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Location of the smart resources in the architecture. Edge interaction is possible when smart resources are physically in contact; that is, when their operating ranges overlap. Interaction in the fog allows communication with real-time restrictions between smart resources, without the need to share physical space. Cloud interaction allows connection to other components and data servers without real-time restrictions.
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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 architectu...
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... Scholars and practitioners [6][7][8][9] have started to analyze relationships between Industry 4.0 technologies and environmental management. In the manufacturing sector specifically, some companies are implementing solutions based on smart sensors and AI, especially to improve energy consumption, while others are focusing on additive manufacturing to conserve and reuse resources [3]. ...
... In this way, by intelligently integrating devices, in construction, industrial, or residential environments, it is possible to approach the concept of a smart grid. This concept, associated with the use of smart sensors, is discussed in [9,40,[71][72][73][74][75]. The works present methods and examples that relate the use of smart sensors to the concept of and application in smart grids and smart cities. ...
This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker’s cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment’s operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.
... Un dispositivo inteligente, por tanto, es un dispositivo que, por medio de servicios, proporciona información y capacidad de actuación en un rango de operación. Cuando un dispositivo inteligente ofrece su operatividad, es decir la de los módulos de control que lo componen, en forma de servicios, se habla de recursos inteligentes, ampliamente tratados en (Poza-Lujan et al., 2020). El recurso inteligente proporciona unos servicios que se forman combinando las funcionalidades ofrecidas por los módulos de control que lo componen. ...
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.
... On the other hand, the sustainable management of water resources is gaining more attention in terms of energy consumption, enabling smart water management platforms for intelligent water consumption measurement and crisis detection [126]. The integration of heterogeneously distributed information enables the identification of object architectures in smart environments, in which information is used for particular processes, such as reconstructing environmental maps of the smart city or for the intelligent navigation of vehicles [127]. Figure 17 illustrates 11 co-occurring keywords for automation control systems, with the central keywords being IoT and Industry 4.0. ...
At present, the smart city offers the most desired state of urban development, encompassing, as it does, the concept of sustainable development. The creation of a smart city is closely associated with upgrading the construction industry to encompass many emerging concepts and technologies, such as Construction 4.0, with its roots in Industry 4.0, and the deployment of building information modeling (BIM) as an essential tool for the construction industry. Therefore, this paper aims to explore the current state of the art and development trajectory of the multidisciplinary integration of Construction 4.0, Industry 4.0, BIM, and sustainable construction in the context of the smart city. It is the first attempt in the literature to use both macro-quantitative analysis and micro-qualitative analysis methods to investigate this multidisciplinary research topic. By using the visual bibliometric tool, VOSviewer, and based on macro keyword co-occurrence, this paper is the first to reveal the five keyword-constructed schemes, research hotspots, and development trends of the smart city, Construction 4.0, Industry 4.0, BIM, and sustainable construction, from 2014 to 2021 (a period of eight years). Additionally, the top 11 productive subject areas have been identified with the help of VOSviewer software keyword-clustering analysis and application. Furthermore, the whole-building life cycle is considered as an aid to identifying research gaps and trends, providing suggestions for future research with the assistance of an upgraded version of BIM, namely, city information modeling (CIM) and the future integration of Industry 5.0 and Construction 5.0, or even of Industry Metaverse with Construction Metaverse.
... Los entornos de ciudades inteligentes necesitan de arquitecturas adaptadas a las necesidades de cada espacio [4]. Basándose en el espacio común en el que interaccionan varios dispositivos, en [9] se define el 'Edge' como la zona donde el rango de operación de dichos dispositivos. Basándose en las premisas anteriores, en este artículo se presenta una arquitectura modular de control inteligente distribuido que es posible ubicar en el modelo de arquitecturas de industria 4.0 y que cumple el paradigma de computación en Cloud/Fog/Edge. ...
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.
... Wireless Sensor Networks (WSN) refers to an infrastructure-less network that refers to a group of interconnected small embedded, inexpensive and elegant computing devices called sensors, which nowadays are being used in all advanced sectors; for developing and deploying smart sensors [7]. These smart sensors get geographically distributed in the network in such a way and embedded by the process to ecological devices with various purposes like measuring and monitor environment effectively like temperature, sound, humidity, pollution levels, wind, and so on [8]. The sensor nodes of the network using radio signals can interact and exchange information among themselves [9]. ...
Wireless sensor networks (WSN’s) comprise limited energy small sensor nodes having the ability to monitor the physical conditions and communicate information among the various nodes without requiring any physical medium. Over the last few years, with the rapid advancements in information technology, there has been an increasing interest of various organizations in making the use of wireless sensor networks (WSN’s). The sensor nodes in WSN having limited energy detects an event, collect data and forward this collected data to the base node, called sink node, for further processing and assessment. Few attributes of WSN’s like the energy consumption and lifetime can be impacted by the design and placement of the Sink node. Despite various useful characteristics WSN’s is being considered vulnerable and unprotected. There is a large class of various security attacks that may affect the performance of the system among which sinkhole an adversary attack puts dreadful threats to the security of such networks. Out of various attacks, a sinkhole attack is one of the detrimental types of attacks that brings a compromised node or fabricated node in the network which keeps trying to lures network traffic by advertising its wrong and fake routing update. Sinkhole attacks may have some other serious harmful impacts to exploit the network by launching few other attacks. Some of these attacks are forwarding attacks, selective acknowledge spoofing attacks, and they may drop or modify routing information too. It can also be used to send fake or false information to the base station. This study is analyzing the challenges with sinkhole attacks and exploring the existing available solutions by surveying comparatively which used to detect and mitigate sinkhole attacks in the wireless sensor network.
... Wireless Sensor Networks (WSN) refers to an infrastructure-less network that refers to a group of interconnected small embedded, inexpensive and elegant computing devices called sensors, which nowadays are being used in all advanced sectors; for developing and deploying smart sensors [7]. These smart sensors get geographically distributed in the network in such a way and embedded by the process to ecological devices with various purposes like measuring and monitor environment effectively like temperature, sound, humidity, pollution levels, wind, and so on [8]. The sensor nodes of the network using radio signals can interact and exchange information among themselves [9]. ...
Wireless sensor networks (WSN’s) comprise limited energy small sensor nodes having the ability to monitor the physical conditions and communicate information among the various nodes without requiring any physical medium. Over the last few years, with the rapid advancements in information technology, there has been an increasing interest of various organizations in making the use of wireless sensor networks (WSN’s). The sensor nodes in WSN having limited energy detects an event, collect data and forward this collected data to the base node, called sink node, for further processing and assessment. Few attributes of WSN’s like the energy consumption and lifetime can be impacted by the design and placement of the Sink node. Despite various useful characteristics WSN’s is being considered vulnerable and unprotected. There is a large class of various security attacks that may affect the performance of the system among which sinkhole an adversary attack puts dreadful threats to the security of such networks. Out of various attacks, a sinkhole attack is one of the detrimental types of attacks that brings a compromised node or fabricated node in the network which keeps trying to lures network traffic by advertising its wrong and fake routing update. Sinkhole attacks may have some other serious harmful impacts to exploit the network by launching few other attacks. Some of these attacks are forwarding attacks, selective acknowledge spoofing attacks, and they may drop or modify routing information too. It can also be used to send fake or false information to the base station. This study is analyzing the challenges with sinkhole attacks and exploring the existing available solutions by surveying comparatively which used to detect and mitigate sinkhole attacks in the wireless sensor network.
... This summary view of the RAMI reference model gives an idea of the extraordinary complexity of the task of approaching the complete implementation of a middleware system that serves as a platform for the development of I4.0 applications. There are currently experimental prototypes in the field of research [40] and industrial products [41] that, following the ecosystem defined by the manufacturer, enable the development of applications with a many of the properties demanded by current industry (such as modularity, flexibility, digital twin, and data analysis). ...
Embedded systems used in critical systems, such as aeronautics, have undergone continuous evolution in recent years. In this evolution, many of the functionalities offered by these systems have been adapted through the introduction of network services that achieve high levels of interconnectivity. The high availability of access to communications networks has enabled the development of new applications that introduce control functions with higher levels of intelligence and adaptation. In these applications, it is necessary to manage different components of an application according to their levels of criticality. The concept of “Industry 4.0” has recently emerged to describe high levels of automation and flexibility in production. The digitization and extensive use of information technologies has become the key to industrial systems. Due to their growing importance and social impact, industrial systems have become part of the systems that are considered critical. This evolution of industrial systems forces the appearance of new technical requirements for software architectures that enable the consolidation of multiple applications in common hardware platforms—including those of different criticality levels. These enabling technologies, together with use of reference models and standardization facilitate the effective transition to this approach. This article analyses the structure of Industry 4.0 systems providing a comprehensive review of existing techniques. The levels and mechanisms of interaction between components are analyzed while considering the impact that the handling of multiple levels of criticality has on the architecture itself—and on the functionalities of the support middleware. Finally, this paper outcomes some of the challenges from a technological and research point of view that the authors identify as crucial for the successful development of these technologies.
... The emergence of the Internet of Things (IoT) paradigm is the key in enabling seamless and massive interconnection of smart devices, machines or things over the Internet (Figure 3). Coupled with the Big Data Infrastructure and using a machine learning approach, the smart intelligent layer is essential for smart resources to be able to process information (Poza-Lujan et al., 2019). The ubiquitous framework of system architecture can be summarized in three-folds: data acquisition, data curation and data presentation; where this architecture has been proven to be able to make positive impacts and provide useful solutions for landslide management (Karunarathne et al., 2020). ...
Urban trees are beneficial to our environment and important to human inhabitants. However, they are exposed to natural and anthropogenic stressors, such as strong windstorms, extreme wind events and accidents; inducing tree falling which can cause personal damages, economic losses and infrastructural destructions. The current study is the first of its kind, presenting a tree monitoring system, and using smart sensing devices installed on more than 8,000 trees in Hong Kong's rural and urban landscapes. A description of the key components of the system, followed by big data analysis and three case studies of strong wind events over the past 2 years, are presented. A network of smart sensing devices was deployed to develop a large-scale, long-term, smart tree monitoring framework; to help identify potentially hazardous trees in urban areas, particularly during extreme weather events. The changes in tree tilt angle under natural wind loading were recorded. Patterns and responses of tree tilt angles were analyzed, with prediction using time series models based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting time series forecasting (xGBoost). The results showed the highest correlation for 1-hour forward forecasting, by applying xGBoost model on tree tilt data and weather observations (R²=0.90). On the other hand, SARIMA model produced one-step-ahead prediction with correlation (R²) ranging from 0.77 to 0.93, while lower correlation (R² ≤ 0.55) was observed for long term prediction (15 days) of the tree tilt angles. Finally, a dashboard and mobile applications of tree monitoring systems were developed, to transfer knowledge and engage the public in understanding associated hazards with tree failures in the urban area.
... The collaboration between various sensors residing in different, or even nearby locations is essential for complete and accurate knowledge. However, with the growth in smart sensors, magnitude and heterogeneity of data pose an issue [48]. Autonomy and performance are essential characteristics to motivate any intelligent smart city application. ...
... Object detection is a common desirable functionality [48] for smart city applications such as license plate recognition [47], vehicle and pedestrian detection [26,[36][37][38][39]43], and traffic sign recognition [6]. While object recognition provides value for several applications, it also poses several issues. ...
Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, etc. which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, etc., which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.
In this chapter, we describe how to adopt different software components based on the previously presented conceptual solution and generate an integrating digital twin for a business workflow as a service extension. The focus of this software system lies in the implementation of methods from computer vision that can reliably recognize the existing objects in a process plant with their structure and interconnections. This intention benefits from numerous methods which were developed in the past years to tackle the challenge of the recognition of 3D objects under various conditions. A brief review of such methods is presented here, in particular concerning difficult environmental impact (vapor, dust, smoke, darkness, and dirt). Methods are distinguished according to data input: image, point cloud, or video. For several reasons, the implementation of an automatic object recognition procedure is realized by using existing convolutional neural networks (CNN frameworks), also known as deep learning. Literature review shows that effective and versatile automation capabilities of deep learning combined with large-scale processing may be an adequate means for the challenges of the extent and complexity of a process plant. Further is the recognition procedure described in more detail, in particular how the piping system is built up in its full complexity coming from singular components. This recognition runs iteratively in four steps with a mutual interdependence. The entire point cloud is processed by segmentation, where the pipe system is extracted. In two subsequent steps (clustering and classification) the point cloud is subdivided further to recognize the singular parts. While piping systems consist of forked structures with complex configurations and partial occlusion, the exact recognition of piping centerlines is conducted based on the position of singular parts. A robust clustering and graph-based aggregation yield a coherent pipe model before it is linked with piping and instrumentation diagram to the digital twin. Due to the high complexity and variance of the tasks, some individual tasks must run fully manually or with partial human assistance. Finally, we present how the singular steps are automated and orchestrated by using a workflow automation platform. Our workflow promises good results on pipe models with varying complexity and density both in synthetic and real cases.
