysis of Global Research Production in Respiratory Medicine. Chest, 128(6), 3993-3998. doi:10.1378/chest.128.6.3993Lefaivre, K. A., Shadgan, B., & O’Brien, P. J. (2011). 100 Most Cited Articles in Orthopaedic Surgery. Clinical Orthopaedics & Related Research, 469(5), 1487-1497. doi:10.1007/s11999-010-1604-1Kelly, J. C., Glynn, R. W., O’Briain, D. E., Felle, P., & McCabe, J. P. (2010). 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A bibliometric analysis of articles identified by editors as representing excellence in nursing publication: Replication and extension. Journal of Advanced Nursing, 76(5), 1247-1254. doi:10.1111/jan.14316Liu, W., Wang, Z., & Zhao, H. (2020). Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview. Electronic Markets, 30(4), 735-757. doi:10.1007/s12525-020-00395-7Cronin, B. (2001). Bibliometrics and beyond: some thoughts on web-based citation analysis. Journal of Information Science, 27(1), 1-7. doi:10.1177/016555150102700101Durieux, V., & Gevenois, P. A. (2010). Bibliometric Indicators: Quality Measurements of Scientific Publication. Radiology, 255(2), 342-351. doi:10.1148/radiol.09090626Guerola Navarro, V., Oltra Badenes, R. F., Gil Gomez, H., & Gil Gomez, J. A. (2020). Customer Relationship Management (CRM): A Bibliometric Analysis. 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An energy-aware algorithm for electric vehicle infrastructures in smart cities

[EN] The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results.This work was partially supported by MINECO/FEDER, Spain RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by UPV, Spain PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana -Fondo Social Europeo, Spain.Palanca Cámara, J.; Jordán, J.; Bajo, J.; Botti Navarro, VJ. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems. 108:454-466. https://doi.org/10.1016/j.future.2020.03.001S454466108Gan, L., Topcu, U., & Low, S. H. (2013). Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28(2), 940-951. doi:10.1109/tpwrs.2012.2210288Ma, T., & Mohammed, O. A. (2014). Optimal Charging of Plug-in Electric Vehicles for a Car-Park Infrastructure. IEEE Transactions on Industry Applications, 50(4), 2323-2330. doi:10.1109/tia.2013.2296620Needell, Z. A., McNerney, J., Chang, M. T., & Trancik, J. E. (2016). Potential for widespread electrification of personal vehicle travel in the United States. Nature Energy, 1(9). doi:10.1038/nenergy.2016.112Franke, T., & Krems, J. F. (2013). Understanding charging behaviour of electric vehicle users. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 75-89. doi:10.1016/j.trf.2013.09.002Shukla, A., Pekny, J., & Venkatasubramanian, V. (2011). An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles. Computers & Chemical Engineering, 35(8), 1431-1438. doi:10.1016/j.compchemeng.2011.03.018Nie, Y. (Marco), & Ghamami, M. (2013). A corridor-centric approach to planning electric vehicle charging infrastructure. Transportation Research Part B: Methodological, 57, 172-190. doi:10.1016/j.trb.2013.08.010Tu, W., Li, Q., Fang, Z., Shaw, S., Zhou, B., & Chang, X. (2016). Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65, 172-189. doi:10.1016/j.trc.2015.10.004Dong, J., Liu, C., & Lin, Z. (2014). Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transportation Research Part C: Emerging Technologies, 38, 44-55. doi:10.1016/j.trc.2013.11.001He, J., Yang, H., Tang, T.-Q., & Huang, H.-J. (2018). An optimal charging station location model with the consideration of electric vehicle’s driving range. Transportation Research Part C: Emerging Technologies, 86, 641-654. doi:10.1016/j.trc.2017.11.026Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding Human Mobility from Twitter. PLOS ONE, 10(7), e0131469. doi:10.1371/journal.pone.0131469Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-