Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities

[EN] Smart cities provide new applications based on Internet of Things (loT) technology. Moreover, Software Defined Networks (SDNs) offer the possibility of controlling the network based on applications requirements. One of the main problems that arise when an emergency happens is minimizing the delay time in emergency resource forwarding so as to reduce both human and material damages. In this paper, a new control system based on the integration of SDN and loT in smart city environments is proposed. This control system actuates when an emergency happens and modifies dynamically the routes of normal and emergency urban traffic in order to reduce the time that the emergency resources need to get to the emergency area. The architecture is based on a set of loT networks composed by traffic lights, traffic cameras and an algorithm. The algorithm controls the request of resources and the modification of routes in order to ease the movement of emergency service units. Afterwards, the proposal is tested by emulating a Smart City as a SDN-utilizing Mininet. The experiments show that the delay of the emergency traffic improves in a 33% when the algorithm is running. Moreover, the energy consumed by the loT nodes is modeled and the obtained results display that it increases linearly with the number of nodes, therefore, the proposal is scalable. (C) 2018 Elsevier B.V. All rights reserved.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 2015)". Grant number FPU15/06837, by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D - P rograma Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", project TIN 2014-57991- C 3 - 1 - P and through the "Convocatoria 2016 - Proyectos I+D+I - P rograma Estatal De Investigacion, Desarrollo e Innovacion Orientada a los retos de la sociedad" (Project TEC 2016 - 76795 - C 6 - 4 - R). This work has also been partially supported 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.Rego Mañez, A.; García-García, L.; Sendra, S.; Lloret, J. (2018). Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities. Future Generation Computer Systems. 88:243-253. https://doi.org/10.1016/j.future.2018.05.054S2432538

Dynamic Metric OSPF-Based Routing Protocol for Software Defined Networks

[EN] Routing protocols are needed in networking to find the optimal path to reach the destination. However, networks are changing both their use finality and their technology. Paradigms like Software Defined Networks (SDNs) introduce the possibility and the necessity to improve the routing protocols. In this paper, a modification of the Open Shortest Path First (OSPF) routing protocol is proposed in order to allow the protocol to change the metric calculation dynamically according to the network requirements. Experiments, which compare our proposal against the OSPF protocol, are performed in five different scenarios. In these scenarios, the performance of the multimedia traffic has been increased 33% in terms of bandwidth utilization, 80% of loss rate reduction and delay reduction on VoIP communications.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 2015)". Grant No. FPU15/06837, by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D - Programa Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", project TIN2014-57991-C3-1-P, through the "Convocatoria 2016 - Proyectos I+D+I - Programa Estatal De Investigacion, Desarrollo e Innovacion Orientada a los retos de la sociedad" (Project TEC2016-76795-C6-4-R) and through the "Convocatoria 2017 - Proyectos I+D+I - Programa Estatal de Investigacion, Desarrollo e Innovacion, convocatoria excelencia" (Project TIN2017-84802-C2-1-P).Rego Mañez, A.; Sendra, S.; Jimenez, JM.; Lloret, J. (2019). Dynamic Metric OSPF-Based Routing Protocol for Software Defined Networks. Cluster Computing. 22(3):705-720. https://doi.org/10.1007/s10586-018-2875-7S705720223Coltun, R., Ferguson, D., Moy, J.: OSPF for IPv6, RFC 5340. https://doi.org/10.17487/rfc5340 , July 2008. https://rfc-editor.org/rfc/rfc5340.txtSoftware-Defined Networking (SDN) Definition. https://www.opennetworking.org/sdn-definition/ . Accessed 15 Dec 2017Jimenez, J.M., Romero, O., Rego, A., Dilendra, A., Lloret, J.: Study of multimedia delivery over software defined networks. Netw. Protoc. Algorithms 7(4), 37–62 (2015). https://doi.org/10.5296/npa.v7i4.8794Egea, S., Rego, A., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Intelligent IoT traffic classification using novel search strategy for fast based-correlation feature selection in industrial environments. IEEE Internet Things J. 5(3), 1616–1624 (2018). https://doi.org/10.1109/JIOT.2017.2787959Rego, A., Sendra, S., Jimenez, J.M., Lloret J.: OSPF routing protocol performance in software defined networks. In: Fourth International Conference on Software Defined Systems (SDS 2017), 8–11 May 2017, Valencia, Spain, https://doi.org/10.1109/SDS.2017.7939153Sendra, S., Fernández, P.A., Quilez, M.A., Lloret, J.: Study and performance of interior gateway IP routing protocols. Netw. Protoc. Algorithms 2(4), 88–117 (2010). https://doi.org/10.5296/npa.v2i4.547Rakheja, P., Kaour, P., Gupta, A., Sharma, A.: Performance analysis of RIP, OSPF, IGRP and EIGRP routing protocols in a network. Int. J. Comput. Appl. 48(18), 6–11 (2012). https://doi.org/10.5120/7446-0401Sendra, S., Rego, A., Lloret, J., Jimenez, J.M., Romero, O.: Including artificial intelligence in a routing protocol using software defined networks. In: IEEE International Conference on Communications Workshops (ICC Workshops 2017), 21–25 May 2017, Paris, France. https://doi.org/10.1109/ICCW.2017.7962735Barbancho, J., León, C., Molina, J., Barbancho, A., SIR: a new wireless sensor network routing protocol based on artificial intelligence. In: Advanced Web and Network Technologies, and Applications. APWeb 2006. Lecture Notes in Computer Science (LNCS), vol. 3842, pp. 271–275. https://doi.org/10.1007/11610496_35Barbancho, J., León, C., Molina, F.J., Barbancho, A.: Using artificial intelligence in wireless sensor routing protocols. In: Knowledge-Based Intelligent Information and Engineering Systems. (KES 2006). Lecture Notes in Computer Science, vol. 4251, pp. 475–482. Springer, New York. https://doi.org/10.1007/11892960_58Arabshahi, P., Gary, A., Kassabalidis, I., Das, A., Narayanan, S., Sharkawi, M.E., Marks, R.J.: Adaptive routing in wireless communication networks using swarm intelligence. In: AIAA 19th Annual Satellite Communications System Conference, Toulouse, France, April 17, 2001Gunes, M., Sorges, U., Bouazizi I.: ARA-the ant-colony based routing algorithm for MANETs. In: International Conference on Parallel Processing Workshops, Vancouver, BC, Canada, 21–21 Aug 2002. https://doi.org/10.1109/ICPPW.2002.1039715Ducatelle, F., Di Caro, G.A., Gambardella, L.M.: Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell. 4(3), 173–198 (2010). https://doi.org/10.1007/s11721-010-0040-xRajagopalan, S., Shen, C.: ANSI: a swarm intelligence-based unicast routing protocol for hybrid ad hoc networks. J. Syst. Archit. 52(8–9), 485–504 (2006). https://doi.org/10.1016/j.sysarc.2006.02.006RFC 3561 Ad hoc On-Demand Distance Vector (AODV) Routing, July 2003. https://www.rfc-editor.org/info/rfc3561 . Accessed 08 may 2018Zungeru, A.M., Ang, L., Seng, K.P.: Classical and swarm intelligence based routing protocols for wireless sensor networks: a survey and comparison. J. Netw. Comput. Appl. 35(5), 1508–1536 (2012). https://doi.org/10.1016/j.jnca.2012.03.004Karaboga, D., Okdem, S., Ozturk, C.: Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel. Netw. 18(7), 847–860 (2012). https://doi.org/10.1007/s11276-012-0438-zGinsberg, L., Litkowski, S., Previdi, S.: IS-IS route preference for extended IP and IPv6 reachability, RFC 7775. https://doi.org/10.17487/rfc7775 , February 2016. https://www.rfc-editor.org/rfc/rfc7775.txtRekhter, Y., Li, T., Hares, S.: A border gateway protocol 4 (BGP-4), RFC 4271. https://doi.org/10.17487/rfc4271 . Jan 2006. https://rfc-editor.org/rfc/rfc4271.txtCaria, M., Das, T., Jukan, A.: Divide and conquer: partitioning OSPF networks with SDN. In: IFIP/IEEE International Symposium on Integrated Network Management (IM 2015), 11–15 May, Ottawa (ON), Canada, 2015. https://doi.org/10.1109/INM.2015.7140324Rothenberg, C.E., Nascimento, M.R., Salvador, M.R., Corrêa, C.N.A., Cunha de Lucena, S., Raszuk, R.: Revisiting routing control platforms with the eyes and muscles of software-defined networking. In: HotSDN ‘12 Proceedings of the first workshop on Hot topics in software defined networks, August 13–17 (2012), Helsinki (Finland), pp. 13–18. https://doi.org/10.1145/2342441.2342445Zhu, M., Cao, J., Pang, D., He, Z., Xu, M.: SDN-based routing for efficient message propagation in VANET, In: Wireless Algorithms, Systems, and Applications (WASA 2015), Lecture Notes in Computer Science, vol. 9204, pp. 788–797. https://doi.org/10.1007/978-3-319-21837-3_77Ye, T., Hema, T.K., Kalyanaraman, S., Vastola, K.S, Yadav S.: Minimizing packet loss by optimizing OSPF weights using online simulation. Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2003. MASCOTS 2003. In: 11th IEEE/ACM International Symposium on, Orlando, FL, USA, 27 Oct 2003. https://doi.org/10.1109/MASCOT.2003.1240645O’Halloran, C.: Dynamic adaptation of OSPF interface metrics based on network load. In: 26th Irish Signals and Systems Conference (ISSC), Ireland, Jun 2015. https://doi.org/10.1109/ISSC.2015.7163767Şimşek, M., Doğan, N., Akcayol, M.A.: A new packet scheduling algorithm for real-time multimedia streaming. Netw. Protoc. Algorithms 9(1–2), 28–47 (2017). https://doi.org/10.5296/npa.v9i1-2.12410Sanchez-Iborra, R., Cano, M.D., Garcia-Haro, J.: Revisiting VoIP QoE assessment methods: are they suitable for VoLTE? Netw. Protoc. Algorithms 8(2), 39–57 (2016). https://doi.org/10.5296/npa.v8i2.912

Adapting reinforcement learning for multimedia transmission on SDN

[EN] Multimedia transmissions require a high quantity of resources to ensure their quality. In the last years, some technologies that provide a better resource management have appeared. Software defined networks (SDNs) are presented as a solution to improve this management. Furthermore, combining SDN with artificial intelligence (AI) techniques, networks are able to provide a higher performance using the same resources. In this paper, a redefinition of reinforcement learning is proposed. This model is focused on multimedia transmission in a SDN environment. Moreover, the architecture needed and the algorithm of the reinforcement learning are described. Using the Openflow protocol, several sample actions are defined in the system. Results show that using the system users perceive an increase in the image quality three times better. Moreover, the loss rate is reduced more than half the value of losses recorded when the algorithm is not applied. Regarding bandwidth, the maximum throughput increases from 987.16 kbps to 24.73 Mbps while the average bandwidth improves from 412.42 kbps to 7.83 Mbps.Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU (Convocatoria 2015), Grant/Award Number: FPU15/06837; Programa Estatal de Investigación Científica y Técnica de Excelencia (Convocatoria 2017), Grant/Award Number: TIN2017-84802-C2-1-P; Programa Estatal De Investigación, Desarrollo e Innovación Orientada a los retos de la sociedad (Convocatoria 2016), Grant/Award Number: TEC2016-76795-C6-4-R; ERANETMED, Grant/Award Number: ERANETMED3-227 SMARTWATIRRego Mañez, A.; Sendra, S.; García-García, L.; Lloret, J. (2019). Adapting reinforcement learning for multimedia transmission on SDN. Transactions on Emerging Telecommunications Technologies. 30(9):1-15. https://doi.org/10.1002/ett.3643S11530

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

Artificial intelligent system for multimedia services in smart home environments

[EN] Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.This work has been partially supported 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. This work has also been partially founded by the Universitat Polite`cnica de Vale`ncia through the postdoctoral PAID-10-20 program.Rego Mañez, A.; Gonzalez Ramirez, PL.; Jimenez, JM.; Lloret, J. (2022). Artificial intelligent system for multimedia services in smart home environments. Cluster Computing. 25(3):2085-2105. https://doi.org/10.1007/s10586-021-03350-zS2085210525

Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments

[EN] Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.This paper was supported in part by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional within the project Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software under Grant TIN2014-57991-C3-1-P, in part by the Ministerio de Educacion, Cultura y Deporte, through the Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015) under Grant FPU15/06837, 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 TIN2017-84802-C2-1-P. (Corresponding author: Jaime Lloret.)Egea, S.; Rego Mañez, A.; Carro, B.; Sánchez-Esguevillas, A.; Lloret, J. (2018). Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments. IEEE Internet of Things. 5(3):1616-1624. https://doi.org/10.1109/JIOT.2017.2787959S161616245

A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

[EN] Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formation del Profesorado Universitario FPU (Convocatoria 2015)", grant number FPU15/06837 and by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigation Cientffica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Canovas Solbes, A.; Rego Mañez, A.; Romero Martínez, JO.; Lloret, J. (2020). A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN. Journal of Network and Computer Applications. 150:1-14. https://doi.org/10.1016/j.jnca.2019.102498S114150Cánovas, A., Taha, M., Lloret, J., & Tomás, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications, 104, 107-116. doi:10.1016/j.jnca.2017.12.020Canovas, A., Jimenez, J. M., Romero, O., & Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network, 32(6), 100-107. doi:10.1109/mnet.2018.1800121Burden, F., & Winkler, D. (2008). Bayesian Regularization of Neural Networks. Artificial Neural Networks, 23-42. doi:10.1007/978-1-60327-101-1_3Goodman, S. N. (2005). Introduction to Bayesian methods I: measuring the strength of evidence. Clinical Trials, 2(4), 282-290. doi:10.1191/1740774505cn098oaHirschen, K., & Schäfer, M. (2006). Bayesian regularization neural networks for optimizing fluid flow processes. Computer Methods in Applied Mechanics and Engineering, 195(7-8), 481-500. doi:10.1016/j.cma.2005.01.015Huang, X., Yuan, T., Qiao, G., & Ren, Y. (2018). Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking. IEEE Network, 32(6), 35-41. doi:10.1109/mnet.2018.1800097Lin, Y. (2002). Data Mining and Knowledge Discovery, 6(3), 259-275. doi:10.1023/a:1015469627679Lopez-Martin, M., Carro, B., Lloret, J., Egea, S., & Sanchez-Esguevillas, A. (2018). Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets. IEEE Communications Magazine, 56(9), 110-117. doi:10.1109/mcom.2018.1701156Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. doi:10.1109/72.329697Nguyen, T. T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56-76. doi:10.1109/surv.2008.080406Queiroz, W., Capretz, M. A. M., & Dantas, M. (2019). An approach for SDN traffic monitoring based on big data techniques. Journal of Network and Computer Applications, 131, 28-39. doi:10.1016/j.jnca.2019.01.016Rego, A., Canovas, A., Jimenez, J. M., & Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access, 6, 31580-31598. doi:10.1109/access.2018.2842034Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010). Study of Subjective and Objective Quality Assessment of Video. IEEE Transactions on Image Processing, 19(6), 1427-1441. doi:10.1109/tip.2010.2042111Soysal, M., & Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451-467. doi:10.1016/j.peva.2010.01.001Tan, X., Xie, Y., Ma, H., Yu, S., & Hu, J. (2019). Recognizing the content types of network traffic based on a hybrid DNN-HMM model. Journal of Network and Computer Applications, 142, 51-62. doi:10.1016/j.jnca.2019.06.004Tongaonkar, A., Torres, R., Iliofotou, M., Keralapura, R., & Nucci, A. (2015). Towards self adaptive network traffic classification. Computer Communications, 56, 35-46. doi:10.1016/j.comcom.2014.03.02

An Intelligent System for Video Surveillance in IoT Environments

© 2018 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.[EN] Multimedia traffic has drastically grown in the last few years. In addition, some of the last paradigms proposed, like the Internet of Things (IoT), adds new types of traffic and applications. Software-defined networks (SDNs) improve the capability of network management. Combined with SDN, artificial intelligence (AI) can provide solutions to network problems based on classification and estimation techniques. In this paper, we propose an artificial intelligence system for detecting and correcting errors in multimedia transmission in a surveillance IoT environment connected through a SDN. The architecture, algorithm, and messages of the SDN are detailed. The AI system design is described, and the test-bed and the data set are explained. The AI module consists of two different parts. The first one is a classifying part, which detects the type of traffic that is sent through the network. The second part is an estimator that informs the SDN controller on which kind of action should be executed to guarantee the quality of service and quality of experience. Results show that with the actions performed by the network, like jitter can be reduced up to 70% of average and losses can be reduced from 9.07% to nearly 1.16%. Moreover, the presented AI module is able to detect critical traffic with 77% accuracyThis work was supported in part by the Ministerio de Educacion, Cultura y Deporte, through the Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015) under Grant FPU15/06837, in part by the Programa para la Formacion de Personal Investigador de la Universitat Politecnica de Valencia 2014, Subprograma 2, (Codigo del contrato: 884), 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 TIN2014-57991-C3-1-P and Grant TIN2017-84802-C2-1-P.Rego Mañez, A.; Canovas Solbes, A.; Jimenez, JM.; Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access. 6:31580-31598. https://doi.org/10.1109/ACCESS.2018.2842034S3158031598