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RETRACTED ARTICLE: Model and algorithm of innovation performance evaluation for coordination of supply and demand based on wireless sensor network

This article was retracted on 10 September 2022

This article has been updated


In recent years, with the continuous development and progress of wireless communication and sensors, people's production and life have also undergone tremendous changes. This article aims to apply wireless sensor networks to the construction of a supply–demand coordination innovation performance evaluation model, in order to improve its influence and application scope in real life. This paper deeply researches the architecture and node organization structure of wireless sensor network, and strengthens its theoretical foundation in the application of performance evaluation model. This paper designs performance evaluation indicators and compares performance evaluation methods at home and abroad based on the evaluation indicators, compares and analyzes the factor analysis method, fuzzy comprehensive evaluation method, comprehensive index evaluation method, etc., draws the advantages and disadvantages of each method, and uses them reasonably. The performance evaluation model constructed in this paper adopts the production function method and the analytic hierarchy process. According to the principles of scientificity, feasibility, and economy, the performance indicators for evaluating the balance of supply and demand are screened out, and score evaluation and comparison of each indicator are carried out. Finally, this paper analyzes the corporate performance evaluation index composition, model regression, sensor performance and performance evaluation scores, etc., and has a comprehensive application analysis of the model constructed in this paper. As can be seen from the overall score we selected five companies, enterprises composite score is 83.574, ranking first, followed by a score of 78.421.

1 Introduction

In recent years, with the further development of information acquisition and communication networks, all aspects of people's production and life have also undergone earth-shaking changes. Sensor nodes have the ability to perceive monitoring targets, the ability to calculate and analyze collected data, and the ability to communicate with other sensor nodes. The sensor nodes are randomly or manually deployed in the monitoring area, and a distributed multi-hop self-organizing wireless sensor network is formed through wireless communication.

Wireless sensor network has the characteristics of large network scale, strong dynamics, high reliability, specific application and data-centricity, which makes it widely used in the fields of national defense and military, system control and environmental monitoring. However, sensor nodes have certain limitations in terms of computing capabilities, storage capabilities, and their own energy resources, which leads to many problems and challenges in actual deployment and use. Among them, privacy protection of wireless sensor networks has largely affected wireless sensors large-scale deployment and use of the network.

The application of wireless sensors is becoming more and more widespread, and it has also attracted more scholars' attention. The Saini RK wireless sensor network consists of multiple sensor nodes, which are used to monitor any target area, such as forest fire detection by our military personnel and any industrial activities of industrial managers. Some cities have deployed wireless sensor networks to monitor the concentration of dangerous gases for citizens. In wireless sensor networks, when sensor nodes communicate with each other, routing protocols are used between protocol layers to communicate. The wireless sensor network protocol stack is composed of five layers: application layer, transport layer, network layer, MAC layer, and physical layer. He researched and analyzed the Bellman-Ford routing algorithm, and inspected the data flow between these protocol layers. For simulation purposes, they use the Qualnet 5.0.2 simulator tool. However, the simulation of wireless sensors was still subject to many restrictions [1]. Hu B In a supplier-to-buyer supply chain, uncertainty not only occurs on the demand side, but also on the supply side, which is a common phenomenon in business. For this kind of supply chain optimization and coordination, there are few studies in the literature, especially in the case of service demand. Under the condition of uncertain supply and demand, he established a supply chain model with revenue sharing contract and service demand. First, they deduced the optimal strategy for suppliers and buyers, and found the conditions for coordinating the supply chain. Second, they proved that the optimal number of buyers and suppliers is non-decreasing service demand. They also found that the supplier has an optimal supply. If the buyer's order based on its service demand exceeds the supplier's optimal supply, the supplier's profit is a non-increasing function of the buyer's order; otherwise, the supplier's profit is a non-decreasing function of the buyer's order quantity. However, the supply chain service demand model he designed is not applicable to all industries [2]. One of the main goals of Giri C modern supply chain management is to deal with the increasingly dispersed relationships between related entities, thereby minimizing the dual marginal effects within the supply chain, especially when the end customer’s needs are uncertain. To this end, he studied a three-tier supply chain consisting of a raw material supplier, a manufacturer, and a retailer. The effects of supply chain coordination and sub-supply chain coordination are studied. For the centralized system describing the collaborative scenario, the optimal order quantity and production quantity are obtained. Under the commonly used pure price contract, the optimal order quantity and expected profit of each channel participant in the decentralized system are locally maximized. The semi-integrated model under pure price contract is studied. The optimal strategies under different power structures are compared, and the influence of channel parameters on the optimal strategies is analyzed. Numerical examples illustrate the effectiveness of the model. However, the effect of this supply chain coordination is limited by many factors [3]. Evaluating employee performance is a complex task, and all aspects and indicators must be considered. In addition, each employee of the company has participated in multiple projects during this period, and its overall performance is the sum of the individual performance of a specific project. The concentration is based on the weight of the item. This usually depends on the number of days employees have participated in the project or their financial contribution. Lidinska L focuses on implementing the Analytical Hierarchy Process (AHP) process to evaluate the performance of management consulting company employees. AHP is a tool for constructing and analyzing complex decision-making problems, and seems to be the ideal tool for this task. The proposed AHP model combines correlation and absolute measurement, making it easy to obtain an employee’s overall performance score using a simple MS Excel tool, without the need for special software [4].

The innovations of this paper are: (1) Breaking the limitations of the network energy consumption of wireless sensor nodes, and enhancing its practical application capabilities such as reliability and privacy protection in applications; (2) Reasonable performance evaluation of supply and demand indicators selected to coordinate innovation, compared to traditional evaluation methods more innovative, comprehensive, enterprise for performance evaluation with a good application.

2 Innovation performance evaluation method of supply and demand coordination based on wireless sensor network

2.1 Wireless sensor

  1. (1)

    Wireless sensor network

Wireless Sensor Networks (Wireless Sensor Networks, WSN) is a new system network technology that integrates sensor technology, microelectronic technology, wireless communication technology and distributed processing technology. Through the collaboration between sensor nodes, the sensor network performs real-time data perception, data collection, and perceptual data analysis and processing of the physical environment or monitoring target in the monitoring area, and can send the results of the analysis and processing to the corresponding network terminal users [5, 6]. The wireless sensor network has become a bridge connecting the Internet from the virtual world to the physical world, connecting the information world and the physical world to realize the concept of interconnection of all things, so as to integrate the two. American Business Weekly, MIT Technology, in its forecasting future technology development report, rated wireless sensor network technology as one of the most influential 21 technologies in the twenty-first century and one of the top ten technologies that will change the world in the twenty-first century. Sensor network technology, bionic human organ technology and plastic electronics technology are known as the three high-tech industries in the world in the future [7]. This paper studies the architecture and node organization structure of wireless sensor networks, strengthens its theoretical basis in the application of performance evaluation models, and applies it to constructing a supply–demand coordination innovation performance evaluation model to improve its influence and application scope in real life.

Up to now, wireless sensor networks have mainly experienced two stages of development. The first stage is mainly to design miniaturized and miniaturized sensing node devices through micro-electromechanical system (MEMS) technology [8]. The second phase of research work is mainly focused on its own sensor network problems and possible problems, which is the main direction of current research in the field of wireless sensor networks. Schematic diagram of wireless sensor network structure is shown in Fig. 1.

  1. (2)

    The architecture of wireless sensor network

As can be seen from Fig. 1, a wireless sensor network usually consists of four parts: sensor node, sink nodes, communication systems, and remote terminals. The main functions of each component are as follows: (1) Sensor nodes: limited by the working environment and working methods, sensor nodes usually use batteries with limited energy for power; limited by the design cost of hardware devices, batteries usually cannot be replenished in time Electricity [9]. This type of sensor node constitutes the ordinary sensing node of the wireless sensor network. It needs to collect data and perform preliminary data processing on the status of the monitoring target in the monitoring area. It also needs to forward, receive and process the data of neighbor nodes [10]. The sensor nodes cooperate with each other to complete the information collection tasks of the network and other tasks that need to be processed. (2) Sink node (sink node): Compared with ordinary sensor nodes, Sink nodes have greater advantages in terms of storage resources, energy resources, and computing capabilities [11]. It usually acts as a gateway between the internal sensor network and the external network. It typically acts as a gateway sensor internal and external networks, one can perceive from the sensor network external network to forward the data acquisition and processing, by the external network to the remote sensing data is sent to the terminal system [12]. The sensory data information obtained by the node draws up corresponding decisions and measures for the node monitoring area; on the other hand, it can receive the task sent by the terminal system through the external network, perform preliminary analysis and processing on it, and send the task to the sensor network the terminal node [13, 14]. In addition, the sink node can also only include a wireless communication interface without a gateway device with monitoring functions. (3) Communication network system: It is mainly responsible for the information exchange and communication between the remote terminal system and the sensor system. It usually includes: the Internet, satellites, and mobile communication networks [15]. (4) Remote terminal system: It mainly manages and maintains the wireless sensor network in real time. The system user can realize the information query, access and management of the status of the node monitoring area through the user terminal [16].

  1. (3)

    The structure of the wireless sensor node

Sensor nodes are the core components of wireless sensor networks. It is a small communication device that can perceive the surrounding environment and match or store information, as shown in Fig. 2. With the advancement of semiconductor technology, the cost of these devices continues to drop. The sensor node includes the following main components. (1) Microcontroller: It is a single-chip computer. Although its size is small, it can perform many complex tasks, including controlling the operation of other devices interconnected with it [17, 18]. Generally speaking, the microcontroller is composed of a microprocessor, RAM memory and related external devices. There are also other devices on the market today, which can be used to replace microcontrollers to achieve the same functions, but these devices have their own advantages and disadvantages. Due to lower energy consumption and strong computing power, microprocessors are still small [19]. (2) Transceiver: A transmitter–receiver used to send data, receive data and instructions in the communication process. Wireless sensor networks communicate via radio signals [20, 21]. These sensor nodes generally use industrial, scientific and medical frequency bands. (3) External memory: Flash memory has become the external memory commonly used by wireless sensor network nodes due to its smaller size and increasing storage capacity [22, 23]. Based on the requirements of the node, we can have user memory and program memory. The size of the external memory depends on the specific application. (4) Energy: The energy consumption value of a node refers to the power consumption of node programming, sensing and collecting data, data processing and data communication [24]. Normally, most of the energy is used to transmit data. Energy is stored inside the sensor node in the form of a battery. The cost of batteries has recently fallen sharply, especially for disposable batteries. (5) Sensors: The sensor works can be divided into the following categories: physical sensors, temperature sensors, chemical sensors and the like; a sensor is used to collect data from the monitoring area and produce typical hardware device in response to the certain measurable nature. Figure 3 is the sensor organization chart.

Fig. 1
figure 1

Schematic diagram of wireless sensor network structure

Fig. 2
figure 2

Physical map of wireless sensor (Picture is from Baidu gallery)

Fig. 3
figure 3

Organizational structure diagram of sensor nodes

The sensor node is mainly composed of a sensing part, a processing part, a communication part and a power supply part. Among them, the processing part is the core part of the sensor node, responsible for the entire node's equipment control, task allocation, task scheduling, data integration, data transmission and other functions.

2.2 Performance evaluation methods

There are many ways to evaluate the performance of domestic and foreign enterprises. These performance evaluation methods reflect the unique economic ideas of scholars and at the same time become more perfect with economic development and changes of the times. The research summarizes the enterprise performance evaluation methods widely adopted at home and abroad:

  1. (1)

    Factor analysis method: a statistical analysis method that uses the statistical index system to analyze the degree of influence of each factor in the total change of the phenomenon [25]. It characterized by qualitative analysis, and for the extension of the main indicators of business performance. He began to study the effect is the most direct and most easily recognized characteristic. It gradually extends to the deeper attributes and factors of corporate performance evaluation.

  2. (2)

    Fuzzy comprehensive evaluation method: a method in which things or objects are restricted by multiple factors and an overall evaluation is given to the things or objects. It is characterized by the combination of qualitative description and quantitative analysis [26]. It is classified according to four levels: strong, strong, general and poor, which is suitable for solving various non-deterministic problems [27, 28]. The effect is that the performance level is generally divided into four levels: very strong, strong, fair and poor, and the criteria for defining each level are different. This method is used to vaguely evaluate corporate performance [29, 30].

  3. (3)

    Comprehensive index evaluation method: a method for evaluating multiple objects to be evaluated through multiple indicators [31]. The characteristic is the comparative analysis among multiple enterprises. The evaluation process is to evaluate many indicators at the same time through some special ways, not to evaluate one by one in order [32]. The function is to filter out some of the most suitable indicators based on the collection of a large number of indicators, including quantitative indicators and qualitative indicators. And then use non-dimensional methods for processing, and convert different indicators of magnitude into quantification of the same level for comparison, so as to obtain specific Corporate Performance Index.

2.3 Performance evaluation model algorithm

  1. (1)

    Production function method

To investigate the relationship between inputs and outputs of business growth, the general form of the production function has been expanded, production inputs enterprise as a factor of production, and the production function is introduced into the expression of the production function to obtain the output growth production function, which is:

$$T = {\text{Bg}}(L,M,O)$$

In the formula, T is the output of informatization; B is the level of technological progress; L is the amount of capital input; M is the amount of labor input; O is the amount of enterprise information input.

Write the above formula as the commonly used Cobb-Grass production function form, namely:

$$T = BL^{\alpha } M^{\beta } O^{\kappa }$$

Among them: \(\alpha ,\beta ,\kappa\) the output elasticity of capital, labor, and information input respectively.

Take the full differential of both ends of the formula with respect to time t, we get:

$$\frac{{{\text{d}}T}}{{{\text{d}}t}} = \frac{{{\text{d}}B}}{{{\text{d}}t}}g\left( {L,M,O} \right) + \frac{\partial T}{{\partial L}}\frac{\partial L}{{\partial t}} + \frac{\partial T}{{\partial t}}\frac{\partial M}{{\partial t}} + \frac{\partial T}{{\partial t}}\frac{\partial O}{{\partial t}}$$

Dividing both ends of the formula by T, we get:

$$\frac{{{\text{d}}T/{\text{d}}t}}{T} = \frac{{\left( {{\text{d}}B/{\text{d}}t} \right) \cdot g\left( {L,M,O} \right)}}{{{\text{Bg}}\left( {L,M,O} \right)}} + \frac{\partial T}{{\partial L}}\frac{L}{T}\frac{{{\text{d}}L/{\text{d}}t}}{L}$$
$$y = b + \alpha l + \beta m + \kappa o$$

Among them, α is the output elasticity of capital, the output elasticity of labor \(\beta\), and the output elasticity of information \(\kappa\), y is the growth rate of output, l is the growth rate of capital, m is the growth rate of labor, and o is the growth rate of information input, b is the speed of technological progress.

The contribution of the total enterprise production input to the output growth rate is:

$${\text{EU}} = \kappa o/y$$

The production function is mainly used to analyze the relationship between the input and output of the macro enterprise performance evaluation, such as the efficiency of the national enterprise informatization and the analysis of the informatization of a certain industry.

  1. (2)

    Analytic Hierarchy Process

To construct the analytic hierarchy function, we must first establish a hierarchical structure, including the target layer, the criterion layer and the scheme layer. Secondly, we must construct the judgment matrix. Then calculate the relative weight of each indicator. Finally, the consistency check is calculated.

Calculate the consistency index CU. The formula is as follows:

$${\text{CU}} = \frac{{\gamma_{{{\text{max}}}} - n}}{n - 1}$$

Among them, n is the order of the judgment matrix and the maximum eigenvalue of the judgment matrix \(\gamma_{\max }\). The CU value is the smaller, the greater the consistency; the larger the CU, the smaller the consistency. The special case is when it is n, that is, when CU = 0, it is complete consistency.

Calculate the consistency ratio CT. The formula is as follows:

$${\text{CT}} = \frac{{{\text{CU}}}}{{{\text{RU}}}}$$

When CT < 0.1 is considered by matrix consistency test, if the deviation should be immediately corrected until the meet results. Calculate overall consistency. The formula is as follows:

$${\text{CT}} = \frac{{\sum\nolimits_{j = 1}^{n} {{\text{CU}}_{j} *R_{j} } }}{{\sum\nolimits_{j = 1}^{n} {{\text{RU}}_{j} *R_{j} } }}.$$

3 Innovation performance evaluation model of supply and demand coordination based on wireless sensor network

3.1 Principles for constructing the innovation performance evaluation model for supply and demand coordination

The principles of constructing the innovation performance evaluation index system for the coordination of supply and demand based on sensor networks: (1) Scientific principles. The design index system of innovation performance evaluation must be established on the basis of science. First of all, we must accurately and scientifically understand what are the performance of independent innovation and the related theoretical basis of performance and performance evaluation. Furthermore, through scientifically searching and analyzing the influencing factors of the independent innovation performance evaluation model of enterprises. Then, establish a performance evaluation index system for various influencing factors. Finally, put forward countermeasures and suggestions on the basis of science. (2) The principle of feasibility. The construction of the independent innovation performance evaluation index system needs to be practical and feasible. Whether it is the process of screening indicators or the process of constructing a model, it must be implemented in accordance with the principles of feasibility and operability. (3) The principle of growth. As the name suggests, it means that the construction of the independent innovation performance evaluation index system of enterprises needs to keep pace with the times and constantly change to adapt to the new situation. Growth needs not only to develop according to the company's own environment, but also to carry out independent innovation based on the industry in which the company is located, the national policy environment and the international environment, and to study the company's future independent innovation capabilities and future development potential.

3.2 Index screening

This paper uses questionnaire survey method and Delphi method to allow respondents to assign values according to their views on the necessity of various indicators, and select the most necessary indicators to form an indicator system for enterprise informatization performance evaluation. In this paper, the design of the "buyer coordination performance evaluation index system" questionnaire survey of production inputs and outputs included in the index, asked respondents to judge the necessity of the indicators used to evaluate the performance of enterprise supply and demand coordination.

The credibility index is the credibility coefficient, which can theoretically be expressed as the ratio of the true value variance to the measured value variance. The confidence factor is:

$$T_{x} = \frac{{\theta_{i}^{2} }}{{\theta_{x}^{2} }} = 1 - \frac{{\theta_{E}^{2} }}{{\theta_{x}^{2} }}$$

The Kendall synergy coefficient test is mainly used to analyze whether the judgment criteria of the judges are consistent and fair. The formula for the synergy coefficient E is:

$$E = \sum\limits_{i}^{n} {\frac{{\left[ {T_{i} - m\left( {n + 1} \right)/2} \right]^{2} }}{{\left[ {m^{2} n\left( {n^{2} - 1} \right)} \right]/2}}}$$

The closer E is to 1, the stronger the correlation between the rows of data, and the more consistent the evaluation criteria of the judges.

3.3 Enterprise input–output supply and demand balance innovation performance evaluation model

The value of cognitive variables after selection is used to analyze the impact of the enterprise's input–output status and the structural relationship between the two on enterprise performance. Data analysis steps.

  1. (1)

    Calculate the value of each index in the enterprise performance evaluation index system

After the items set in the questionnaire passed the analysis of validity and validity, the cognitive variables that have a good correlation with the indicators of enterprise informatization input and output were selected. In this paper, the factor loading coefficient of the cognitive variables contained in each index is used as the weight, and the value of each index is obtained by weighted average. Specifically according to the following formula:

$$X_{i} = \sum\limits_{j = 1}^{{n_{j} }} {\phi_{j} x_{ij} }$$
$$\phi_{j} = u_{{x_{j} }} /\sum\limits_{j} {u_{{x_{o} }} }$$

Which \(x_{ij}\) represents the value of the j-th cognitive variable of the i-th production input index. \(u_{{x_{ij} }}\) indicates the factor loading value; the indicated weight \(\phi_{j}\) is the factor loading value \(x_{ij}\) calculated \(x_{ij}\) according to the above formula.

$$Y_{i} = \sum\limits_{j = 1}^{{l_{j} }} {\phi^{\prime}_{j} y_{ij} }$$
$$\phi^{\prime}_{j} = u_{{y_{ij} }} /\sum\limits_{j} {u_{{y_{ij} }} }$$

where \(y_{ij}\) is the value of the j-th cognitive variable representing the i-th informatization.

  1. (2)

    Calculate the scores of each indicator in the enterprise performance evaluation index system

Calculate the empirical statistical data according to the formula to obtain samples with a sample size of t for Xi and Yi respectively. Use xi and yi to represent one of the samples. The calculation formula for finding the sample mean is:

$$\left\{ {\begin{array}{*{20}c} {\overline{X}_{i} = \frac{1}{i}\sum\limits_{i = 1}^{t} {x_{{t_{i} }} } } \\ {\overline{Y}_{i} = \frac{1}{i}\sum\limits_{i = 1}^{t} {y_{{t_{i} }} } } \\ \end{array} } \right.$$

The calculation result of the formula is the score of each indicator of enterprise informatization input and output, and examines the input–output situation of enterprise informatization.

3.4 Mathematical statistics

The questionnaire uses a 9-level scale, with 1 for "not necessary", 3 for "general", 5 for "necessary", 7 for "very necessary", and 9 for "very necessary"; 2, 4, 6, 8 Represents the intermediate value of two adjacent judgments. This article uses SPSS statistical analysis software to analyze the results of each survey to test the consistency of the respondents’ opinions.

4 Based on wireless sensor network supply and demand coordination innovation performance evaluation model and algorithm analysis

4.1 Composition of corporate performance evaluation indicators

Table 1 is an introduction to the composition of the theoretical indicators of enterprise performance evaluation. The performance of the enterprise is evaluated from the aspects of enterprise goals, partners, customer satisfaction, and internal enterprise. As can be seen that, from the perspective of the target level of the enterprise, seizing the rapidly changing market opportunity is the main purpose of enterprise formation. Only in the process of realizing the opportunity can modern information technology be used to realize the resource sharing of each member enterprise. Cost reduction, risk and cost sharing. The indicators that reflect the corporate goals include five indicators: reduction of business costs, specificity of corporate resources, acquisition and utilization of skilled personnel, expansion of business volume, and reduction of business risks. Consider the relationship between business partners from the environmental level and relationship of partners. From the environmental aspects of the relationship between partners and to consider the relationship between business partners, the higher the performance level, the higher the degree of close cooperation, its likelihood of success is greater. So get members of the customer's identity will be a key factor in the success of virtual enterprise operation. From the perspective of customer satisfaction, three indicators are selected: on-time delivery rate, customer complaint rate, and product pass rate. From the perspective of the internal process of the company, two indicators are selected: enhancement of business competitiveness and enhancement of corporate brand.

Table 1 The composition of theoretical indexes of enterprise performance evaluation

4.2 Regression analysis of corporate performance evaluation model

Figure 4 shows that there is a large gap between the maximum and minimum scores of each index, indicating that the consistency of the scores of the experts is low. From the results in Fig. 4, the same conclusion can be drawn. The synergy coefficient is only 0.68, which is far less than 0.85. Therefore, it is necessary to conduct the next stage of investigation. During the first phase of the survey, several experts pointed out that the second-level indicator "talent construction" should not be included in the third-level index "enterprise informatization talent incentive policy", so the second phase of the survey included this indicator in the same survey.

Fig. 4
figure 4

Descriptive statistics of survey data of business operation process

From the economic performance regression analysis in Fig. 5 and the management performance regression analysis in Fig. 6, the regression models have achieved good results. In order to be able to see the relationship between the various input factors and output factors of production, firstly adopt the forced entry regression method, and modify the regression model. In the case of large samples, the goodness of fit is considered acceptable. Significance level = 0.001, indicating that the regression equation is highly significant, and the independent variable has a highly significant linear effect on the dependent variable. The variance inflation factor VIF of the independent variables in the regression model is all less than 10, indicating that there is no multicollinearity between the independent variables.

Fig. 5
figure 5

Economic performance regression analysis results

Fig. 6
figure 6

Management performance regression analysis results

Figure 7 show that the output of this part is analysis of variance data. From the data in the figure, it can be seen that: the statistic F = 47.539; the associated probability p < 0.005. Explain that there is a linear regression relationship between the evaluation index and the dependent variable supply chain performance.

Fig. 7
figure 7

Data regression coefficient analysis

4.3 Wireless sensor performance analysis

The coordinates of the source node are (50, 50), and Fig. 8 shows the energy consumption of the two privacy protocols that are simulated. The result is when the number of transmissions increases and the network energy consumption continues to increase. The energy consumption of the RDPRPP protocol is significantly lower than that of the PNDBPR protocol. In the process of submitting data from the source node to the sink node, the RDPRPP protocol thoroughly checks the energy overhead of the data transmission node. According to the energy consumption model, the energy consumption of nodes is proportional to the physical distance between nodes, so the algorithm in this chapter is the lowest transmission power consumption, and the data upgrade adopts the improved minimum power consumption start protocol.

Fig. 8
figure 8

Schematic diagram of wireless sensor network energy consumption

When the number of sensing nodes N is in the two-layer sensor network changes, the energy consumption of the sensing nodes in the network is the number of sensing nodes N and the network energy consumption. As shown in Table 2, the main energy consumption of the sensing nodes is concentrated on the data transmission and reception of the nodes. Therefore, the total network consumption of the OSERQ protocol and the Encoding protocol will increase accordingly. The OSERQ protocol uses the unit group ID to qualitatively select the network unit area, avoiding unnecessary range query methods and reducing unnecessary data forwarding work. This effectively avoids the phenomenon of a significant increase in the number of forwarding caused by the increase in the density of sensing nodes. Therefore, as the number of sensing nodes N increases, the network energy consumption of the OSERQ protocol is significantly lower than that of the Encoding protocol.

Table 2 Number of sensing nodes N and network energy consumption expression

Figure 9 is the comparison of the target value between the algorithm in this paper and the comparison algorithm. It can be seen that as the number of nodes continues to increase, the target values of the algorithm Rmin and Rmax steadily rise, while the target value of the algorithm MCAND tends to undergo a slight oscillation. The target value curve of algorithm LMA, LMN and FCTP tends to be flat after a sharp decline, and the curve of FCTP is between LMA and LMN. But the target value of MCAND is closer to the target value of Rmin. In the process of increasing the number of nodes, it is always lower than LMA, LMN and FCTP, and the less the number of nodes, the greater the gap between the target values. This is because the algorithm MCAND dynamically changes the node communication radius to make the degree of the node equal to the minimum connectivity Average degree, on the premise of ensuring the given connection probability, try to reduce the communication radius of the node, reduce the degree of the node to reduce communication overhead and signal interference. In addition, the number of nodes is the more, the greater the node density, and the smaller the communication radius at this time which is closer to Rmin.

Fig. 9
figure 9

Comparison of target values of several algorithms with network coverage

4.4 Supplies-demand coordination innovation performance evaluation score

The quantitative data mainly quoted the data collected by the official websites and company reports of 5 large companies, and classified and processed them according to the planned independent innovation performance evaluation model. Figure 10 shows the result of error-free processing of the original data. The independent entrepreneurial performance evaluation model mainly combines expert evaluation with survey methods and questionnaire surveys to obtain quality indicators such as information collection and processing capabilities, innovation trends, innovation strategies, business innovation quality, and popularity. Because data such as the development speed of innovative products is difficult to calculate, it can be achieved through qualitative data processing.

Fig. 10
figure 10

Data dimensionless results

This paper analyzes the professional level and practical experience of many experts and students, selects 20 experts with excellent professional level and rich experience, and evaluates the importance of various indicators of the company's independent innovation performance. According to the scoreboard, the analysis hierarchy process is used for processing. In terms of software, the weights of companies independently measuring innovation performance are ultimately determined by calculations. The calculation process and results are shown in Table 3.

Table 3 Judgment matrix

According to the comprehensive score of the five companies in Fig. 11, it can be seen that the comprehensive score of the company is 83.574, ranking first, followed by a score of 78.421. It can be seen that companies that can well balance the interests of all stakeholders have the highest scores, and only care about the interests of shareholders and investors. Compared with other companies, companies that only focus on the interests of shareholders and investors have the lowest scores. Taking account into the interests of several stakeholders, so the score is in the middle.

Fig. 11
figure 11

Comprehensive performance evaluation scores of enterprises based on the perspective of stakeholders

5 Conclusion

This article mainly studies the innovation performance evaluation model and algorithm of supply–demand coordination based on wireless sensor network, adopts analytic hierarchy process and mathematical model method to make reasonable selection of performance evaluation indicators, innovate performance evaluation methods, improves performance evaluation level, and strengthens the efficiency of the performance evaluation of the enterprise. The model constructed in this paper has a good application in corporate performance evaluation. The innovation of this article lies in the application of wireless sensor network to the performance evaluation model to better filter the evaluation factors and improve its performance evaluation efficiency. The research results of this paper also show that the innovation performance evaluation model based on wireless sensor network studied in this paper can effectively evaluate the comprehensive performance of enterprises. The disadvantage of this paper is that the application scope of the constructed evaluation model is not wide enough, and it needs to be tested by a wide range of practices. In the future, the application of wireless sensors will become more and more extensive, which will bring greater changes to people’s lives.

Availability of data and materials

Data sharing does not apply to this article because no data set was generated or analyzed during the current research period.

Change history



Media Access Control


Wireless Sensor Networks


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Authors’ information

Ye Zhao was born in Changchun, Jilin P.R. China, in 1990. She received the Master’s degree from University of Leicester, United Kingdom. Now, she studies in School of Management, Jilin University. Her research interests include innovation performance, technical economy and big data analysis. Beiwei Li was born in P.R. China, in 1963. He received the Post-doctoral degree in Applied Economics from Business School of Jilin University, P.R. China. Now, he is a professor in the School of Management, Jilin University. His research interests include theory and method of investment economy, theory and method of technological innovation.


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YZ: writing—editing; BL: Data analysis. Both authors read and approved the final manuscript.

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Correspondence to Beiwei Li.

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Zhao, Y., Li, B. RETRACTED ARTICLE: Model and algorithm of innovation performance evaluation for coordination of supply and demand based on wireless sensor network. EURASIP J. Adv. Signal Process. 2021, 108 (2021).

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