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The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.

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One of the key technologies in smart cities is the use of next generation networks such as 5G networks. Mainly because this new mobile technology offers massive connections in densely populated areas in smart cities, thus playing a crucial role for numerous subscribers anytime and anywhere. Indeed, all the most important infrastructure to promote a connected world is being related to next generation networks. Specifically, the small cells transmitters is one of the 5G technologies more relevant to provide more connections and to attend the high demand in smart cities. In this article, a smart small cell positioning is proposed in the context of a smart city. The work proposal aims to do this through the development of a hybrid clustering algorithm with meta-heuristic optimizations to serve users, with real data, of a region satisfying coverage criteria. Furthermore, the problem to be solved will be the best location of the small cells, with the minimization of attenuation between the base stations and its users. The possibilities of using multi-objective optimization algorithms based on bioinspired computing, such as Flower Pollination and Cuckoo Search, will be verified. It will also be analyzed by simulation which power values would allow the continuity of the service with emphasis on three 5G spectrums used around the world: 700 MHz, 2.3 GHz and 3.5 GHz.

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Resumo As cidades inteligentes vêm crescendo ao redor do mundo, impulsionadas por inovações tecnológicas. Com elas surgem diversas oportunidades, mas também novas ameaças à segurança e privacidade do usuário nessa realidade interconectada. Este estudo tem como objetivo investigar a percepção de segurança e confiança na tecnologia por parte dos cidadãos e como esta afeta a propensão ao seu uso e, consequentemente, à vida na cidade inteligente. Para tanto, conduziu-se um survey (n = 601), por meio do método PLS-SEM, para testar as hipóteses formuladas. Os resultados obtidos confirmam que o modelo proposto demonstra ser consistente. As relações "confiança e segurança subjetiva" e "segurança objetiva e privacidade de dados" obtiveram relações mais consistentes, assegurando a forte influência das barreiras "tangíveis" e "intangíveis" da percepção de segurança. Dessa forma, para obter e manter a confiança dos usuários, as instituições por trás da tecnologia precisam estar atentas à opinião deles e da sociedade, de forma a manter uma boa reputação para que possam, assim, perpetuar uma percepção positiva de segurança. Conclui-se, assim, que o conceito de segurança adquire uma nova dimensão no contexto da cidade inteligente por ser um componente crucial para toda a sua base e estar intimamente ligado à tecnologia, além de se apresentar como uma preocupação fundamental para os governos e as entidades que buscam implementar soluções e aplicações do conceito.

Resumen Las ciudades inteligentes están creciendo en todo el mundo, impulsadas por las innovaciones tecnológicas. Con ellas surgen diversas oportunidades, pero también nuevas amenazas a nuestra seguridad y privacidad en esta realidad interconectada. Este estudio tiene como objetivo investigar la percepción de los ciudadanos sobre la seguridad y la confianza en las tecnologías y cómo afectan la propensión a usarlas y, en consecuencia, la vida en la ciudad inteligente. Para ello, se realizó una encuesta (n = 601) utilizando el método PLS-SEM para contrastar las hipótesis formuladas. Los resultados obtenidos confirman que el modelo propuesto resulta ser consistente. Las relaciones 'confianza y seguridad subjetiva' y 'seguridad objetiva y privacidad de datos' obtuvieron relaciones más consistentes, confirmando la fuerte influencia de las barreras 'tangibles' e 'intangibles' de la percepción de seguridad. De esta forma, para obtener y mantener la confianza de los usuarios, las instituciones que están detrás de las tecnologías deben estar atentas a su opinión y a la de la sociedad a los efectos de mantener una buena reputación, para que puedan, así, mantener una percepción positiva de la seguridad. Se concluye que el concepto de seguridad adquiere una nueva dimensión en el contexto de la ciudad inteligente, ya que es un componente crucial de toda su base, estrechamente vinculado a la tecnología además de presentarse como una preocupación fundamental para los gobiernos y entidades que buscan implementar soluciones conceptuales y aplicaciones.

Abstract Smart cities are growing around the world, driven by technological innovations. With them come several opportunities and new threats to our security and privacy in this interconnected reality. This study investigates citizens' perception of security and trust in technologies and how they affect the propensity to use them and, consequently, life in the smart city. Therefore, a survey was conducted (n = 601) using the PLS-SEM method to test the formulated hypotheses. The results obtained confirm that the proposed model proves to be consistent. The relationships between 'trust and subjective security' and 'objective security and data privacy' obtained stronger relationships, confirming the strong influence of the 'tangible' and 'intangible' barriers of the perception of security. Thus, to obtain and maintain users' trust, the institutions behind the technologies need to be attentive to the opinion of their users and society to keep a good reputation and a positive perception of security. The users' opinion is a crucial component of smart cities' entire base, closely linked to technology, and presents as a fundamental concern for governments and entities that seek to implement concept solutions and applications.

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In this paper, a dual-band graphene coplanar waveguide antenna is designed for smart cities and internet of things applications. A graphene film is chosen as the conductive material for the radiation patches and ground plane with a thickness of 240 µm and an electric conductivity of 3.5 × 105 S/m. The dielectric is glass with a dielectric permittivity of 6 and a thickness of 2 mm. The implementation of the antenna on glass permits the integration of the antenna in smart cities and IoT applications. This antenna is based on two trapezoidal patches that generate the dual-band behavior. The overall dimensions of the antenna are 30 mm × 30 mm × 2 mm. The reflection coefficient, gain, and radiation patterns were measured and compared with the simulations. The antenna covers two frequency bands; the lower band covers the 2.45 GHz ISM band, and the upper band range covers from 4 to 7 GHz.

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Abstract This article examines data-driven policy for smart cities and how the institutional analysis and development framework (IAD) is a tool to analyze data governance and data policy design. The research assumes that data-driven policy underpins smart city initiatives and incorporates collective action dilemmas, directly impacting policy design and data governance. The article's motivation is to examine the elements of the IAD framework and decompose the constitutive components of data policy in smart cities, obtaining principles that guide data governance in complex situations of digital transformation. The article is a theoretical essay to discuss data policy in smart cities and how policy design understands dilemmas and conflicts of collective action motivated by data control.

Resumen Este artículo examina la política basada en datos para ciudades inteligentes y cómo el Institutional Analysis and Development Framework (IAD) constituye una herramienta para analizar el diseño de la gobernanza de datos y de la política de datos. El examen realizado en el artículo parte de la premisa de que la política basada en datos, que sustenta las iniciativas de ciudades inteligentes, incorpora dilemas de acción colectiva e impacta directamente en el diseño de las políticas y de la gobernanza de datos. La motivación para la elaboración del artículo fue examinar los elementos del IAD framework y descomponer los componentes constitutivos de la política de datos en las ciudades inteligentes, para luego derivar de ellos los principios que guían la gobernanza de datos en situaciones complejas de transformación digital. El artículo es un ensayo teórico que pretende generar discusiones sobre la política de datos en las ciudades inteligentes y cómo el diseño de políticas entiende los dilemas y conflictos de la acción colectiva motivados por el control de datos.

Resumo Este artigo examina a política orientada por dados para cidades inteligentes e como o Institutional Analysis and Development Framework (IAD) se posiciona como uma ferramenta para analisar o design da governança de dados e de política de dados. A pesquisa realizada neste artigo sobre políticas baseadas em dados parte da premissa de que um projeto orientado por dados sustenta iniciativas de cidades inteligentes e incorpora dilemas de ação coletiva, impactando diretamente o desenho da política e da governança de dados. A motivação do artigo foi examinar dos componentes da estrutura IAD e componentes constitutivos da política de dados que orientam sua governança em sistemas complexos de transformação digital. O artigo é um ensaio que pretende levantar discussões sobre dados em cidades inteligentes e como o desenho de políticas compreende os conflitos de ação coletiva motivados pelo controle de dados.

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The pandemic by COVID-19 is causing a devastating effect on the health of the global population. Currently, there are several efforts to prevent the spread of the virus. Among those efforts, cleaning and disinfecting public areas have become important tasks and they should be automated in future smart cities. To contribute in this direction, this paper proposes a coverage path planning method for a spraying drone, an unmanned aerial vehicle that has mounted a sprayer/sprinkler system, that can disinfect areas. State-of-the-art planners consider a camera instead of a sprinkler, in consequence, the expected coverage will differ in running time because the liquid dispersion is different from a camera's projection model. In addition, current planners assume that the vehicles can fly outside the target region; this assumption can not be satisfied in our problem, because disinfections are performed at low altitudes. Our method presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning method that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The algorithm has been tested in several simulation scenes, showing that it is effective and covers more areas with respect to two approaches in the literature. Note that the proposal is not limited to disinfection applications, but can be applied to other ones, such as painting or precision agriculture.

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Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets' quality and map the areas with the most significant anomalies.

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

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In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.

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Real-time detection of possible deforestation of urban landscapes is an essential task for many urban forest monitoring services. Computational methods emerge as a rapid and efficient solution to evaluate bird's-eye-view images taken by satellites, drones, or even street-view photos captured at the ground level of the urban scenery. Identifying unhealthy trees requires detecting the tree itself and its constituent parts to evaluate certain aspects that may indicate unhealthiness, being street-level images a cost-effective and feasible resource to support the fieldwork survey. This paper proposes detecting trees and their specific parts on street-view images through a Convolutional Neural Network model based on the well-known You Only Look Once network with a MobileNet as the backbone for feature extraction. Essentially, from a photo taken from the ground, the proposed method identifies trees, isolates them through their bounding boxes, identifies the crown and stem, and then estimates the height of the trees by using a specific handheld object as a reference in the images. Experiment results demonstrate the effectiveness of the proposed method.

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Cities have high demand and limited availability of water and energy, so it is necessary to have adequate technologies to make efficient use of these resources and to be able to generate them. This research focuses on developing and executing a methodology for an urban living lab vocation identification for a new water and energy self-sufficient university building. The methods employed were constructing a technological roadmap to identify global trends and select the technologies and practices to be implemented in the building. Among the chosen technologies were those for capturing and using rain and residual water, the generation of solar energy, and water and energy generation and consumption monitoring. This building works as a living laboratory since the operation and monitoring generate knowledge and innovation through students and research groups that develop projects. The insights gained from this study may help other efforts to avoid pitfalls and better design smart living labs and off-grid buildings.

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The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP's markets was less than 5%. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

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Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

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Air, water, and soil are essential for terrestrial life, but pollution, overexploitation, and climate change jeopardize the availability of these primary resources. Thus, assuring human health and food production requires efficient strategies and technologies for environmental protection. Knowing key parameters such as soil moisture, air, and water quality is essential for smart farming and urban development. The MeteoMex project aims to build simple hardware kits and their integration into current Internet-of-Things (IoT) platforms. This paper shows the use of low-end Wemos D1 mini boards to connect environmental sensors to the open-source platform ThingsBoard. Two printed circuit boards (PCB) were designed for mounting components. Analog, digital and I2C sensors are supported. The Wemos ESP8266 microchip provides WiFi capability and can be programed with the Arduino IDE. Application examples for the MeteoMex aeria and terra kits demonstrate their functionality for air quality, soil, and climate monitoring. Further, a prototype for monitoring wastewater treatment is shown, which exemplifies the capabilities of the Wemos board for signal processing. The data are stored in a PostgreSQL database, which enables data mining. The MeteoMex IoT system is highly scalable and of low cost, which makes it suitable for deployment in agriculture 4.0, industries, and public areas. Circuit drawings, PCB layouts, and code examples are free to download from https://github.com/robert-winkler/MeteoMex.

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For some IoT applications, mobile entities are considered as the main source of sensed data, requiring the attachment of sensor modules on them. The endowing of sensing capabilities to such mobile entities can be performed in different ways, but the adoption of a reference hardware framework can bring a series of advantages, specially in dynamic complex scenarios. This article exploits the MSensorMob2 multi-sensor hardware framework for monitoring in areas with disconnection periods, comprising sensing, transmission and reconfiguration functions. Comprehensive analyses on multiple open-source hardware platforms are conducted, assessing their costs, deployment constraints and performance issues when implementing this development framework.

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The volume of research that associates the theme of city management with crises resulting from emerging infectious disease is modest, even after the occurrences of Ebola and Severe Acute Respiratory Syndrome. Similarly, the Coronavirus disease (COVID-19) pandemic has thus far contributed only modestly to the expansion of attention to people's health, through city management, in times of crisis. This study, by means of a systematic literature review, analyzes the gap in research on urban theory on how epidemics are confronted. The term "cities" had 2,440,607 articles published and were identified 665 that presents the combination of the term "pandemic". After the development of content analysis were identified 11 articles prior to 2019 and 10 articles published between January and June 2020, adhering to the objective of this investigation. Prior to 2019 studies addressed topics related to the construction of an urban structure aimed at reducing people's vulnerability to infectious diseases, starting in 2020, the focus of researchers' attention is on the use of information and communication technologies used as tools for prevention and control. Theories of the management of cities indicate the need to extrapolate the urban perimeter, incorporating the relations of dependence in cities with the other actors within the surroundings, especially in times of crisis. Studies have emphasized that cities are not isolated islands; rather, they are parts of a complex system with multiple exchanges. This thematic field of study enhances research that presents urban planning solutions by using data-driven management to consider conduct, parameters, and protocols relating to public health in moments of crisis.

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Mercosur (a.k.a. Mercosul) is a trade bloc comprising five South American countries. In 2018, a unified Mercosur license plate model was rolled out. Access to large volumes of ground truth Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for license plate detection (LPD) in automatic license plate recognition (ALPR) systems. To address this problem, a Mercosur license plate generator was developed to generate artificial license plate images meeting the new standard with sufficient variety for ALPR training purposes. This includes images with variation due to occlusions and environmental conditions. An embedded system was developed for detecting legacy license plates in images of real scenarios and overwriting these with artificially generated Mercosur license plates. This data set comprises 3,829 images of vehicles with synthetic license plates that meet the new Mercosur standard in real scenarios, and equivalent number of text files containing label information for the images, all organized in a CSV file with compiled image file paths and associated labels.

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The significant growth of wireless communications systems in the last years has led to the adoption of a wide range of applications not only for the general public but, also, including utilities and administrative authorities. In this context, the notable expansion of new services for smart cities requires, in some specific cases, the construction of underground tunnels in order to enable the maintenance and operation works of utilities, as well as to reduce the visual impact within the city center. One of the main challenges is that, inherently, underground service tunnels lack coverage from exterior wireless communication systems, which can be potentially dangerous for maintenance personnel working within the tunnels. Accordingly, wireless coverage should be deployed within the underground installation in order to guarantee real-time connectivity for safety maintenance, remote surveillance or monitoring operations. In this work, wireless channel characterization for complex urban tunnel environments was analyzed based on the assessment of LoRaWAN and ZigBee technologies operating at 868 MHz. For that purpose, a real urban utility tunnel was modeled and simulated by means of an in-house three-dimensional ray-launching (3D-RL) code. The utility tunnel scenario is a complex and singular environment in terms of radio wave propagation due to the limited dimensions and metallic elements within it, such as service trays, user pathways or handrails, which were considered in the simulations. The simulated 3D-RL algorithm was calibrated and verified with experimental measurements, after which, the simulation and measurement results showed good agreement. Besides, a complete wireless sensor network (WSN) deployment within the tunnels was presented, providing remote cloud data access applications and services, allowing infrastructure security and safety work conditions. The obtained results provided an adequate radio planning approach for the deployment of wireless systems in complex urban utility scenarios, with optimal coverage and enhanced quality of service.

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Resumo A pandemia da COVID-19 vem afetando o modo de vida nas cidades. Em alguns países - como o Brasil - a curva epidemiológica cresce significativamente, exigindo respostas rápidas da administração pública. Esta pesquisa, conduzida em abril de 2020, visa caracterizar a utilização de ferramentas digitais na adaptação das cidades brasileiras à pandemia, baseada no conceito de smart city. Para tanto, buscou-se traçar um panorama da adoção de ferramentas digitais pela administração pública nas cem maiores cidades brasileiras, como resposta aos desafios impostos pela situação pandêmica, contribuindo para efetivar o isolamento ou para adaptar o funcionamento de atividades. Os resultados indicam a aplicação de tais estratégias em 83% das cidades pesquisadas, segundo a distribuição geográfica de casos da doença confirmados. A concentração das iniciativas sugere que a adoção da tecnologia acompanha a previsão da curva de infecção, e aponta para a tendência de adoção das estratégias em locais de contexto historicamente favorável à inovação. Característica fundamental das cidades inteligentes, o recurso à tecnologia digital na otimização dos serviços indica que as cidades pesquisadas estão, em algum nível, seguindo uma tendência mundial.

Resumen La pandemia de COVID-19 ha estado afectando la forma de vida en las ciudades. En algunos países ‒como Brasil‒ la curva epidemiológica crece significativamente, lo que requiere respuestas rápidas de la administración pública. Esta investigación, realizada en abril de 2020, tiene como objetivo caracterizar el uso de herramientas digitales en la adaptación de las ciudades brasileñas a la pandemia, a la luz del concepto de ciudad inteligente. Para ello, buscamos esbozar un panorama de la adopción de estas herramientas por parte de la administración pública en las cien ciudades brasileñas más grandes, como una forma de responder a los desafíos impuestos por la situación de pandemia, contribuyendo a efectuar el aislamiento o adaptar el desempeño de las actividades. Los resultados indicaron la presencia de tales estrategias en el 83% de las ciudades encuestadas. La concentración de las iniciativas sugiere que la adopción de la tecnología sigue el pronóstico de la curva de infección, y señala la tendencia a adoptar estrategias en lugares con un contexto históricamente favorable a la innovación. Característica fundamental de las ciudades inteligentes, el uso de tecnología digital en la optimización de los servicios indica que las ciudades encuestadas están siguiendo, en algún nivel, una tendencia mundial.

Abstract COVID-19 has been affecting the way of life in cities. The pandemic curve grows significantly in some countries, such as Brazil, requiring rapid responses from the public administration. This research, conducted in April 2020, characterizes the use of digital tools in adapting Brazilian cities to the pandemic in light of the concept of smart cities, presenting a panorama of the current situation. The results indicated that 83% of the cities surveyed used digital tools in measures to fight the pandemic, such as increasing social distancing and adapting public services. The concentration of initiatives tends to follow the geographic distribution of confirmed cases, i.e., digital tools are more used in places where the pandemic curve is more accentuated. Also, cities that are historically more open to innovation demonstrated a heavier use of digital technologies and strategies to fight the pandemic. Finally, the results indicate that the largest Brazilian cities follow, at some level, the trends of digital optimization observed worldwide.

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Global industry is undergoing major transformations with the genesis of a new paradigm known as the Internet of Things (IoT) with its underlying technologies. Many company leaders are investing more effort and money in transforming their services to capitalize on the benefits provided by the IoT. Thereby, the decision makers in public waste management do not want to be outdone, and it is challenging to provide an efficient and real-time waste management system. This paper proposes a solution (hardware, software, and communications) that aims to optimize waste management and include a citizen in the process. The system follows an IoT-based approach where the discarded waste from the smart bin is continuously monitored by sensors that inform the filling level of each compartment, in real-time. These data are stored and processed in an IoT middleware providing information for collection with optimized routes and generating important statistical data for monitoring the waste collection accurately in terms of resource management and the provided services for the community. Citizens can easily access information about the public waste bins through the Web or a mobile application. The creation of the real prototype of the smart container, the development of the waste management application and a real-scale experiment use case for evaluation, demonstration, and validation show that the proposed system can efficiently change the way people deal with their garbage and optimize economic and material resources.