Modelling a Sustainable Smart City Based on Human and User Centred Design

Guoan Wei

Department of Engineering Management, Henan Technical College of Construction, Zhengzhou, Henan 450000, China
E-mail: guaonwei6879@outlook.com

Received 11 November 2021; Accepted 12 January 2022; Publication 28 December 2022

Abstract

A smart, sustainable city that employs ICTs (information and communication technologies) improves urban businesses and services’ quality of life, productivity, and competitiveness. A sustainable smart city’s significant challenges are legislation and regulations, financing, technical and infrastructural dimensions. While at the same time providing for the economic, social, environmentally, and cultural needs of the present and future generations. Continuous population and urban development have stepped up creative ways of tackling urbanization, with limited implications supporting smart cities’ environments. This cram aimed to build a user-centred design using a sustainable big data analytics assisted optimization framework (SBDAAO) to step up creative ways of tackling urbanization with limited environmental implications. This research represents a first move to provide perspectives and value in creating intelligent cities to instil sustainability. Simulation analysis has been carried out based on the data collected based on city infrastructure. The experimental results have been executed, and the suggested method achieves an accuracy ratio (98.2%), the efficiency ratio (95.5%), energy consumption (20.1%), average communication delay (17.6%), and renewable power (93.9 %).

Keywords: User-centered design, modelling, sustainable smart city, big data analytics.

1 Introduction to Sustainable Smart City on Human and User-Centered Design

With an advanced edge, cloud,and big data, information and communication technology have been employed in smart city systems [1]. Large-scale smart city and big data implementations can be introduced with secure, distributed, safe, and end-user technology availability [2]. Edge will now incorporate solution computing, a good way to help only the existing cloud smart city services for all future services [3]. These services provide intelligent transportation, intelligent waste treatment, and intelligent tool [4]. Moreover, mobile and edge computing have been the key result of exponential research and development in the past decade, primarily due to edge-oriented networks’ added market value that takes advantage of edge servers’ low network and computers [5]. Implementing state-of-the-art computing technologies facilitates the accessibility of applications for broad consumer segments while meeting strong computer requirements without overloading their finite resources [6]. Improve the functionality of cutting-edge technologies, ultimately contributing to the network’s improved output without unnecessary power usage [7, 8]. The benefits of using modern computing with a zero delay and scalability of connectivity have led to a new type of application built at the edge instead of cloud-based systems that rely heavily on resource-rich data centres [9, 10].

Smart City offers big chances and stimulating challenges [11]. In general, community functions and utilities such as hospitals, education, transport, parking, and power grids are funded by ICT facilities to boost performance, and activity can be considered intelligent in the metropolitan region [12]. In certain instances, the services will be finding a job, requesting a driver’s license, purchasing a car and land, changing address, requesting a passenger passport, starting a new company, disclosing an offence, declaring income taxes, seeking healthcare services [13]. Both these programs require multiple services coordination programs [14]. Intelligent urban planning must be based on the resident, and the infrastructure needs to offer residents incentives, regardless of their ICT skills, in terms of city structures’ complexities [15, 16].

The information must be secured to avoid misuse while processing and evaluating the data [17]. The goal is to provide the right person with the right facts, and no concession should be made on secrecy, honesty, and availability [18]. Data masking is the sanitization of information to preserve privacy [19]. This technology Encrypts, covers, or deletes data from the collections so that entities described by the data remain confidential [20]. Data masking allows information to be transferred between frontiers, as within an agency or two agencies, thus mitigating the chance or danger of unexpected exposure [21].

Therefore, the purpose of this article is to map the different aspects of the smart city, turn them into an Environment strategy for the growth of the city, and provide an overview of architectural variables. IoT modelling is intended to place and define core aspects of smart city growth and dynamics. It isn’t easy to cover all facets of a smart city, and our goal is to create a seamless model that will help accomplish the key design decisions. It helps carry out a dynamic analysis of diverse fields represented in smart cities to contribute to our input and output model. The model offers graphic and straightforward facets of smart cities, allowing policymakers, city planners, architects, and engineers to see the acceptable planning factors, what decisions they should make, and the sort of smart city they have selected.

The main contributions of the paper are,

• Legislation and regulations, funding, and technical and infrastructural are major hurdles for a sustainable smart city.

• Sustainable big data analytics assisted optimization framework (SBDAAO) focuses on the user’s needs to boost innovation in urbanization management while minimizing negative environmental impacts.

• Data gathered from the city’s infrastructure has been used to conduct a simulation analysis

The rest of the article is structured as follows. Section 2 deals with efficient storage, intelligent cities, and network application development. The proposed model is defined in Section 3. The experimental findings are discussed in Section 4. Section 5 eventually ends the suggested work and outlines our plans.

2 Related Works

This study culminated in a comprehensive analysis of literature reviewing the localization techniques analyzed by peers to correlate analytics, travel, and smart cities. The research undertaken by Google Scholar is restricted to journal articles as a metric of consistency.

Clément et al. [22] discussed smart city programs have attracted considerable interest by suggesting smart city development enablers using structural equation modelling (SEM) to improve urban development. In several studies, technological and non-technical enablers have been implemented to enhance control of smart city development. However, after important achievements, there has not been a complete quantification of intelligent urban facilitators’ direct and indirect impact on urban efficiency. As a result, knowledgeable city officials have trouble developing correct growth plans because they lack thorough comprehension. Psychological studies have shown that non-technical and technical supporting clusters considerably affect their strongly interrelated situational dynamics on clever cities’ outputs. Based on these results, local planners will reinforce strategy for the transition to smart cities through careful monitoring of their policies.

Mimica et al. [23] planned smart city modelling key indicators in Serbia using interval type-2 fuzzy sets (IT2FS) for Previous projects designed to build and achieve a smart, healthy community have proven that a city “smarter” and more sustainable is no simple solution. Each city is a unique structure that includes various stakeholders, municipal authorities, service providers, and residents, creating a network of interactions and interdependence. Establishing the right approach to encourage establishing a healthy and intelligent community has become increasingly important to consider the city’s ecological and social context and its goals, history, and unique features. The entire structure is divided into various criteria, and subcriteria by an integrative approach focused on the statistical method hybrid fuzzy multi-criteria decision-making model based on (IT2FS), acknowledging experts’ views. The goal is to provide smart cities with model solutions for our EU-integrated region.

Adeeb et al. [24] recommended a system thinking approach for harmonizing smart and sustainable city initiatives with united nations sustainable development goals for throughout culture, technology has been an integral aspect of urban evolution. In recent years, smart cities’ growth trend has shifted, as smart cities aim to take sustainable practices using Information and Communication Technologies (ICT) and other smart solutions. This study will promote comprehensive policy interventions and best practices for business experts, policy-makers, and city planners to develop policies to allow smart cities to be turned over to sustainable cities’ potential mark.

Jianjun et al.[25] suggested Blockchain-based sharing services (BSS) blockchain technology can contribute to smart cities for The idea of smart cities has become popular over the past few years. It embraces many aspects depending on the sense of the term “smart” and benefits from creative uses of new kinds of information and communications technologies to enable communal exchange. This document presents a philosophical structure with three dimensions based on previous literature, (1) person, (2) technology, and (3) the organization, addressing a range of fundamental factors that make a town intelligent from the point of view of the economy of sharing. Using this triangle structure, we explore the contribution of new blockchain technologies to these variables and how their components will enable intelligent cities to build sharing services.

In this paper, SBDAAO has been suggested to increase accuracy, efficiency, energy consumption, and renewable power and decrease average communication delay. The following section discusses the proposed SBDAAO model briefly.

3 Sustainable Big Data Analytics Assisted Optimization Framework (SBDAAO)

Big data analytics is the practice of processing massive data to discover cached patterns, hidden associations, and other relevant details for decision-making. Big data analytics employ sophisticated statistical processing, text analysis, and statistical analysis methods. It could help recognize patterns, blind points, and conditions that may be significant to the region involved in making smarter, quicker decisions about the future. Using Big Data analytics, useful details from terabytes can be retrieved and analyzed to turn strategic business decisions, and it allows getting a deeper view of the datasets. With the finest forecast of what is to come, firms can be assured that they are moving forward with the right business decisions. Fraud detection, risk management, operations, and marketing are just a few of the many uses for predictive analytics. The analysis and management of vast amounts of IoT big data can therefore aid firms in identifying how it affects their operations. Consequently, businesses and other organizations can make more efficient and well-informed decisions due to this technology.

The key aim of smart cities is to maximize the tools available to all stakeholders and optimize profits. This will enable consumers to transition first to smart city technologies and innovations and belief them. (i.e., residents, municipalities, utility providers, and enterprises). Many factors, particularly service reliability and security, play a part in the widespread acceptance of smart city systems. Furthermore, intelligent cities’ sustainable use is a central aspect of today’s intelligent cities’ long-term planning. An ecosystem’s provision to sustain consumers’ effective service offering represents another big obstacle for smart cities. This infrastructure would require state-of-the-art computing and connectivity.

Figure 1 Proposed SBDAAO.

Figure 1 shows the proposed SBDAAO model. In smart cities, big data in all fields, from personal to business to global, can expand exponentially. It comes from a wide range of enterprise systems, website logs, directory providers, social media, mobile devices, sensors, GPS, etc. It is organized in multiple ways, semi-structured and unstructured. The interpretation and arrangement of data sets became highly significant. Big data includes integrating data from several disparate channels, stored using multiple systems to have an incorporated view of the data. A complete data integration approach includes exploration, cleansing, tracking, converting, and distributing data from various sources to an integrated venue. Knowledge is searched, compiled, and displayed in a summary way in the data aggregation process report, dashboard, etc., that will assist in evaluating. Inaccurate data validation is needed to avoid important program entry info. Data validation guarantees that the data meets the consistency criteria and specifications. This guarantees complete, correct, stable, and reliable data sent to linked applications. In the context of data integration, a data warehouse is used to consolidate data from several sources into a single, easily accessible area. Operational use cases benefit greatly from the integration of applications. A comprehensive data integration technique encompasses the investigation, cleansing, tracking, converting, and distribution of data from diverse sources to one integrated venue. Big data is a method for repairing or scrubbing the disposal of incorrect or corrupt results. This method is important because wrong data can lead to wrong results and bad assessments, particularly when large volumes of large data are available. In water delivery and construction models, mathematical models describe simulating network operation or responses and estimate network statements, operational and load conditions. This section explores some outstanding approaches to address water supply and construction issues. Some of the greatest approaches for addressing water supply and building difficulties are discussed in section 3. Combining data from multiple sources and storing it in multiple systems is important for big data strategy. The cornerstone of urban growth plans and efforts is strategic planning organizations, particularly sustainability. While each city has its own unique set of challenges to overcome, the two have a lot in common regarding their planning procedures.

Model 1: Building Model

In this study, the replica of the grey box has been used to monitor building model construction. This model is an ordinary differential equation of the heat balance in first-order (ODE). Equation (1) denotes a standard functional example in ${\mathit{D}}_{\mathit{n}\mathit{o}\mathit{d}\mathit{e}}$ at a temperature corresponding $({\mathit{D}}_{\mathit{a}\mathit{d}\mathit{j}})$ input of exogenous/risk $({\mathit{R}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}})$ and output monitoring $({\mathit{R}}_{\mathit{c}\mathit{t}\mathit{r}\mathit{l}})\cdot {\mathit{D}}_{\mathit{n}\mathit{o}\mathit{d}\mathit{e}},{\mathit{S}}_{\mathit{a}\mathit{d}\mathit{j}}$ and ${\mathit{b}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}$ are estimated node capacity parameters, the fraction between the goal node and the neighbouring node, and the factor multiplied to the ${\mathit{R}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}$. To create Smart Cities, these Smart Buildings must serve as the foundation. Energy-efficient buildings can only be achieved and maintained over a Smart Building’s life cycle if equipped with cutting-edge information and communication technologies (ICT). In a network of other devices, a “node” is any gadget that can send, receive, or forward data. Nodes like personal computers are very common, and nodes on the Internet are referred to as “computers” or “nodes.” Goal nodes are nodes in a graph that include criteria for success or failure that have been explicitly specified. A* defines the distance to the goal node as zero once it has been reached, and all other nodes are defined as having positive distances to the goal node. A node is only displayed as a neighbour once if an edge connects it to itself.

$$\mathit{M}{\mathit{P}}_{\mathit{i}\mathit{n}}{\mathit{D}}_{\mathit{i}\mathit{n}}(\mathit{t})=\frac{\mathbf{1}}{{\mathit{S}}_{\mathit{a}\mathit{d}\mathit{j}}}({\mathit{D}}_{\mathit{a}\mathit{d}\mathit{j}}\mathbf{-}{\mathit{D}}_{\mathit{i}\mathit{n}})+{\mathit{b}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}{\mathit{R}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}+{\mathit{R}}_{\mathit{c}\mathit{t}\mathit{r}\mathit{l}}$$

(1)

In general, multiple structures consist of all building dynamics and require space and envelope dynamics. Applications may be fusion with a matrix formed by a state-space representation Equation (2). Notice that for the state variable, the regular $\mathit{y}$ notation is used when Equation (1) uses the $\mathit{D}$ reflecting the building model temperature. $\mathit{P},\mathit{Q},\mathit{R},$ and $\mathit{S}$ shall consist of the approximate variables such as $\mathit{M}{\mathit{P}}_{\mathit{i}\mathit{n}},{\mathit{S}}_{\mathit{a}\mathit{d}\mathit{j}}$ and ${\mathit{b}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}$ in Equation (1) state, input, output, and feed-through matrice. The input image and time are $\mathit{v}$ and $\mathit{x}$. The “smart city” is a new concept proposed by major and local districts. It depicts a community of average technology size networked and sustainable and comfortable, appealing, and safe. Both the landscape and answers to local concerns are key elements. IoT sensors, analytics software, a user interface and networking are the four components.

$$\mathit{a}={\mathit{P}}_{\mathit{y}}+{\mathit{Q}}_{\mathit{v}},\mathit{b}={\mathit{R}}_{\mathit{y}}+{\mathit{S}}_{\mathit{v}}$$

(2)

These formulations discreetly and successfully applied to the thermal device of timing and actuation calculations operating/control system. Sensors equipped with Internet of Things (IoT) capabilities are used in smart water and energy systems to collect real-time data. Leak detection and network monitoring allow users to make more informed decisions about water management, enabling water infrastructure optimisation. To meet the needs of current and future generations in terms of economic, social, environmental, and cultural factors, a smart, sustainable city employs cutting-edge technologies such as information and communication technologies (ICTs) and other methods to enhance the quality of life, efficiency of city operations and services, and competitiveness.

Model 2: Water Management

The static hydraulic model defines the hydraulic network in a specific moment variable, and the water source network is presented with a snapshot. The first rule on managing the pipe structure flow at node mass Kirchhoff is conservation.

${\displaystyle \sum _{\mathit{m}}}{\mathit{B}}_{\mathit{m},\mathit{n}}\mathit{n}=\mathbf{1},\mathrm{\dots }\mathit{N}\text{(junction nodes)}$

(3)

As shown in Equation (3) where ${\mathit{B}}_{\mathit{m},\mathit{n}}$ is flow in nodes of relation $\mathit{m}$ and $\mathit{n}$, $\mathit{N}$ is the sum of nodes, ${\mathit{B}}_{\mathit{n}}$ is the demand at the node $\mathit{n}.$

The second protection Law in Kirchhoff is known as the Water Act. The cumulative network loop top loss is $\mathbf{0}$.

$$\sum _{\mathit{n},\mathit{m}\in \mathit{i}}{\mathit{h}}_{\mathit{m},\mathit{n}}=\mathbf{0}\mathit{\hspace{1em}}\mathit{i}=\mathbf{1},\mathrm{\dots }\mathit{T}\text{(loops)}$$

(4)

As inferred from Equation (4) where ${\mathit{h}}_{\mathit{i}}$ is the loss of head pipe $(\mathit{m},\mathit{n}),$ and $\mathit{T}$ is the number of loops. For the sake of convenience, this paper presumes that there is only one tank, so pseudo loops are not required involving fixed heads. Later that’s all-embracing.

In Equation (5), the association of stream loss is shown. This form of Ohm’s statute. The statement is seen in a condensed manner,

$${\mathit{h}}_{\mathit{m},\mathit{n}}={\mathit{D}}_{\mathit{m},\mathit{n}\mathrm{\dots }}\mathit{B}_{\mathit{m},\mathit{n}}{}^{\mathit{i}=\mathbf{1}}{\mathit{B}}_{\mathit{m},\mathit{n}}$$

(5)

As discussed in Equation (5) where ${\mathit{D}}_{\mathit{m},\mathit{n}}$ is the confrontation of the pipe related nodes $\mathit{m}$, and $\mathit{n}$ is the exponent of the fixed loss factor. This refers to a control device with a struggle coefficient dependent on certain local coefficients’ percentage error and valve diameter.

${\mathit{R}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}$	The input of exogenous/risk
$({\mathit{R}}_{\mathit{c}\mathit{t}\mathit{r}\mathit{l}})$	output monitoring
${\mathit{b}}_{\mathit{d}\mathit{i}\mathit{s}\mathit{t}}$	Estimated node capacity parameters
${\mathit{h}}_{\mathit{i}}$	Loss of head pipe
${\mathit{B}}_{\mathit{m},\mathit{n}}$	Flow in nodes of relation $\mathit{m}$ and $\mathit{n}$

Figure 2 Big data analytics in Smart cities.

Figure 2 shows big data analytics in smart cities. Big data preparation in intelligent cities. Intelligent urban developed enormous quantities of data, and big data networks use it to include data to enable smart urban platforms. Big data technology can effectively save, archive, and store information from smart cities to produce information to develop different smart urban programs. The use of ICT to enhance these organizations’ performance is required by sustainability and urban expansion. Big data analytics can significantly impact urban operations, functions, services, designs, plans, and policies. Applied intelligence in big data computing enables improved decision-making and deeper insights. As a result, most large-scale studies into big data technologies concentrate on enhancing economic growth while neglecting or underestimating the potential of such applications to help promote sustainability in intelligent and even more intelligent cities. Sustainably designing and building smart cities presents various issues and challenges that must be addressed in advance. Tools like Apache Storm, MongoDB, Cloudera, and OpenRefine are most utilized by data scientists working with large data. Big data would help decision-makers prepare to expand their programs, resources, and areas in smart communities.

Furthermore, big data administrators are known for some features and characteristics. To be a successful big data analyst, you must be familiar with the five V’s of the field (velocity, volume, value, variety, and integrity). In terms of Big Data features, value is the most significant, and it must still be accurate and useful as rapidly as data is generated.

Volume refers to the data size generated by all sources.

Speed submit to the speed of information invention, retrieval, analysis, and processing. The funding of real-time Big Data research has recently been emphasized.

Variety refers to the various data types that are generated. Most data are now generally formless and cannot effectively be sorted or tabularized.

Variability refers to how information arrangement and interpretation continuously changes, particularly with natural language processing data. The grammatical structure of sentences and the meaning of words can be studied and comprehended in context when human language is broken up into fragments in natural language processing. This aids computers in reading and comprehending written or spoken text in the same way that people do.

Value Big data could give an organization the potential value of the processing, administration, and analysis of better big data.

For instance, variance refers to keeping organized data from multiple sources. There is authenticity, which applies to data consistency, accuracy, and validation. Truthfulness refers to the data’s consistency and accuracy to the significance of data outcome for some problems. Using big data technology, smart city data can be successfully saved, archived, and stored, producing data for the development of various smart urban initiatives. Smart communities’ activities, resources, and locations would benefit from the use of big data if it were available to decision-makers. A few more traits and features of big data managers are well-known. Large amounts of information can be harvested from smart city data systems more quickly and effectively than ever before. In addition, big data can be used to prepare for any future growth of smart city services, resources, or locations. The diverse features of big data reveal immense advantages and developments. There are infinite options, constrained by available technology and instruments. Big data requires the best resources and methods to be analyzed and effectively categorized to meet its targets and developments in smart cities’ tools and methods. By recognizing the capacities and vulnerabilities present, we can use big data to capture more possibilities for improved programs and implementations in smart cities. Big Data can provide useful information for businesses. Big Data is used by businesses to boost their marketing efforts. A wide range of cutting-edge analytics projects, including machine learning and predictive modelling, make use of it. It’s difficult to pin down the exact size of “big data” to understand it.

Figure 3 Different Layers of smart city.

Figure 3 shows different layers of a smart city. The keystone of a smart city is the ICT infrastructure. It is the basic layer that all other components rely on. High-speed broadband networks, high-end data centres, physical space improvement of smart machines, cameras, drives, and many more are all part of the ICT infrastructure. Intelligent use of information and communication technology (ICT) is critical to advancing both climate change mitigation and sustainable urban planning. Users’ energy usage may be automatically analyzed and optimally controlled thanks to this new technology that connects the grid, the buildings, and the users. Urban services can be improved by using ICT technology in a Smart City to improve quality, performance, and interaction while reducing costs and resource consumption. The e-governance layer promotes establishing strategic ties between different public sector departments. In this layer, laws, regulations, and legislation enhance governmental organizations’ efficiency and provide people with future benefits. Following the facilities and strategies, people and other stakeholders can be provided securely, reliably, and efficiently by different public utilities on the e-governance layer. Traditionally, the city departments have been functioning separately, exchanging negligible knowledge with other departments of the total smart city. ICT systems and facilities that are in use in departments only concentrate on the on service. This setup causes much confusion and slows the implementation or performance of a service. People face privations when information from various agencies is needed. Similarly, departments’ response time where shared contact and collaboration is needed in a catastrophe gets much slower. Resource preparation based onreal-time data is not feasible. An ecosystem’s provision to sustain consumers’ effective service offering represents another big obstacle for smart cities. This infrastructure would require state-of-the-art computing and connectivity. Big data analytics has a very different methodology than classical statistical experimentation. Most of the time, one try to explain a reaction by modelling the data. Predicting response behaviour or figuring out how input variables affect response behaviour are the primary goals of this technique.

This architecture focuses on exchanging knowledge between departments and providing data resources through an open data model. This paper claims that residents should receive cross-application services in smart cities. The use of real-time data from different domains can accelerate the process response/completion time, which improves the overall performance of urban services. By exchanging, knowledge, which is the first source of value, the other realms and the city can learn how to solve the challenges and recognize imminent problems it occurs or escalate. Likewise, the provision of real-time data can contribute to effective resource planning that seeks to maximize capital usage at both the departmental and the city levels. The communication delay has been minimized using the proposed SBDAAO model.

Figure 4 Conceptual framework.

Figure 4 shows the conceptual framework. Utilizing the data-driven urban development approach, the study of population-related data enables us to understand the evolving request for different locations. Data-driven technologies in planning are related to planning, based on the processing of information about the mobility of people and events, regions, roads, and urban infrastructure. Different technological and operational competencies, i.e., the sequence of provable features and skills, allow and strengthen quality and productivity of urban running, organization, and preparation as a set of interlinked development and behaviour, are at the heart of information sustainable smart cities. Urban operating systems connect diverse, intelligent technology to the urban framework and domain coordination. Intelligent cities reflect constellations of resources on several fronts linked by numerous intelligence-enhancing networks, which organize ongoing data on the various facets of urbanism of the flow of choices about the city’s spatial, cultural, financial, and societal types. For example,the urban operating system and control panels, the smarter cities, and the urban operating system of plan IT represent business capital planning structures designed to execute and coordinate the major city-related businesses. Therefore, this instrumentation is the ICT field’s realm, which provides comprehensive operating systems for smart cities. The ICT systems and facilities used in departments exclusively focus on the service. This setup creates a lot of confusion and slows down the implementation or performance. It’s difficult for people to get the information they need from many agencies. In addition to minimizing power outages, intelligent grids minimize suppliers’ energy generation and delivery costs. No GHGs are emitted by these devices since they employ carbon-free fuel or power sources. Carbon dioxide from fossil fuels is reduced by using biomass as an energy source, reducing greenhouse gas emissions. The amount of methane released into the atmosphere is reduced when wood waste and coal are co-fired in a power plant. It gives an incentive and encouragement for customers to maximize their use of energy and reduce bills. Smart grids allow renewable energy sources that do not emit greenhouse gases such as wind and solar energy to be incorporated. Restart the computer if it hasn’t already been shut down. Using computers in an office building consumes a lot of electricity. Eliminate vampire power by unplugging unused electronics and choosing the appropriate lighting. Reduce the number of outlets needs for the plugin by using a power strip.

In general, city process centres and control panels outline big urban data collection and provide an interactive view and city synoptic knowledge. Often urban control centres are designed to track the entire city; data sources from several city authorities and agencies (including public transit and traffic, mobility, electricity grids, municipality and infrastructure, emergency management, weather information services, and public transmission information) are gathered or compiled in real-time. The huge volume of live service data for real-time decision-making and problem-solving is then stored, analyzed, visualized, and tracked from smartphones and social media systems in a particular data analysis midpoint. Analytical centres have been set up and are now operational inside the major data centre.

Strategic planning organizations, particularly for sustainability, are the key to urban growth programs and efforts. Both cities foster intelligent practices in planning processes, use knowledge widely to advise municipal preparation and architecture and enable developers to introduce digital technology for emerging developments in the future. Strategic scheduling and approach offices use a specific information analysis centre to easily and effectively connect and interlace local agencies and departments data to coordinate and organize the city. Data sources are vast everyday in such departments, where a review of cross-reference data is carried out, models are identified, and city issues can be understood and addressed. In addition, various developing cities have widespread use of specialist research programs for computer discipline and big data analysis correlated to environmental discipline, municipal computing, city learnings, inner-city analytics, etc. In this article’s framework, the degree to which the relevant technologies are built for urban operations, planning, and management are indicated in the extent to which the data-driven city concept is introduced. Therefore, several cities in the developing countryside have set up advanced laboratories and investigate centres, which actively promote ground breaking resolution, and communication delay is decreased using the SBDAAO model.

4 Result and Discussion

The proposed SBDAAO model’s experimental results have been performed based on the performance metrics such as efficiency ratio, energy consumption, average communication delay, renewable power, and accuracy ratio. A smart city’s key strategic feature would be sustainability to search for participatory drivers, create efficient energy storage and consumption patterns, and use renewable power to recycle and care for natural resources.

Figure 5 Efficiency ratio.

Figure 5 shows the efficiency ratio. Smart cities are linked to a direct outcome, announcing robust Energy Conservation and Green Energy Development. This study examines how an efficient legal framework is enforced for smart future cities in national and democratic decision-making. The use of renewable energy in metropolitan areas can be improved based on local and domestic sources. Solar farms or wind turbines may be installed in cities that meet energy needs for lighting schemes or support electricity. Using local resources, metropolitan communities can increase their use of renewable energy. Lighting a room more brightly is more cost-effective and practical when using energy-efficient lights. Lighting equipment’s output can be adjusted based on the amount of light required by using energy-saving technology. The smart network promotes renewable energies and intelligent loads while building and local energy efficiency. It integrates distributed processing, distributed generation, and purchasing power charge management applications to make all power systems more effective and efficient (generation, transmission, delivery, and consumption). One of the strongest links between smart cities and energy conservation and green energy development strategy is the announcement of such an aggressive approach (ECGED). For smart future cities, this study examines how democratic and national decision-making procedures can enforce a strong legal foundation for those cities. SBDAAO’s experimental findings are based on performance metrics such as efficiency ratios, energy consumptions, average communication delays, renewable power and accuracy ratios.

Figure 6 Energy consumption.

Figure 6 shows energy consumption. The transportation field’s energy consumption takes a large part of the overall consumption, and the development of urban transportation will decrease energy production, heating, and cities’ emissions. Smart transportation systems (electrification of transportation through the smart grid), enhanced road signs, and smart rooting of vehicles would have advantages on energy efficiency and ecological problems in minimizing carbon pollution. Construction can be designed to require less electricity for the building. In the architecture and planning of cities, natural ventilation and heat transfer should be taken into account to maximize energy efficiency in homes and cities. At the same time, cooling, heating, and lighting methods should be used in houses. Using adequate structural insulation, planting trees around the building, and implementing special energy conservation techniques will limit consumption.

Figure 7 Renewable power.

Figure 7 shows renewable power. Utilities use wind and solar power to achieve price and output parity with traditional electricity sources worldwide, add cost-effective grid equilibrium, and become more critical tools by increasingly cost-effective storage and other new technologies. These renewable sources are now closest to the increasing need for energy sources that utilities aim to supply, efficient, economical, and environmentally sound. For core users such as cities, renewables have favoured energy sources. Another essential city and utility customer is companies that receive record quantities of renewable energy. Wind and solar power in and around the city are available compared to other power suppliers. The energy sources are people and wind. Many communities and organizations are increasingly demanding green energy. They are encouraged to install it on their properties and resources or buy shares in solar, wind, or energy projects through community energy initiatives.

Figure 8 Average communication delay.

Figure 8 shows the average communication delay. The reliable and deterministic connectivity of wireless networks is important. Determinism means that any data packet must be given within a finite amount of time. It must ensure that future congestion or interference does not impair the network’s predictable characteristics. This study explores the transmission process and provides algorithms that guarantee network stability and limited latency. By grouping endpoints that often connect, subnetting can also minimize latency across your network. In addition, consider traffic shaping and bandwidth allocation strategies to improve network latency for important business processes. Whereas 3G and 4G mobile networks currently present a variety of problems with the spectrum of services needed for smart, sustainable city appliances, designing 5G with a connection to mobile applications in the link devices with Internet and other devices in a secure fashion, to speed up transport traffic and process high volumes of data. All protocols provide asynchronous and synchronization data relationships. The former can be used for smart city traffic applications, which can accommodate the delay and can be used for applications that need more strict quality of service (QoS) requirements, including greater bandwidth and reduced delay. These can be used for real-time applications, and multimedia communication is part of these applications. In addition, stability and security services are available in these protocols. However, most protection features require more processing, leading to increased delays and energy usage. These considerations should be taken into account before these features are allowed.

Figure 9 Accuracy ratio.

Figure 9 shows the accuracy ratio. The big advances in wireless and sensor-based technology have paved the way for the extensive introduction of IoT-based technologies in the world of smart cities. The smart city must incorporate core infrastructures such as IoT, Big Data, cameras, machine learning, and GPS-based apps, which contribute to major risks to citizens’ protection and honesty. Systems with sufficient protection measures to avoid data violations and reveal vulnerabilities are expected to be technologically robust. Smart city network architectures may adapt to the rising amount of data from various interaction devices, sensors, and systems. The poor quality and slightly different design of the intelligent city data will harm critical systems’ reliability and accuracy. These considerations pose an added challenge when deploying a large number of multi-supplier networks. An essential infrastructural component of a Smart City is providing safe and abundant drinking water, reliable energy, sanitation and recycling of solid waste, efficient urban mobility and public transportation, affordable housing, robust IT connectivity, and good governance. To build a smart city, you must consider five types of human characteristics. There are several aspects to consider here, including social, technological, economic, and environmental.

The proposed SBDAAO model for the sustainable smart city on human and user-centred design and enhances the efficiency ratio, accuracy ratio and reduces energy consumption, average communication delay, and renewable power when compared to another existing structural equation modelling (SEM), interval type-2 fuzzy sets (IT2FS), Blockchain-based sharing services (BSS) methods.

Experimental Results

Smart City and Big Data are two recent and essential ideas that led many to merge smart digital applications to produce economic development, resilience, and good governance. This paper analyzed both terms and their various meanings and defined several similar characteristics. Each concept has a set of characteristics that describe each concept considering its diversified meanings. Building on the key advantages of using big data, smart urban applications were planned and supported. From that point on, the paper addressed the different possibilities, which would result in a smart application that will leverage any available data to optimize operations and performance. In addition, the different obstacles and listed some concerns that could hamper growth activities in Big Data applications. Moreover, these requests often aim to overcome the issues and recommend new approaches to address those problems and achieve better outcomes. Finally, this paper has explored some key unanswered questions which should be examined deeper and answered to reach a more detailed understanding of smart cities and build a practical framework. Powerful development and implementation models, well-trained human capital, model-simulation, and the planning and the well-supported governing bodies are the obstacles and open problems to developing and implementing effective smart urban big data applications. With both success factors and a better understanding of solutions, society will be intelligent and built more intelligently with intelligent models and services. The experimental results have been executed, and the suggested method achieves an accuracy ratio (98.2%), the efficiency ratio (95.5%), energy consumption (20.1%),average communication delay (17.6%), and renewable power (93.9 %).

Acknowledgement

A brief introduction to the author: Wei Guoan (1981–), male, Henan Zhengzhou people, master of Xi’an University Of Architecture And Technology, Associate Professor of Engineering Management Department of Henan Technical College of Construction, main research area: Civil Engineering and engineering management. Express address: the 27 District of Henan city of Zhengzhou province Ruhe Road No. 28 hospital.

Funding

Training Program for Young Backbone Teachers in Higher Vocational Schools in Henan Province (2019GZGG073).

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Biography

Guoan Wei (1981–), male, Henan Zhengzhou people, master of Xi’an University Of Architecture And Technology, Associate Professor of Engineering Management Department of Henan Technical College of Construction, main research area: Civil Engineering and engineering management.

Strategic Planning for Energy and the Environment, Vol. 42_1, 137–160.
doi: 10.13052/spee1048-5236.4218
© 2022 River Publishers

Abstract

1 Introduction to Sustainable Smart City on Human and User-Centered Design

2 Related Works

3 Sustainable Big Data Analytics Assisted Optimization Framework (SBDAAO)

Model 1: Building Model

Model 2: Water Management

4 Result and Discussion

Experimental Results

Acknowledgement

Funding

References

Biography