# Sensor Network Security Risk Prediction and Control Method Based on Big Data Analysis

Authors: Bingxia Yuan
Journal: Security and Communication Networks
 (2022)

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## Abstract

The security prediction and control program of sensor network based on big data analysis is studied. First of all, in view of the shortcomings of existing security measures, this paper regards security standards as a challenge. Based on the changes of network security before and after the attack, the concept of “network security line” is proposed to select and simplify the security measures that can meet the needs and affect the real security and monitor the counting process of the network. Then, for counterattack, several models of topology security measurement based on stochastic process are developed to check the topology security. Finally, the experimental design of the risk assessment method is found by creating a risk receiving data model, deleting data features, and recreating distributed data. Experimental results show that the efficiency of this method is above 90% when the estimated distance is 10–40 m. The working power is 196∼461 Hz, lower than the normal standard. The time delay is less than 0.3 s, and the real-time performance is better than ordinary models. The relative error is less than 3%, and the accuracy is higher.


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## Body

## 1. Introduction

With the rapid development of microelectronics technology, computer technology, wireless communication technology, and sensor technology, wireless sensor network (WSN), as a concentrated embodiment of these technologies, has gradually entered the conceptual level. This kind of microsensor network with computing and communication capability is a multihop self-organizing network formed by several low-cost microsensor nodes through wireless communication. It can complete the collection, transmission, and integration of various analysis data in the joint venture area. In most noncommercial applications, such as environmental protection, forest fire prevention, bird migration, and so on, safety is not an important issue. It is also applied in other fields, such as commercial community wireless security network, military sensor network, data sampling, and sending program. In the second application, security is particularly important [1].Routing protocol solves the problem of data transmission and is one of the key technologies in wireless sensor networks (Figure1). The performance of the routing process is close to that of the entire network. Due to the limitation of wireless sensor network nodes, traditional Internet routing protocols are not applicable. The main goal of WSN routing protocol is to reduce network overhead as much as possible, make full use of resources, expand network capabilities, and improve network connectivity while satisfying application programs. Topology control of wireless sensor networks is to eliminate unnecessary communication connections between nodes and establish an optimization model of power management and team data transmission on the premise of satisfying network planning. Cluster is an important part of topology management. By joining together, nodes are divided into two groups: a header group and the same nodes. Since the power consumption of the wireless communication module of the sensor node in idle standby state is equal to that in receiving and sending state, the power consumption can be reduced by shutting down the communication module on the same node and only turning on the communication module. In the process of data collection, data are transmitted to the board of the node [2].Figure 1
Wireless sensor network.Wireless sensor network nodes have both router and host functions. They not only perform their own application tasks (such as sensing and transmitting data) but also participate in the discovery and maintenance of routing and the construction of network topology organization. Therefore, Routing protocol and topology control are the key technologies of wireless sensor networks. With the in-depth research of wireless sensor networks, many routing protocols and topology control algorithms have been proposed in recent years, such as energy-aware routing (such as multipath routing), query-based routing (such as directed diffusion routing), geographic location routing (such as geographically efficient routing), and reliable routing (such as multipath routing (MPR)). Cluster-based routing is a common topology control algorithm with specific security mechanism. However, how to select a safe and effective control algorithm according to the specific needs of these routing protocols and topology control is the key to realize the data communication of wireless sensor networks [3]. Therefore, according to the particularity and characteristics of wireless sensor networks, it is of great significance to propose a set of security evaluation model of routing protocol and topology control algorithm in wireless sensor networks. This paper analyzes the protocol according to the evaluation model, compares its advantages and disadvantages, and provides a reference for establishing secure routing according to the specific application environment. At the same time, based on big data technology, this paper scientifically predicts the security attributes such as network transmission data integrity, confidentiality, and availability and realizes the accurate prediction of sensor network information transmission risk, which is the common goal of the world.


## 2. Literature Review

According to Hussain et al., WSN combines electronics, computers, modern networking, and wireless communication technologies and distributes data into technologies. It enables people to obtain detailed and reliable data from the data received and processed on sensor nodes anytime and anywhere in any environment and brings about a revolution in information insight and technology purchase [4]. Vaneeta and Kumar believe that in the long run, WSN is a heat engine that will have a significant impact on people’s life after the Internet. The Internet has changed the way people communicate because WSN combines global historical data with the real world, which will change the way people interact with events. Wireless sensor networks may be as important as the Internet [5]. Cheng and others believe that due to the characteristics of wireless sensor networks, the network is vulnerable to a variety of attacks, which seriously affects the accuracy and integrity of data transmission. Especially when the network routing and topology are attacked, the data cannot be transmitted smoothly or correctly to the destination node [6]. Hare and others proposed the application of optical fiber sensor in all-optical network security and prevention. Using all-optical network, using its characteristics of high transmission bandwidth and high processing capacity, they calculated the network transmission threat value and hidden risk probability, constructed the importance weight of data transmission through three-scale analytic hierarchy process, and realized the risk prediction of network data transmission [7]. The Bayesian network transmission risk prediction method proposed by Javeed and others predicts the transmission risk probability by constructing the network data transmission risk prediction model and analyzes the risk factors according to the fuzzy importance theory [8]. Babaeer and others believe that since the network system can include a large number of sensor nodes, the fault tolerance of the system is enhanced, and the reliability of diagnosis of the whole system is doubled. Due to the integration of various types of sensors, the detection performance is enhanced when the exact location for monitoring cannot be determined. By arranging the sensors near the monitoring location, the effect of monitoring through distributed detection is better than using only a single sensor [9]. Zhang and others believe that wireless sensor networks are data centric, unlike traditional networks, which are connection centric, so nodes need to process data aggregation, fusion, caching, and compression. Data fusion and compression can reduce redundant data, save power consumption caused by communication, prolong network lifetime, and reduce the load of processing center [10]. Dong and others believe that the measurement accuracy of a single sensor is lower than that of traditional large-scale sensing equipment due to its weak ability, but multiple sensors can improve the measurement accuracy through data fusion. In addition, through distributed data processing, the sensor nodes conduct collaborative computing locally and only transmit the user requirements and partially processed data, which reduces the amount of data transmission, especially the energy consumed in radio signal transmission when the distance is long [11].


## 3. Method

Sensor network is a large-scale peer-to-peer network. Each node is both a terminal and a routing node. The communication between two nodes often goes through many hops, which gives enemy personnel more opportunities to destroy the normal transmission of data packets. To carry out network layer attacks, enemy personnel must have an understanding of the deployment of a wireless network. Therefore, wireless sensor networks in open space are vulnerable to various attacks. The security state of the network will change with the change of attack [12].


### 3.1. Security Requirements of WSN and Limitations of Existing Evaluation Models

In traditional communication, security has always been a concern of many scientists. However, wireless sensor networks have different requirements from traditional wireless networks. In traditional wireless networks, management and routing are designed primarily to maximize service efficiency (QoS) and high bandwidth usage. Power consumption is a secondary concern and the battery can be replaced for as long as possible. However, wireless sensor networks have hundreds of nodes. After configuration, most nodes are the same, and a few are removable. Traffic has the same characteristics as traditional connections. In addition, the network topology of most sensor networks is unpredictable prior to deployment. Meanwhile, after transmission, the topology of the entire network and the role of sensor nodes in the network have changed for many times [13].Considering the characteristics of WSN, the security of WSN can be discussed from the following two aspects.(1)
Message security: it refers to the security of various messages transmitted between nodes, including not only general data confidentiality, data authentication, and data integrity but also data freshness. Data confidentiality means that even if the enemy gets the ciphertext, he/she cannot decipher the original text, which can be realized by an appropriate symmetric key encryption system. Data identification refers to identifying whether the data are sent by a legitimate node. Data integrity implies that the data should be complete and not tampered with. It can be realized through corresponding summary functions or obtained through data identification [14].(2)
Node security: when the sensor node is received and turned into a malicious node, the network can quickly detect different nodes and prevent more risks. WSN nodes are more prone to failure and damage than traditional networks. Therefore, it is not enough to simply provide an encryption algorithm that can secure messages. When a bomb goes off and sensitive information such as keys is stolen, the real decision is how to ensure security. It is therefore worth examining the main benefits of distribution in detail. A well-designed security system should be able to prevent malicious behaviors caused by such malicious activities [15]. In addition, different applications require varying degrees of variability, which is also required in other aspects of practical applications to maximize the distribution efficiency of critical components.


### 3.2. Establishment of Evaluation Model Based on Attack Effect

In wireless sensor networks, routing security plays a very important role in the safe data transmission of the whole network. The routing security evaluation model is used to evaluate the network routing security, so as to establish a secure route and put forward guiding suggestions during networking. The establishment of routing security evaluation model based on attack effect includes the selection of evaluation index, the description of routing attack, and the determination of node security and relevant weight [16].


#### 3.2.1. Selection of Evaluation Indicators

With the development and application of WSN technology, the connotation of information security is constantly extending. At present, the security of information usually corresponds to a variety of performance indicators. How to determine the indicators that can objectively and truly reflect the security to evaluate the effect of routing attacks is the key problem. A good metric should have the following characteristics: (1) consistent measurement, no subjective standard; (2) easy to collect, more suitable for active acquisition; (3) it should appear in the form of numerical value or percentage, not qualitative with labels such as “high,” “medium,” and “low”; (4) it should use at least one measurement unit to express [17]. According to the above characteristics, combined with the characteristics of WSN, this paper adopts systematic analysis method to divide the security measurement into protocol layer, attack effect layer, and security index layer, and the relationship is gradually refined, as shown in Figure 2.Figure 2
Safety measurement hierarchy.Figure2 describes the security measurement level of the model. Firstly, the indicators of the security index layer are measured through routing attacks. The security index layer can obtain the direct data through measurement, the main security indexes, and their data representation. Secondly, the security index layer calculates the network security difference of each attack effect according to the weight of the attack effect layer. The attack effect layer mainly includes the important aspects of routing security, such as quality of service parameters, data security, and transmission node security. Finally, according to the direct difference of attack effect and the weight of each attack effect on the routing protocol, the routing security direct difference is calculated [18]. Through the statistical quantitative method proposed in this paper to reflect the security of network data transmission process, the routing security of WSN is comprehensively evaluated. Different routing attacks have different effects on each index of the security index layer. The attack effect is weighted by testing the consequences of the attack.


#### 3.2.2. Description of Routing Attack Effect

For a safety factorxi, its safety degree pxi0≤pxi≤1 can be used to express its ability to complete the safety function. The size of the safety degree can be analyzed from the perspective of the safety degree. It must have a safety degree corresponding to the safety degree to represent the uncertainty, confusion, and disorder of the safety factor. The greater the degree of safety, the smaller the degree of uncertainty, confusion, and disorder, and vice versa. The security difference of WSN before and after attack can be used as an evaluation standard of attack effect. Since the evaluation of attack effect only considers the changes of security performance before and after the system is attacked, the concept of “network security” is proposed based on the concept of “information entropy” in information theory, which can well describe the changes of security performance [19]. Starting from the essential meaning of heirloom, heirloom is a measure s of the state probability p of a system, that is, S=klnp, where k is an undetermined coefficient of constant parameter value. In the calculation of this paper, k = 1 is taken. “Network security direct” is a description of information security, and the smaller the direct value, the better the security of the network. For a certain security index xi of WSN, its direct value can be defined as(1)Sxi=−log2Pxi.It can be seen from formula (1) that when the safety degree Pxi is greater, the corresponding safety degree Sxi is smaller. Obviously, after the WSN network is attacked, the security decreases and the direct value should increase. Therefore, “direct difference” (2) can be adopted:(2)ΔS=−log2Pxi′Pxi.


#### 3.2.3. Determination of Node Security

Construct a probability model to unify the security evaluation indexes related to the attack effect of WSN routing protocol into a process of data flow. The process model is established according to the Monte Carlo method, and then the weighted entropy of the index is calculated according to the level of security index, so as to evaluate the security of Routing protocol according to the change of soil moisture value [20].The so-called Monte Carlo method is to construct an appropriate probability model or random process according to the change and statistical law of the random problem to be solved and carry out a large number of statistical experiments according to the model. This process is simulated through a series of random numbers, and then the parameters are calculated through the observation or sampling experiment of the model or process. Finally, the approximate value of the solution is given [21]. Its analysis is essentially a complex probability calculation, which can be used to simulate attacks, normalize security indicators, and evaluate security.Network security under routing attack is a complex change process, so all security related indicators can be abstracted as data flow and established process model. Each index is simulated by a process: a failure rate corresponds to0,1, which is a random number, representing normal and failure states, respectively. The probability model is based on the time process of simulated attack [22].Take a simulation as a random test to simulate the network attack. We suppose that you want to test and the “one scan” index R for each test. The number of nodes in the normal range isX1,X2,…Xs, independent and identically distributed. Introduce random function:(3)fZ=0,z≥β,1,z<β,where β is the user-defined minimum number of nodes with acceptable single indicator [23], that is, the critical condition for indicator xi to be normal. According to the principle of the Monte Carlo method, the probability that index xi is normal is as follows, that is, the safety degree is(4)Pxi≈1s∑j=1sfXj,where j refers to the j-th attack test.


## 3.1. Security Requirements of WSN and Limitations of Existing Evaluation Models

In traditional communication, security has always been a concern of many scientists. However, wireless sensor networks have different requirements from traditional wireless networks. In traditional wireless networks, management and routing are designed primarily to maximize service efficiency (QoS) and high bandwidth usage. Power consumption is a secondary concern and the battery can be replaced for as long as possible. However, wireless sensor networks have hundreds of nodes. After configuration, most nodes are the same, and a few are removable. Traffic has the same characteristics as traditional connections. In addition, the network topology of most sensor networks is unpredictable prior to deployment. Meanwhile, after transmission, the topology of the entire network and the role of sensor nodes in the network have changed for many times [13].Considering the characteristics of WSN, the security of WSN can be discussed from the following two aspects.(1)
Message security: it refers to the security of various messages transmitted between nodes, including not only general data confidentiality, data authentication, and data integrity but also data freshness. Data confidentiality means that even if the enemy gets the ciphertext, he/she cannot decipher the original text, which can be realized by an appropriate symmetric key encryption system. Data identification refers to identifying whether the data are sent by a legitimate node. Data integrity implies that the data should be complete and not tampered with. It can be realized through corresponding summary functions or obtained through data identification [14].(2)
Node security: when the sensor node is received and turned into a malicious node, the network can quickly detect different nodes and prevent more risks. WSN nodes are more prone to failure and damage than traditional networks. Therefore, it is not enough to simply provide an encryption algorithm that can secure messages. When a bomb goes off and sensitive information such as keys is stolen, the real decision is how to ensure security. It is therefore worth examining the main benefits of distribution in detail. A well-designed security system should be able to prevent malicious behaviors caused by such malicious activities [15]. In addition, different applications require varying degrees of variability, which is also required in other aspects of practical applications to maximize the distribution efficiency of critical components.


## 3.2. Establishment of Evaluation Model Based on Attack Effect

In wireless sensor networks, routing security plays a very important role in the safe data transmission of the whole network. The routing security evaluation model is used to evaluate the network routing security, so as to establish a secure route and put forward guiding suggestions during networking. The establishment of routing security evaluation model based on attack effect includes the selection of evaluation index, the description of routing attack, and the determination of node security and relevant weight [16].


### 3.2.1. Selection of Evaluation Indicators

With the development and application of WSN technology, the connotation of information security is constantly extending. At present, the security of information usually corresponds to a variety of performance indicators. How to determine the indicators that can objectively and truly reflect the security to evaluate the effect of routing attacks is the key problem. A good metric should have the following characteristics: (1) consistent measurement, no subjective standard; (2) easy to collect, more suitable for active acquisition; (3) it should appear in the form of numerical value or percentage, not qualitative with labels such as “high,” “medium,” and “low”; (4) it should use at least one measurement unit to express [17]. According to the above characteristics, combined with the characteristics of WSN, this paper adopts systematic analysis method to divide the security measurement into protocol layer, attack effect layer, and security index layer, and the relationship is gradually refined, as shown in Figure 2.Figure 2
Safety measurement hierarchy.Figure2 describes the security measurement level of the model. Firstly, the indicators of the security index layer are measured through routing attacks. The security index layer can obtain the direct data through measurement, the main security indexes, and their data representation. Secondly, the security index layer calculates the network security difference of each attack effect according to the weight of the attack effect layer. The attack effect layer mainly includes the important aspects of routing security, such as quality of service parameters, data security, and transmission node security. Finally, according to the direct difference of attack effect and the weight of each attack effect on the routing protocol, the routing security direct difference is calculated [18]. Through the statistical quantitative method proposed in this paper to reflect the security of network data transmission process, the routing security of WSN is comprehensively evaluated. Different routing attacks have different effects on each index of the security index layer. The attack effect is weighted by testing the consequences of the attack.


### 3.2.2. Description of Routing Attack Effect

For a safety factorxi, its safety degree pxi0≤pxi≤1 can be used to express its ability to complete the safety function. The size of the safety degree can be analyzed from the perspective of the safety degree. It must have a safety degree corresponding to the safety degree to represent the uncertainty, confusion, and disorder of the safety factor. The greater the degree of safety, the smaller the degree of uncertainty, confusion, and disorder, and vice versa. The security difference of WSN before and after attack can be used as an evaluation standard of attack effect. Since the evaluation of attack effect only considers the changes of security performance before and after the system is attacked, the concept of “network security” is proposed based on the concept of “information entropy” in information theory, which can well describe the changes of security performance [19]. Starting from the essential meaning of heirloom, heirloom is a measure s of the state probability p of a system, that is, S=klnp, where k is an undetermined coefficient of constant parameter value. In the calculation of this paper, k = 1 is taken. “Network security direct” is a description of information security, and the smaller the direct value, the better the security of the network. For a certain security index xi of WSN, its direct value can be defined as(1)Sxi=−log2Pxi.It can be seen from formula (1) that when the safety degree Pxi is greater, the corresponding safety degree Sxi is smaller. Obviously, after the WSN network is attacked, the security decreases and the direct value should increase. Therefore, “direct difference” (2) can be adopted:(2)ΔS=−log2Pxi′Pxi.


### 3.2.3. Determination of Node Security

Construct a probability model to unify the security evaluation indexes related to the attack effect of WSN routing protocol into a process of data flow. The process model is established according to the Monte Carlo method, and then the weighted entropy of the index is calculated according to the level of security index, so as to evaluate the security of Routing protocol according to the change of soil moisture value [20].The so-called Monte Carlo method is to construct an appropriate probability model or random process according to the change and statistical law of the random problem to be solved and carry out a large number of statistical experiments according to the model. This process is simulated through a series of random numbers, and then the parameters are calculated through the observation or sampling experiment of the model or process. Finally, the approximate value of the solution is given [21]. Its analysis is essentially a complex probability calculation, which can be used to simulate attacks, normalize security indicators, and evaluate security.Network security under routing attack is a complex change process, so all security related indicators can be abstracted as data flow and established process model. Each index is simulated by a process: a failure rate corresponds to0,1, which is a random number, representing normal and failure states, respectively. The probability model is based on the time process of simulated attack [22].Take a simulation as a random test to simulate the network attack. We suppose that you want to test and the “one scan” index R for each test. The number of nodes in the normal range isX1,X2,…Xs, independent and identically distributed. Introduce random function:(3)fZ=0,z≥β,1,z<β,where β is the user-defined minimum number of nodes with acceptable single indicator [23], that is, the critical condition for indicator xi to be normal. According to the principle of the Monte Carlo method, the probability that index xi is normal is as follows, that is, the safety degree is(4)Pxi≈1s∑j=1sfXj,where j refers to the j-th attack test.


## 3.2.1. Selection of Evaluation Indicators

With the development and application of WSN technology, the connotation of information security is constantly extending. At present, the security of information usually corresponds to a variety of performance indicators. How to determine the indicators that can objectively and truly reflect the security to evaluate the effect of routing attacks is the key problem. A good metric should have the following characteristics: (1) consistent measurement, no subjective standard; (2) easy to collect, more suitable for active acquisition; (3) it should appear in the form of numerical value or percentage, not qualitative with labels such as “high,” “medium,” and “low”; (4) it should use at least one measurement unit to express [17]. According to the above characteristics, combined with the characteristics of WSN, this paper adopts systematic analysis method to divide the security measurement into protocol layer, attack effect layer, and security index layer, and the relationship is gradually refined, as shown in Figure 2.Figure 2
Safety measurement hierarchy.Figure2 describes the security measurement level of the model. Firstly, the indicators of the security index layer are measured through routing attacks. The security index layer can obtain the direct data through measurement, the main security indexes, and their data representation. Secondly, the security index layer calculates the network security difference of each attack effect according to the weight of the attack effect layer. The attack effect layer mainly includes the important aspects of routing security, such as quality of service parameters, data security, and transmission node security. Finally, according to the direct difference of attack effect and the weight of each attack effect on the routing protocol, the routing security direct difference is calculated [18]. Through the statistical quantitative method proposed in this paper to reflect the security of network data transmission process, the routing security of WSN is comprehensively evaluated. Different routing attacks have different effects on each index of the security index layer. The attack effect is weighted by testing the consequences of the attack.


## 3.2.2. Description of Routing Attack Effect

For a safety factorxi, its safety degree pxi0≤pxi≤1 can be used to express its ability to complete the safety function. The size of the safety degree can be analyzed from the perspective of the safety degree. It must have a safety degree corresponding to the safety degree to represent the uncertainty, confusion, and disorder of the safety factor. The greater the degree of safety, the smaller the degree of uncertainty, confusion, and disorder, and vice versa. The security difference of WSN before and after attack can be used as an evaluation standard of attack effect. Since the evaluation of attack effect only considers the changes of security performance before and after the system is attacked, the concept of “network security” is proposed based on the concept of “information entropy” in information theory, which can well describe the changes of security performance [19]. Starting from the essential meaning of heirloom, heirloom is a measure s of the state probability p of a system, that is, S=klnp, where k is an undetermined coefficient of constant parameter value. In the calculation of this paper, k = 1 is taken. “Network security direct” is a description of information security, and the smaller the direct value, the better the security of the network. For a certain security index xi of WSN, its direct value can be defined as(1)Sxi=−log2Pxi.It can be seen from formula (1) that when the safety degree Pxi is greater, the corresponding safety degree Sxi is smaller. Obviously, after the WSN network is attacked, the security decreases and the direct value should increase. Therefore, “direct difference” (2) can be adopted:(2)ΔS=−log2Pxi′Pxi.


## 3.2.3. Determination of Node Security

Construct a probability model to unify the security evaluation indexes related to the attack effect of WSN routing protocol into a process of data flow. The process model is established according to the Monte Carlo method, and then the weighted entropy of the index is calculated according to the level of security index, so as to evaluate the security of Routing protocol according to the change of soil moisture value [20].The so-called Monte Carlo method is to construct an appropriate probability model or random process according to the change and statistical law of the random problem to be solved and carry out a large number of statistical experiments according to the model. This process is simulated through a series of random numbers, and then the parameters are calculated through the observation or sampling experiment of the model or process. Finally, the approximate value of the solution is given [21]. Its analysis is essentially a complex probability calculation, which can be used to simulate attacks, normalize security indicators, and evaluate security.Network security under routing attack is a complex change process, so all security related indicators can be abstracted as data flow and established process model. Each index is simulated by a process: a failure rate corresponds to0,1, which is a random number, representing normal and failure states, respectively. The probability model is based on the time process of simulated attack [22].Take a simulation as a random test to simulate the network attack. We suppose that you want to test and the “one scan” index R for each test. The number of nodes in the normal range isX1,X2,…Xs, independent and identically distributed. Introduce random function:(3)fZ=0,z≥β,1,z<β,where β is the user-defined minimum number of nodes with acceptable single indicator [23], that is, the critical condition for indicator xi to be normal. According to the principle of the Monte Carlo method, the probability that index xi is normal is as follows, that is, the safety degree is(4)Pxi≈1s∑j=1sfXj,where j refers to the j-th attack test.


## 4. Experimental Analysis

In order to verify the performance of this method in realizing the risk prediction of information transmission in high reliability fiber Bragg grating sensor networks, simulation experiments are carried out. The experiment is established in the MATLAB simulation environment. The transmission risk data acquisition frequency band of high reliability fiber Bragg grating sensor network is 3–12 kHz, the number of monitoring data acquisition nodes is set to 2000, and the clustering center is initialized (0.3, 0.6). The sampling carrier frequency time width of FBG sensor network information transmission risk is 1.25 ms, the normalized initial frequency of risk prediction isf11=0.05, f12=0.15, the number of sampling points of risk information data is N=120, and the variation range of interference signal-to-noise ratio is −12∼12 dB. According to the above simulation environment and parameter settings, other algorithms and this algorithm are selected to collect the risk data of wireless sensor networks, respectively, and take the collected data as the test object to simulate the dynamic prediction of information transmission risk of high reliability fiber Bragg grating sensor networks [24, 25], and the comparison of time cost of information transmission risk prediction of high reliability fiber Bragg grating sensor network is shown in Figure 3.Figure 3
Prediction efficiency comparison (in %).It can be seen from Table1 that the prediction efficiency of this method is significantly higher than that of other algorithms and always maintains a high level.Table 1
Load overhead comparison (unit: Hz).
Distance (s)/time delayThe method of this paperOther methods150.130.25240.170.24400.250.34300.290.41It can be seen from Table2 that the load overhead increases in proportion to the predicted distance. In the distance of 10∼40 m, the load overhead of this method is 196∼461 Hz, which is smaller than 246∼797 Hz of other methods. This shows that the demand for a priori sample knowledge is smaller and more cost-effective.Table 2
Delay comparison (unit: s).
Distance (m)/relative errorThe method of this paperOther methods151.696.72240.255.03402.596.48300.280.83According to the analysis of Table2, the time delay of information transmission risk prediction of high reliability fiber Bragg grating sensor network using this method is not higher than 0.3 s when the distance is 10∼40 m. Compared with 0.25∼0.52 Hz of other methods, the load is smaller and the real-time performance is significantly improved.Accordingly,(5)error=xi−xjxi×100%.Test the prediction error of risk information transmission by different methods, wherexi represents the prediction data and xj represents the real data. The results are shown in Table 3.Table 3
Comparison of risk prediction errors (unit: %).
Distance (m)/relative errorThe method of this paperOther methods151.696.72240.255.03402.596.48300.280.83It can be seen from Table3 that when the prediction distance is 10∼40 m, the relative error of the risk prediction method in this paper is not higher than 3%, which is far lower than that of the two traditional risk prediction methods.


## 5. Conclusion

This paper summarizes and analyzes the existing models of WS security evaluation. Aiming at the advantages and disadvantages of the existing models, a security evaluation model based on attack effect is proposed. According to the changes of network security performance before and after routing attack, this paper puts forward the concept of “network security line,” selects and simplifies the security evaluation index that can objectively and truly reflect the security, and analyzes the calculation method of network security moisture. By constructing the big data structure model, the correlation feature reorganization method is used to reconstruct the distributed information of information transmission risk in high reliability fiber Bragg grating sensor networks. Combined with the big data feature clustering method, the big data fusion clustering analysis is carried out to extract the dynamic distribution feature of big data. According to the results of data clustering and information fusion, the dynamic prediction of information transmission risk of high reliability fiber Bragg grating sensor network is realized. The research shows that the efficiency of the proposed method for dynamic prediction of 10∼40 m information transmission risk maintains a high level of more than 90%, and the overhead is 196∼461 Hz, which is smaller than that of the traditional method. The delay is less than 0.3 s, and the real-time performance is better. The relative error is no more than 3%, which has higher accuracy and can be popularized.The security of sensor networks is a big problem. This paper only studies several aspects related to security, and there are still many problems about security that need to be deeply studied. Due to the limitation of conditions, no large-scale networking and other tests and experiments are carried out. Based on the prototype system, only the key configuration, algorithm performance, and battery energy consumption are tested. In the next step, we need to design more sensor nodes to verify the performance and effect of clustering and distributed intrusion detection algorithms in the actual test.



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*Source: 7116013-2022-05-19.xml*