Future Smart Cities Research Articles

Efficient Resource Allocation in Blockchain-Assisted Health Care Systems

Smart health care will be a major application in future smart cities. Timely and precise delivery of patients’ data to their medical consultant, to allow the necessary actions, is one of the basic needs in health care systems. Blockchain technology, with the provisioning of recording and tracking of data blocks, guarantees secure and error-free data delivery. The vital sign data from patients’ sensors are placed in different data blocks. To become a part of the blockchain, the block must contain a valid key, based on a hash function. Mining nodes with high processing capabilities generate the required key using a 32-bit number, known as a nonce, which is changed for every new block. Finding a nonce that meets the hash function requirements is a time-intensive process in blockchain technology and is performed by several fog mining nodes. However, an efficient resource allocation that results in the fair placement of data in these fog mining nodes, while maintaining the priority and sensitivity of patients’ data, is a challenge. This work proposes two algorithms for the resource allocation of mining nodes. The first algorithm uses a load balancing technique to distribute the load of nonce computing tasks. The second algorithm utilizes the knapsack algorithm to allocate the caching space of the mining nodes. The simulation results highlighted that the proposed resource allocation techniques outperformed the existing techniques, in terms of quick mining of the most sensitive patient data blocks.

Autonomous Behavioral Decision for Vehicular Agents Based on Cyber-Physical Social Intelligence

In future smart cities supported by cyber-physical social intelligence, autonomous behavioral decision for vehicular agents is going to become a general demand. Despite much progress achieved in autonomous behavioral decision of vehicular agents, the existing works can just be used in scenarios of short-distance behavioral decision. Naturally, they are not well suitable for long-distance behavioral decision tasks, posing much challenge in realistic cyber-physical environment. To bridge the existing gaps, this article proposes an autonomous behavioral decision framework for vehicular agents using cyber-physical social intelligence. First, it is expected to establish a dynamic planning model with multiple objectives and constraints. This can be embedded into the control unit of a vehicular agent to endow it with proper social intelligence. On this basis, an iterative search algorithm is specifically designed for it to find the optimal solutions from the whole solution space. Finally, two typical situation cases are implemented with use of simulation modeling to display the working architecture of the proposed method. In addition, a universal optimization search algorithm is selected as the baseline to be compared with the proposed method. The comparison results reveal both planning utility and running efficiency of the proposed method.

Integrated Node Infrastructure for Future Smart City Sensing and Response

Emerging smart cities and digital twins are currently built from heterogenous cutting-edge low-power remote sensing systems limited by diverse inefficient communication and information technologies. Future smart cities delivering time-critical services and responses must transition towards utilizing massive numbers of sensors and more efficient integrated systems that rapidly communicate intelligent self-adaptation for collaborative operations. Here, we propose a critical futuristic integrated communication element named City Sensing Base Station (CSBS), inspired by base stations for cell phones that address similar concerns. A CSBS is designed to handle massive volumes of heterogeneous observation data that currently need to be upgraded by middleware or registered. It also provides predictive and interpolation modelling for the control of sensors and response units such as emergency services and drones. A prototype of CSBS demonstrated that it could unify readily available heterogeneous sensing devices, including surveillance video, unmanned aerial vehicles, and ground sensor webs. Collaborative observation capability was also realized by integrating different object detection sources using advanced computer-vision technologies. Experiments with a traffic accident and water pipeline emergency showed sensing and intelligent analyses were greatly improved. CSBS also significantly reduced redundant Internet connections while maintaining high efficiency. This innovation successfully integrates high-density, high-diversity, and high-precision sensing in a distributed way for the future digital twin of cities.

A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

• Advanced co-simulation of rooftop PV production and households electricity demand. • Urban Energy Systems design and RES integration in the context of future smart cities. • Enabling considerations on Demand Side Management and Demand Response strategies. • High spatio-temporal resolution (25 cm and 10 minutes respectively) • Integration in a GIS environment of open-data and models. The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO 2 emissions.

Urban quantum leap: A comprehensive review and analysis of quantum technologies for smart cities

Contemporary smart city solutions rely on standardized von Neumann architecture, in which single data units are coded as “0” or “1.” Conversely, urban quantum technologies rely on the fundamental principles of quantum physics, transcending the conventions of the current computational paradigm. On the one hand, urban quantum technologies hold managerial relevance for future smart cities. On the other hand, they are often overlooked by smart city researchers. Accordingly, their value as a breakthrough technological paradigm is still largely unexplored. In this article, we look at how quantum technologies may contribute to existing smart city solutions, including the Internet of Things, cloud computing, big data, ICT, smart transportation, artificial intelligence, and blockchain. First, through a semi-systematic review of eighty articles on quantum computing within the social science domain, we identify two relevant classes of urban quantum technologies: quantum communication and quantum computing. Second, we establish a comprehensive taxonomy of conventional smart city solutions based on the automated content analysis of 567 abstracts of articles on the technological aspects of smart cities. Third, we investigate potential associations between two classes of technologies (conventional smart city solutions and urban quantum technologies) by analyzing the semantic relationships between eighty articles on quantum technologies according to the frequency of keywords denoting different types of conventional smart city solutions. Finally, we triangulate our findings through a thematic analysis of potential uses of quantum technologies within identified categories of smart city solutions.

Toward efficient vehicular-based virtual network infrastructure for smart cities

Future smart cities will undoubtedly generate and process an immense volume of data. Developing a smart network infrastructure is thus becoming a key component for the better development and efficient governance of smart cities. The main objective of this article is to examine the feasibility of utilizing moving vehicles to develop an efficient Vehicular-based Virtual Network Infrastructure (VVNI) for smart cities. The basic idea here is to divide the whole city into distinct cells, then predict and evaluate the vehicle eligibility to act efficiently as a centralized communication and information hub for each cell. Specifically, we present the basic framework of the proposed VVNI, and identify the corresponding efficiency requirements in terms of service availability, cost, and quality; then we introduce novel metrics to characterize the vehicle's ability to cope with these requirements. Several simulation scenarios were conducted to assess the efficiency of the proposed VVNI. The simulation results demonstrate how the proposed model outperforms existing models and improves system performance in three benchmark areas: throughput, packet delivery ratio, and latency, especially in medium- and high-density network scenarios with slower vehicle velocities. The simulation results are promising to further enhance the development of the proposed network infrastructure.

Deflection-aware smart piezoelectric buildings come as Central elements of the future smart cities empowered by artificial intelligence algorithm

This study follows a novel path toward alarming the dangerous conditions of the buildings by means of piezoelectricity. piezoelectric materials have the ability to translate electrical voltage and displacement to each other interchangeably. This feature is employed in this research to define a critical threshold for the displacements. The case study of this research is a graphene-platelets reinforced functionally-graded piezoelectric (GPLR-FGP) rectangular wall with one free edged at its top. The voltage would be gradually increased until the natural frequency approaches to zero. This circumstance puts the wall in the direct danger of failing its stability. Accordingly, displacements of the wall at this condition can be considered as the critical deflections of the system. The wall’s governing equations are established in the background of higher-order shear deformation theory (HOSDT). Differential quadrature approach (DQA) is employed to obtain the natural frequency of the wall. Deep-learning is utilized to accelerate the computation process by means of training a deep network of neurons through the instances determined by the explained numerical approach. Efficiency of the innovative implemented solution in this research is verified through a comparative analysis with the results of the published articles. This research can be considered as the main stone of developing innovative solutions for alarming safety issues in the smart cities of near future.

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 novel and lean data-based method to calculate the actual HVAC zone energy consumption and cooling load in sustainable smart cities using a single temperature sensor

Sustainability and energy savings are two intertwined research aims for humanity’s long-term survival, aligned with the Paris Agreement and the Egyptian vision of 2030. The purpose of this research is to minimize the cost needed to measure the HVAC energy consumption in educational and commercial buildings to save energy for energy efficiency strategies to serve the future smart and sustainable cities. These facilities are among the most energy-consuming loads, and this research proposes a technique to determine real HVAC energy consumption and cooling load using a single-sensor method. Calculating the as-built HVAC energy usage and real cooling load, especially in complex HVAC systems like VRV, which are some of the most challenging gaps, is achieved through a revolutionary and cost-effective approach. The proposed approach computes the actual HVAC energy consumption of each specific zone in the building using data from a single temperature sensor. Furthermore, the real and instantaneous cooling load of each zone was calculated, and the results show a promising agreement between the proposed techniques and the real measured consumption of the HVAC system. This approach can be integrated into the BMS building management system to determine the actual HVAC energy usage and cooling load for each zone individually in real time, enabling significant energy reduction decisions with optimal cost efficiency. Furthermore, it can be used by the BMS system as a real-time On-Board Diagnostic of the HVAC system.

Adaptive Recursive Sliding Mode Control (ARSMC)-Based UAV Control for Future Smart Cities

The rapid expansion of the Internet and communication technologies is leading to significant changes in both society and the economy. This development is driving the evolution of smart cities, which utilize cutting-edge technologies and data analysis to optimize efficiency and reduce waste in their infrastructure and services. As the number of mobile devices and embedded computers grows, new technologies, such as fifth-generation (5G) cellular broadband networks and the Internet of Things (IoT), are emerging to extend wireless network connectivity. These cities are often referred to as unmanned aerial vehicles (UAVs), highlighting their innovative approach to utilizing technology. To address the challenges posed by continuously varying perturbations, such as unknown states, gyroscopic disturbance torque, and parametric uncertainties, an adaptive recursive sliding mode control (ARSMC) has been developed. The high computational cost and high-order nonlinear behavior of UAVs make them difficult to control. The controller design is divided into two steps. First, a confined stability analysis is performed using controllability and observability to estimate the system’s stability calculation. Second, a Lyapunov-based controller design analysis is systematically tackled using a recursive design procedure. The strategy design aims to enhance robustness through Lyapunov stability-based mathematical analysis in the presence of considered perturbations. The ARSMC introduces new variables that depend on state variables, controlling parameters, and stabilizing functions to minimize unwanted signals and compensate for nonlinearities in the system. The paper’s significant contribution is to improve the controlled output’s rise time and stability time while ensuring efficient robustness.

Deep Learning–Based Autonomous Road Condition Assessment Leveraging Inexpensive RGB and Depth Sensors and Heterogeneous Data Fusion: Pothole Detection and Quantification

Poor condition of roads is a major factor for traffic accidents and damage to vehicles. A significant portion of car accidents is attributed to severe three-dimensional (3D) pavement distresses such as potholes, ruttings, and ravelings. Insufficient road condition assessment is responsible for the poor condition of roads. To inspect the condition of the pavement surfaces more frequently and efficiently, an inexpensive data acquisition system was developed that consists of a consumer-grade RGB-D sensor and an edge computing device that can be mounted on vehicles and collect data while driving vehicles. The RGB-D sensor is used for collecting two-dimensional (2D) color images and corresponding 3D depth data, and the lightweight edge computing device is used to control the RGB-D sensor and store the collected data. An RGB-D pavement surface data set is generated. Furthermore, encoder-decoder deep convolutional neural networks (DCNNs) consisting of one or two encoders, and one decoder trained on heterogeneous RGB-D pavement surface data are used for pothole segmentation. Comprehensive experiments using different depth encoding techniques and data fusion methods including data- and feature-level fusion were performed to investigate the efficacy of defect detection using DCNNs. Experimental results demonstrate that the feature-level RGB-D data fusion based on the surface normal encoding of depth data outperform other approaches in terms of segmentation accuracy, where the mean intersection over union (IoU) over 10-fold cross-validation of 0.82 is achieved that shows a 7.7% improvement compared with a network trained only on RGB data. In addition, this study explores the efficacy of indirectly using depth information for pothole detection when depth data are not available. Additionally, the semantic segmentation results were utilized to quantify the severity level of the potholes assisting in maintenance decision-making. The result from these comprehensive experiments using an RGB-D pavement surface data set gathered through the proposed data acquisition system is a stepping stone for opportunistic data collection and processing through crowdsourcing and Internet of Things in future smart cities for effective road assessment. Finally, suggestions about the improvement of the proposed system are discussed.

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends.

As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

Analyzing the Challenges for Future Smart and Sustainable Cities

The fast growth of the urban population increases the demand for energy, water, and transportation, amongst other needs. This study explores the current state and future scenarios of Smart Cities and the environmental, economic, and social challenges that must be overcome. We used the Rapid Review method to understand the challenges of implementing Smart Cities in different urban contexts and the potential impact of research on Smart City planning in future Smart Cities. The study offers insights into the potential for Smart City growth while identifying obstacles that must be addressed to ensure sustainability. Results serve as a foundation for planning and decision-making, highlighting aspects such as the adoption of alternative energies, reduction in car use, preservation of ecosystems, waste reduction, citizen participation, infrastructure, and citizen data privacy, among others. These aspects are essential to overcome obstacles and promoting Smart Cities’ development.

Hydrogen fuel as an important element of the energy storage needs for future smart cities

A considerable amount of non-dispatchable photovoltaic and wind power have always been planned in smart cities, however, the problem of massive energy storage has not yet been solved which limits the use of green energy on larger scale. At present the only battery energy storage is available, and it is effective only for storing modest quantities of energy for short periods of time. The other storage technology options are not often commercially available items; rather, they are just good concepts that need to be tested for viability. Currently, the only alternative options for turning an urban development into one that exclusively uses green energy is to use that energy to generate hydrogen through electrolyzers, then use this fuel to generate the required electricity in order to stabilize the grid. Even more appealing is the idea of using wind and photovoltaic energy to transform smart communities into a centre for producing hydrogen in addition to a city that solely uses renewable energy. The most likely solution, absent an urgent debate inside the science establishment, will be to import electricity from the burning of hydrocarbons while continuing to pay carbon offsets, which is incompatible with the goal of using only renewables. The smart city has not officially accepted this issue, just like the science establishment.

Detecting the Type of Attack in Healthcare Cloud Computing System Using Termite Deep Belief Neural Network (TDBNN) for Future Smart Cities

Abstract With the popularity of smart electronic devices, along with the development of clouds and cloudlet technology, there has been an increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage, data sharing, etc. The traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. This issue motivates construction to maintain the security and privacy of healthcare data by applying the neural network approach. During this process, the system examines the various intermediate attacks; malwares for detecting the threats also eliminate the unauthorized access in the cloud storage-based healthcare system. In this paper, A termite colony optimization deep belief neural network (TDBNN) is proposed for the feature selection process with the combination of Levenberg-Marquardt for the back-propagation stage, which hence detects the list of attacks that occurred in the cloud and categorizes it. The proposed TDBNN is compared with two well-known, state-of-the-art methods, the recurrent neural network oppositional crow search algorithm and voting extreme learning machine, in terms of precision, sensitivity, specificity, f1-score, and kappa score. The proposed method achieves 99.03 % accuracy, 99.03 % precision, 99.03 % sensitivity, 99.76 % specificity, a 99.03 % f1-score, and a 98.79 % of kappa score.

Agent Data Encryption and Storage Algorithm for Wireless Sensor Networks Based on Blockchain Technology for Future Smart City Networks

Abstract Aiming at the problems of long storage time and low encryption accuracy of the traditional agent data encryption storage algorithm in wireless sensor networks, an agent data encryption storage algorithm based on blockchain technology is proposed. Analyze the blockchain hierarchy and data, take the sensor node with the signal energy value exceeding the predetermined threshold as the source node for generating sensing data, and obtain the agent data transmission path of the wireless sensor network. Based on the obtained agent data transmission path in the data layer of data block, the agent data are encrypted by generating an encryption key and data encryption. According to the agent data encryption results, the agent data encryption storage model of the wireless sensor network is constructed. The simulation results show that the proposed algorithm has shorter storage time and higher encryption accuracy.

Water Resources Balanced Scheduling Method Using Particle Swarm Optimization for Future Smart Cities

Abstract In order to solve the problem of a balanced water resources operation more effectively, the improved particle swarm optimization (PSO) algorithm is applied to the optimization of water resources and a mathematical model is constructed of water resources to optimize the operation of cascading hydropower plants. The objective function of the model is to solve the optimal output process of each hydropower plant so as to maximize the total power generation during the operation period. The constraints of the model are water level, output, discharge, the water balance of the reservoir, and the water flow relationship between the reservoirs. The particle swarm optimization algorithm is used to optimize the water resources of cascade hydropower plants. The objective function is taken as the fitness function of the particle swarm and the particle flight speed is dynamically adjusted to the ultimate optimal point. The time dimension of optimal scheduling is taken as the particle space dimension to realize the calculation of the multi-stage process of the scaling period. On this basis, from the two aspects of local convergence improvement and PSO, we improve the resource balance scheduling process of the particle algorithm and improve the balanced scheduling of water resources. The results show that the power generation of cascading hydropower plants is 4.52 % higher than that of the conventional operation when using the PSO algorithm; the average annual power generation of cascading hydropower plants is increased by 2.0 %, the guaranteed output is increased by 1.9 %, and the average annual waste water is reduced by 1.6 %. The guaranteed degree of water supply of the reservoir during the operation period is reduced and the storage capacity of the reservoir at the end of the year is increased by 50 %. The feasibility and effectiveness of this method in the optimization of water resources balance are verified, which shows that this method can provide a new idea for the balanced operation of water resources.

Modeling between Water Resources Security and Regional Economic Growth in the Yellow River Basin Based on Entropy Weight Method—A Case Study for Future Smart City

Abstract In order to explore the relationship between water resource security and regional economic growth, the Yellow River Basin was taken as the study area, and a relationship model between water resource security and regional economic growth in the Yellow River Basin based on the entropy weight method was constructed. After investigating the geographical location, changes in water resources, and economic status of the study area, combined with the principle of coupling coordination in water resources and economic systems, the Yellow River Basin water resources security index system was constructed based on the five aspects, including water resources characteristics, water supply facilities, and water resources management capacity and regional economic growth index system was constructed based on survival and development poverty two aspects. The entropy weight method was used to measure the variation degree of the indexes, and the information entropy was used to calculate the weight of each index. The TOPSIS method can be applied in optimizing the coupling algorithm and building a coupling coordination relationship model. Based on the coupling coordination index, the relationship between water resources security and regional economic growth in the Yellow River Basin was analyzed. The results show that the model analysis results can reflect the actual situation of water resources security and regional economic growth in the Yellow River Basin and show that there is a high degree of coupling between water resources security and regional economic growth in most provinces (cities). The cities showing extreme coupling relationships are mainly distributed in the economic zones, including Henan, Ningxia, Shanxi, and Gansu, and the cities showing moderate coupling relationships are mainly distributed in Xining and Tibetan Autonomous Prefectures. At the same time, policy suggestions are put forward based on the relationship between water resources security and regional economic growth in the Yellow River Basin.

Double Encryption Algorithm for Massive Personal Biometric Authentication Images Based on Chaotic Mapping for Future Smart Cities

Abstract To address the problems of low encryption security, slow encryption speed, and small key space of personal biometric authentication image, a double encryption algorithm of image based on chaotic mapping is proposed. The global block-matching strategy of sparse clustering is used to remove the image noise, and the encrypted key information in the biometric authentication image is extracted by mathematical morphology. The logistic index is used to disrupt the image information distribution, bit scrambling is used to double scramble the image, and the Secure Hash Algorithm (SHA)-256 hash function is used to generate the initial value of the chaotic system to obtain the chaotic map and the random variable sequence of hyperchaotic Chen system, and the chaotic sequence is transformed into the ciphertext image matrix after exclusive OR (XOR) operation in order to realize the double encryption of personal biometric authentication image. The results show that the proposed method has large key space, high encryption accuracy, fast efficiency, and better security performance.

Metamaterials for Acoustic Noise Filtering and Energy Harvesting.

Artificial methods for noise filtering are required for the twenty-first century's Factory vision 4.0. From various perspectives of physics, noise filtering capabilities could be addressed in multiple ways. In this article, the physics of noise control is first dissected into active and passive control mechanisms and then further different physics are categorized to visualize their respective physics, mechanism, and target of their respective applications. Beyond traditional passive approaches, the comparatively modern concept for sound isolation and acoustic noise filtering is based on artificial metamaterials. These new materials demonstrate unique interaction with acoustic wave propagation exploiting different physics, which is emphasized in this article. A few multi-functional metamaterials were reported to harvest energy while filtering the ambient noise simultaneously. It was found to be extremely useful for next-generation noise applications where simultaneously, green energy could be generated from the energy which is otherwise lost. In this article, both these concepts are brought under one umbrella to evaluate the applicability of the respective methods. An attempt has been made to create groundbreaking transformative and collaborative possibilities. Controlling of acoustic sources and active damping mechanisms are reported under an active mechanism. Whereas Helmholtz resonator, sound absorbing, spring-mass damping, and vibration absorbing approaches together with metamaterial approaches are reported under a passive mechanism. The possible application of metamaterials with ventilation while performing noise filtering is reported to be implemented for future Smart Cities.