Smart system for children's chronic illness monitoring

[EN] Sick children need a continuous monitoring, but this involves high costs for the government and for the parents. The use of information and communication technologies (ICT) jointly with artificial intelligence and smart devices can reduce these costs, help the children and assist their parents. This paper presents a smart architecture for children's chronic illness monitoring that will let the caregivers (parents, teachers and doctors) to remotely monitor the health of the children based on the sensors embedded in the smartphones and smart wearable devices. The proposed architecture includes a smart algorithm developed to intelligently detect if a parameter has exceeded a threshold, thus it may imply an emergency or not. To check the correct operation of this system, we have developed a small wearable device that is able to measure the heart rate and the body temperature. We have designed a secure mechanism to stablish a Bluetooth connection with the smartphone. In addition, the system is able to perform the data fusion in both the information packetizing process, which contributes to improve the protocol performance, and in the measured values combination, where it is used a stochastic approach. As a result, our system can fusion data from different sensors in real-time and detect automatically strange situations for sending a warning to the caregivers. Finally, the consumed bandwidth and battery autonomy of the developed device have been measured.This work has been partially supported by the "Ministerio de EducaciOn, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Sendra, S.; Parra-Boronat, L.; Lloret, J.; Tomás Gironés, J. (2018). Smart system for children's chronic illness monitoring. Information Fusion. 40:76-86. https://doi.org/10.1016/j.inffus.2017.06.002S76864

Smart resource allocation for improving QoE in IP Multimedia Subsystems

[EN] IP Multimedia Subsystem (IMS) is a robust multimedia service. IMS becomes more important when delivering multimedia services. Multimedia service providers can benefit from IMS to ensure a good QoE (Quality of Experience) to their customers with minimal resources usage. In this paper, we propose an intelligent media distribution IMS system architecture for delivering video streaming. The system is based primarily on uploading a multimedia file to a server in the IMS. Later, other users can download the uploaded multimedia file from the IMS. In the system, we also provide the design of the heuristic decision methods and models based on probability distributions. Thus, our system takes into account the network parameters such as bandwidth, jitter, delay and packet loss that influence the QoE of the end -users. Moreover, we have considered the other parameters of the energy consumption such as CPU, RAM, temperature and number connected users that impact the result of the QoE. All these parameters are considered as input to our proposal management system. The measurements taken from the real test bench show the real performance and demonstrate the success of the system about ensuring the upload speed of the multimedia file, guaranteeing the QoE of end users and improving the energy efficiency of the IMS.This work has been partially supported by the "Ministerio de Ciencia e Innovation", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigation Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary projects.Canovas Solbes, A.; Taha, M.; Lloret, J.; Tomás Gironés, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications. 104:107-116. https://doi.org/10.1016/j.jnca.2017.12.020S10711610

IoT-WLAN Proximity Network for Potentiostats

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The implementation of potentiostats as portable and communicated devices has reached significant progress to benefit research, industry, and education. The Internet of Things (IoT) is a good opportunity to interconnect devices such as the potentiostats together with electronics, communication technologies, and chemistry into a single system. This work proposes a network for potentiostats using machine-to-machine (M2M) protocols, modifying its functioning mechanism in the broker to check the payload of the message that passes through it and synchronize the sensors depending on its content. Although one sensor can be synchronized directly to another, the broker decides which sensor to pair. This modification was made in the M2M protocol algorithm, both in the Broker and in the Client (sensor). In addition to this, the network uses an interconnection architecture of IoT smart networks of proximity with centralized management. The results of the tests carried out showed that the use of a modified M2M such as the one proposed in the architecture allows synchronization and comparison of the measurements of several sensors in real-time.González, P.; Lloret, J.; Tomás Gironés, J.; Rodríguez, O.; Hurtado, M. (2020). IoT-WLAN Proximity Network for Potentiostats. IEEE. 94-99. https://doi.org/10.1109/FMEC49853.2020.9144776S949

An Integrated IoT Architecture for Smart Metering

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Advanced meter infrastructures (AMIs) are systems that measure, collect, and analyze utilities distribution and consumption, and communicate with metering devices either on a schedule or on request. AMIs are becoming a vital part of utilities distribution network and allow the development of Smart Cities. In this article we propose an integrated Internet of Things architecture for smart meter networks to be deployed in smart cities. We discuss the communication protocol, the data format, the data gathering procedure, and the decision system based on big data treatment. The architecture includes electricity, water, and gas smart meters. Real measurements show the benefits of the proposed IoT architecture for both the customers and the utilities.Lloret, J.; Tomás Gironés, J.; Canovas Solbes, A.; Parra-Boronat, L. (2016). An Integrated IoT Architecture for Smart Metering. IEEE Communications Magazine. 54(12):50-57. doi:10.1109/MCOM.2016.1600647CMS5057541

Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning

[EN] The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. One of the most important mechanisms is the use of facemasks. However, the wearing the facemasks incorrectly makes this prevention method useless. Artificial Intelligence (AI) and especially facial recognition techniques can be used to detect misuses and reduce virus transmission, especially indoors. In this paper, we present an intelligent method to automatically detect when facemasks are being worn incorrectly in real-time scenarios. Our proposal uses Convolutional Neural Networks (CNN) with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading. The main problem that we have detected is that there is currently no training set for this task. It is for this reason that we have requested the participation of citizens by taking different selfies through an app and placing the mask in different positions. Thus, we have been able to solve this problem. The results show that the accuracy achieved with transfer learning slightly improves the accuracy achieved with convolutional neural networks. Finally, we have also developed an Android-app demo that validates the proposal in real scenarios.Tomás Gironés, J.; Rego Mañez, A.; Viciano-Tudela, S.; Lloret, J. (2021). Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning. Healthcare. 9(8):1-17. https://doi.org/10.3390/healthcare90810501179

An m-health application for cerebral stroke detection and monitoring using cloud services

[EN] Over 25 million people suffered from cerebral strokes in a span of 23 years. Many systems are being developed to monitor and improve the life of patients that suffer from different diseases. However, solutions for cerebral strokes are hard to find. Moreover, due to their widespread utilization, smartphones have presented themselves as the most appropriate devices for many e-health systems. In this paper, we propose a cerebral stroke detection solution that employs the cloud to store and analyze data in order to provide statistics to public institutions. Moreover, the prototype of the application is presented. The three most important symptoms of cerebral strokes were considered to develop the tasks that are conducted. Thus, the first task detects smiles, the second task employs voice recognition to determine if a sentence is repeated correctly and, the third task determines if the arms can be raised. Several tests were performed in order to verify the application. Results show its ability to determine whether users have the symptoms of cerebral stroke or not.This work has been partially supported by the pre-doctoral student grant "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)" by the "Ministerio de Educacion, Cultura y Deporte", with reference: FPU14/02953.García-García, L.; Tomás Gironés, J.; Parra-Boronat, L.; Lloret, J. (2019). An m-health application for cerebral stroke detection and monitoring using cloud services. International Journal of Information Management. 45:319-327. https://doi.org/10.1016/j.ijinfomgt.2018.06.004S3193274

An Intelligent Algorithm for Resource Sharing and Self-Management of Wireless-IoT-Gateway

[EN] Internet of Things (IoT) is rapidly gaining momentum in the scenario of telecommunications. Conventional networks allow for interactivity and data exchange, but these networks have not been designed for the new features and functions of IoT devices. In this paper, an algorithm is proposed to share common recourse among Things, that is, between different types of smart appliances. This proposal is based on an IoT network with centralized management architecture, controlled by an Artificial Intelligence (AI). The AI controller uses an algorithm which based on machine learning techniques, collecting information on the network through an information protocol. Every smart thing that connects to the network is announces through a protocol message called Function and Service Discovery Protocol (DFSP) over the queued message telemetry transport protocol (MQTT). The proposed algorithm is responsible for discovering and allocating resources in the networks. As a result, using our proposed algorithm in communication system provides the outperform efficiency and availability than that used in conventional communication systems for the integrate IoT devices.This work was supported in part by the "Ministerio de Economia y Competitividad'', through the "Convocatoria 2014 Proyectos I+D - Programa Estatal de Investigacion Cientica y Tecnica de Excelencia'' in the "Subprograma Estatal de Generacion de Conocimiento'', under Grant TIN2014-57991-C3-1-P and through the "Convocatoria 2017 -Proyectos I+D+I -Programa Estatal de Investigacion, Desarrollo e Innovacion, convocatoria excelencia'' under Grant TIN2017-84802-C2-1-PGonzalez Ramirez, PL.; Taha, M.; Lloret, J.; Tomás Gironés, J. (2019). An Intelligent Algorithm for Resource Sharing and Self-Management of Wireless-IoT-Gateway. IEEE Access. 8:3159-3170. https://doi.org/10.1109/ACCESS.2019.2960508S31593170

An architecture and protocol for smart continuous eHealth monitoring using 5G

[EN] Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth, lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to collect measures from the body, and a smartphone at the patient side in order to process the data received from the wearable devices. We also need a DataBase with an intelligent system able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data received from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there are few users (less than 200 users), we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G The Packet Loss is almost null in 4G until 300 users, while in 5G it is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use the 5G network because it offers low delays and guarantees the availability of bandwidth for all users.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Lloret, J.; Parra-Boronat, L.; Abdullah, MTA.; Tomás Gironés, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks. 129(2):340-351. https://doi.org/10.1016/j.comnet.2017.05.018S340351129

An Intelligent System to Detect the Type of Devices Sending and Receiving Data in the Network

Nowadays mobile and fixed devices are used interchangeably for surfing the web due to the huge improvements performed in mobile devices in the recent years. Both mobile and fixed devices with Internet connectivity are supplied with different types of connection, thus users can select the best one at any time depending on their environment. In general, the mobile devices allow users access to Internet using the 3G network or a common WiFi connection, and the fixed ones generally use a wireless or wired connection. Selecting one or another type of connection implies different features of the network environment, so Internet Service Providers need to adapt their infrastructure to guarantee acceptable levels of Quality of Service in every type of connection. In this paper we study the behavior of the devices according to their nature, that is, if it is a mobile or fixed device. First, we have classified the most significant network parameters and software application values in order to know the nature of the device. Our proposal uses an intelligent system based on neural networks and finite state machines that lets the Internet Service Provider know the type of device belongs to the traffic going to its network. The system analyzes the transport and application layers from TCP packets to discriminate the percentage of Internet traffic generated by mobile and fixed devices. Test results show the success of the developed system.Bri Molinero, D.; Canovas Solbes, A.; Tomás Gironés, J.; Lloret, J. (2013). An Intelligent System to Detect the Type of Devices Sending and Receiving Data in the Network. Network Protocols and Algorithms. 5(2):72-91. doi:10.5296/npa.v5i2.3833S72915

Speech Translation Statistical System for Teaching Environments and Conference Speeches

The synergic combination of different sources of knowledge is a key aspect in the development of modern statistical translators. The effect and implications of adding additional other-than-voice information in a voice translation system for teaching environments and conference speakers is described in this work. The additional information serves as the bases for the log-linear combination of several statistical models. A prototype that implements a real-time speech translation system from Spanish to English is presented. In the scenario of analysis a teacher, or presenter, as speaker giving its presentation could use a real time translation system for foreign students or participants. The speaker could add slides or class notes as additional reference to the voice translation system. Should notes be already translated into the destination language the system could have even more accuracy. In this paper, first, we present the theoretical framework of the problem, then, we summarize the overall architecture of the system, next, we specify the speech recognition module and the machine translation module, then, we show how the system is enhanced with capabilities related to capturing the additional information, and, finally, we present the performance results of the developed system.Tomás Gironés, J.; Canovas Solbes, A.; Lloret, J.; García Pineda, M. (2011). Speech Translation Statistical System for Teaching Environments and Conference Speeches. International Journal on Advances in Intelligent Systems. 4(1):20-30. http://hdl.handle.net/10251/46962S20304