9 research outputs found

    Network of multi-hop wireless sensors for low cost and extended area home automation systems

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    [EN] Wireless sensor networks have a wide range of applications and many pending challenges, especially those related to the evolution of digital electronics, bandwidth, reduction of implementation costs, network coverage and processing capacity. This document proposes a configuration of multi-hop wireless network oriented to intelligent domotic installations, based on 32-bit microcontrollers and low cost wireless communication modules, which allows to have complete coverage between the devices of the home automation system with a reduced loss of data, improvement in the processing capacity, adaptability and scalability in the nodes. The evaluation of network performance considers the following metrics: response time, network reach, scalability and precision. The experimental results determined a successful adaptation of the AODV multi-hop protocol, allowing sufficient coverage for a single-family house, at transmission speeds of 250Kbps, which guarantees the integrity and security of the data.[ES] Las redes de sensores inalámbricos disponen de un campo muy amplio de aplicaciones y aún muchos desafíos pendientes, especialmente aquellos relacionados con la evolución de la electrónica digital, ancho de banda, reducción de costos de implementación, cobertura de red y capacidad de procesamiento. Este documento propone una configuración de red inalámbrica multisalto orientada a instalaciones domóticas inteligentes, basadas en microcontroladores de 32 bits y módulos de comunicación inalámbrica de bajo costo, que permita tener cobertura completa entre los dispositivos del sistema domótico con una reducida pérdida de datos, mejora en la capacidad de procesamiento, adaptabilidad y escalabilidad en los nodos. La evaluación del desempeño de la red considera las siguientes métricas: tiempo de respuesta, alcance de red, escalabilidad y precisión. Los resultados experimentales determinaron una adaptación exitosa del protocolo multisalto AODV, permitiendo una cobertura suficiente para una vivienda unifamiliar, a una velocidad de transmisión de 250Kbps, que garantiza la integridad y seguridad de los datos.Mendoza, E.; Fuentes, P.; Benítez, I.; Reina, D.; Núñez, J. (2020). Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida. Revista Iberoamericana de Automática e Informática industrial. 17(4):412-423. https://doi.org/10.4995/riai.2020.12301OJS412423174Ahmad, A., Roslan, M. F., & Amira, A., 2017. Throughput, latency and cost comparisons of microcontroller-based implementations of wireless sensor network (WSN) in high jump sports. In AIP Conference Proceedings (Vol. 1883, No. 1, p. 020010). 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Journal of Ambient Intelligence and Humanized Computing, 10(4), 1325-1338. https://doi.org/10.1007/s12652-018-0906-0Belagali, R., Anusha, A. M., Sangulagi, P., 2015. Energy-Efficient Secure Routing and Aggregation in Military Sensor Network using Multi-Agent Approach. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2015 International Conference on 286-292. IEEE. https://doi.org/10.1109/ICATCCT.2015.7456897Benítez, J. D., Sosa, E. O., Godoy, D. A., Belloni, E. A., Favret, F., Bareiro, H., Urdinola, R., Olivera, M., 2017. Ampliando la Vida Útil de las WSN por Medio de los Protocolos de Ruteo, Modificación de AODV. In XIX Workshop de Investigadores en Ciencias de la Computación (WICC 2017, ITBA, Buenos Aires). URL: http://sedici.unlp.edu.ar/handle/10915/61567Bondorf, S., Jens, B. S., 2010. Statistical response time bounds in randomly deployed wireless sensor networks. In Local Computer Networks (LCN). IEEE 35th Conference on 340-343. IEEE. https://doi.org/10.1109/LCN.2010.5735738Campamá, D. S., 2012. Sistema operativo para redes inalámbrica de sensores. Tesis de maestría, Pontificia Universidad católica de Chile. URL: https://repositorio.uc.cl/handle/11534/1723Di Nisio, A., Di Noia, T., Carducci, C. G. C., & Spadavecchia, M., 2016. High dynamic range power consumption measurement in microcontroller-based applications. IEEE Transactions on Instrumentation and Measurement, 65(9), 1968-1976. https://doi.org/10.1109/TIM.2016.2549818Escribano, J., García, A., de la Fuente, M., 2011. Monitorización de la Condición Física de Personas en Espacios Confinados Mediante Etiquetas RFID con Sensores y Redes Inalámbricas Eficientes. Revista Iberoamericana de Automática e Informática industrial 8(4), 371-384. https://doi.org/10.1016/j.riai.2011.09.004Espressif Systems, 2018. ESP8266 Non-OS SDK. Version 3.0. URL: https://www.espressif.com/sites/default/files/documentation/2c- esp8266_non_os_sdk_api_reference_en.pdfEspressif, 2016. ESP8266 Mesh User Guide. Version 1.2. URL: https://docplayer.net/33922006-Esp8266-mesh-user-guide.htmlFajriansyah, B., Ichwan, M., & Susana, R., 2016. Evaluasi Karakteristik XBee Pro dan nRF24L01 sebagai Transceiver Nirkabel. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, 4(1), 83. https://doi.org/10.26760/elkomika.v4i1.83Fischione, C., 2014. An Introduction to Wireless Sensor Networks. Royal Institute of technology. Draft, version 1.8. URL: https://www.kth.se/social/files/5431a388f276540a05ad2514/An_Introduc tion_WSNS_V1.8.pdf.García, D., 2015. Estudio de 6loWPAN para su aplicación a Internet de las Cosas. Trabajo de fin de grado. URL: https://riull.ull.es/xmlui/bitstream/handle/915/945/Estudio+de+6loWPAN+para+su+aplicacion+a+Internet+de+las+Cosas.pdf?sequence=1.Hong, S. H., Kim, B., Eom, D. S., 2007. A base-station centric data gathering routing protocol in sensor networks useful in home automation applications. IEEE Transactions on Consumer Electronics 53(3), 945- 951. https://doi.org/10.1109/TCE.2007.4341570Hsieh, F. S., Lin, J. B., 2014. A multiagent approach for managing collaborative workflows in supply chains. In Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) 71-76. IEEE. https://doi.org/10.1109/CSCWD.2014.6846819Jaggi, S., and Wasson, E., 2016. Enhanced OLSR Routing Protocol Using Link-Break Prediction Mechanism for WSN. Industrial Engineering & Management Systems, 15(3), 259-267. https://doi.org/10.7232/iems.2016.15.3.259Kailas, A., Cecchi, V., & Mukherjee, A., 2012. A survey of communications and networking technologies for energy management in buildings and home automation. Journal of Computer Networks and Communications, 2012(932181), 1-6. https://doi.org/10.1155/2012/932181.Kelly, S. D. T., Suryadevara, N. K., Mukhopadhyay, S. C., 2013.Towards the Implementation of IoT for Environmental Condition Monitoring in Homes. IEEE Sensors Journal 13(10), 3846-3853. https://doi.org/10.1109/JSEN.2013.2263379Li, M., Lin H. J., 2015. Design and Implementation of Smart Home Control Systems Based on Wireless Sensor Networks and Power Line Communications. IEEE Transactions On Industrial Electronics 62(7). 4430-4442. https://doi.org/10.1109/TIE.2014.2379586Liao, C., Zhu, K., Tang, J., Zhang, S., 2016. Wireless Sensor Network Performance Research for LEACH Based on Multi-Agent Simulation. IEEE International Conference on Agents (ICA) 98-99. IEEE. https://doi.org/10.1109/ICA.2016.031López Torres, V. 2014. Diseño de un modelo de red domótica libre basada en componentes OpenDomo para aplicación a un pequeño hotel.Magno, M., Polonelli, T., Benini, L., Popovici, E., 2015. A Low Cost, Highly Scalable Wireless Sensor Network Solution to Achieve Smart LED Light Control for Green Buildings. IEEE Sensors Journal 15(5), 2963-2973. https://doi.org/10.1109/JSEN.2014.2383996Manda, S., Shukla, Y., Shrivastava, K., Patil, T. B., & Sawant-Patil, S. T., 2018. A Literature Survey on Wireless Sensor Network in Home Automation Based on Internet of Things. https://doi.org/10.26438/ijcse/v6i6.13621368Medina, C., 2017. Control de Congestión en Redes Inalámbicas de Sensores. Tesis de maestría, Pontificia Universidad Javeriana. Bogota-Colombia.Mezghani, M., Abdellaoui, P., 2015. WSN intelligent communication based on Khalimsky theory using multi-agent systems. In 2015 SAI Intelligent Systems Conference (IntelliSys) (pp. 871-876). IEEE. https://doi.org/10.1109/IntelliSys.2015.7361245Microchip, 2020. URL: https://www.microchip.com/wwwproducts/en/ PIC16F628AMostafaei, H., 2019. Energy-efficient algorithm for reliable routing of wireless sensor networks. IEEE Transactions on Industrial Electronics, 66(7), 5567-5575.https://doi.org/10.1109/TIE.2018.2869345Narten, T., Nordmark, E., Simpson, W., Soliman, H., 2007. Neighbor Discovery for IP version 6 (IPv6). RFC 4861, https://doi.org/10.17487/RFC4861.Nikoukar, A., Raza, S., Poole, A., Güneş, M., & Dezfouli, B., 2018. Low- power wireless for the internet of things: Standards and applications. IEEE Access, 6, 67893-67926. https://doi.org/10.1109/ACCESS.2018.2879189Nordic Semiconductor. (2008). nRF24L01 Single Chip 2.4GHz Transceiver. URL: https://www.nordicsemi.com/DocLib?Product=nRF24Núñez, José Ricardo et al., 2019. Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red. Revista Iberoamericana de Automática e Informática industrial, [S.l.], v. 17, n. 1, p. 94-105. https://doi.org/10.4995/riai.2019.11449Nuñez, J. R., Benítez, I.F., Rodriguez, A., Diaz, S., Oliveira, D., 2019. Tools for the implementation of a SCADA system in a desalination process. 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IEEE transactions on cybernetics, 47(3), 772-783. https://doi.org/10.1109/TCYB.2016.2526683.Randhawa, S., 2014. Research Challenges in Wireless Sensor Network: A State of the Play. Conference Proceeding of National Conference of Science, Engineering y Management in Education and Research. arXiv preprint arXiv:1404.1469v1 [cs.NI]Rawat, P., Singh, K. D., Chaouchi, H., Bonnin, J. M., 2014. Wireless sensor networks: A survey on recent developments and potential synergies. The Journal of Supercomputing 68(1), 1-48. https://doi.org/10.1007/s11227-013-1021-9 https://doi.org/10.1007/s11227-013-1021-9Rodríguez, A., 2011. Sistemas SCADA. Tercera Edición. Marcombo: Barcelona. ISBN: 978-8426717818.Saha, Himadri & Mandal, Shashwata & Mitra, Shinjan & Banerjee, Soham & Saha, Urmi., 2017. Comparative Performance Analysis between nRF24L01+ and XBEE ZB Module Based Wireless Ad-hoc Networks. International Journal of Computer Network and Information Security. 9. 36-44. https://doi.org/10.5815/ijcnis.2017.07.05.Saravanan, S., Poovazhaki, R., Shanker, N., 2018. Cluster Topology in WSN with SCPS for QoS. Wireless Personal Communications, 99(3), 1295- 1314. https://doi.org/10.1007/s11277-017-5185-0STMicroelectronics, 2018. STM32F103xC STM32F103x, STM32F103xE. DS5792 Rev 13. URL:https://www.st.com/resource/en/datasheet/stm32f103rc.pdfSTMicroelectronics, 2019. STM32F030x4 STM32F030x6 STM32F030x8 STM32F030xC. DS9773 Rev 4. URL: https://www.st.com/resource/en/datasheet/stm32f030f4.pdfSnigdh, I., & Gupta, N. 2016. Quality of service metrics in wireless sensor networks: A survey. Journal of The Institution of Engineers (India): Series B, 97(1), 91-96. https://doi.org/10.1007/s40031-014-0160-6Suárez, A., and Núñez, J. R., 2019. 1D Convolutional Neural Network for Detecting Ventricular Heartbeats. IEEE Latin America Transactions, 17(12), 1970-1977. https://doi.org/10.1109/TLA.2019.9011541.Sutagundar, A., Bennur, V., Anusha, A., Bhanu, K., 2016. Agent Based Fault Tolerance in Wireless Sensor Networks. 2016 International Conference on Inventive Computation Technologies (ICICT) 1, 1-6. IEEE. https://doi.org/10.1109/INVENTIVE.2016.7823265Valencia, G., Núñez, J., Vanegas, M., 2020. Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar-Colombia. Data in Brief, 27, 104753. https://doi.org/10.1016/j.dib.2019.104753Vidhya, S., Sasilatha, T., 2018. Secure Data Transfer Using Multi Layer Security Protocol with Energy Power Consumption AODV in Wireless Sensor Networks. Wireless Personal Communications, 103(4), 3055- 3077. https://doi.org/10.1007/s11277-018-5994-9Villarrubia, G., De Paz, J., De La Iglesia, D., Bajo, J., 2017. Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation. 17(8), 1775. 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    Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida

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    Wireless sensor networks have a wide range of applications and many pending challenges, especially those related to the evolution of digital electronics, bandwidth, reduction of implementation costs, network coverage and processing capacity. This document proposes a configuration of multi-hop wireless network oriented to intelligent domotic installations, based on 32-bit microcontrollers and low cost wireless communication modules, which allows to have complete coverage between the devices of the home automation system with a reduced loss of data, improvement in the processing capacity, adaptability and scalability in the nodes. The evaluation of network performance considers the following metrics: response time, network reach, scalability and precision. The experimental results determined a successful adaptation of the AODV multi-hop protocol, allowing sufficient coverage for a single-family house, at transmission speeds of 250Kbps, which guarantees the integrity and security of the data.Las redes de sensores inalámbricos disponen de un campo muy amplio de aplicaciones y aún muchos desafíos pendientes, especialmente aquellos relacionados con la evolución de la electrónica digital, ancho de banda, reducción de costos de implementación, cobertura de red y capacidad de procesamiento. Este documento propone una configuración de red inalámbrica multisalto orientada a instalaciones domóticas inteligentes, basadas en microcontroladores de 32 bits y módulos de comunicación inalámbrica de bajo costo, que permita tener cobertura completa entre los dispositivos del sistema domótico con una reducida pérdida de datos, mejora en la capacidad de procesamiento, adaptabilidad y escalabilidad en los nodos sensores. La evaluación del desempeño de la red considera las siguientes métricas: tiempo de respuesta, alcance de red, escalabilidad y precisión. Los resultados experimentales determinaron una adaptación exitosa del protocolo multisalto AODV, permitiendo una cobertura suficiente para una vivienda unifamiliar, a una velocidad de transmisión de 250Kbps, que garantiza la integridad y seguridad de los datos

    Energy aware routing protocols in ad hoc wireless networks

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    In Mobile Ad hoc Network, communication at mobile nodes can be achieved by using multi-hop wireless links. The architecture of such a network is based, not on a centralized base station but on each node acting as a router to forward data packets to other nodes in the network. The aim of each protocol, in an ad hoc network, is to find valid routes between two communicating nodes. These protocols must be able to handle high mobility of the nodes which often cause changes in the network topology. Every ad hoc network protocol uses some form of a routing algorithm to transmit between nodes based on a mechanism that forwards packets from one node to another in the network. These algorithms have their own way of finding a new route or modifying an existing one when there are changes in the network. The novel area of this research is a proposed routing algorithm which improves routing and limits redundant packet forwarding, especially in dense networks. It reduces the routing messages and consequently power consumption, which increases the average remaining power and the lifetime of the network. The first aim of this research was to evaluate various routing algorithms in terms of power. The next step was to modify an existing ad hoc routing protocol in order to improve the power consumption. This resulted in the implementation of a dynamic probabilistic algorithm in the route request mechanism of an ad hoc On-Demand Distance Vector protocol which led to a 3.0% improvement in energy consumption. A further extension of the approach using Bayesian theory led to 3.3% improvement in terms of energy consumption as a consequence of a reduction in MAC Load for all network sizes, up to 100 nodes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

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    The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

    Advances in Reinforcement Learning

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    Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

    Communication platform for inter-satellite links in distributed satellite systems

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Personality Identification from Social Media Using Deep Learning: A Review

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    Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed
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