Intelligent Municipal Heritage Management Service in a Smart City: Telecommunication Traffic Characterizationand Quality of Service

[EN] The monitoring of cultural heritage is becoming common in cities to provide heritage preservation and prevent vandalism. Using sensors and video cameras for this task implies the need to transmit information. In this paper, the teletraffic that cameras and sensors generate is characterized and the transmissions¿ influence on the municipal communications network is evaluated. Then, we propose models for telecommunication traffic sources in an intelligent municipal heritage management service inside a smart sustainable city. The sources were simulated in a smart city scenario to find the proper quality of service (QoS) parameters for the communication network, using Valencia City as background. Specific sensors for intelligent municipal heritage management were selected and four telecommunication traffic sources were modelled according to real-life requirements and sensors datasheet. Different simulations were performed to find the proper CIR (Committed Information Rate) and PIR (Peak Information Rate) values and to study the effects of limited bandwidth networks. Packet loss, throughput, delay, and jitter were used to evaluate the network¿s performance. Consequently, the result was the selection of the minimum values for PIR and CIR that ensured QoS and thus optimized the traffic telecommunication costs associated with an intelligent municipal heritage management service.This work was partially supported by Spanish Government Projects TIN2013-47272-C2-1-R and TEC2015-71932-REDTRodríguez-Hernández, MA.; Jiang, Z.; Gomez-Sacristan, Á.; Pla, V. (2019). Intelligent Municipal Heritage Management Service in a Smart City: Telecommunication Traffic Characterizationand Quality of Service. Wireless Communications and Mobile Computing (Online). 1-10. https://doi.org/10.1155/2019/8412542S11

Servicios de una Smart City, caracterización del tráfico y de la calidad de servicio

La implantación masiva de dispositivos IoT representa un desafío para las ciudades que tienen que adaptar sus redes de telecomunicaciones para cumplir con requisitos de tráfico heterogéneos y administrarlos con el fin de garantizar una buena Calidad de Servicio. Este proyecto se centra en analizar tres servicios verticales del ayuntamiento: estacionamiento regulado en la calle, alumbrado público y videovigilancia. Todos ellos compartirán un acceso de telecomunicaciones común. Diferentes patrones de tráfico están involucrados y un escenario de simulación será necesario para lograr un dimensionamiento correcto del ancho de banda.Massive implementation of IoT devices represent a challenge for municipalities that have to adapt their telecomunication networs to acommplish heterogeneous traffic requeriments and manage them in order to guarantte a good Quality of Service. This project focuses on analize three city council vertical services: regulated street parking, public lighting and video surveillance. All of them will share a common telecom access. Different trafffic patterns are involved and a simulation scenario will be neccesary to achieve a correct dimensioning of bandwidht.Jiang, Z. (2018). Servicios de una Smart City, caracterización del tráfico y de la calidad de servicio. http://hdl.handle.net/10251/108863TFG

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We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. The experiment of the proposed method on the video sequences has also been conducted and has shown its effectiveness in capturing the target in a moving background and with non-rigid object motion

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We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. The experiment of the proposed method on the video sequences has also been conducted and has shown its effectiveness in capturing the target in a moving background and with non-rigid object motion

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