Problems In Smart Cities Research Articles

IoT Frameworks: Comparing Efficiency and Scalability in Smart City Applications

As Smart City apps change quickly, IoT platforms are very important for making it possible to install linked devices and services quickly and on a large scale. The main goal of this study is to compare how well and how easily different IoT systems can be expanded to work in Smart City settings. Being efficient in IoT systems means that they can make the best use of resources, reduce delay, use less energy, and keep data transfer stable. Concerning scalability, on the other hand, we look at how well these frameworks can grow to support more devices and people without affecting security or speed. This study looks at a number of well-known IoT systems, each with its own layout and set of rules made to solve specific Smart City problems. Frameworks like MQTT (Message Queuing Telemetry Transport) focus on lightweight communication and publish-subscribe messaging models. This makes them perfect for situations where low bandwidth and fast data sharing between many devices in cities are needed. CoAP (Constrained Application Protocol) is unique because of its RESTful architecture, which allows for scalability thanks to its stateless design and support for resource discovery. This makes it ideal for use in situations where device interactions are dynamic and spread out, like in traffic management systems or environmental monitoring. The study also looks into systems such as LwM2M (Lightweight Machine-to-Machine), which is known for its effective device control and support for devices that don't have a lot of memory or processing power. Its protocol design makes sure that data sharing and device control work reliably, which is important for keeping service going in large-scale setups like those found in Smart City infrastructures. The study also looks at how these frameworks compare in terms of performance measures like speed, delay, and flexibility when networks are changed and workloads are distributed in ways that are common in Smart City settings.

Future Smart City with Less Traffic Congestion Model Enabled by (IoT)

The advancement in wireless telecommunication network has increase the accessibility of more users to wireless connectivity. With the advent of the fifth-generation (5G) wireless network, a seamless connectivity is available for internet users globally. A future smart city is a metropolis that utilizes information and communication technologies (ICT) to grow its functionality effectively to disseminate information among the public and to develop the quality of government facilities and the welfare of the citizen. The Internet of Things (IoT) refer to the interconnection of several systems, devices or physical objects/things which are driven by sensors, software, and other equipment in order to interconnect and interchange data with other devices and systems through the internet. The Internet of things (IoT), is a revolutionary method that allows a diverse number of applications to be interconnected in order to create a single communication architecture. Future smart city has resulted in the increase in population, hence there is need to develop a smart traffic light system to help in managing the problem of future smart city; traffic congestion. The Internet of Things (IoT) a key features necessary for employing a large-scale in IoTS are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications which helps in solving these challenges associated with traffic congestion. It enables a smart environment, smart energy, and smart transportation system. In this paper, we shall discuss IoT technology, review some literatures on application area of Internet of Things (IoT), and challenges of IoT. And also discuss the applications of IoT, in smart city development, and traffic congestion management in smart city design, and how it proffers solution to future smart city problem.

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

Solar Photovoltaic Cell Parameter Identification Based on Improved Honey Badger Algorithm

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.

Implementation of Dijkstra’s Algorithm for Traffic Flow in Smart City using Raspberry Pi Board

Today, Traffic congestion Problems are avoid less in road network application such as in smart cities. The traffic density in a city changes from time to time sometimes there are increases huge amount of traffic and sometimes there are very minimum amount of traffic density also traffic profiles describe the time needed to pass the road based on time which also differs for work days and weekend. So it is very difficult to choose shortest time route form source to destination. The basic idea behind this is the implementation of one efficient method which is helpful for smooth and faster traffic flow in smart cities. Now a day’s traffic congestion is the biggest problem in smart cities. Proposed system creates data base based on history of traffic conditions and find where traffic density is high according to time, day and date. Major role to the people live in various states, cities, town and villages, from each and every day they travel to work, to schools, to business meetings, and to transport their goods. Even in this modern era whole world used roads, remain one of the most useful mediums used most frequently for transportation and travel. The manipulation of shortest paths between various locations appears to be a major problem in the road networks. The large range of applications and product was introduced to solve or overcome the difficulties by developing different shortest path algorithms. Even now the problem still exists to find the shortest path for road networks. Shortest Path problems are inevitable in road network applications such as city emergency handling and drive guiding system. Basic concepts of network analysis in connection with traffic issues are explored. The traffic condition among a city changes from time to time and there are usually huge amounts of requests occur, it needs to find the solution quickly.

Urban Data Science Education: A Key Actor towards Improving Data-Driven Policy-Making for Solving Urban Problems


 This paper explains the benefits of Urban Data Science Education in improving data driving policy-making and highlights the impact of vital use of available open data using a practical demonstration from the Summer-Winter School (SWS) at CEPT University Ahmedabad India. Good Urban data policies can be an important driver for smart city research and the implementation of good data management practices. Many authors are also becoming interested in the data revolution that is enhancing the way we study and understand cities. It now becomes appropriate to respond to this need in an evidence-informed manner by working with stakeholders to encourage a more resilient approach via education for the younger enthusiast, leading to more transparent, improved, sustainable and safer urban cities. However, the major challenge we foresee is the inability to utilize the large amount of urban data generated daily. With the drive to mobilize more education towards urban data science and analytics, generated data can be effectively utilized to make informed and evidence-based decisions. This paper points to a practical methodology for reproducibility, the key steps taken, as it views the inclusion of an Urban Data Science curriculum as priceless, and a decision that will bring into notice the importance of engaging the younger generation in scholastic learning on data principles and analytics. Data along with spatial enhancement can be the driving factor towards improving data-driven policy-making for solving urban problems in smart cities.
 

Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation

We live in a complex world characterised by complex people, complex times, and complex social, technological, economic, and ecological environments. The broad aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we introduce the concept of deep journalism, a data-driven deep learning-based approach, to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems that industry and academia seem to ignore. In contrast, academia produces much broader and deeper knowledge on subjects such as pollution that are not sufficiently explored in industry. Our deep journalism approach could find the gaps in information and highlight them to the public and other stakeholders.

IoT Analytics and Agile Optimization for Solving Dynamic Team Orienteering Problems with Mandatory Visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewards.

Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.

Where Am I Parking: Incentive Online Parking-Space Sharing Mechanism With Privacy Protection

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

Exploring the Usability of Process Mining in Smart City

The aim of this paper is to explore the usability of Process Mining approaches in Smart City applications. Our research was triggered by the following three research questions, concerning the initial investigation of: i) the most researched Smart City problems using the methods of Process Mining; ii) the most utilized Process Mining methods in Smart City applications; iii) the most popular Smart City topics that were not approached yet from the perspective of Process Mining. This analysis will result in a set of challenges and opportunities of utilizing the most innovative Process Mining methods for solving emergent Smart City problems.

Socialized Learning for Smart Cities: Cognitive Paradigm, Methodology, and Solution

Along with the unprecedented boom of 5G, the Internet of Things (IoT), and artificial intelligence, smart cities are making a great clamor. When billions of IoT devices are streaming in the smart city, realizing low-latency and high-bandwidth services has a significant impact on the benefits of smart cities. Thus, managing resources efficiently and intelligently to meet various service requirements has become a challenge for smart cities. With the rapid development of machine intelligence, it can be considered that many machines constitute a machine society just like that of human beings. Inspired by the efficient and collaborative operation mechanism of human society, we propose a socialized learning scheme to address various needs in smart cities (e.g., reasonable resource allocation). Specifically, we present the socialized learning scheme from three perspectives. First, we design a cognitive paradigm of socialized learning to enlighten the long-term vision of applying learning-based resource management to smart cities, with comprehensive discussions of three dimensions consisting of architecture, decision, and knowledge. Second, we also probe the socialized learning methodology including socialized training and socialized inference. It highlights social relationships and introduces a series of technologies to realize social relations within and across layers. Finally, we give a socialized learning solution for solving the practical resource allocation problem in smart cities, thereby jointly improving decision accuracy and reducing training costs. Illustrative simulations are provided to show the effectiveness of the proposed scheme.

TaxiBJ21: An open crowd flow dataset based on Beijing taxi GPS trajectories

Crowd flow prediction has been a significant problem in smart cities. Real‐world datasets are the basis of designing data‐driven models, for example, machine learning and deep learning models. However, some well‐known datasets, for example, TaxiBJ, become unavailable when the original sharing links are deleted as the data providers move to new jobs. As an alternative choice, TaxiBJ21 is proposed in this letter for future crowd flow prediction studies. This new open dataset contains the taxi inflow and outflow matrices in Beijing for 3 months, with the same data format as TaxiBJ and can be used in the current literature seamlessly. In this letter, the process of constructing TaxiBJ21 is introduced and three baselines including two simple historical models and a multilayer perceptron neural network are evaluated on this new dataset as benchmarks. Both the data and baselines are publicly available in a Github repository.

Iot based intelligent route selection of wastage segregation for smart cities using solar energy

The waste management is a major problem all over the world. The smart is an innovative automated device for the separate collection of used absorbent hygiene products. The Automatic Intelligence Smart Bin helps to solve waste management problem in smart cities. The smart bin will play a important role in smart cities. The smart lifestyle begins with cleanliness environment within the city which originates with smart bin. The smart bin utilized HC-SR04 Ultrasonic Sensor is used to identify human being and TowerPro SG90 Servo Motor is used to open/close the smart bin lid automatically. The Arduino Uno microcontroller was used to control the smart bin overall functionality. The smart bin separates dry/wet garbage collection using IoT sensor within the smart bin. The smart bin is full up to 80% then the microcontroller sends warning message to garbage collector automatically. The top of the smart bin solar panel is fixed, which is used to collect energy from sun. The collected energies are stored in the battery bank which is used to operate smart bin automatically. The applications of smart bins are shopping malls, Colleges/University campus, Railway stations, Tourist areas, Parks etc.

Predictive Estimation of Optimal Signal Strength From Drones Over IoT Frameworks in Smart Cities

The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math></inline-formula> to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength.

Surveillance System in Smart Cities: A Dependability Evaluation Based on Stochastic Models

Surveillance monitoring systems are highly necessary, aiming to prevent many social problems in smart cities. The internet of things (IoT) nowadays offers a variety of technologies to capture and process massive and heterogeneous data. Due to the fact that (i) advanced analyses of video streams are performed on powerful recording devices; while (ii) surveillance monitoring services require high availability levels in the way that the service must remain connected, for example, to a connection network that offers higher speed than conventional connections; and that (iii) the trust-worthy dependability of a surveillance system depends on various factors, it is not easy to identify which components/devices in a system architecture have the most impact on the dependability for a specific surveillance system in smart cities. In this paper, we developed stochastic Petri net models for a surveillance monitoring system with regard to varying several parameters to obtain the highest dependability. Two main metrics of interest in the dependability of a surveillance system including reliability and availability were analyzed in a comprehensive manner. The analysis results show that the variation in the number of long-term evolution (LTE)-based stations contributes to a number of nines (#9s) increase in availability. The obtained results show that the variation of the mean time to failure (MTTF) of surveillance cameras exposes a high impact on the reliability of the system. The findings of this work have the potential of assisting system architects in planning more optimized systems in this field based on the proposed models.

Content delivery enhancement in Vehicular Social Network with better routing and caching mechanism

Vehicular Social Network (VSN) emerges as one meaningful scenario of Mobile Social Network (MSN) and aims at addressing the traffic congestion and security problems in smart cities. However, the current IP based network connection cannot efficiently solve the high mobility and dynamically changing topology issues caused by VSN, which results in long routing latency and low cache hit ratio. In this regard, this work intends to optimize the routing latency and the cache hit ratio by solving the content delivery problem in VSN, during which the concepts of fog/edge computing and Information-Centric Networking (ICN) are introduced, since they can provide a new perspective of network connection in VSN by concentrating on contents instead of IP addresses. Firstly, the ICN routing table structure is expanded for the purpose of providing key routing basis for both interest and data packets. Secondly, the vehicle social information is fully explored from the points of relationship strength and interest degree to offer reliable data foundation for community detection and packet forwarding. Thirdly, the dynamic vehicle communities are constructed based on an improved community overlap propagation algorithm. By monitoring the content popularity and cache distribution in the dynamic communities, this paper proposes the vehicle-adaptive caching scheme which guarantees the cache diversity and improves the packet delivery rate. After that, a vehicle-adaptive interest packet routing scheme is proposed based on relationship strength and interest degree for both inter/intra communities. The experiment results indicate that the proposed mechanism can improve the network performance in terms of the packet delivery rate, the average hop, the average delay and the overhead.

Framework for Prioritization of Open Data Publication: An Application to Smart Cities

Public Sector Information is considered to play a fundamental role in the growth of the knowledge economy and improvements in society. Given the difficulty in publishing and maintaining all available data, due to budget constraints, institutions need to select which data to publish, giving priority to data most likely to generate social and economic impact. Priority of publication could become an even more significant problem in Smart Cities: as huge amounts of information are generated from different domains, the way data is prioritized and thus reused, could be a determining factor in promoting, among others, new and sustainable business opportunities for local entrepreneurs, and to improve citizen quality of life. However, people in charge of prioritizing which data to publish through open data portals (such as Chief Data Officers, or CDOs) do not have available any specific support in their decision-making process. In this work, a proposal of a framework for prioritization of open data publication as well as its application to Smart Cities is presented. This specific application of the framework relies on OSS (Open Source Software) indicators to help making decisions on the most relevant data to publish focused on developers and businesses operating within the Smart City context.

Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects

The purpose of smart city is to enhance the optimal utilization of scarce resources and improve the resident’s quality of live. The smart cities employed Internet of Things (IoT) to create a sustainable urban life. The IoT devices such as sensors, actuators, and smartphones in the smart cities generate data. The data generated from the smart cities are subjected to analytics to gain insight and discover new knowledge for improving the efficiency and effectiveness of the smart cities. Recently, the application of deep learning in smart cities has gained a tremendous attention from the research community. However, despite raise in popularity and achievements made by deep learning in solving problems in smart cities, no survey has been dedicated mainly on the application of deep learning in smart cities to show recent progress and direction for future development. To bridge this gap, this paper proposes to conduct a dedicated survey on the applications of deep learning in smart cities. In this paper, recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled. The paper can provide opportunities for experts in the research community to propose a novel approach for developing the research area. On the other hand, new researchers interested in the research area can use the paper as an entry point.

Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities

One of the main challenges in urban development faced by large cities is related to traffic jam. Despite increasing efforts to maximize the vehicle flow in large cities, to provide greater accuracy to estimate the traffic jam and to maximize the flow of vehicles in the transport infrastructure, without increasing the overhead of information on the control-related network, still consist in issues to be investigated. Therefore, using artificial intelligence method, we propose a solution of inter-vehicle communication for estimating the congestion level to maximize the vehicle traffic flow in the transport system, called TRAFFIC. For this, we modeled an ensemble of classifiers to estimate the congestion level using TRAFFIC. Hence, the ensemble classification is used as an input to the proposed dissemination mechanism, through which information is propagated between the vehicles. By comparing TRAFFIC with other studies in the literature, our solution has advanced the state of the art with new contributions as follows: (i) increase in the success rate for estimating the traffic congestion level; (ii) reduction in travel time, fuel consumption and CO2 emission of the vehicle; and (iii) high coverage rate with higher propagation of the message, maintaining a low packet transmission rate.