ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish

[EN] Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet's area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen's d effect size of 2.55.This work acknowledges the research project Desarrollo Colaborativo de Soluciones AAL with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. This work has been supported by the program Estancias de movilidad en el extranjero José Castillejo para jóvenes doctores funded by the Spanish Ministry of Education, Culture and Sport with reference CAS17/00005. We also acknowledge support from Universidad de Zaragoza , Fundación Bancaria Ibercaja and Fundación CAI in the Programa Ibercaja-CAI de Estancias de Investigación with reference IT24/16. We acknowledge the research project Construcción de un framework para agilizar el desarrollo de aplicaciones móviles en el ámbito de la salud funded by University of Zaragoza and Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. It has also been supported by Organismo Autónomo Programas Educativos Europeos with reference 2013-1-CZ1-GRU06-14277. We also aknowledge support from project Sensores vestibles y tecnología móvil como apoyo en la formación y práctica de mindfulness: prototipo previo aplicado a bienestar funded by University of Zaragoza with grant number UZ2017-TEC-02. Furthermore, we acknowledge the Fondo Social Europeo and the Departamento de Tecnología y Universidad del Gobierno de Aragón for their joint support with grant number Ref-T81.García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2017). ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish. Sensors. 17(11):1-19. https://doi.org/10.3390/s17112606S1191711Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors, 13(9), 11782-11796. doi:10.3390/s130911782Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: research challenges. 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An agent-based bioenergetics model for predicting impacts of environmental change on a top marine predator, the Weddell seal. Ecological Modelling, 351, 36-50. doi:10.1016/j.ecolmodel.2017.02.002Berman, M., Nicolson, C., Kofinas, G., Tetlichi, J., & Martin, S. (2004). Adaptation and Sustainability in a Small Arctic Community : Results of an Agent-based Simulation Model. ARCTIC, 57(4). doi:10.14430/arctic517Kadir, H. A., & Arshad, M. R. (2015). Cooperative Multi Agent System for Ocean Observation System based on Consensus Algorithm. Procedia Computer Science, 76, 203-208. doi:10.1016/j.procs.2015.12.343Trygonis, V., Georgakarakos, S., Dagorn, L., & Brehmer, P. (2016). Spatiotemporal distribution of fish schools around drifting fish aggregating devices. Fisheries Research, 177, 39-49. doi:10.1016/j.fishres.2016.01.013De Kerckhove, D. T., Milne, S., & Shuter, B. J. (2015). Measuring fish school swimming speeds with two acoustic beams and determining the angle of the school detection. Fisheries Research, 172, 432-439. doi:10.1016/j.fishres.2015.08.001Source Code of the Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fishes Called ABS-FishCounthttp://dx.doi.org/10.17632/yzmt73x8j8.1Cossentino, M., Gaud, N., Hilaire, V., Galland, S., & Koukam, A. (2009). ASPECS: an agent-oriented software process for engineering complex systems. Autonomous Agents and Multi-Agent Systems, 20(2), 260-304. doi:10.1007/s10458-009-9099-4García-Magariño, I., Palacios-Navarro, G., & Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107. doi:10.1016/j.simpat.2017.05.006García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., & Palacios-Navarro, G. (2015). PEABS: A Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112. doi:10.1016/j.engappai.2015.09.003Rosenthal, J. A. (1996). Qualitative Descriptors of Strength of Association and Effect Size. Journal of Social Service Research, 21(4), 37-59. doi:10.1300/j079v21n04_0

Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics

[EN] Internet-of-Things (IoT) can allow healthcare professionals to remotely monitor patients by analyzing the sensors outputs with big data analytics. Sleeping conditions are one of the most influential factors on health. However, the literature lacks of the appropriate simulation tools to widely support the research on the recognition of sleeping postures. This paper proposes an agent-based simulation framework to simulate sleeper movements on a simulated smart bed with load sensors. This framework allows one to define sleeping posture recognition algorithms and compare their outcomes with the poses adopted by the sleeper. This novel presented ABS-BedIoT simulator allows users to graphically explore the results with starplots, evolution charts, and final visual representations of the states of the bed sensors. This simulator can also generate logs text files with big data for applying offline big data techniques on them. The source code of ABS-BedIoT and some examples of logs are freely available from a public research repository. The current approach is illustrated with an algorithm that properly recognized the simulated sleeping postures with an average accuracy of 98%. This accuracy is higher than the one reported by an existing alternative work in this area.This work was supported in part by the Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores Program through the Spanish Ministry of Education, Culture and Sport under Grant CAS17/00005, in part by the Universidad de Zaragoza, Fundacion Bancaria Ibercaja, and Fundacion CAI in the Programa Ibercaja-CAI de Estancias de Investigacion under Grant IT24/16, in part by the Desarrollo Colaborativo de Soluciones AAL through the Spanish Ministry of Economy and Competitiveness under Grant TIN2014-57028-R, in part by the Organismo Autonomo Programas Educativos Europeos under Grant 2013-1-CZ1-GRU06-14277, and in part by the Fondo Social Europeo and the Departamento de Tecnologia y Universidad del Gobierno de Aragon for their joint support under Grant Ref-T81.García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2018). Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics. IEEE Access. 6:366-379. https://doi.org/10.1109/ACCESS.2017.2764467S366379

Survivability Strategies for Emerging Wireless Networks With Data Mining Techniques: a Case Study With NetLogo and RapidMiner

[EN] Emerging wireless networks have brought Internet and communications to more users and areas. Some of the most relevant emerging wireless technologies are Worldwide Interoperability for Microwave Access, Long-Term Evolution Advanced, and ad hoc and mesh networks. An open challenge is to ensure the reliability and robustness of these networks when individual components fail. The survivability and performance of these networks can be especially relevant when emergencies arise in rural areas, for example supporting communications during a medical emergency. This can be done by anticipating failures and finding alternative solutions. This paper proposes using big data analytics techniques, such as decision trees for detecting nodes that are likely to fail, and so avoid them when routing traffic. This can improve the survivability and performance of networks. The current approach is illustrated with an agent based simulator of wireless networks developed with NetLogo and data mining processes designed with RapidMiner. According to the simulated experimentation, the current approach reduced the communication failures by 51.6% when incorporating rule induction for predicting the most reliable routes.This work was supported in part by the research project Construccion de un framework para agilizar el desarrollo de aplicaciones moviles en el a mbito de la salud through the University of Zaragoza and Foundation Ibercaja under Grant JIUZ-2017-TEC-03, in part by the Universidad de Zaragoza, in part by the Fundacion Bancaria Ibercaja, in part by the Fundacion CAI in the Programa Ibercaja-CAI de Estancias de Investigacion under Grant IT1/18, in part by the program Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores through the Spanish Ministry of Education, Culture and Sport under Grant CAS17/00005, in part by the Desarrollo Colaborativo de Soluciones AAL through the Spanish Ministry of Economy and Competitiveness under Grant TIN2014-57028-R, in part by the Organismo Autonomo Programas Educativos Europeos under Grant 2013-1-CZ1-GRU06-14277, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the project under Grant TIN2017-84802-C2-1-P.García-Magariño, I.; Gray, G.; Lacuesta Gilabert, R.; Lloret, J. (2018). Survivability Strategies for Emerging Wireless Networks With Data Mining Techniques: a Case Study With NetLogo and RapidMiner. IEEE Access. 6:27958-27970. https://doi.org/10.1109/ACCESS.2018.2825954S2795827970

System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

[EN] The conditions of the environment where a person lives have a great impact on his wellness state. When buying a new house, it is important to select a place that aids in improving the wellness state of the buyer or, at least, keeps it at the same level. A deficient wellness state implies an increase of stress and the appearance of some effects associated with it. Heart rate variability (HRV) allows measuring the stress or wellness levels of a person by measuring the difference in time between heartbeats. A low HRV is related to high stress levels whereas a high HRV is associated with a high wellness state. In this paper, we present a system that measures the wellness and stress levels of home buyers by employing sensors that measure the HRV. Our system is able to process the data and recommend the best neighborhood to live in considering the wellness state of the buyer. Several tests were performed utilizing different locations. In order to determine the best neighborhood, we have developed an algorithm that assigns different values to the area in accordance with the HRV measures. Results show that the system is effective in providing the recommendation of the place that would allow the person to live with the highest wellness state.Lacuesta Gilabert, R.; García-García, L.; García-Magariño, I.; Lloret, J. (2017). System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability. IEEE Access. 5:10594-10604. doi:10.1109/ACCESS.2017.2702107S1059410604

ABS-DDoS: An Agent-Based Simulator about Strategies of Both DDoS Attacks and Their Defenses, to Achieve Efficient Data Forwarding in Sensor Networks and IoT Devices

[EN] Sensor networks and Internet of Things (IoT) are useful for many purposes such as military defense, sensing in smart homes, precision agriculture, underwater monitoring in aquaculture, and ambient-assisted living for healthcare. Efficient and secure data forwarding is essential to maintain seamless communications and to provide fast services. However, IoT devices and sensors usually have low processing capabilities and vulnerabilities. For example, attacks such as the Distributed Denial of Service (DDoS) can easily hinder sensor networks and IoT devices. In this context, the current approach presents an agent-based simulation solution for exploring strategies for defending from different DDoS attacks. The current work focuses on obtaining low-consuming defense strategies in terms of processing capabilities, so that these can be applied in sensor networks and IoT devices. The experimental results show that the simulator was useful for (a) defining defense and attack strategies, (b) assessing the effectiveness of defense strategies against attack ones, and (c) defining efficient defense strategies with low response times.The authors acknowledge the research project "Construccion de un Framework para Agilizar el Desarrollo de Aplicaciones Moviles en el Ambito de la Salud" funded by University of Zaragoza and Foundation Ibercaja with Grant Reference JIUZ-2017-TEC-03. This work has been supported by the program "Estancias de Movilidad en el Extranjero Jose Castillejo para Jovenes Doctores" funded by the Spanish Ministry of Education, Culture and Sport with Reference CAS17/00005. The authors also acknowledge support from "Universidad de Zaragoza", "Fundacion Bancaria Ibercaja", and "Fundacion CAI" in the "Programa Ibercaja-CAI de Estancias de Investigacion" with Reference IT1/18. This work acknowledges the research project "Desarrollo Colaborativo de Soluciones AAL" with reference TIN2014-57028-R funded by the Spanish Ministry of Economy and Competitiveness. It has also been supported by "Organismo Autonomo Programas Educativos Europeos" with Reference 2013-1-CZ1-GRU06-14277. Furthermore, they acknowledge the "Fondo Social Europeo" and the "Departamento de Tecnologia y Universidad del Gobierno de Aragon" for their joint support with Grant no. Ref-T81.González-Landero, F.; García-Magariño, I.; Lacuesta Gilabert, R.; Lloret, J. (2018). ABS-DDoS: An Agent-Based Simulator about Strategies of Both DDoS Attacks and Their Defenses, to Achieve Efficient Data Forwarding in Sensor Networks and IoT Devices. Wireless Communications and Mobile Computing. 2018:1-11. https://doi.org/10.1155/2018/7264269S1112018García-Magariño, I., Lacuesta, R., & Lloret, J. (2017). ABS-FishCount: An Agent-Based Simulator of Underwater Sensors for Measuring the Amount of Fish. Sensors, 17(11), 2606. doi:10.3390/s17112606Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics. IEEE Access, 6, 366-379. doi:10.1109/access.2017.2764467Anagnostopoulos, T., Kolomvatsos, K., Anagnostopoulos, C., Zaslavsky, A., & Hadjiefthymiades, S. (2015). Assessing dynamic models for high priority waste collection in smart cities. Journal of Systems and Software, 110, 178-192. doi:10.1016/j.jss.2015.08.049Alomari, E., Manickam, S., B. Gupta, B., Karuppayah, S., & Alfaris, R. (2012). Botnet-based Distributed Denial of Service (DDoS) Attacks on Web Servers: Classification and Art. International Journal of Computer Applications, 49(7), 24-32. doi:10.5120/7640-0724Anwar, Z., & Malik, A. W. (2014). Can a DDoS Attack Meltdown My Data Center? A Simulation Study and Defense Strategies. IEEE Communications Letters, 18(7), 1175-1178. doi:10.1109/lcomm.2014.2328587Huda, S., Islam, R., Abawajy, J., Yearwood, J., Hassan, M. M., & Fortino, G. (2018). A hybrid-multi filter-wrapper framework to identify run-time behaviour for fast malware detection. Future Generation Computer Systems, 83, 193-207. doi:10.1016/j.future.2017.12.037García-Magariño, I., Palacios-Navarro, G., & Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107. doi:10.1016/j.simpat.2017.05.006García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., & Palacios-Navarro, G. (2015). PEABS: A Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112. doi:10.1016/j.engappai.2015.09.003Akhunzada, A., Sookhak, M., Anuar, N. B., Gani, A., Ahmed, E., Shiraz, M., … Khurram Khan, M. (2015). Man-At-The-End attacks: Analysis, taxonomy, human aspects, motivation and future directions. Journal of Network and Computer Applications, 48, 44-57. doi:10.1016/j.jnca.2014.10.009Yan, Q., Yu, F. R., Gong, Q., & Li, J. (2016). Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges. IEEE Communications Surveys & Tutorials, 18(1), 602-622. doi:10.1109/comst.2015.248736

Integration of Data from Vehicular Ad Hoc Networks Using Model-Driven Collaborative Tools

[EN] Ubiquitous environments such as Vehicular Ad Hoc Networks need applications that allow them to integrate data and services to build knowledge that can be used to make decisions and to improve standards of living and user safety, among others. We have designed a collaborative virtual environment that covers the needs of integration of knowledge from different vehicles to endow the final user with the necessary information. This environment has been carried out following a model-driven approach that generates a groupware application for improving collaborative work and access to services. The implemented tool facilitates the development and implementation of collaborative frameworks in VANETs, where every vehicle acts as a node.Lacuesta Gilabert, R.; Gallardo, J.; Lloret, J.; Palacios-Navarro, G. (2016). Integration of Data from Vehicular Ad Hoc Networks Using Model-Driven Collaborative Tools. Mobile Information Systems. 2016:1-15. doi:10.1155/2016/4291040S115201