# Information Extraction and Data Planning of Smart City Based on Internet of Things

Authors: Zeyad Farisi
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to solve the problem of slow resource scheduling in the process of smart city management, the author proposes a smart city information extraction and data planning system based on the Internet of Things. IoT requires different technologies for analysis for different data types. Managers use different IoT applications to analyze data from different devices and integrate relevant data for possible machine failure or emergency in smart home applications. Situation is predicted. The system includes a cloud platform intelligence center, uses the cloud management module to monitor various hardware devices, constructs the cloud computing resource scheduling objective function, uses the cultural particle swarm algorithm to solve the objective function, and obtains the cloud computing resource scheduling scheme. The experimental results show that the overall utilization rate of the system is the best, close to 100%.Conclusion. The system can realize the effective management of the smart city and monitor the city situation in real time. When implementing resource scheduling, the task completion time is short, the system utilization rate is high, and the resources can be maximized.


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## Body

## 1. Introduction

In the current process of social competition, the importance of science and technology is becoming more and more obvious, and all walks of life need to develop and use new technologies, only in this way can they occupy their own position in the market. Big data is a product that emerged in the process of modern high-tech development and has strong market potential, and through relevant practical analysis, it is found that big data can play an important role in the process of urban planning. First, the government can use big data technology to understand and master the problems that arise in the process of urban spatial planning, make timely decisions, and deal with these problems [1]. Secondly, in the traditional model, the information channel is single, and the accuracy of the data obtained is limited, which can no longer match the requirements of modern urban planning; the application of big data technology can quickly expand the information and sources of urban and rural planning data. Third, the current speed of informatization is further accelerated, and the usage of the Internet is further increased, which provides great convenience for the realization of big data [2].In the process of building a smart city, it is necessary to use the Internet of Things technology to connect with each terminal, and then realize the collection and application of data, and then it can be applied to actual production and life to realize intelligent construction [3]. In the actual process of building a smart city, terminal equipment needs to be installed in many scenarios for information collection and access, but due to the restrictions and influence of the actual environment, it is impossible to connect to external power. These results in that the power supply cannot be connected during the use of the terminal device, and only external batteries can be used for power supply, which makes the application of the terminal have power consumption problems. If the device consumes a lot of power, the battery needs to be replaced frequently, which seriously affects the effective performance of the IoT function and increases the workload of the relevant personnel [4].Smart city refers to the full use of information technology in the process of continuous construction of the city and can be applied in various fields of social development. This is also the advanced stage of smart urbanization construction, which can not only promote a better urban environment, at the same time, based on technologies such as cloud computing and big data, it has a certain positive significance for smart city construction, environmental protection, security protection, and other services and can respond accurately and quickly, making urbanization construction more sensitive and complete, make people’s life more convenient and fast, provide people with a better living environment, and then improve the humanization and intelligence of urban construction [5]. Therefore, people’s thoughts will not be limited by region and time, and the distance between urban residents will be shortened [6]. Especially under the background that the basic functions of my country’s cities are becoming more and more perfect, the application of the Internet of Things can ensure the information interaction of various building equipment in the city, that is, the “Internet of Everything” in the smart city [7]. At the same time, the application of Internet of Things technology is also an important part of urban development, it can not only realize the informatization of the whole city but also achieve precise control; the relationship between the two should rely on and promote each other, specifically, it includes ecological construction, engineering construction, people’s livelihood construction, road construction, public safety, logistics and transportation, and mature medical treatment through intelligent response to optimize and improve service quality, so as to meet the actual needs of our people [8].


## 2. Literature Review

With the continuous development of information technology, the current production and life of people are inseparable from the Internet and information technology, under this situation; people have higher requirements for the quality of the Internet and communication services [9]. At the same time, in the process of building a smart city, the number of various information collection, terminals, and smart devices used in production and life is increasing, which puts forward higher requirements for the capacity and communication quality of urban networks [10]. Therefore, in order to ensure the construction effect of smart city, in the process of practical application of IoT technology, the problem of network capacity must be solved. At present, the 5G network that China is gradually promoting and applying is an important preparation for building a smart city [11]. The comparative analysis of 5G network and 4G network is shown in Table 1.Table 1
Comparative analysis of 5G network and 4G network.
Network4G5GDelay10 msLess than 1 msPeak data rate1 Gbps20 GbpsNumber of mobile connections8 billion (2016)11 billion (2021)Channel broadband20 MHz 2200kHZ(applies to Cat-NBI IoT)100 MHz (below 6GHz) 400 MHz (above 6GHz)Frequency band600 MHz-5.925 GHz600 MHz-mmWaveUser terminal (UE) transmit power+23 dBm (+26 dBm in n41 band)+26 dBm for 2.5 GHz and above (in Sub6G band)The construction of a smart city requires the Internet of Things technology to cover a wider range as much as possible, so as to ensure a more comprehensive collection of data and information, so that the data resources in the city can be fully utilized, only in this way can the level of urban intelligence be further improved, so as to provide better services for the production and life in the city [12]. Therefore, in the process of smart city construction, it is necessary to expand the coverage of IoT technology as much as possible, however, in terms of the current technical level of various aspects in China, the promotion and application of Internet of Things technology will still be limited [13].China’s urbanization process is accelerating, and a series of social problems such as urban environment pollution, lack of public resources, lack of public safety management, rapid urban population growth, and public traffic congestion have seriously affected the normal life of urban residents [14]. Many cities focus on building smart cities and establish smart city management systems and related platforms, smart city management systems have become the focus of relevant research under the needs of the social environment [15]. Cloud computing is a product of the highly developed network information technology, all available resources are shared in the form of “cloud”, and users can apply to the “cloud” to provide required services and resources through the network [16]. The resources in the cloud computing platform are heterogeneous and dynamic, when scheduling and resource allocation of large-scale data tasks, the completion time and throughput of the applied system need to be considered, and the load balancing of system resources needs to be considered, therefore, the research on resource scheduling of cloud computing platform is also a difficult problem in the current research community [17]. Taking the cloud computing platform as the basis, the smart city management system is designed to meet the development requirements of the modern city process [18].


## 3. Methods

### 3.1. Smart City Management System Based on Cloud Computing Platform

#### 3.1.1. The Overall Structure of the System

Considering the overall positioning and management principles of the system, the business functions of the smart city system are analyzed, and the overall structure of the smart city management system based on the cloud computing platform is constructed. The results are shown in Figure1.Figure 1
Overall structure of the system.The basic layer of the system is the IaaS (Basic Knowledge as a Service) resource layer, and the main construction content is the PaaS (Platform as a Service) platform layer. This layer mainly implements system management functions, and the cloud management module and business management module are designed to realize system operation and maintenance and business operation [19].
(1)
The hardware resource used in the IaaS layer is a unified resource, and its main function is to provide services to the PaaS platform layer. The IaaS layer consists of network resource pools, storage resource pools, computing resource pools, and security resource pools, which are composed of security devices, storage, networks, and servers [20]. This layer provides basic equipment resources to the SaaS layer and PaaS of the system. This layer is also responsible for the management and healthy operation of each resource pool and physical devices and allocates and controls the capacity of various resource pools(2)
The PaaS layer provides standardized shared cloud services to each application. This layer includes business operation and application incubation environment, and the three functional components are cloud service engine component, middleware component, and data component. The main functions of the cloud service engine component are scheduling and management of service measurement, service resources, service monitoring, service routing, service authentication, etc.; the middleware component is responsible for the dynamic sharing and unified management of resources; the data component completes the dynamic sharing of resources and unified management of the database. The cloud management module in this layer provides management services for the cloud management operation in the entire smart city management system and realizes the management of submodules such as resource management; the business management module provides management services for the cloud platform business operation in the entire smart city management system and realizes the normal operation of submodules such as service management(3)
In the SaaS (software as a service) layer, applications are deployed in detail according to the seven major areas of smart cities, mainly including national direct management applications and provincial and municipal applications; the division is based on industry division and geographical division. The way to present the application is through the cloud platform [21]


#### 3.1.2. Design of Monitoring and Management Module

In the entire smart city management system, it is necessary to monitor the city’s situation. In the cloud platform management module, the monitoring and management submodule plays an important role, the structure diagram of this submodule is shown in Figure2. The monitoring and management submodule is mainly composed of six functions. A complete monitoring and management submodule requires audio and video acquisition equipment, transmission medium equipment, control equipment, and terminal monitoring and monitoring equipment. They are installed in the camera and jointly realize the six functions of the monitoring and control submodule. Real-time monitoring is achieved by arranging surveillance cameras in all directions in the city.Figure 2
Monitoring and management submodule structure.


### 3.2. Cloud Computing Resource Scheduling Strategy

#### 3.2.1. Scheduling Principle

Under the cloud computing platform, the relationship between computing resources and tasks is not one-to-one, but resources are mapped by tasks first and related physical devices are mapped to resources. Currently, the most commonly used programming model for cloud computing platforms is Map/Reduce proposed by Google. A quintuple is used to describe the resource scheduling model of cloud computing.(1)S=T,V,D,MTV,MVD.In the formula,V=v1,v2,⋯,vm,D=d1,d2,⋯,dm, and T=t1,t2,⋯,tm represent the resource set, the physical device set, and the task set, respectively, and MVD and MTV, respectively, represent the correspondence between physical devices and resources and the mapping strategy between resources and tasks.According to the user task computing center, theMTV is allocated, and the resource utilization scheduler is scheduled to the corresponding physical device to realize the MVD, therefore, resource scheduling is to realize the scheduling of resources to physical devices. Suppose that after MVD mapping, a certain task ti is mapped to the resource vj, and the physical device dk receives the task assigned by the resource vj and executes it. According to the corresponding relationship between the task and the resource, after resource transfer, the expected execution time of task ti arriving on device dk is expressed by ETCti,dk, therefore, the entire distribution matrix of D subject to T is uniformly called the ETC matrix.
(2)ETCmn=ETCtiMTV,dk,1≤i≤m,1≤k≤n.The essence of equation (2) is the execution time matrix, the main content is that on n physical devices, and the execution time matrix of m tasks after resource mapping tiMTV. The earliest completion time of task ti on material equipment dk is expressed by
(3)FinishtiMTV,dk=Startdk+ETCtiMTV,dk.In the formula, the earliest execution start time of physical devicedk is represented by Startdk, which represents the total time spent by the physical device dk to achieve task allocation and execution
(4)Sumdk=∑i=1mcikFinishtiMTV,dk,cik=1tiMTV=dk,0tiMTV≠dk.In the formula, the representative physical devicedk is finally mapped and executed by the task ti. The total execution time of all tasks T=t1,t2,⋯,tm is expressed by
(5)totalT=∑k=1nSumdk.The ultimate goal of cloud computing resource scheduling is to ensure that equation (5) is a minimum value, and equation (6) is the objective function of cloud computing resource scheduling.
(6)GoalT=min∑k=1nSumdk.


#### 3.2.2. Cloud Computing Resource Scheduling Strategy Based on Cultural Particle Swarm Algorithm

In the regular binary encoding used by the traditional particle swarm algorithm, cloud computing resource scheduling is not applicable. In order to comply with the characteristics of cloud computing resource scheduling, the author uses decimal encoding rules.d2,d5,⋯,dk⋯,di represents the particle position encoding method, di represents the i-th physical device, and the number of tasks determines the particle code length. If 5,4,1,7 is in the form of particle position encoding, then t1M1V⟶d2,t2M2V⟶d5,t3M3V⟶d1 and t4M4V⟶d7 represent four tasks corresponding to resource access physical devices [22].Controlling the task completion goal to the shortest is the ultimate goal of cloud computing resource scheduling, if the particle quality is to be guaranteed to be high, the corresponding fitness value needs to be large, so that a better cloud computing resource scheduling scheme can be obtained. Equation (7) defines the particle fitness function.
(7)fit=1min∑k=1nsumdk.The common particle swarm algorithm generates the initial particle swarm in a random way, which is easy to cause the particles to be concentrated in a certain local area, and an uneven feasible solution will appear. In the author’s study, the uniform method is used to generate the initial particle swarm, make sure that the initial particle swarm is distributed uniformly.The cloud computing resource scheduling process based on cultural particle swarm is shown in Figure3. First determine the scale of cloud computing resources, and then set the parameters of the cultural particles in the algorithm, initialize the knowledge space and the swarm particle swarm at the same time, determine the number of iterations, update the knowledge space, and calculate the particle fitness at the same time. After the update of the knowledge space is completed, the evolution of the group itself is carried out, and it is determined whether the evolution result satisfies the termination condition. If it cannot be satisfied, the evolution of the group itself will be reimplemented; if it can be satisfied, it will affect the particle swarm space according to the influence operation. At this time, the particle fitness is calculated, and the individual extreme values and fresh extreme values are updated according to the calculation results, at the same time, the particle position and velocity are updated, and then determine whether the termination condition is satisfied. If not, rerun the specified number of iterations, and if it can be satisfied, the optimal particle position is obtained, and finally the optimal cloud computing resource scheduling scheme is obtained [23].Figure 3
Cloud computing resource scheduling process.


### 3.3. Implementation and Performance Test of Smart City Management System

In order to verify the performance of the system, a city is taken as the research object. The city is a new first-tier city with 26 districts and 8 counties under its jurisdiction, with a total area of about 80,000 square kilometers and a resident population of about 31 million. According to statistics in 2019, the total regional production reached 2.36 trillion yuan and the subtropical monsoon humid climate prevails in the region, with hot and sultry summers and cold and humid winters. The system includes functions such as smart government, smart finance, smart transportation, smart energy, smart security, smart medical care, and smart transportation. According to the user’s needs, by clicking the corresponding button in the interface, jump to the corresponding page to expand the specific operation; the system also includes a search function and enter the keywords in the search box to find what users need.The system displays the map of all streets and districts in the city, by dragging the mouse to browse the location-related content required by the user, and the black dots on the map are the monitoring distribution locations. If you need to check the monitoring situation of a certain location, you need to double-click the black dot at the location, that is, you can operate the monitoring equipment at this location, and you can also call the historical video of the monitoring of the location to realize the visual management of the smart city [24].


## 3.1. Smart City Management System Based on Cloud Computing Platform

### 3.1.1. The Overall Structure of the System

Considering the overall positioning and management principles of the system, the business functions of the smart city system are analyzed, and the overall structure of the smart city management system based on the cloud computing platform is constructed. The results are shown in Figure1.Figure 1
Overall structure of the system.The basic layer of the system is the IaaS (Basic Knowledge as a Service) resource layer, and the main construction content is the PaaS (Platform as a Service) platform layer. This layer mainly implements system management functions, and the cloud management module and business management module are designed to realize system operation and maintenance and business operation [19].
(1)
The hardware resource used in the IaaS layer is a unified resource, and its main function is to provide services to the PaaS platform layer. The IaaS layer consists of network resource pools, storage resource pools, computing resource pools, and security resource pools, which are composed of security devices, storage, networks, and servers [20]. This layer provides basic equipment resources to the SaaS layer and PaaS of the system. This layer is also responsible for the management and healthy operation of each resource pool and physical devices and allocates and controls the capacity of various resource pools(2)
The PaaS layer provides standardized shared cloud services to each application. This layer includes business operation and application incubation environment, and the three functional components are cloud service engine component, middleware component, and data component. The main functions of the cloud service engine component are scheduling and management of service measurement, service resources, service monitoring, service routing, service authentication, etc.; the middleware component is responsible for the dynamic sharing and unified management of resources; the data component completes the dynamic sharing of resources and unified management of the database. The cloud management module in this layer provides management services for the cloud management operation in the entire smart city management system and realizes the management of submodules such as resource management; the business management module provides management services for the cloud platform business operation in the entire smart city management system and realizes the normal operation of submodules such as service management(3)
In the SaaS (software as a service) layer, applications are deployed in detail according to the seven major areas of smart cities, mainly including national direct management applications and provincial and municipal applications; the division is based on industry division and geographical division. The way to present the application is through the cloud platform [21]


### 3.1.2. Design of Monitoring and Management Module

In the entire smart city management system, it is necessary to monitor the city’s situation. In the cloud platform management module, the monitoring and management submodule plays an important role, the structure diagram of this submodule is shown in Figure2. The monitoring and management submodule is mainly composed of six functions. A complete monitoring and management submodule requires audio and video acquisition equipment, transmission medium equipment, control equipment, and terminal monitoring and monitoring equipment. They are installed in the camera and jointly realize the six functions of the monitoring and control submodule. Real-time monitoring is achieved by arranging surveillance cameras in all directions in the city.Figure 2
Monitoring and management submodule structure.


## 3.1.1. The Overall Structure of the System

Considering the overall positioning and management principles of the system, the business functions of the smart city system are analyzed, and the overall structure of the smart city management system based on the cloud computing platform is constructed. The results are shown in Figure1.Figure 1
Overall structure of the system.The basic layer of the system is the IaaS (Basic Knowledge as a Service) resource layer, and the main construction content is the PaaS (Platform as a Service) platform layer. This layer mainly implements system management functions, and the cloud management module and business management module are designed to realize system operation and maintenance and business operation [19].
(1)
The hardware resource used in the IaaS layer is a unified resource, and its main function is to provide services to the PaaS platform layer. The IaaS layer consists of network resource pools, storage resource pools, computing resource pools, and security resource pools, which are composed of security devices, storage, networks, and servers [20]. This layer provides basic equipment resources to the SaaS layer and PaaS of the system. This layer is also responsible for the management and healthy operation of each resource pool and physical devices and allocates and controls the capacity of various resource pools(2)
The PaaS layer provides standardized shared cloud services to each application. This layer includes business operation and application incubation environment, and the three functional components are cloud service engine component, middleware component, and data component. The main functions of the cloud service engine component are scheduling and management of service measurement, service resources, service monitoring, service routing, service authentication, etc.; the middleware component is responsible for the dynamic sharing and unified management of resources; the data component completes the dynamic sharing of resources and unified management of the database. The cloud management module in this layer provides management services for the cloud management operation in the entire smart city management system and realizes the management of submodules such as resource management; the business management module provides management services for the cloud platform business operation in the entire smart city management system and realizes the normal operation of submodules such as service management(3)
In the SaaS (software as a service) layer, applications are deployed in detail according to the seven major areas of smart cities, mainly including national direct management applications and provincial and municipal applications; the division is based on industry division and geographical division. The way to present the application is through the cloud platform [21]


## 3.1.2. Design of Monitoring and Management Module

In the entire smart city management system, it is necessary to monitor the city’s situation. In the cloud platform management module, the monitoring and management submodule plays an important role, the structure diagram of this submodule is shown in Figure2. The monitoring and management submodule is mainly composed of six functions. A complete monitoring and management submodule requires audio and video acquisition equipment, transmission medium equipment, control equipment, and terminal monitoring and monitoring equipment. They are installed in the camera and jointly realize the six functions of the monitoring and control submodule. Real-time monitoring is achieved by arranging surveillance cameras in all directions in the city.Figure 2
Monitoring and management submodule structure.


## 3.2. Cloud Computing Resource Scheduling Strategy

### 3.2.1. Scheduling Principle

Under the cloud computing platform, the relationship between computing resources and tasks is not one-to-one, but resources are mapped by tasks first and related physical devices are mapped to resources. Currently, the most commonly used programming model for cloud computing platforms is Map/Reduce proposed by Google. A quintuple is used to describe the resource scheduling model of cloud computing.(1)S=T,V,D,MTV,MVD.In the formula,V=v1,v2,⋯,vm,D=d1,d2,⋯,dm, and T=t1,t2,⋯,tm represent the resource set, the physical device set, and the task set, respectively, and MVD and MTV, respectively, represent the correspondence between physical devices and resources and the mapping strategy between resources and tasks.According to the user task computing center, theMTV is allocated, and the resource utilization scheduler is scheduled to the corresponding physical device to realize the MVD, therefore, resource scheduling is to realize the scheduling of resources to physical devices. Suppose that after MVD mapping, a certain task ti is mapped to the resource vj, and the physical device dk receives the task assigned by the resource vj and executes it. According to the corresponding relationship between the task and the resource, after resource transfer, the expected execution time of task ti arriving on device dk is expressed by ETCti,dk, therefore, the entire distribution matrix of D subject to T is uniformly called the ETC matrix.
(2)ETCmn=ETCtiMTV,dk,1≤i≤m,1≤k≤n.The essence of equation (2) is the execution time matrix, the main content is that on n physical devices, and the execution time matrix of m tasks after resource mapping tiMTV. The earliest completion time of task ti on material equipment dk is expressed by
(3)FinishtiMTV,dk=Startdk+ETCtiMTV,dk.In the formula, the earliest execution start time of physical devicedk is represented by Startdk, which represents the total time spent by the physical device dk to achieve task allocation and execution
(4)Sumdk=∑i=1mcikFinishtiMTV,dk,cik=1tiMTV=dk,0tiMTV≠dk.In the formula, the representative physical devicedk is finally mapped and executed by the task ti. The total execution time of all tasks T=t1,t2,⋯,tm is expressed by
(5)totalT=∑k=1nSumdk.The ultimate goal of cloud computing resource scheduling is to ensure that equation (5) is a minimum value, and equation (6) is the objective function of cloud computing resource scheduling.
(6)GoalT=min∑k=1nSumdk.


### 3.2.2. Cloud Computing Resource Scheduling Strategy Based on Cultural Particle Swarm Algorithm

In the regular binary encoding used by the traditional particle swarm algorithm, cloud computing resource scheduling is not applicable. In order to comply with the characteristics of cloud computing resource scheduling, the author uses decimal encoding rules.d2,d5,⋯,dk⋯,di represents the particle position encoding method, di represents the i-th physical device, and the number of tasks determines the particle code length. If 5,4,1,7 is in the form of particle position encoding, then t1M1V⟶d2,t2M2V⟶d5,t3M3V⟶d1 and t4M4V⟶d7 represent four tasks corresponding to resource access physical devices [22].Controlling the task completion goal to the shortest is the ultimate goal of cloud computing resource scheduling, if the particle quality is to be guaranteed to be high, the corresponding fitness value needs to be large, so that a better cloud computing resource scheduling scheme can be obtained. Equation (7) defines the particle fitness function.
(7)fit=1min∑k=1nsumdk.The common particle swarm algorithm generates the initial particle swarm in a random way, which is easy to cause the particles to be concentrated in a certain local area, and an uneven feasible solution will appear. In the author’s study, the uniform method is used to generate the initial particle swarm, make sure that the initial particle swarm is distributed uniformly.The cloud computing resource scheduling process based on cultural particle swarm is shown in Figure3. First determine the scale of cloud computing resources, and then set the parameters of the cultural particles in the algorithm, initialize the knowledge space and the swarm particle swarm at the same time, determine the number of iterations, update the knowledge space, and calculate the particle fitness at the same time. After the update of the knowledge space is completed, the evolution of the group itself is carried out, and it is determined whether the evolution result satisfies the termination condition. If it cannot be satisfied, the evolution of the group itself will be reimplemented; if it can be satisfied, it will affect the particle swarm space according to the influence operation. At this time, the particle fitness is calculated, and the individual extreme values and fresh extreme values are updated according to the calculation results, at the same time, the particle position and velocity are updated, and then determine whether the termination condition is satisfied. If not, rerun the specified number of iterations, and if it can be satisfied, the optimal particle position is obtained, and finally the optimal cloud computing resource scheduling scheme is obtained [23].Figure 3
Cloud computing resource scheduling process.


## 3.2.1. Scheduling Principle

Under the cloud computing platform, the relationship between computing resources and tasks is not one-to-one, but resources are mapped by tasks first and related physical devices are mapped to resources. Currently, the most commonly used programming model for cloud computing platforms is Map/Reduce proposed by Google. A quintuple is used to describe the resource scheduling model of cloud computing.(1)S=T,V,D,MTV,MVD.In the formula,V=v1,v2,⋯,vm,D=d1,d2,⋯,dm, and T=t1,t2,⋯,tm represent the resource set, the physical device set, and the task set, respectively, and MVD and MTV, respectively, represent the correspondence between physical devices and resources and the mapping strategy between resources and tasks.According to the user task computing center, theMTV is allocated, and the resource utilization scheduler is scheduled to the corresponding physical device to realize the MVD, therefore, resource scheduling is to realize the scheduling of resources to physical devices. Suppose that after MVD mapping, a certain task ti is mapped to the resource vj, and the physical device dk receives the task assigned by the resource vj and executes it. According to the corresponding relationship between the task and the resource, after resource transfer, the expected execution time of task ti arriving on device dk is expressed by ETCti,dk, therefore, the entire distribution matrix of D subject to T is uniformly called the ETC matrix.
(2)ETCmn=ETCtiMTV,dk,1≤i≤m,1≤k≤n.The essence of equation (2) is the execution time matrix, the main content is that on n physical devices, and the execution time matrix of m tasks after resource mapping tiMTV. The earliest completion time of task ti on material equipment dk is expressed by
(3)FinishtiMTV,dk=Startdk+ETCtiMTV,dk.In the formula, the earliest execution start time of physical devicedk is represented by Startdk, which represents the total time spent by the physical device dk to achieve task allocation and execution
(4)Sumdk=∑i=1mcikFinishtiMTV,dk,cik=1tiMTV=dk,0tiMTV≠dk.In the formula, the representative physical devicedk is finally mapped and executed by the task ti. The total execution time of all tasks T=t1,t2,⋯,tm is expressed by
(5)totalT=∑k=1nSumdk.The ultimate goal of cloud computing resource scheduling is to ensure that equation (5) is a minimum value, and equation (6) is the objective function of cloud computing resource scheduling.
(6)GoalT=min∑k=1nSumdk.


## 3.2.2. Cloud Computing Resource Scheduling Strategy Based on Cultural Particle Swarm Algorithm

In the regular binary encoding used by the traditional particle swarm algorithm, cloud computing resource scheduling is not applicable. In order to comply with the characteristics of cloud computing resource scheduling, the author uses decimal encoding rules.d2,d5,⋯,dk⋯,di represents the particle position encoding method, di represents the i-th physical device, and the number of tasks determines the particle code length. If 5,4,1,7 is in the form of particle position encoding, then t1M1V⟶d2,t2M2V⟶d5,t3M3V⟶d1 and t4M4V⟶d7 represent four tasks corresponding to resource access physical devices [22].Controlling the task completion goal to the shortest is the ultimate goal of cloud computing resource scheduling, if the particle quality is to be guaranteed to be high, the corresponding fitness value needs to be large, so that a better cloud computing resource scheduling scheme can be obtained. Equation (7) defines the particle fitness function.
(7)fit=1min∑k=1nsumdk.The common particle swarm algorithm generates the initial particle swarm in a random way, which is easy to cause the particles to be concentrated in a certain local area, and an uneven feasible solution will appear. In the author’s study, the uniform method is used to generate the initial particle swarm, make sure that the initial particle swarm is distributed uniformly.The cloud computing resource scheduling process based on cultural particle swarm is shown in Figure3. First determine the scale of cloud computing resources, and then set the parameters of the cultural particles in the algorithm, initialize the knowledge space and the swarm particle swarm at the same time, determine the number of iterations, update the knowledge space, and calculate the particle fitness at the same time. After the update of the knowledge space is completed, the evolution of the group itself is carried out, and it is determined whether the evolution result satisfies the termination condition. If it cannot be satisfied, the evolution of the group itself will be reimplemented; if it can be satisfied, it will affect the particle swarm space according to the influence operation. At this time, the particle fitness is calculated, and the individual extreme values and fresh extreme values are updated according to the calculation results, at the same time, the particle position and velocity are updated, and then determine whether the termination condition is satisfied. If not, rerun the specified number of iterations, and if it can be satisfied, the optimal particle position is obtained, and finally the optimal cloud computing resource scheduling scheme is obtained [23].Figure 3
Cloud computing resource scheduling process.


## 3.3. Implementation and Performance Test of Smart City Management System

In order to verify the performance of the system, a city is taken as the research object. The city is a new first-tier city with 26 districts and 8 counties under its jurisdiction, with a total area of about 80,000 square kilometers and a resident population of about 31 million. According to statistics in 2019, the total regional production reached 2.36 trillion yuan and the subtropical monsoon humid climate prevails in the region, with hot and sultry summers and cold and humid winters. The system includes functions such as smart government, smart finance, smart transportation, smart energy, smart security, smart medical care, and smart transportation. According to the user’s needs, by clicking the corresponding button in the interface, jump to the corresponding page to expand the specific operation; the system also includes a search function and enter the keywords in the search box to find what users need.The system displays the map of all streets and districts in the city, by dragging the mouse to browse the location-related content required by the user, and the black dots on the map are the monitoring distribution locations. If you need to check the monitoring situation of a certain location, you need to double-click the black dot at the location, that is, you can operate the monitoring equipment at this location, and you can also call the historical video of the monitoring of the location to realize the visual management of the smart city [24].


## 4. Results and Discussion

After the initial display of the system interface, the resource scheduling performance of the system is verified. Two types of task numbers are allocated to each cloud computing node, namely, the case of 300 tasks, which is a small number of tasks, and the case of 10,000 tasks, that is, a large number of tasks. At the same time, the use of a certain city smart management and control system is compared with the new smart city management platform and system based on the city information model, the comparison results are shown in Figure4. It can be seen from Figure 4(a) that if the number of tasks is small, it will not affect the completion time of the resource scheduling task, the completion time of each system task is almost the same, and the task completion time is parity. In Figure 4(b), the number of tasks is large, the number of tasks is increasing, and the time spent in resource scheduling of each system has increased. Compared with the other two systems, system resource scheduling takes the shortest time and has absolute advantages in resource scheduling.Figure 4
Resource scheduling completion time for different tasks.
(a)
When the number of tasks is small(b)
In the case of a large number of tasksThe number of cloud computing resource nodes will also affect resource scheduling. In 60 cloud computing nodes, 5000 tasks are allocated for scheduling, and the three systems are still compared, the comparison results are shown in Figure5.Figure 5
Number of nodes and task completion time.In the smart city management system, the resource utilization rate represents the resource scheduling evaluation index in the cloud computing platform, and it is also the busy and idle degree of the resources in the system. The ultimate goal of the cloud computing platform is to maximize the utilization of resources and to share resources efficiently; comparing the overall utilization of the three systems, the results are shown in Figure6. As can be seen from Figure 6, the overall utilization of the system is the best, close to 100%, while the overall utilization of the other two systems is lower, below 90%. It can be seen that, compared with similar systems, the system has a high overall utilization rate [25].Figure 6
Overall utilization of the system.


## 5. Conclusion

The author proposes the information extraction and data planning of smart city based on the Internet of Things, constructs the overall structure of the system, and realizes the scheduling of cloud computing resources through the cultural particle swarm algorithm. The system can realize the effective management of smart city and monitor the city management situation in real time, and the resource scheduling effect is good. Even if the number of scheduling tasks is huge, it still maintains a relatively fast speed to complete the scheduling and has a good resource scheduling effect compared with similar systems. The design of the system lays the foundation for further research on smart city management. In future research, we can start from more detailed aspects, such as systematic research on smart medical care or smart security, in order to create better theoretical support for the healthy development of cities in the future.



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*Source: 2893702-2022-09-30.xml*