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Arquitectura modular para la gestión automática del tráfico en rotondas
Abstract
En los entornos de movilidad, calles o carreteras, los cruces y las rotondas pueden generar problemas de atascos. Poder optimizar el tráfico entrante y saliente de un cruce o rotonda es uno de los campos de investigación de los sistemas de control inteligente. Para optimizar el tráfico se debe disponer de dispositivos capaces de detectar los vehículos así como de actuar, regulando el tráfico, de forma dinámica para adaptarse a las distintas circunstancias. El sistema presentado busca la adaptación a las necesidades de tráfico en una rotonda. Dependiendo de la saturación de cada carril de entrada se intenta crear un tráfico fluido y continuo en el interior de la misma. Para lograr mejorar el tráfico, en este trabajo se presenta una arquitectura modular que permite adaptarse a cualquier cruce o rotonda para, a partir del control específico de un sector, mejorar el rendimiento global. El sistema simulado está compuesto por dispositivos independientes, que, dependiendo de la información adquirida varían el tiempo de paso. Se presenta, asimismo, un experimento de simulaci ón en el que se pone en valor la capacidad de reducir el tráfico adaptando los tiempos de paso en función de la demanda. Los resultados muestran que es posible descongestionar una rotonda cuando se automatizan dinámicamente los tiempos de paso sobre los que se tiene control.
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Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.
BY 2030, THE world's population is projected to be 8.5 billion and increase to 9.7 billion by 2050 and 11.2 billion by 2100. Half of humanity today lives in cities. Many cities are experiencing exponential growth as people move from rural areas in search of better jobs and education. Consequently, cities' services and infrastructures are being stretched to their limits in terms of scalability, environment, and security as they adapt to support this population growth. Visionaries and planners are thus seeking a sustainable, post-carbon economy20 to improve energy efficiency and minimize carbon-emission levels. Along with cities' growth, innovative solutions are crucial for improving productivity (increasing operational efficiencies) and reducing management costs. A smart city is an ultra-modern urban area that addresses the needs of businesses, institutions, and especially citizens. Here we should differentiate between a smart city and smart urbanism. The objective of these concepts is the same-the life of citizens. The architects of ancient cities did not take into consideration long-Term scalability- housing accessibility, sustainable development, transport systems, and growth-and there is no scalable resource management that may be applied from one decade to another. Unfortunately, smart urbanism is not well represented in smart cities' development. Smart urbanism must also be considered as an aspect of a smart city, including information-communication technologies. In recent years, a significant increase in global energy consumption and the number of connected devices and other objects has led government and industrial institutions to deploy the smart city concept. Cities' demographic, economic, social, and environmental conditions are the major reasons for the dramatic increase in pollution, congestion, noise, crime, terrorist attacks, energy production, traffic accidents, and climate change. Cities today are the major contributors to the climate problem. They cover less than 2% of the Earth's surface yet consume 78% of the world's energy, producing more than 60% of all CO2 emissions (http://unhabitat.org/).
Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.
The main postulate of the Internet of things (IoT) is that everything can be connected to the Internet, at anytime, anywhere. This means a plethora of objects (e.g. smart cameras, wearables, environmental sensors, home appliances, and vehicles) are ‘connected’ and generating massive amounts of data. The collection, integration, processing and analytics of these data enable the realisation of smart cities, infrastructures and services for enhancing the quality of life of humans. Nowadays, existing IoT architectures are highly centralised and heavily rely on transferring data processing, analytics, and decision-making processes to cloud solutions. This approach of managing and processing data at the cloud may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. Furthermore, in many applications, such as health monitoring and emergency response services, which require low latency, delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. The idea of allowing data processing closer to where data is generated, with techniques such as data fusion, trending of data, and some decision making, can help reduce the amount of data sent to the cloud, reducing network traffic, bandwidth and energy consumption. Also, a more agile response, closer to real-time, will be achieved, which is necessary in applications such as smart health, security and traffic control for smart cities. Therefore, this chapter presents a review of the more developed paradigms aimed to bring computational, storage and control capabilities closer to where data is generated in the IoT: fog and edge computing, contrasted with the cloud computing paradigm. Also an overview of some practical use cases is presented to exemplify each of these paradigms and their main differences.
This paper presents a novel smart street lighting (SmSL) system in which energy consumption by a group of street lighting poles is minimized based on Brute-Force search algorithm. While outdoor lighting imposes considerable cost, maintenance, safety, and environmental issues; utilization of advanced street lighting system with energy saving, autonomous fault detection, and monitoring capabilities benefits all the players involved including municipalities and distribution companies. The proposed SmSL has a hierarchical platform. The segment or intermediate controller determines the scheduling, switching, and dimming level of each pole based on the proposed optimization subroutine and transmits the controller set points to the local pole controller through Power Line Communications (PLC). Optimization of street lighting electrical energy is achieved by minimizing a cost function, considering operational constraints, ambient luminance, and local traffic flow. The local controller acts as an actuator and applies the received commands. The controller inherently responses to lamp fault. Moreover, pole electrical parameters and status of the lamp and its capacitor is transmitted to the intermediate controller. The supervisory controller which is installed on a server in distribution center monitors the whole system and sends appropriate commands such as minimum required luminance in the area to the segment controller based on WiMAX wireless communications. The whole system is developed and implemented in a pilot street. The experimental results show considerable energy saving with the proposed SmSL and reduced maintenance costs.
Fog/edge computing has been proposed to be integrated with Internet-of-Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between Cyber-Physical Systems (CPS) and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications
The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of Edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative Edge to materialize the concept of Edge computing. Finally, we present several challenges and opportunities in the field of Edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
A review on optimization techniques for the deployment and scheduling of distributed real-time systems
- Andoni Amurrio
- Ekain Azketa
- Javier Gutierrez
- Mario Aldea
- Jorge Parra
Andoni
Amurrio,
Ekain
Azketa,
J Javier Gutierrez, Mario Aldea, and
Jorge Parra. A review on optimization techniques for the deployment and scheduling
of distributed real-time systems. Revista
Iberoamericana de Automática e Informática
Industrial, 16(3):249-263, 2019.