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Method to detect the vehicle's front and side using US rays (a), and the device implemented with US modules (b).
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In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article present...
Contexts in source publication
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... way to measure vehicle speed and length is to compare different distance measurements of a vehicle as it approaches the US sensor. Figure 4a shows how a sensor with an inclination of 45° to the road axis can detect the front and side of a vehicle. These distance measurements are the maximum possible when no vehicle is present. ...
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... is a channel widely used in embedded systems due to its simplicity to manage it. Figure 4b shows the experimental device. This experimental device has three modules or CNs. ...
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... shown in this figure, a module that acts as a CN can detect a vehicle and determine its speed, as well as the length of it. As previously discussed, Figure 4 illustrates how vehicle detection is initiated when the sensor starts to detect a distance less than the maximum distance determined. Consequently, the first step is to sample and filter the data, because the ultrasound signal is subject to many problems, including echoes and material-dependent responses. ...
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... the first step is to sample and filter the data, because the ultrasound signal is subject to many problems, including echoes and material-dependent responses. This first step (1 in Figure 4) consists of sampling five distances and calculating their average to obtain the main data, distance d(t). Not all samples are correct, for example, echoes can produce false measurements. ...
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... if the filter detects two continuous erroneous samples, five-window samples are discarded. In the second phase (2 in Figure 4), the vehicle is detected. Vehicle detection occurs when the values d(t) > d(t + 1) over N consecutive operation cycles. ...
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... spatial characteristics of the vehicle are its length, while the kinematic characteristic is its speed. The module changes to instantaneous speed detection when an approaching vehicle has been detected (phase 3 in Figure 4). During instantaneous detection, the speed is calculated by comparing the two distances obtained by consecutive measurements of the vehicle's front. ...
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... the difference of distances detected and time between these distances is available, the vehicle speed calculation is immediate. From the instantaneous speed detected, the fourth phase (4, in Figure 4) updates the speed value. As a result of this update, an average speed and standard deviation can be detected. ...
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... the difference between two consecutive distances is less than a certain threshold, 5% in the experiments, the side of the vehicle is considered detected. From this point on-wards, the length update phase, 5 in Figure 4, starts. Based on the speed calculated in the previous phase, this phase works while the vehicle is being detected to determine the vehicle length. ...
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Citations
... Consecuentemente, un sistema de controlóptimo debe ser capaz de conocer la cantidad de vehículos en cola, o con posibilidad de llegar al semáforo en un corto tiempo. Sistemas como el presentado en Poza-Lujan et al. (2022) son los que dan soporte al sistema de control de tráfico presentado. ...
La gestión de la movilidad de personas y vehículos es un aspecto de continuo estudio debido a la relevancia que tiene en la contribución a la polución. El control de los semáforos determina las colas que en los cruces se pueden formar. Habitualmente este control no está adaptado al tráfico existente en un momento concreto, dado que la adaptación implica conocer los peatones y vehículos que se encuentran circulando en cada momento. Para resolver este problema, en el artículo se propone el uso de unos dispositivos inteligentes modulares que permiten detectar los vehículos y cambiar los tiempos de acceso al cruce dependiendo de las circunstancias. Para validar el sistema se ha realizado una simulación generando cargas en MatLab y simulando el control con Simulink. Se ha simulado un ciclo de semáforo con tiempos fijos y se ha comparado con ciclos de tiempos variables en función de la carga de peatones y de vehículos. En el artículo se proponen los indicadores Op y Sat como método de medición de la optimización del algoritmo de control sobre el estado del cruce. Por medio de dichos indicadores se ha comprobado que en el mejor de los casos es posible optimizar en un 50 % el tiempo de espera de forma casi independiente de la carga de tráfico.
... The MAS proposed by the authors allows for the interconnection of IoT devices. Within the area of IoT, Poza-Lujan et al. [2] introduce a distributed modular architecture that allows embedding some intelligence in IoT devices, making it possible to shift from a hierarchical pyramid to an inverted pyramid. In the architecture, IoT devices can join together to form abstract nodes called control nodes. ...
Artificial Intelligence (AI) and its applications have undergone remarkable experimental development in the last decade and are now the basis for a large number of decision support systems. [...]
In this constantly evolving landscape of urbanization, the relationship between technology and automation,
in regards to sustainability, holds immense significance. The intricate strands of human intelligence are
seamlessly interwoven with the fabric of technological progress, giving rise to exquisite patterns of synergy
and collaborative innovation. Automation is just another step in this process which started with the industrial
revolution and now has paved way towards urbanization. Smart homes or home automation is a subset of
Internet of Things (IoT) based automation that has added into the comfort, ease, and quality of our living
standards and is now being integrated to form the concept of Smart Cities. In the past decade, various
techniques and processes of smart home automation have been proposed and implemented. To extend and
translate the existing methods into new one, the understanding of the former is imperative to the research
procedure. This review stands as a comprehensive exploration, diving into the pivotal role of intelligent
systems and expert knowledge in driving the transformation of smart homes into sustainable smart cities. By
meticulously analyzing and aggregating an array of contemporary techniques used in smart homes, this paper
offers profound contributions to the intersection of urban evolution and technological innovation. The review’s
holistic approach not only facilitates a deep understanding of smart homes’ contributions but also charts a
course for innovative strategies in city planning, infrastructure, and technological integration. In bridging the
gap between technology and sustainable urban development, this exploration underscores the transformative
power of leveraging smart home techniques to lay the foundation for harmonious and forward-thinking smart
cities. The technologies cover a wide range of methodologies and intelligent systems used for communication,
security and management in an urban infrastructure. The paper focuses on analysis of the technology to provide
an outlook into achieving the goal of sustainable smart cities and deal with challenges like scalability and big
data computation. Our comprehensive analysis yields a holistic set of technology comparisons and illuminates
the promising future prospects within this domain. The information is highly insightful in creating a bigger
picture for adopting state of the art technologies like Federated Learning (FL), Digital Twin and Embedded
Edge computing in better planning and infrastructure management in smart cities. These findings offer reliable
and potent methods to chart not only the course of research but also to enhance these technologies for the
betterment of mankind’s convenience and advancement.
The management of people and vehicles’ mobility is an aspect of continuous study due to its contribution to pollution. Traffic light control determines the queues that can form at crossroads. Usually, this control is not adapted to the existing traffic at a specific time since the adaptation implies knowing the pedestrians and vehicles that are circulating at all times. The article proposes using intelligent method that allow the detection of vehicles and changing access times to the intersection depending on the circumstances to solve this problem. A simulation has been carried out to validate the system, generating loads in MatLab and simulating the control with Simulink. A traffic light cycle with varying times depending on pedestrians and vehicles load has been simulated and has been compared with a fixed time cycle simulation. In this article, Op and Sat indicators are proposed to measure optimisation of the control algorithm on the state of the crossing. Using these indicators verified that it is possible to optimise the waiting time, almost independently of the traffic load in the best of cases.KeywordsSmart citiesIntelligent traffic controlTraffic parameters
