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Intelligent and Holistic Internet of Things (IoT) Solutions for Sustainable Cities and Society

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Volume 2021 |Article ID 3344987 | https://doi.org/10.1155/2021/3344987

Wenze Ning, Mei Lu, "Informatization Construction of Digital Assets in Smart Cities", Wireless Communications and Mobile Computing, vol. 2021, Article ID 3344987, 9 pages, 2021. https://doi.org/10.1155/2021/3344987

Informatization Construction of Digital Assets in Smart Cities

Academic Editor: Peng Yu
Received21 Jun 2021
Revised18 Sep 2021
Accepted30 Sep 2021
Published22 Oct 2021

Abstract

With the rapid popularization of the Internet of Things (IoT), smart cities driven by IoT technology have become a matter of concern. This study mainly investigates the construction of digital asset based on the data collected from government departments, enterprises and institutions. Particularly digital asset personnel should not only get familiar with the process of smart city information management, but also be able to identify the risks in digital assets. Strict accountability mechanism can ensure the data privacy, dispel the worries of data providers, and help weaken data barriers. Supported by comprehensive numerical results, we reach a conclusion that our proposed digital asset construction scheme has practical significance in many aspects of smart city development and will help the regional Informatization considerably.

1. Introduction

Internet of Things (IoT) refers to a number of smart physical devices that are connected to each other through the Internet. Through the information transmission via the Internet, smart devices complete the communication and interaction behaviors between each other and with users. With the continuous progress of wireless communications and Internet, IoT technology is also developing rapidly. Internet of Things has already played its role in a variety of critical applications such as remote healthcare, environment monitoring, smart grid, etc. However, raw data from IoT device is often not easily understood, and thus requires conversion to human-readable information. With those pre-processed data, people enter the era of information explosion and sink in the ocean of information.

IoT empowered Smart cities have become the evolution trend of a new type of town. This tendency serves as not only a catalyst for the development of a new kind of community, but also as a strong motivator and a clear vision. Due to the new kind of town’s greenness and outstanding environmental and energy-saving features, the smart city built on IoT is the top option. The smart city powered by IoT may help accelerate the development of new cities. The networked smart city solves many issues associated with urbanization’s growth and is an efficient solution for new urbanization.

With the popularization of digital devices and social network applications, the explosive growth of multimedia big data has brought many challenges for users to securely obtain them in various application scenarios. Zhu C first reviewed the latest work of multimedia big data, where the key issues are identified to ensure the success of secure multimedia big data in trust-assisted sensor cloud (TASC). He proposed two types of TASC: TASC-S (TASC with a single trust value threshold) and TASC-M (TASC with multiple trust value thresholds). Although the throughput of TASC-M in his research may fluctuate with the same trust value threshold, the research process lacks data [1]. Hashem I A T proposes a future business model with the goal of managing big data in smart cities. And identified and discussed business and technical research challenges. By focusing on how big data fundamentally changes the urban population at different levels, the vision of supporting big data analysis for smart cities was discussed. This research can serve as a benchmark for researchers and industries to advance and develop smart cities in the context of big data [2]. Li Y studied the current situation of data over-collection and studied some of the most common cases of data over-collection. By proposing a mobile cloud framework, by putting all user data in the cloud, the security of user data can be greatly improved [3]. Menouar H proposed that there is no smart city without a reliable and efficient transportation system. The realization of next-generation intelligent transportation system (ITS) relies on the effective integration of connected cars and autonomous vehicles. It is also worth noting that ITS that supports drones can be used in the next-generation smart cities. Potential and challenges [4].

Smart city operation center must comply with relevant data management and control standards for high-quality collection when gather data directly by itself. Alternatively the smart city operation center may also need to indirectly collect data from government departments, enterprises and institutions. This requires the centre to negotiate with the data supplier and establish a generally acceptable agreement with the assistance of high-level management agency. Data creation has a direct impact on the quality of subsequent data and the market acceptability of data products. The department of digital asset management may use appropriate tools and technology to create market-ready data products while maintaining data quality and security. Manager of digital asset plays a critical role in the strategic planning of information management in smart cities. Thus, the novelty of this work is that it integrates the digital asset of smart cities with the process of information building to examine the relationship between the two and better support the process of contemporary information construction.

2. Smart City Digital Assets

2.1. Smart City

Recent IoT revolution is growing rapidly with the development of communication technology. Smart cities are the main scenarios for IoT applications, allowing it to contribute to the improvement and progress of cities. The implementation of IoT brings great complexity, and its application requires great efforts to achieve. The deployment of IoT is critical to building smart cities due to communication and data management. With the development and implementation of IoT in cities, many problems will arise. These issues can be categorized by network type, flexibility and scalability, heterogeneity, and end user participation. The need for high-speed connectivity to support connections between different IoT devices is huge. Another major requirement is data storage for all connected devices. To complement existing communication and data management solutions, a large number of software applications and hardware devices are needed to manage and operate user-facing applications. Sustainability and efficiency of software applications and hardware devices are difficult to achieve. Smart cities can connect healthcare. A central connection manager with pollution control, water management, power management, traffic control, security and privacy control devices/tools. It is difficult for a single central management facility to manage and deliver services using existing technical solutions.

The main road of industrial development is definitely the construction of smart city based on IoT. However, in the future, there is no technical support for the implementation of information architecture and big data in other fields [5, 6]. The urban management pattern is large. From the beginning, it has a high positioning and a long-term vision. It fully considers the current situation and future development needs of the digital construction of urban management. In the system construction, sufficient access ports are reserved for future urban management content refinement and urban management content expansion. At the same time, it pays attention to the construction and sharing of resources within the city. In terms of components and events, the urban management content more comprehensively refines the urban management content at all levels of the city. With the continuous improvement of the digital operation of urban management in the main urban area, it gradually advances to the sub-city area and expands the management scope of urban management digitalization. Reduce the urban management gap between the main urban area and the sub-urban area in the city, and comprehensively improve the digital level of urban management [7]. The overall layout of the smart city is shown in Figure 1.

Adjust urban buildings and public facilities, optimize alternatives, implement 3D spatial analysis of urban buildings, and carry out digitalization and automation of building approval management [8, 9].

Among them, and are corresponding digital asset information management services [10]. As an important direction of future GIS technology development, 3D GIS uses 3D space coordinates to simulate and visualize the real world [11, 12]. Index eigenvalue relative member matrix R, standard eigenvalue relative member matrix S and relative member matrix of all levels.

The expected information entropy required to classify the tuple of the data set T according to the attribute A is [13]:

Define the length , the string length is [14, 15].

Here, is the number of rules. The information gain after dividing the data by attribute A is:

represents the number of defined gains. There are some unquantifiable indicators in the smart city information security risk assessment index system [16].

Among them, is the expected value. When the evaluation index attribute is benefit type, the standardization of L is described as [17]:

2.2. Asset Informationization

In the digital construction of city management, make full use of the existing platform construction and information resources, do a good job of horizontal resource integration while doing vertical integration, and connect the platforms of other city-level city management related departments and the platforms of subordinate district-level departments. Effectively avoid duplication of construction to achieve resource sharing. In the newly constructed digital platform, high-level configuration and reserved interfaces can be avoided, which can avoid the inability to expand in later construction. It can be found that by integrating the resources of the original urban management related departments and deploying new data resources at a high level, the urban management digital system between the city-level departments and the urban two-level departments can be interconnected, resource-sharing, keep pace, and improve the processing capacity and resource allocation of urban management digitization [18, 19].

The output layer uses the Log-Sigmoid excitation function, so that the model output value range is in the interval [0, 1] [20]:

The output value of the smart city model is denormalized as follows [21, 22]:

In the formula, the range of is in the interval of [0.2, 0.8]. Standardized collection of original indicator data p-dimensional random vector [23]:

The data is standardized and transformed, as shown below [24].

Find the correlation coefficient matrix for the standardized matrix Z, as shown below [25].

Solve the p characteristic roots of the correlation coefficient matrix, arrange the characteristic roots from large to small, and obtain the orthogonal eigenvectors corresponding to the characteristic roots.

3. Digital Asset Construction for Smart Cities

3.1. Establish a Digital Asset Information Management Framework

The digital asset information management framework consists of five sub-modules: digital asset information management, data circulation, value-added services and data privacy protection, data right confirmation and traceability, and digital asset value evaluation.

3.1.1. Digital Asset Information Management

(1) Data Acquisition. Many organizations have established their own databases and information systems in the process of informatization, which leads to differences in the data structures collected by smart city operation centers. These data with different structures, poor quality, and even incomplete data need to be cleaned. In the process of data cleaning, filtering out junk data and correcting data that does not meet the corresponding rules can improve the quality of the data and reduce the problems that may arise in the subsequent digital asset information management work.

(2) Data Circulation. The process of data from the provider to the user is data circulation. From the perspective of smart city data circulation, the amount of data generated by the smart city operation center itself is very small, and most of its data comes from governments, enterprises, social groups, and individuals. The operation center integrates and develops the data collected from these channels, and provides it to government departments, enterprises, social groups and individuals within the scope permitted by relevant regulations. The significance of smart city data circulation is to improve the ability of data users to obtain data resources, and its purpose is to realize the value-added utilization of data through the synchronization of data circulation.

(3) Value-Added Services. To realize the value of digital assets in smart cities, value-added services are the key. In the value-added business of digital assets, data products or data services can generate value through the use of data users. Value-added services achieve the purpose of realizing their potential value by providing additional services or products to data users.

The smart city operation center collects various types of data, and has built a big data analysis platform, so it can provide diversified value-added services. It can not only provide simple data query and download services, but also provide differentiated data products or services as needed, such as customized data visualization analysis or data processing services. The Smart City Operation Center of W City cooperated with the City Traffic Police Brigade to open a reminder of vehicle violations. During this epidemic, the Smart City Operation Center of W City reported the city’s epidemic prevention and control situation in a timely manner to the people of the city through text messages, which produced good social benefits.

(4) Valuation of Digital Assets. When evaluating the value, while taking into account the economic benefits of smart city digital assets, the social benefits of their digital assets must also be considered. The reason for evaluating the value of digital assets is to be able to quantify the value of digital assets. The smart city operation center can classify and manage it based on the quantified digital assets. For example, for data with no value or high value, reduce the collection without violating relevant regulations, and focus on high-value data. In this case, the Smart City Operation Center of W City needs to establish a multi-dimensional value evaluation system based on government benefits, social benefits, and economic benefits.

(5) Data Confirmation and Traceability. Data confirmation mainly refers to clarifying the ownership of data. The property rights here include ownership, possession, control, use, profit and disposal. The concept of data traceability comes from the data life cycle theory, which refers to the evolution of the entire life cycle of traceable data from generation, to use and transaction, to maintenance and destruction. On the basis of data confirmation, the smart city operation center can consider establishing a data traceability system, through blood relationship tracking analysis and other technologies, to manage the whole life cycle of data. Smart city digital asset information management agencies can control the entire process of data from generation to use to destruction, preventing data from being misused or leaked. The organizational structure of digital asset information management is shown in Figure 2.

3.2. Organizational Structure of Digital Asset Information Management

The Smart City Operation Center of W City establishes a management model in which a single department takes the lead and multiple departments coordinate and cooperate. Under the management of the smart city operation center, a separate digital asset information management department can be set up. The management department can conduct professional management of smart city digital assets, be responsible for the daily maintenance and management of digital assets, make overall plans for various expenses in the development or utilization of digital assets, and promote the circulation and value realization of smart city digital assets. Manage the management system and rights protection work exclusively. At the same time, other business departments of the Smart City Operation Center in W City should be separated and assisted in management to promote the effective management of digital assets informatization, and finally maximize the value of the digital assets of the Smart City Operation Center in W City. Builders of smart cities should place a premium on this. By using the city’s digital assets as a focal point and value point, it can be foreseen that the future smart city will be more dazzling in terms of culture and tourism. The exhibition of smart cities will emphasize the city’s extensive digital assets, rather than burdensome technological solutions.

The management tasks of digital assets are arduous, and there is less experience for special posts. Although the smart city operation center can establish an independent digital asset information management department, it is far from enough to rely on other departments to assist in the management of digital asset identification and value realization. The Digital Asset Information Management Department should be able to independently carry out management work, and consider other departments for assistance when necessary.

3.3. Responsibility Mechanism for Informatization Management of Digital Assets

Through the establishment of a personnel accountability mechanism, all parties involved in the information management of smart city digital assets can clarify their rights and responsibilities, and the rights and responsibilities of various roles in management activities can be refined and ensure the efficient implementation of digital asset informatization management of smart city.

3.3.1. Data Provision

The foundation of digital assets is data. If there is a problem with the basic data provided, the subsequent digital asset information management will be difficult to achieve. Therefore, the data provider is the foundation of the entire digital asset information management. Data providers need to provide corresponding data in accordance with the collection standards of smart city data. When the smart city operation center actively collects data independently, it must also comply with relevant data management and control standards and collect high-standard and high-quality data. On the other hand, smart city operation centers need to indirectly collect data from government departments and enterprises and institutions, and cannot restrict data providers through the agency’s responsibility mechanism. This requires the smart city operation center to communicate and negotiate with the data provider, and with the help of the higher-level management organization, reach a consensus on a generally accepted preciousness mechanism.

3.3.2. Data Development

Data development directly determines the quality of the later data and the market acceptance of the demand for data products, whether the value of digital assets in smart cities can be realized, and whether the potential economic benefits of digital assets can be dug out, data development has played a big role. At the same time, the quality and security of the data are also directly responsible for the data developers. The developers of the smart city digital asset information management department can use relevant tools and technologies to develop data products that meet the needs of the market on the basis of ensuring data quality and safety.

3.3.3. Digital Asset Management

Digital asset managers play an overall role in the overall planning of digital asset in smart cities. They must not only know well the process of smart city informatization management, but also be able to identify the risks and timely discover and decisively take appropriate measures for problem fixing. At the same time, they need to have strong communication and collaboration skills and can meet the work requirements of communicating and coordinating with all parties. Through the responsibility recognition mechanism, on the one hand, the responsibility awareness of smart city data providers can be improved, and data providers can be urged to provide qualified data in accordance with the responsibility requirements. On the other hand, a strict responsibility recognition mechanism can ensure the privacy of data providers’ data, alleviate the worries of some data providers and help remove data barriers.

4. Numerical Results and Discussion

4.1. Existing Problems in Digital Asset Information Management for Smart Cities

Data operation management statistics are shown in Table 1. Judging from the survey results, most of the respondents do not think that the data operation management of smart cities is doing well. In data life cycle management, nearly 80% of the respondents believe that the performance of smart cities in data life cycle management needs to be improved and general. The data storage of smart cities generally adopts the method of government purchase, and there is no need to consider the cost and profit. For the stored data, there is no need to maximize the value. After the data is collected, further application analysis and archiving are not very active. In order to avoid unnecessary troubles, some junk data is not destroyed, and it is still stored in the database. Relatively speaking, respondents have a high evaluation of data security management in smart cities. This is due to the fact that the smart city operation center is also responsible for the construction of government e-government affairs. In order to reduce the risk of government data leakage, the management of smart cities has to increase the construction of data security management. As for the main digital management, respondents generally believe that the digital asset information management of smart cities is relatively poor in this regard. The existing smart city technology framework and solutions also do not reflect the main digital management content.


Data operation managementDifferenceNeeds improvementGeneralGoodExcellent

Data life cycle6.19%44.62%39.90%9.30%
Data security management9.28%36. 42%34.20%16.20%4.10%
Master digital management43.36%46.39%8.25%2.06%

Data integration and sharing statistics are shown in Table 2. From the survey results, the respondents generally believe that the performance of smart city digital asset information management in data integration and sharing is a need for improvement and a general level. In theory, smart city data should cover all aspects of city operation. However, the reality is that data barriers exist everywhere. As for data integration, most of the respondents believe that the work of connecting, cleaning, converging and integrating smart city data is not in place. When researching on the spot, some research institutions found that the data of some smart city operation centers is only passively stored. This phenomenon is especially seen in some unstructured data. Most of the respondents believe that the sharing of smart city data is not performing well. In the investigation and research on the smart city digital asset link, it is found that the management of the smart city operation center has a negative attitude towards data sharing. It is emphasized that smart cities are not just about implementing next-generation information technologies like the Internet of Things and cloud computing; they are also about establishing a sustainable urban innovation ecosystem characterized by user innovation, open innovation, and popularization. The key data sources of smart city operations are rich, and some of the data belong to government big data. Involving some government data, the management has no legal and regulatory basis for whether these data can be shared. In order to avoid more responsibilities, it is natural not to actively promote the sharing of data. On the whole, in terms of data integration and sharing, smart city digital asset information management still has a long way to go.


Data operation managementDifferenceNeeds improvementGeneralGoodExcellent

Data life cycle9.28%59.82%26.80%3.10%1.00%
Data security management17.53%42.27%37.15%2.06%1.00%
Master digital management18.56%45.36%35.08%1.00%

Based on interviews with experts from the Smart City Operation Center in W City and on-site field surveys, this article summarizes six problems in the digital asset information management of smart cities. Whether these issues fit with the current status of digital asset management in smart city operation centers, this article, based on interviews, invited 5 experts in related research fields to evaluate the problems identified in the digital asset information management of smart cities. The scoring is from 1 to 10 points, with 1 being the most non-conforming and 10 being the most conforming. Table 3 shows the problems existing in the information management of digital assets in smart cities.


Problems with digital asset managementWYLJTotal score

Failure to fully identify and determine797932
Failure to establish an effective asset management framework8107833
Missing asset management organizational structure867829
The asset value is not reasonably assessed879832
Assets lack operational value-added and circulation787830
Barrier phenomenon899834

4.2. Countermeasures for Smart City Digital Asset Information Management

This research takes the smart city of W city as an example, and proposes solutions to the existing problems in the digital asset information management of smart cities. The effect of these countermeasures needs to be analyzed. Since these are specific countermeasures and suggestions, there is no practical case to support them, so consider using the expert scoring method to analyze their effects. In the research, ten experts in related fields were invited to rate the effects of the proposed solutions (scores range from 1 to 5, with 1 being insignificant, 3 being effective, and 5 being significant).

Smart city refers to the combination of urban composition systems and services with various information technologies or innovative ideas to improve resource utilization efficiency, optimize urban management and services, and improve the life quality of citizens. Smart cities make full use of the new generation of information technology in all walks of life in the city, and build an advanced form of urban informatization on the basis of the next generation of innovation in the knowledge society (Innovation 2.0). It enables the comprehensive integration of information technology, industrialization, and urbanisation, thus alleviating “big city illnesses” and promoting urban management performance. The countermeasure of combing digital assets for digital asset audits has a high score, indicating that most experts believe that based on the current status of the development of smart city digital asset information management, it is necessary to conduct digital asset audits to further sort out the smart city operation center. Digital assets. On the whole, basically all the countermeasures can promote the management of smart city digital assets. Among them is a countermeasure, that is, whether the establishment of personnel recognition system can help break the data barrier, and the score is only 2.6. This shows that most experts believe that this countermeasure may be effective, and have no confidence in its effect. In subsequent exchanges, some experts said that data barriers exist widely on the one hand, and on the other hand, there are many influencing factors. From the perspective of the recognition system alone, the data barrier phenomenon of smart cities can only play a certain mitigation effect, and the impact capacity is limited. More effective measures should be taken, such as administrative intervention and multi-party consultation. The evaluation result of the expert scoring method is shown in Figure 3.

Invite 10 experts in the direction of smart city and digital asset information management, and the experts will assign the above six indicators pair by pair according to the nine-level scale method, and then find the arithmetic average value to establish a judgment matrix. Bring the constructed judgment matrix into MATLAB software to check the consistency of the matrix, and calculate the weight of each index for the matrix that passes the check. The constructed judgment matrix is shown in Figure 4.

From this, the analysis of the weight of the development of digital asset information management in smart cities is shown in Figure 5. The running results of the software are as follows, the consistency index CR =0.049, CI = 0. 0608, and the maximum characteristic value B = 6.3040. CR< 0.1, it can be considered that the judgment matrix passes the consistency test and is suitable for further analysis. The weight vector Q of each indicator is: Q = (0.0564, 0.1177, 0.0842, 0.3085, 0.1655, 0.2678) From this, the final weight result can be obtained. The weight value represents the weight division of experts on the further development of smart city digital asset information management. Through the above analysis, it can be found that the focus of the future development of digital asset information management in smart cities should be the data circulation link, followed by digital asset value evaluation and data appreciation. This shows that the existing smart city digital asset information management is relatively lacking in the data circulation link. Data circulation is the basis for the realization of the value of digital assets in smart cities. At this stage, the management of many smart city operation centers does not pay attention to data circulation. Regarding how to do a good job in data circulation, this article believes that the first is to improve the convenience of data use. In the data circulation link, it is necessary to provide data products that can meet their needs from the perspective of potential data users. On the one hand, it is necessary to ensure the quality of data products, and on the other hand, to make the use of data products as convenient and fast as possible. The second is to strengthen the construction of its own big data platform and actively participate in external big data platforms.

10 appraisal experts were given scores for the judgment value of 9 relative importance in 2 levels constructed by the smart city model for the first round of scoring. The descriptive statistical analysis of the survey data eliminated 39 minor factors. Through the further investigation of the remaining 25 factors, some changes in the status of the index system were made. Simultaneously, the coefficient level has been revised by the original 22-factors in 8-elements and the coefficient level has been revised by the original 72-coefficient in 28-elements As can be observed, there are eight factors with higher than 2.0 eigenvalues, i.e., 20.828, 2.088, 2.080, 2.688, 2.898, 2.828, 2.288, and 2.282. Those eight common factors exported can explain the 92.8% variation. The comparison result of each index system is shown in Figure 6.

5. Conclusion

This paper mainly studies the construction of digital asset in smart city, where informatization process is supported by relevant tools to meet the market demand. With respect to data quality and safety, data management and control standards must kick in to guarantee high standard and high-quality data collection. Not only should the people responsible for digital asset management be acquainted with the process of smart city informatization, but they should also be able to recognize hazards associated with smart city digital assets. Strict accountability mechanisms may protect data providers’ privacy, remove certain data providers’ concerns, and contribute to the lowering of data barriers. This work demonstrates the value of the proposed smart city digital asset construction scheme by comprehensive numerical results.

Data Availability

The author keeps the analysis and simulation datasets, but the datasets are not public.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by Natural Science Foundation of Shanxi Province, China (Grant No. 2014JM9371); Research on reachability of home care model in old urban residential areas in Shaanxi Province, From the perspective of digital Asset, bureau level project (21JZ034).

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Copyright © 2021 Wenze Ning and Mei Lu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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