# The Human-in-the-Loop Data Sensing Architecture Based on Edge Cloud Computing

Authors: Chen Gao
Journal: Journal of Sensors
 (2022)

---

## Abstract

The cloud system structure is limited by sensor data processing to handle real-time constancy and mobility. To solve such a problem, this paper proposes a system structure based on edge cloud computing and human perception of data in the ring and explores its application in big data analysis. Specifically, a three-layer data flow computing structure composed of a wireless network, edge cloud system, and central cloud system is constructed. In terms of data flow structure, a mobile service structure supporting real-time operation and mobility is proposed. Based on such a structure, an edge cloud data awareness structure is further proposed. Moreover, a flexible storage structure that can be used to support different types of applications is designed to optimize the sensor system and improve the reliability of data storage. The experimental results show that the system structure based on edge cloud terms reduces the server failure rate and total utilization due to the capacity of VMs. Besides, its performance is much better than the existing structure in terms of maintenance and data processing time. Finally, the result of the edge of the cloud computing solution can save 19% on time than the original scheme.


---

## Body

## 1. Introduction

Information technology has developed rapidly and is highly popular and is now present in all corners of our lives, with widespread applications such as e-commerce industry, financial industry, biotechnology, and mobile devices, especially in science and technology [1]. To improve the QoS and QoE for users, various service providers deploy edge computing clusters around users to compensate for the long data processing latency in cloud data computing centers, which wastes available resources and imposes a great burden on cross-domain link bandwidth [2]. To address this problem, a cross-domain scheduling scheme with edge cloud collaboration is proposed to solve the problems of untimely data processing and uneven load due to edge backlogs, as well as the previous waste of resources by scheduling data to cloud computing centers. At present, large enterprises and organizations such as Huawei and Google have deployed a large number of edge computing clusters and multiple large data centers worldwide, applying a collaborative edge and cloud resource processing approach to achieve data computation and storage [3]. Mobile edge computing is proposed by the European Telecommunications Standardization Association, and the use of edge computing to solve latency problems is also clearly proposed in the literature [4].Cloud computing is an extended and interactive model based on Internet technology with a cloud data computing center that has powerful computing capabilities and enables cross-domain data scheduling with multichannel parallel control [5]. Data has increased massively, and for a single enterprise, the single application of local computers to process millions of data has been difficult to meet the needs of users, so the acquisition of additional servers has also become an inevitable choice, but the cost of additional servers is too high [6]. Moreover, when the acquisition of one service is difficult to meet the demand, multiple servers are required to provide support. As the number of servers increases, there is also a need for data sharing between servers to work together, which requires a computing center that provides centralized control equipment to achieve overall scheduling. Cloud computing centers have high cost performance, scalability, and on-demand deployment to meet large-scale data processing and storage requirements [7]. And because it is itself a virtualization technology, it breaks the space limitation and is not constrained by the physical platform.These advanced multidisciplinary systems often use more advanced human-machine interfaces to observe human actions and emotional state responses and thus infer human intentions. After understanding the human intent, HitlCPS usually initiates a robotic system to complete the task specified by the human. System development can be achieved by adapting feedback to human needs (as shown in Figure1). HitlCPS can be used to revolutionize a wide variety of industries and potentially impact the daily lives of hundreds of millions of people [8]. Then, a combined method which consists of edge cloud computing and human-in-the-loop is proposed to tackle such an issue. In the proposed method, edge cloud computing is used to maintain mobility in data transmission with few servers. Human-in-the-loop is utilized to reduce the cost of computing and optimal in storage, transmission, failure rate, and processing time. Compared with the original structure, the proposed is much more effective to process data with real-time constancy and mobility.Figure 1
Feedback loop of HitlCPS.


## 2. Related Work

In the past, transferring data to cloud data centers and then centralizing it in cloud data centers could no longer meet the needs of enterprises and organizations [9]. In response to this demand, technology companies such as Siemens and Huawei have proposed the concept of edge cloud collaboration [10]. The combination of powerful data processing capabilities of cloud data centers and the low latency characteristics of edge clusters can provide users with a more complete service system. It solves the problems of cross-domain difficulty and time extension caused by piling up a large amount of user data and load imbalance at the edge side in the past. It makes cloud computing and edge computing go hand in hand and breaks the bottleneck of data scheduling and storage.Since current robots are far less capable of online perception than humans, cannot recognize abstract commands, such as eyes and emotional states, and thus cannot communicate efficiently with people and lack proper safety mechanisms, [11] stated that “future human-robot cooperation will be the best solution to such problems, and robots that can cooperate with people are the ideal operational equipment, and for robots to well serve people, robots must be integrated with them. The degree of integration with people will be an important coordinate in the development of robots.” In addition, [12] argued that future robots will have three characteristics of communion with people, communion with the environment, and communion with robots and will be able to better understand human needs and accomplish the corresponding tasks [13].Cloud computing has 4 deployment models, namely, private cloud, community cloud, public cloud, and hybrid cloud, which are divided according to the source of consumers of cloud computing services; that is, if all consumers of a cloud only come from specific unit organizations (such as microcomputing technology companies), then it is a private cloud. A cloud is a community cloud if all its consumers come from two or more specific unit organizations. A cloud is a public cloud if all its consumers are from the public. A cloud is a hybrid cloud if its resources come from two or more clouds. The traditional cloud computing model requires a lot of load, bandwidth, growth delay, resource waste, and high dependence on the network [14–18]. Through the study of other structures and migration systems, the edge cloud collaboration structure can be derived, as shown in Figure 2.Figure 2
Edge cloud collaborative network structure.Regarding the introduction of human-in-the-loop systems, according to the autonomy levels classified in the literature [19], some autonomous systems with lower autonomy levels interact passively with humans and are only able to estimate the human state but do not consider the impact of the system state on the human state and behavior and do not constitute a closed loop, as shown in Figure 3.Figure 3
Schematic diagram of passive interaction.A human-in-the-loop control system needs to enable more active interaction between the autonomous system and the human, embedding human intent into the control system to form a closed control loop that considers the possible effects of the autonomous system state and behavior on the human [20]. In addition, observations of human states, actions, and the state of the machine manipulated by the human are needed to use this to make inferences about human intentions and provide the inferred intention information to the autonomous system, which leads to better system control inputs, resulting in the framework structure shown in Figure 4. In the proposed method, the typical human-in-the-loop mode was used to obtain the final results.Figure 4
Structure framework diagram of human-in-the-loop.


## 3. Approach

We design a cloud-based data-aware application architecture that is divided into 3 layers: API for sensing devices, API between sensing devices and the edge cloud, and API for central cloud services. A traditional model cannot handle real-time and mobility data in sensor processing. Based on the mobile service architecture, the proposed structure has the ability to process such data to a certain extent. Furthermore, with the assistance of the human perception system, the performance is more effective than traditional edge cloud computing systems without such two structures.


### 3.1. Cloud-Based Data Flow Architecture

The data flow of the cloud-based system is shown in Figure5. In such architecture, the data comes from wearable and in vitro sensors with a common sight. Hence, the amount of data is ensured. Then, the data after cleaning is transferred to the edge cloud service in an effective way. Finally, the central cloud service can store valuable data to support the next stage of the model.Figure 5
Data sensing flow.


### 3.2. Mobile Service Architecture Supporting Real Time and Mobility

From Figure6, in the traditional mobile service architecture, users are directly connected to the central data service center through sensor devices. In this architecture, failures may occur due to the limitations of mobile resources.Figure 6
Device architecture to support real time and mobility.After receiving the service request from the subscriber, the support service on the peripheral server sends the data and service request related to the subscriber to the master daemon on the central ECs, then models and manages the file, and sends the data related to the subscriber service to the slave daemon. Since the peripheral server is located around the user and keeps moving well, the failure probability is very low. When a user requests a large amount of money to calculate resources, the peripheral server can immediately provide them with reserved resources. Therefore, the mobile service architecture designed in this paper can provide real-time support and mobility.


### 3.3. Edge Cloud-Based Data Sensing Architecture

The data sensing structure based on the edge cloud is shown in Figure7. The bottom layer is used to maintain the sensor. The sensor tracks the user’s body parameters and movement and stores the data on the device in real time or transmits it through the API at the edge of the cloud. At this point, the application can check the service level agreement and then set one or more service requests according to API functions. The data type of the first floor is consistent with the initial data without data cleaning, which is stored as TTL.Figure 7
Edge cloud-based data sensing architecture.The second layer is connected to the peripheral ECs. The main component of error prevention technology is fault prediction. Carry out fault prevention and recovery process according to history and status information: execute fault avoidance process due to fault warning or the result of warning. The top layer is located on the central cloud server and is responsible for data collection, analysis, and processing. Since the main processor is a VM, the cloud center server directly controls the life cycle (creation, destruction, and movement) of the VM. Even when the VM fails, the data is stored in the stable memory of the cloud computing system. When the VM fails, the cloud display device can immediately allocate another VM data storage call to respond to the service or workload.In order to efficiently handle big data analysis, data can be replicated or split into multiple copies. Multiple VMs can split a large data into a fixed size to perform data processing applications simultaneously and migrate it in real time to another host that can afford to run it, so the architecture designed in this paper achieves both efficiency and fault tolerance.


### 3.4. Architecture Optimization Techniques

Figure8 shows the storage system structure designed to support different types of applications. In such a storage structure, different kinds of applications are processed at the same time to ensure the effectiveness of the structure. According to the volume of data from different applications, the storage volume is adaptively adjusted. Hence, the space allocation of the storage system is very reasonable. It is noted that there is a storage volume x to store the common information of different applications. With such operations, storing information from different applications not only has unique parts but also saves space.Figure 8
Application storage architecture.Based on the resources being monitored, the architecture optimizes the aggregated throughput of multiple VMs through linear programming:(1)max∑i=1n∑j∈SiXijYijs.t.∑i=1n∑j∈SiXij⋅Cij≤Cmax,where C represents the aggregated throughput of different VMs.Regarding the person-in-the-loop part of it, it is assumed that the human intention remains constant after it is revealed in the considered time frame.


### 3.5. Data Sensing Architecture Simplification

#### 3.5.1. Opportunity Constraint Processing Strategy

At present, there are two main approaches for the treatment of chance constraints. The first one is to transform them into deterministic constraints by using the noise distribution property and then solve them; the second one is to use the stochastic simulation technique to handle them and solve them by a genetic algorithm. In this paper, the first method is used to deal with the chance constraint.Consider the following initial feasible domain:(2)F=xt:yr,t−yh,t≥2.5if6≤dh,t≤12.The units are meters, which can be represented in Figure9 if we keep yh,t=5m constant.Figure 9
Initial feasible region.In Figure9, F denotes the feasible domain and F~ denotes the infeasible domain, in order to solve for the deterministic feasible domain that makes Prxτi∈F≥p.


#### 3.5.2. KL Scattering Simplification

Before performing the KL scatter reduction, the covariance update is simplified here:(3)∑τ+1i=LτiQiLτiT+Fτi∑τiFτiT∀τ=t,⋯,t+N−2.f the past trajectory and control inputs of the system are also considered the optimal variables to be solved; the whole calculation process becomes very complicated. Therefore, at the initial moment, the optimal trajectory x¯τi and the optimal control input uτi derived from the previous time step are used for precomputation to derive the approximate covariance, and the covariance matrix is approximated and updated by substituting into equations.LetPx1,Px2 be two probability distributions on an n-dimensional random variable x. In the case of a discrete random variable, the KL dispersion is calculated:
(4)KLPx1ρPx2=∑xPx1logPx1Px2=EPx1logPx1Px2,where EPx1 denotes the expectation under the Px1 distribution. Since xτ is considered a random variable obeying a Gaussian distribution, then the KL scatter can be calculated as follows:
(5)KLPxτjPPxτi=12EPxτj−logΣτj−xτj−x¯τjTΣτj−1xτj−x¯τj⏟1+logΣτi+xτi−x¯τiTΣτi−1xτi−x¯τi⏟2,where EPxτj denotes the expectation under the Pxτi distribution. Since xτj,xτi are all column vectors, the calculation of Eq. (5) is scalar, so Eq. (5) can be written by using the property of trace of matrix as
(6)KLPxτjρPxτi=12−EPxτjtrΣτj−1xτj−x¯τjTxτj−x¯τj+EPxτjtrΣτi−1xτi−x¯τiTxτi−x¯τi+logΣτiΣτj.In this respect, the linear constraint is used to replace the previous opportunity constraint, which turns the SMPC problem into a MPC deterministic problem. At the same time, it reduces the complexity of the objective function and greatly promotes its solution. If the expected trust value is lower than the user-defined value, the document will be deleted from the model to further improve computational efficiency. Bayesian inference is used in this paper to update the confidence level:(7)bt+1i≈btiPxt+1xt,ut∗,i,where
(8)Pxt+1,xt,ut∗,i≈12πnΣt+1i.The stability of the data sensing architecture proposed in this paper can be guaranteed by the method of guaranteeing the stability of MPC.


## 3.1. Cloud-Based Data Flow Architecture

The data flow of the cloud-based system is shown in Figure5. In such architecture, the data comes from wearable and in vitro sensors with a common sight. Hence, the amount of data is ensured. Then, the data after cleaning is transferred to the edge cloud service in an effective way. Finally, the central cloud service can store valuable data to support the next stage of the model.Figure 5
Data sensing flow.


## 3.2. Mobile Service Architecture Supporting Real Time and Mobility

From Figure6, in the traditional mobile service architecture, users are directly connected to the central data service center through sensor devices. In this architecture, failures may occur due to the limitations of mobile resources.Figure 6
Device architecture to support real time and mobility.After receiving the service request from the subscriber, the support service on the peripheral server sends the data and service request related to the subscriber to the master daemon on the central ECs, then models and manages the file, and sends the data related to the subscriber service to the slave daemon. Since the peripheral server is located around the user and keeps moving well, the failure probability is very low. When a user requests a large amount of money to calculate resources, the peripheral server can immediately provide them with reserved resources. Therefore, the mobile service architecture designed in this paper can provide real-time support and mobility.


## 3.3. Edge Cloud-Based Data Sensing Architecture

The data sensing structure based on the edge cloud is shown in Figure7. The bottom layer is used to maintain the sensor. The sensor tracks the user’s body parameters and movement and stores the data on the device in real time or transmits it through the API at the edge of the cloud. At this point, the application can check the service level agreement and then set one or more service requests according to API functions. The data type of the first floor is consistent with the initial data without data cleaning, which is stored as TTL.Figure 7
Edge cloud-based data sensing architecture.The second layer is connected to the peripheral ECs. The main component of error prevention technology is fault prediction. Carry out fault prevention and recovery process according to history and status information: execute fault avoidance process due to fault warning or the result of warning. The top layer is located on the central cloud server and is responsible for data collection, analysis, and processing. Since the main processor is a VM, the cloud center server directly controls the life cycle (creation, destruction, and movement) of the VM. Even when the VM fails, the data is stored in the stable memory of the cloud computing system. When the VM fails, the cloud display device can immediately allocate another VM data storage call to respond to the service or workload.In order to efficiently handle big data analysis, data can be replicated or split into multiple copies. Multiple VMs can split a large data into a fixed size to perform data processing applications simultaneously and migrate it in real time to another host that can afford to run it, so the architecture designed in this paper achieves both efficiency and fault tolerance.


## 3.4. Architecture Optimization Techniques

Figure8 shows the storage system structure designed to support different types of applications. In such a storage structure, different kinds of applications are processed at the same time to ensure the effectiveness of the structure. According to the volume of data from different applications, the storage volume is adaptively adjusted. Hence, the space allocation of the storage system is very reasonable. It is noted that there is a storage volume x to store the common information of different applications. With such operations, storing information from different applications not only has unique parts but also saves space.Figure 8
Application storage architecture.Based on the resources being monitored, the architecture optimizes the aggregated throughput of multiple VMs through linear programming:(1)max∑i=1n∑j∈SiXijYijs.t.∑i=1n∑j∈SiXij⋅Cij≤Cmax,where C represents the aggregated throughput of different VMs.Regarding the person-in-the-loop part of it, it is assumed that the human intention remains constant after it is revealed in the considered time frame.


## 3.5. Data Sensing Architecture Simplification

### 3.5.1. Opportunity Constraint Processing Strategy

At present, there are two main approaches for the treatment of chance constraints. The first one is to transform them into deterministic constraints by using the noise distribution property and then solve them; the second one is to use the stochastic simulation technique to handle them and solve them by a genetic algorithm. In this paper, the first method is used to deal with the chance constraint.Consider the following initial feasible domain:(2)F=xt:yr,t−yh,t≥2.5if6≤dh,t≤12.The units are meters, which can be represented in Figure9 if we keep yh,t=5m constant.Figure 9
Initial feasible region.In Figure9, F denotes the feasible domain and F~ denotes the infeasible domain, in order to solve for the deterministic feasible domain that makes Prxτi∈F≥p.


### 3.5.2. KL Scattering Simplification

Before performing the KL scatter reduction, the covariance update is simplified here:(3)∑τ+1i=LτiQiLτiT+Fτi∑τiFτiT∀τ=t,⋯,t+N−2.f the past trajectory and control inputs of the system are also considered the optimal variables to be solved; the whole calculation process becomes very complicated. Therefore, at the initial moment, the optimal trajectory x¯τi and the optimal control input uτi derived from the previous time step are used for precomputation to derive the approximate covariance, and the covariance matrix is approximated and updated by substituting into equations.LetPx1,Px2 be two probability distributions on an n-dimensional random variable x. In the case of a discrete random variable, the KL dispersion is calculated:
(4)KLPx1ρPx2=∑xPx1logPx1Px2=EPx1logPx1Px2,where EPx1 denotes the expectation under the Px1 distribution. Since xτ is considered a random variable obeying a Gaussian distribution, then the KL scatter can be calculated as follows:
(5)KLPxτjPPxτi=12EPxτj−logΣτj−xτj−x¯τjTΣτj−1xτj−x¯τj⏟1+logΣτi+xτi−x¯τiTΣτi−1xτi−x¯τi⏟2,where EPxτj denotes the expectation under the Pxτi distribution. Since xτj,xτi are all column vectors, the calculation of Eq. (5) is scalar, so Eq. (5) can be written by using the property of trace of matrix as
(6)KLPxτjρPxτi=12−EPxτjtrΣτj−1xτj−x¯τjTxτj−x¯τj+EPxτjtrΣτi−1xτi−x¯τiTxτi−x¯τi+logΣτiΣτj.In this respect, the linear constraint is used to replace the previous opportunity constraint, which turns the SMPC problem into a MPC deterministic problem. At the same time, it reduces the complexity of the objective function and greatly promotes its solution. If the expected trust value is lower than the user-defined value, the document will be deleted from the model to further improve computational efficiency. Bayesian inference is used in this paper to update the confidence level:(7)bt+1i≈btiPxt+1xt,ut∗,i,where
(8)Pxt+1,xt,ut∗,i≈12πnΣt+1i.The stability of the data sensing architecture proposed in this paper can be guaranteed by the method of guaranteeing the stability of MPC.


## 3.5.1. Opportunity Constraint Processing Strategy

At present, there are two main approaches for the treatment of chance constraints. The first one is to transform them into deterministic constraints by using the noise distribution property and then solve them; the second one is to use the stochastic simulation technique to handle them and solve them by a genetic algorithm. In this paper, the first method is used to deal with the chance constraint.Consider the following initial feasible domain:(2)F=xt:yr,t−yh,t≥2.5if6≤dh,t≤12.The units are meters, which can be represented in Figure9 if we keep yh,t=5m constant.Figure 9
Initial feasible region.In Figure9, F denotes the feasible domain and F~ denotes the infeasible domain, in order to solve for the deterministic feasible domain that makes Prxτi∈F≥p.


## 3.5.2. KL Scattering Simplification

Before performing the KL scatter reduction, the covariance update is simplified here:(3)∑τ+1i=LτiQiLτiT+Fτi∑τiFτiT∀τ=t,⋯,t+N−2.f the past trajectory and control inputs of the system are also considered the optimal variables to be solved; the whole calculation process becomes very complicated. Therefore, at the initial moment, the optimal trajectory x¯τi and the optimal control input uτi derived from the previous time step are used for precomputation to derive the approximate covariance, and the covariance matrix is approximated and updated by substituting into equations.LetPx1,Px2 be two probability distributions on an n-dimensional random variable x. In the case of a discrete random variable, the KL dispersion is calculated:
(4)KLPx1ρPx2=∑xPx1logPx1Px2=EPx1logPx1Px2,where EPx1 denotes the expectation under the Px1 distribution. Since xτ is considered a random variable obeying a Gaussian distribution, then the KL scatter can be calculated as follows:
(5)KLPxτjPPxτi=12EPxτj−logΣτj−xτj−x¯τjTΣτj−1xτj−x¯τj⏟1+logΣτi+xτi−x¯τiTΣτi−1xτi−x¯τi⏟2,where EPxτj denotes the expectation under the Pxτi distribution. Since xτj,xτi are all column vectors, the calculation of Eq. (5) is scalar, so Eq. (5) can be written by using the property of trace of matrix as
(6)KLPxτjρPxτi=12−EPxτjtrΣτj−1xτj−x¯τjTxτj−x¯τj+EPxτjtrΣτi−1xτi−x¯τiTxτi−x¯τi+logΣτiΣτj.In this respect, the linear constraint is used to replace the previous opportunity constraint, which turns the SMPC problem into a MPC deterministic problem. At the same time, it reduces the complexity of the objective function and greatly promotes its solution. If the expected trust value is lower than the user-defined value, the document will be deleted from the model to further improve computational efficiency. Bayesian inference is used in this paper to update the confidence level:(7)bt+1i≈btiPxt+1xt,ut∗,i,where
(8)Pxt+1,xt,ut∗,i≈12πnΣt+1i.The stability of the data sensing architecture proposed in this paper can be guaranteed by the method of guaranteeing the stability of MPC.


## 4. Experiments

To evaluate the situation data sensing architecture model and the improved algorithm, this paper concludes that the algorithm improvement is effective by changing the magnitude of the weights of the three functions of economic expense, load balance, and completion time several times and also verifies the generalizability and stability of the algorithm. Figure10 shows that the improved algorithm is effective in reducing the cost, and the differential artificial bee colony algorithm tends to slow down the growth in economic cost as the number of tasks gradually increases, indicating that more tasks choose to deploy at the edge and select local servers to schedule data nearby, reducing unnecessary waste of resources and lowering the scheduling cost.Figure 10
Changes in economic costs with the number of tasks.Figure11 shows that the artificial bee colony algorithm has a greater impact on the completion time. As it is a problem, other functions need to be sacrificed, which has some impact on the economic expense and the improvement of load balance.Figure 11
Changes in completion time with the number of tasks.Figure12 shows the variation of the load balancing degree with the growth of the number of tasks, using free resources and bandwidth for task scheduling, dispersing the overall tasks, and deploying the overall tasks in multiple edge clusters and cloud computing centers to achieve the goal of load balancing. When the number of tasks is small, it has a greater impact on the load balancing degree of the PSO algorithm. The overall image shows that the improved artificial bee colony algorithm compared to the other two algorithms has some impact on the load balancing degree. The ABC algorithm and DABC algorithm are smoother, which indicates that the coefficient factor has less influence on the load balancing degree.Figure 12
Changes in load balance with the number of tasks.Select three classic application examples: Pocket Sphinx, OCR, and Aeneas. The evaluation metrics include processing and counting the delay of communication, including the delay of transferring tasks, regardless of the calculation time. Calculation latency is the total time it takes to move a job to the edge or cloud for calculation and return. Figure13 shows that in pocket Sphinx, the time required to transfer all tasks to the cloud is longer than that required to transfer all tasks to the periphery. The boundary cloud combined scheduling algorithm is obviously superior. In the Aeneas application, the cloud front motion graph is longer, while in the other two options, minor differences and significant advantages are noted. The communication delay has been tested in various applications, and the improved cloud edge combined with the scheduler algorithm has achieved satisfactory results.Figure 13
Communication latency under different applications.Figure14 compares the simulation results of calculation delay of different applications. The results show that the pocket Sphinx program provides the minimum time required to transfer all data to the cloud, which also reflects that the data processing speed of pocket Sphinx format is faster than that in Figure 14. Compared with the complete cloud motion control program, it can save 68% of the delay computing time and 61% of the time compared with extreme scheduling.Figure 14
Computation latency under different applications.


## 5. Conclusion

To make the data transmission structure more effective in maintaining mobility, we propose a data transmission structure using edge cloud computing and human-in-the-loop system. In such a structure, the amount of data is reduced by placing peripheral devices between the sensors and the central cloud server. Beneficial from such substructure, the software architecture, transmission protocol, and storage architecture are restored. Experimental results show that the cloud system outperforms previous architectures in terms of failure rate, processing time, and scalability. Furthermore, the power consumption of ECS can be reduced due to the system architecture using few servers. In future work, we would focus on integrating artificial intelligence technology to predict edge cloud server failure, database development, and corresponding application research based on the edge cloud structure.



---

*Source: 9542290-2022-08-21.xml*