Wireless sensor network system

Abstract

The invention relates to the technical field of wireless communication, and provides a wireless sensor network system, which comprises a sensor node, an authentication server and a central server, and is characterized in that the sensor node is connected with the authentication server and the central server; the sensor node is used for sending a network access request including pre-authentication information to the authentication server, accessing a network after authentication is passed, and sending collected real-time data to the central server; pre-authentication information is stored in the authentication server, and the authentication server is used for performing network access authentication on the sensor node according to the pre-authentication information and a dynamic key mechanism; the central server is used for processing real-time data collected by the sensor nodes. The pre-authentication information is stored in the authentication server, and the authentication server has a dynamic key mechanism, so that network authentication can be quickly completed, delay in the authentication process is reduced, the authentication efficiency is improved, and the stability, continuity and security of data transmission are ensured in a scene that the sensor node frequently performs network switching and re-authentication.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a wireless sensor network system. Background Art

[0002] With the rapid development of Wireless Sensor Networks (WSN) technology, its application in environmental monitoring, intelligent transportation, industrial control and other fields is becoming more and more extensive. However, when WSN is applied to high-speed mobile environments (such as high-speed rail, subway, etc.), sensor nodes face some technical challenges, mainly including the following points.

[0003] Frequent network switching: In a high-speed mobile environment, sensor nodes frequently cross different network areas, resulting in delays and instability in the authentication process, increasing the risk of node connection interruption.

[0004] Signal attenuation and interference: Signal attenuation and interference caused by complex electromagnetic environments affect the communication quality between sensor nodes and the network, resulting in an increase in authentication failure rate and reduced system reliability.

[0005] Transmission delay and data loss: Due to the rapid changes in signals, the data transmission link is unstable, resulting in data delay or loss. Traditional methods are difficult to effectively deal with this problem.

[0006] Dynamic security threats: When moving at high speed, sensor nodes are vulnerable to security threats such as man-in-the-middle attacks and replay attacks. Existing authentication mechanisms lack the adaptability to deal with dynamic security threats.

[0007] In summary, the existing wireless sensor authentication transmission methods are mainly designed for static or low-speed mobile environments, and are difficult to cope with the complex challenges in high-speed mobile environments. This not only limits the application of WSN technology in high-speed scenarios such as high-speed rail, but may also lead to problems such as unstable data transmission and reduced system security. Therefore, a new wireless sensor authentication transmission mechanism is urgently needed to solve the problems of low authentication efficiency, unstable data transmission and security risks of wireless sensor nodes in high-speed mobile environments in high-speed rail environments. Summary of the invention

[0008] The present invention provides a wireless sensor network system, which is used to solve the problems in the prior art of low authentication efficiency, unstable data transmission and security risks of wireless sensor nodes in high-speed mobile scenarios.

[0009] The invention provides a wireless sensor network system, comprising: a sensor node, an authentication server and a central server, wherein the sensor node is connected to the authentication server and the central server.

[0010] The sensor node is used to send a network access request including pre-authentication information to the authentication server, and after the authentication is passed, access the network and send the collected real-time data to the central server.

[0011] The authentication server stores the pre-authentication information and is used to perform network access authentication on the sensor node according to the pre-authentication information and a dynamic key mechanism.

[0012] The central server is used to process the real-time data collected by the sensor nodes.

[0013] According to a wireless sensor network system provided by the present invention, the authentication server is specifically used to dynamically generate a key according to the current network status and the historical credibility of the historical data of the sensor node, so as to perform network access authentication on the sensor node.

[0014] According to a wireless sensor network system provided by the present invention, the sensor node is also used to calculate the real-time credibility of the real-time data based on the data feature vector of the real-time data and the historical credibility of the historical data, and send the real-time credibility to the central server. The higher the real-time credibility of the real-time data, the higher the transmission priority.

[0015] The authentication server is also used to determine the authentication failure rate fluctuation coefficient based on the standard deviation of the number of authentication failures and the average number of authentication failures within a predetermined time period, and send the authentication failure rate fluctuation coefficient to the central server.

[0016] According to a wireless sensor network system provided by the present invention, the central server is specifically used to determine the response priority of real-time data according to a comprehensive risk assessment index, and process the real-time data according to the response priority, the comprehensive risk assessment index is determined based on the security risk index, the authentication failure rate fluctuation coefficient and the real-time credibility, and the security risk index is determined based on the channel interference index in the real-time data transmission process and the real-time credibility.

[0017] According to a wireless sensor network system provided by the present invention, the real-time credibility is calculated according to the following formula: T .

[0018] .

[0019] in, T The value range of is [0,1], X ={ x 1, x 2, … , x n}, represents the data feature vector of the real-time data, xi Indicates i The data characteristics of the data items, H ={ h 1, h 2, … , h n}, represents the historical credibility vector of historical data, h i Indicates i The historical credibility of each data item, oh i Indicates i The weight of the data item, n Indicates the number of data items in the real-time data.

[0020] According to a wireless sensor network system provided by the present invention, the authentication failure rate fluctuation coefficient is calculated according to the following formula: F .

[0021] F = s Fobs / F avg .

[0022] in, s Fobs represents the standard deviation of the number of authentication failures within a predetermined time period, F avg Indicates the average number of authentication failures within a predetermined time period.

[0023] According to a wireless sensor network system provided by the present invention, the security risk index is calculated according to the following formula: S .

[0024] S = g ( T , I )= α ·(1- T )+ β · I .

[0025] in, S The value range of is [0,1], T Indicates real-time credibility, I represents the channel interference index during the real-time data transmission process, α and β Represents the weight coefficient.

[0026] According to a wireless sensor network system provided by the present invention, the comprehensive risk assessment index is calculated according to the following formula: R .

[0027] R = h ( T , S , F )= l 1. S + l 2. F ·(1- T ).

[0028] in, R The value range of is [0,1], T Indicates real-time credibility, S represents the security risk index, F Indicates the authentication failure rate fluctuation coefficient, l 1 and l 2 represents the weight coefficient.

[0029] According to a wireless sensor network system provided by the present invention, the central server is also used to execute preset security protection measures when the comprehensive risk assessment index is greater than a preset assessment index threshold, or the security risk index is greater than a preset risk index threshold, and the security protection measures include: the central server controls the authentication server to update the key.

[0030] According to a wireless sensor network system provided by the present invention, the sensor node is also used to pre-process the real-time data, including performing denoising, de-duplication, compression and encryption processing on the real-time data in sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a schematic diagram of the structure of the wireless sensor network system provided by the present invention.

[0033] Reference numerals: 1: sensor node; 2: authentication server; 3: central server. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The wireless sensor network system of the embodiment of the present invention is as follows: Figure 1 As shown, it includes: sensor node 1, authentication server 2 and central server 3, and sensor node 1 is connected to authentication server 2 and central server 3. For high-speed moving trains, sensor nodes 1 are arranged on the train and along the track. The sensor nodes 1 on the train move with the high-speed train, and network switching and authentication and access to the network often occur. Only after the sensor nodes 1 are arranged and connected to the network can the real-time data collected by them be sent to the central server.

[0036] When requesting to join the network, the sensor node 1 is used to send a network access request including pre-authentication information to the authentication server 2, and after the authentication is passed, access the network and send the collected real-time data to the central server 3. Specifically, all sensor nodes 1 are integrated with 802.1x wireless modules, and each sensor node 1 is also configured with pre-authentication information, which includes: identity (such as sensor node ID), network access information (such as service set identifier SSID (ServiceSet Identifier), encryption method) and initial key. The identity is used to distinguish different sensor nodes 1, the network access information ensures that the node can be correctly connected to the network, and the initial key is used to ensure the security of communication during the first authentication process. The configuration of this information is usually automatically completed by the wireless sensor network system when the node is first deployed. In this embodiment, the encryption methods include two categories: one is an encryption mechanism used for transmitting real-time data to ensure the security of data transmission, for example, encrypting the real-time data collected by the sensor node through encryption algorithms such as AES or RSA; the other is an encryption method related to the initial key and the dynamic key. The initial key is used when the node is authenticated for the first time to ensure communication security, while the dynamic key is automatically generated in the scenario of network switching and re-authentication, and is used to dynamically encrypt subsequent authentication data and transmission data during the communication process to ensure that the entire communication process is encrypted communication.

[0037] After the sensor node 1 is started, it will automatically send a network access request to the authentication server 2 in the network. In order to complete this operation quickly and stably in a high-speed mobile environment such as a high-speed rail, the sensor node 1 needs to complete this operation quickly and stably. The authentication server 2 is responsible for receiving and managing these access requests to ensure that each node can smoothly enter the next authentication stage. Specifically, an identity authentication request module is deployed in the authentication server 2, which is dedicated to receiving and managing network access requests issued by sensor nodes.

[0038] The authentication server 2 stores the pre-authentication information, which is used to authenticate the sensor node for network access according to the pre-authentication information and the dynamic key mechanism. After the authentication is successful, the authentication server will return the authentication information to the sensor node 1. After receiving the information, the sensor node 1 can access the network. Of course, after the authentication fails, the authentication failure information will also be sent to the sensor node 1. At the same time, the authentication server 2 will also transmit the identity information and authentication status of the sensor node 1 to the central server 3, so that the central server 3 can monitor the node status in real time and provide support in the subsequent data processing and transmission process. In this embodiment, through the pre-stored pre-authentication information and dynamic key mechanism, the authentication server 2 can quickly complete the authentication process in a high-speed mobile environment, reduce delays, and improve network access efficiency.

[0039] The central server 3 is used to process the real-time data collected by the sensor nodes 1 .

[0040] In the wireless sensor network system of this embodiment, pre-authentication information is stored in the authentication server 2, so that the sensor node 1 can quickly complete network authentication during high-speed movement and frequent network switching, thereby reducing the delay in the authentication process and improving the authentication efficiency; and combined with the dynamic key mechanism, in the scenario where the sensor node 1 frequently switches networks and re-authenticates, the dynamic key mechanism can quickly respond to these changes, thereby ensuring the stability, continuity and security of data transmission while reducing the delay in the authentication process.

[0041] In some embodiments, the authentication server 2 is specifically used to dynamically generate a key based on the current network status and the historical credibility of the historical data of the sensor node 1 to authenticate the sensor node 1. This process ensures that the identity of each sensor node 1 is unique and credible, preventing unauthorized devices from accessing the network.

[0042] Specifically, in the present invention, the authentication server 2 calculates the channel interference degree I based on the current network status, and dynamically generates a key through the channel interference degree I and the historical credibility of the historical data of the sensor node 1. The specific steps are as follows.

[0043] Calculation of channel interference degree: The authentication server 2 monitors the network status in real time and obtains the historical credibility of the historical data of the sensor node 1. The network status includes the signal-to-noise ratio and bit error rate of the network. The channel interference degree I is calculated by the signal-to-noise ratio (SNR) and the bit error rate (BER). The calculation formula is as follows.

[0044] I =1 / (1+ e -γ·(SNR-δ) )+k·BER.

[0045] Among them, 𝛾 and 𝛿 are constants that adjust the sensitivity of the channel interference index, k It is the factor that adjusts the weight of SNR and BER.

[0046] Acquisition of credibility of historical data: Acquisition of credibility of historical data of sensor node 1. The credibility of historical data is obtained through vector is used to quantify the reliability of node behavior. n Indicates the number of data items in the historical data.

[0047] Dynamic key generation: Based on the network status I and the credibility H of the historical data, the authentication server 2 dynamically generates a key through a weighted combination, and the formula is as follows: ,in, h ( H , I ) represents the influence function of network conditions on key generation, which can be an influence function in the form of partial differential or gradient descent. Represents a bitwise exclusive OR operation, Initial_Key It is a pre-stored initial key. The dynamic key generation mechanism can dynamically adjust the key generation process according to the real-time changes in network conditions and the historical credibility of historical data of sensor node 1, thereby improving the security and efficiency of network authentication.

[0048] In some embodiments, the sensor node 1 is also used to calculate the real-time credibility of the real-time data according to the data feature vector of the real-time data and the historical credibility of the historical data, and send the real-time credibility to the central server 3. The higher the real-time credibility of the real-time data, the higher the transmission priority. Specifically, the credibility is used to evaluate the reliability of the real-time data, so as to determine whether the real-time data should be transmitted first or undergo other special processing. Through such a design, the efficiency and security of data transmission are ensured.

[0049] It should be noted that: the sensor node 1 sends the real-time credibility to the central server 3. As time goes by, the real-time credibility of the real-time data will become the historical credibility of the historical data, that is, the sensor node 1 stores the historical credibility of its own historical data, and the central server 3 stores the historical credibility of the historical data of each sensor node 1.

[0050] Among them, other special processing includes the following processing methods.

[0051] Redundant data transmission: When the real-time credibility is lower than the preset first credibility threshold, the sensor node 1 selects a multi-path transmission strategy to send the real-time data whose real-time credibility is lower than the first credibility threshold. Specifically, for real-time data with low credibility, the sensor node 1 may select a multi-path transmission strategy, that is, repeatedly sending the real-time data with low credibility through multiple transmission paths to reduce the possibility of data loss and ensure the integrity of the data arriving at the central server 3.

[0052] Data verification and retransmission: For real-time data that triggers the error detection mechanism during transmission, sensor node 1 verifies or marks the real-time data and retransmits the real-time data. Specifically, real-time data with low credibility may trigger the error detection mechanism during transmission. Sensor node 1 can further verify or mark the real-time data and retransmit the real-time data when necessary to ensure the accuracy of data transmission.

[0053] Local caching and delayed transmission: When the real-time credibility is lower than the preset first credibility threshold, the sensor node 1 temporarily caches the real-time data whose real-time credibility is lower than the first credibility threshold locally, and sends it after the network load is lower than the preset load threshold. Specifically, for data with low credibility or unimportant data, the sensor node 1 will temporarily cache the data locally, delay transmission, and send it when the network load is lower to avoid occupying precious bandwidth resources.

[0054] Data downgrade processing: When the real-time credibility is lower than the preset second credibility threshold, the sensor node 1 reduces the transmission priority of the real-time data whose real-time credibility is lower than the second credibility threshold to the lowest transmission priority, or performs downgrade processing on the real-time data whose real-time credibility is lower than the second credibility threshold, and the downgrade processing includes: at least one of data compression and format conversion, and the second credibility threshold is lower than the first credibility threshold. Specifically, if the real-time credibility score of the real-time data is too low, the sensor node 1 may reduce the transmission priority of the data to the lowest transmission priority, or perform downgrade processing (such as data compression, format conversion, etc.) on it to reduce the bandwidth and storage resources occupied by it.

[0055] The first credibility threshold, the second credibility threshold and the preset load threshold can all be set according to actual conditions.

[0056] The authentication server 2 is further configured to determine an authentication failure rate fluctuation coefficient based on a standard deviation of the number of authentication failures and an average value of the number of authentication failures within a predetermined time period, and send the authentication failure rate fluctuation coefficient to the central server 3 .

[0057] Specifically, the authentication failure rate fluctuation coefficient reflects the fluctuation of the number of authentication failures within a period of time. The higher the value, the greater the fluctuation of the failures in the authentication process. The authentication server 2 monitors the number of authentication failures during the authentication process, and determines the authentication failure rate fluctuation coefficient based on the standard deviation of the number of authentication failures and the average value of the number of authentication failures within a predetermined time period. This monitoring can timely discover problems that may occur during the authentication process, such as: network instability, signal interference, or excessive load on the authentication server 2. When the authentication failure rate fluctuation coefficient is large or exceeds a preset threshold, the authentication server 2 will take corresponding measures, such as: increasing the redundancy of network access requests (for network access requests that fail authentication, increase the number of times the network access request can be repeated), adjusting the load distribution of the authentication server 2, or enhancing the signal strength, etc., to reduce the occurrence of authentication failures. This process helps to improve the overall stability of the system and the efficiency of authentication.

[0058] In some embodiments, the central server 3 is specifically used to determine the response priority of real-time data according to the comprehensive risk assessment index, and process the real-time data according to the response priority. The comprehensive risk assessment index is determined based on the security risk index, the authentication failure rate fluctuation coefficient and the real-time credibility. The security risk index is determined based on the channel interference index and the real-time credibility during the real-time data transmission process. Specifically, the lower the comprehensive risk assessment index, the higher the response priority. Data with high response priority will be processed first and may trigger corresponding operations or alarms, such as adjusting the carriage environment, triggering emergency braking, etc. In this way, the security and effectiveness of data transmission in high-speed mobile environments such as high-speed rails are ensured.

[0059] In some embodiments, the real-time credibility is calculated according to the following formula: T .

[0060] .

[0061] in, T The value range of is [0,1]. The higher the value, the more reliable the data. X ={ x 1, x 2, … , x n}, represents the data feature vector of the real-time data, x i Indicates i The data features of each data item are different environmental parameters collected by the sensor, including temperature, humidity, vibration, etc. For example: x 1 represents the temperature characteristic, that is, the temperature value, x 2 represents humidity characteristics, i.e., humidity value; H ={ h 1, h 2, … , h n}, represents the historical credibility vector of historical data, h i Indicates i The historical credibility of each data item, oh i Indicates i The weight of a data item, historical credibility reflects the reliability of historical data. oh i According to the importance setting in the application scenario, the higher the value, the greater the impact of the data item on the overall data credibility, and vice versa. oh i , the impact of different data items on the final real-time credibility can be controlled, n Indicates the number of data items in the real-time data.

[0062] The weight It is set according to the importance in the application scenario. The higher the value, the greater the impact of the data item on the overall data credibility, and vice versa.

[0063] For example: In environmental monitoring, if temperature is a key factor, the weight of temperature can be set higher, such as oh 温度 =0.6, the weights of humidity and vibration are relatively low.

[0064] In high-speed rail carriage monitoring, carriage vibration data may be key data, and its weight can be set to oh 振动 =0.7, while other data items have lower weights.

[0065] In environmental monitoring of high-speed moving vehicles, the cabin temperature may be the data of primary concern, and its weight can be set to oh 温度 =0.8 to ensure timely response to temperature changes.

[0066] In some embodiments, the authentication failure rate fluctuation coefficient is calculated according to the following formula: F .

[0067] F = s Fobs / F avg .

[0068] in, s Fobs represents the standard deviation of the number of authentication failures within a predetermined time period, F avg Represents the average number of authentication failures within a predetermined time period. The predetermined time period refers to a period of time that is traced back from the current time, and its length can be set according to the application scenario. For example, in a high-speed mobile network environment, the predetermined time period is usually 1 to 5 minutes, while in a relatively stable network environment, the predetermined time period can be set to 10 to 30 minutes to capture the fluctuation of authentication failures under different network conditions. During the authentication process of sensor node 1, authentication server 2 monitors the number of authentication failures in real time, thereby obtaining s Fobs and F avg . s Fobs It is used to measure the degree of dispersion of the number of authentication failures. The larger the standard deviation, the greater the variation in authentication failures within the preset time period. This may be caused by network fluctuations or other system instability factors. By calculating the standard deviation, abnormal fluctuations in the authentication process can be identified. F avg It indicates the average number of authentication failures within the preset time period, reflecting the average frequency of overall authentication failures. F avg Provides an overall reference value, making the standard deviation Relative to the average F avg By comparison, the fluctuation coefficient of authentication failure rate is obtained. F .

[0069] By calculating the authentication failure rate fluctuation coefficient F , the authentication server 2 can identify and quantify the fluctuations and instabilities that occur during the authentication process. F If the value is high, corresponding measures can be taken, such as increasing the redundancy of network access requests, adjusting the load distribution of the authentication server 2, or enhancing the signal strength, to reduce the occurrence of authentication failures. This process helps to improve the overall stability of the system and the authentication efficiency.

[0070] In some embodiments, to quantify the security risk index S , the security risk index is calculated according to the following formula S .

[0071] S = g ( T , I )= α ·(1- T )+ β · I .

[0072] in, S The value range is [0,1]. The higher the value, the greater the security risk in the transmission process. T Indicates real-time credibility, I It indicates the channel interference index during the real-time data transmission process, which is used to evaluate the stability and interference of the transmission channel. The higher the value, the more serious the interference to the channel. α and β Indicates the weight coefficient. Security risk index S Used to assess the risk level during data transmission so that additional security measures can be taken if a high risk is detected.

[0073] Specifically, the central server 3 calculates the security risk index according to the above formula: S , (1- T ) reflects the unreliability of real-time data, which correspondingly affects the security risk index. I Based on the signal-to-noise ratio (SNR) and bit error rate (BER) calculation of the transmission channel, the calculation formula is: I =1 / (1+ e -γ·(SNR-δ) )+k·BER, c and d To adjust the constant of the channel interference index sensitivity, it is adjusted according to different application scenarios to adapt to different channel conditions; k is the factor that adjusts the weight of SNR and BER. SNR and BER are used together to evaluate the stability and interference of the channel to ensure that the complexity of the transmission environment can be reflected.

[0074] α and β Used to balance the impact of data credibility and channel interference on security risks. α The coefficient determines the impact of the real-time credibility of real-time data on security risks. T ) will increase when it has a greater impact on safety risks. α The value of is used to increase the weight of the real-time credibility of real-time data in risk assessment. α The value can be set based on the analysis of historical data and the security requirements of specific application scenarios. βThe coefficient depends on the impact of channel interference on security risks. If the network operates in an environment where channel interference is frequent and severe, then β The value of should be appropriately increased to emphasize the contribution of channel interference to security risks. β The value of should also be adjusted according to the actual network environment and experience data.

[0075] When the unreliability of real-time data (i.e., 1-T) has a greater impact on security risks, it will increase α For example, in a high-speed rail communication system, if the real-time data is highly unreliable, the central server 3 can α The value of is set to 0.8 to ensure the security and stability of real-time data in high-speed movement; in the vehicle network system on the highway, the accuracy of the data is very important, and the central server 3 can α The value is set to 0.9 to ensure the accuracy and timeliness of data transmission.

[0076] β The coefficient depends on the degree of impact of channel interference on security risks. If the system operates in an environment where channel interference is frequent and severe, then β The value of should be appropriately increased. For example, in wireless communications between high-speed railway stations and along the track, when channel interference is frequent, β It can be set to 0.7; in military communication systems, channel stability is crucial. β The value of can be set to 0.85 to ensure additional protection in high interference environments.

[0077] After calculating the security risk index S, the central server 3 will decide whether to take additional security measures based on the value of S. For example, when S exceeds a certain preset risk index threshold, the central server 3 may take a variety of countermeasures. Among them, the central server 3 will control the authentication server 2 to start the key update operation, dynamically generate keys, and ensure that the keys used in the authentication process are the latest; the central server 3 may also control the sensor node 1 to retransmit data, or trigger early warning signals and other operations to ensure that the system can respond quickly when facing potential security threats and protect the security of data transmission.

[0078] In some embodiments, the comprehensive risk assessment index is calculated according to the following formula: R .

[0079] R = h ( T , S , F )= l 1. S + l 2. F·(1- T ).

[0080] Specifically, the central server 3 calculates the comprehensive risk assessment index according to the above formula: R ,in, R The value range of is [0,1]. The larger the value, the greater the overall risk. T Indicates real-time credibility, S represents the security risk index, F Indicates the authentication failure rate fluctuation coefficient, l 1 and l 2 represents the weight coefficient, which is used to balance the impact of the security risk index and the authentication failure rate fluctuation coefficient on the comprehensive risk index.

[0081] In comprehensive risk assessment, the weight coefficient l 1 and l 2 determines the degree of influence of each factor on the final risk assessment result. l 1Used to measure security risk index S Impact on comprehensive risk, when the safety risk index S When it has a greater impact on the overall comprehensive risk, it will increase l The value of 1. l 1 can be determined based on historical data analysis and risk preferences in specific application scenarios. For example, in highly sensitive environments such as financial transactions or medical data transmission, the central server 3 may l 1 is set to 0.7 to more closely assess potential risks; during the transmission of sensitive information within the enterprise, l 1 can be set to 0.6. l 2 is used to measure the fluctuation coefficient of authentication failure rate F Unreliability of real-time data 1- T The impact of the combination of factors on the overall risk will increase when the volatility of authentication failures and the unreliability of real-time data have a greater impact on the overall risk. l The central server 3 will dynamically adjust the authentication failure rate according to the authentication process and the historical credibility of the data. l 2 to meet the risk assessment needs in different environments. For example, in high-interference environments such as airports or subways, l 2 can be set to 0.8; in a highly mobile device network, l 2 can be set to 0.75.

[0082] In some embodiments, the central server 3 is also used to execute preset security protection measures when the comprehensive risk assessment index is greater than a preset assessment index threshold, or the security risk index is greater than a preset risk index threshold, and the security protection measures include: the central server 3 controls the authentication server 2 to update the key or take other additional security protection measures to reduce potential security risks. This design ensures that the system can make more intelligent and secure decisions when facing complex environments and multiple risk factors.

[0083] In the wireless sensor network system provided by this embodiment, pre-authentication information is stored in the authentication server 2, so that the sensor node 1 can quickly complete network authentication during high-speed movement and frequent network switching, thereby reducing the delay in the authentication process and improving the authentication efficiency; and combined with the dynamic key mechanism, in the scenario where the sensor node 1 frequently switches networks and re-authenticates, the dynamic key mechanism can quickly respond to these changes, thereby ensuring the stability, continuity and security of data transmission while reducing the delay in the authentication process.

[0084] The pre-authentication information includes the sensor node's identity, network access information (such as service set identifier SSID) and initial key. Traditional WSNs also store similar identity information, but in high-speed mobile scenarios, the increase in authentication delay mainly comes from frequent network switching and the complexity of real-time key generation in dynamic environments.

[0085] In high-speed mobile environments, such as high-speed rail and subway, sensor node 1 frequently crosses different network areas. If authentication is performed from scratch each time (traditional method), multiple steps are required to verify the identity and generate new keys, resulting in delays. In this embodiment, the advantage of pre-authentication information is that it reduces the steps of the first authentication. Sensor node 1 only needs to be based on the stored pre-authentication information and quickly verified by the authentication server 2, which avoids repeated identity verification processes.

[0086] After authentication, the dynamic key mechanism adjusts the key in real time by monitoring the network status of the node, thereby improving the response speed of authentication.

[0087] In traditional WSN systems, identity authentication relies on a long process and is not optimized for scenarios with high-speed network switching. Although these WSN systems pre-store the identity information of nodes, the authentication process still requires full identity authentication after each network switch, which cannot fully cope with rapidly changing network conditions. In contrast, this solution avoids authentication delays caused by network fluctuations, channel interference and other factors by storing pre-authentication information in advance and using a dynamic key mechanism when moving at high speed, ensuring the stability and continuity of data transmission.

[0088] In some embodiments, in order to ensure data quality and reduce network load, the sensor node 1 is also used to perform local preprocessing on the collected real-time data, and the preprocessing includes sequentially performing denoising, deduplication, compression and encryption on the real-time data.

[0089] Specifically, denoising and deduplication processing ensures data quality, compression can reduce the amount of data transmitted and speed up data transmission, and encryption ensures that data is not stolen or tampered with during transmission, providing basic transmission security. The central server 3 uses a corresponding decryption algorithm to obtain real-time data before encryption.

[0090] In this embodiment, the wireless sensor network system further includes the following functional modules.

[0091] Dynamic access management module: This module is deployed on sensor node 1 and is mainly responsible for managing the network access requests of sensor node 1, especially in high-speed mobile or frequently switched network environments, dynamically adjusting access strategies and priorities to ensure that the node can quickly access the network.

[0092] Function: This module is responsible for managing the network access request (i.e., identity verification request) of sensor node 1 when it attempts to access the network during high-speed movement. It is specially designed for frequently switched network environments. It ensures that the node completes identity verification and accesses the network in the shortest possible time by dynamically adjusting the access strategy and node priority. The node priority is preset when the entire network system is deployed based on the importance and urgency of the task.

[0093] Working principle: When sensor node 1 moves to a new network area, the module dynamically adjusts the access strategy according to the preset node priority of the corresponding sensor node 1 and the current network load. When the network load is low, all nodes are accessed in order of node priority; however, when the network load is high, nodes with high node priority will be allowed to access first, while nodes with low node priority may temporarily postpone access or refuse access if the load is too heavy.

[0094] Edge intelligent authentication module: This module is deployed on the authentication server 2, which is an edge computing device. It is responsible for processing the identity network access request of the sensor node 1 and making intelligent authentication decisions based on the historical credibility of historical data and network conditions. This module is also the core module of dynamic key generation and distribution to ensure system security.

[0095] Function: This module is used to process the authentication requests passed from the dynamic access management module in real time, perform traditional authentication and make intelligent decisions in combination with edge computing technology.

[0096] Working principle: The module can generate dynamic keys based on the historical credibility of the historical data of sensor node 1 and the network status to ensure the authentication security in a high-speed environment. The module can also monitor the fluctuation of the authentication failure rate and dynamically adjust the authentication process according to the monitoring results to ensure the efficiency and security of the authentication process.

[0097] Adaptive edge data processing module: This module is deployed on sensor node 1. It processes the collected data locally, including data filtering, compression and encryption, and selects the optimal data transmission strategy according to the network conditions.

[0098] Function: This module is used to obtain and process environmental data and network conditions locally at sensor node 1. The module can process data in real time and select the best data transmission strategy based on the network conditions.

[0099] Working principle: The module uses an adaptive algorithm to filter, compress and encrypt the collected data and calculate the credibility score of the data. During the data transmission process, the module dynamically selects the best transmission path and strategy according to the network conditions to optimize the efficiency and reliability of data transmission.

[0100] Multi-level risk assessment module: This module is mainly deployed in the central server 3. The module generates a comprehensive security risk index by analyzing the data uploaded by the sensor node 1 and the authentication performance information.

[0101] Function: Used to perform multi-level analysis on the data transmitted by sensor node 1, authentication performance indicator information and network channel characteristics, and generate security risk index S and comprehensive risk assessment index R.

[0102] Working principle: This module uses the above formulas of T, F, S and R, combined with historical data and current network conditions, to update the risk assessment model in real time, predict potential threats, and provide risk assessment results that are more accurate and intelligent than a single threshold. This enables the wireless sensor network system of this embodiment to better prevent and respond to various security threats.

[0103] Intelligent response and protection module: This module is deployed on the central server 3 and the authentication service 2 of the edge device, and is used to receive risk assessment results and trigger corresponding protection measures, such as key update, transmission path optimization, etc.

[0104] Function: Receive the risk assessment index generated by the multi-level risk assessment module and trigger corresponding security protection measures according to the risk level.

[0105] Working principle: The module can dynamically adjust response measures according to the current network status and environmental factors, including real-time adjustment of key generation strategy, optimization of data transmission path, and triggering redundant transmission when necessary to ensure data security and transmission continuity.

[0106] Furthermore, in actual use, the present invention may also include a communication optimization and channel prediction module, which is intended to improve communication efficiency in a high-speed mobile environment. This module can predict the movement trajectory and channel conditions of sensor nodes to optimize channel switching and reduce data loss and delay.

[0107] The distributed early warning and event response module is deployed on the central server 3.

[0108] Function: When the intelligent response and protection module triggers an early warning, it is used to record event information in a distributed manner and send warnings to all relevant nodes. Among them: Early warning based on the comprehensive risk assessment index R: In most cases, the central server 3 will give priority to the comprehensive risk assessment index R. If R exceeds a preset threshold (e.g., 0.7 or higher), indicating that the overall risk is high, the central server 3 will trigger an early warning signal and take countermeasures.

[0109] Warning based on individual indicators (S, F, T): Even if the comprehensive risk assessment index R does not reach the warning threshold, some individual indicators (such as the security risk index S or the authentication failure rate fluctuation coefficient F) may trigger warnings individually. For example: High S value: When the security risk index S alone exceeds the threshold, it indicates that there is a direct threat to network security, and the central server 3 may issue an early warning. Large fluctuations in F value: If the authentication failure rate fluctuation coefficient F fluctuates greatly, it means that the authentication process is unstable, and the central server 3 may also trigger a warning based on the changes in F. Low T value: If the credibility T of real-time data is too low, it means that there are problems with the accuracy of the data, which may affect subsequent decisions. The central server 3 can trigger a warning based on the T value alone.

[0110] Working principle: The module can send out traditional warning signals and dynamically adjust the working mode of each sensor node 1 according to the network status (such as low power mode or switching to an alternative path), ensuring that the system can respond in time and extending the service life of the sensor node.

[0111] The security log and adaptive learning module are deployed on the central server 3.

[0112] Function: Used to record all key operations and abnormal events in real time during system operation, and continuously optimize the network's authentication and transmission strategies through adaptive learning algorithms.

[0113] The advantages of the wireless sensor network system of the above embodiment are specifically described below using an application scenario in a high-speed rail carriage.

[0114] In high-speed rail carriages, environmental monitoring is an important part of ensuring passenger comfort and safety. Existing technologies face challenges in environmental data collection and transmission, especially in high-speed movement, where the stability and security of data transmission cannot be guaranteed. The wireless sensor network system of the above embodiment realizes real-time collection and transmission of environmental data in high-speed rail carriages, ensuring high data reliability and transmission security, especially in the complex environment of high-speed rail movement, improving the network response speed and data accuracy.

[0115] The application scenarios mainly include temperature, humidity and air quality monitoring in high-speed rail carriages, which can provide real-time feedback on changes in the environment in the carriage and ensure security during data transmission. In the application scenario in high-speed rail carriages, the performance comparison between the wireless sensor network system of the present invention and the wireless sensor network system of the prior art is shown in Table 1 below.

[0116] It can be seen from the comparison in Table 1 that the application of the present invention in the environmental monitoring of high-speed rail carriages has significant advantages. In particular, in terms of the stability and security of data transmission, the system performs well and can provide efficient and safe environmental data monitoring in a high-speed mobile environment.

[0117] Table 1 Performance comparison data in high-speed rail carriage application scenarios

[0118] The advantages of the wireless sensor network system of the above embodiment are specifically described below using the application scenario of equipment status monitoring along the track.

[0119] Real-time monitoring of equipment along the track (such as signal lights, track status sensors, etc.) is crucial to ensure the safety of train operation. Traditional monitoring systems have deficiencies in the stability of equipment status data collection and transmission, especially in complex track environments, which are prone to data transmission delays and safety hazards. By applying the wireless sensor network system of the present invention, real-time monitoring and safe transmission of equipment status along the track can be achieved, ensuring the accuracy of equipment status information and the immediacy of transmission, so as to improve the overall safety level of the rail transit system. In the application scenario of equipment status monitoring along the track, the performance comparison of the wireless sensor network system of the present invention and the wireless sensor network system of the prior art is shown in Table 2 below.

[0120] Table 2 Performance comparison data in the application scenario of equipment status monitoring along the track

[0121] It can be seen from the comparison in Table 2 that the application of the present invention in the equipment status monitoring along the track has obvious advantages. The wireless sensor network system can not only improve the accuracy and security of data transmission, but also significantly improve the fault response speed, thereby ensuring the overall safety of the rail transit system.

[0122] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0123] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless sensor network system, characterized in that: include: A sensor node, an authentication server and a central server, wherein the sensor node is connected to the authentication server and the central server; The sensor node is used to send a network access request including pre-authentication information to the authentication server, and after the authentication is passed, access the network and send the collected real-time data to the central server; The authentication server stores the pre-authentication information and is used to perform network authentication on the sensor node according to the pre-authentication information and a dynamic key mechanism; The central server is used to process the real-time data collected by the sensor nodes.

2. The wireless sensor network system according to claim 1, characterized in that: The authentication server is specifically used to dynamically generate a key according to the current network status and the historical credibility of the historical data of the sensor node, so as to perform network access authentication on the sensor node.

3. The wireless sensor network system according to claim 1, characterized in that: The sensor node is also used to calculate the real-time credibility of the real-time data according to the data feature vector of the real-time data and the historical credibility of the historical data, and send the real-time credibility to the central server, and the real-time data with higher real-time credibility has higher transmission priority; The authentication server is also used to determine the authentication failure rate fluctuation coefficient based on the standard deviation of the number of authentication failures and the average number of authentication failures within a predetermined time period, and send the authentication failure rate fluctuation coefficient to the central server.

4. The wireless sensor network system according to claim 3, characterized in that: The central server is specifically used to determine the response priority of real-time data based on a comprehensive risk assessment index, and process the real-time data according to the response priority. The comprehensive risk assessment index is determined based on a security risk index, the authentication failure rate fluctuation coefficient and the real-time credibility. The security risk index is determined based on a channel interference index during the real-time data transmission process and the real-time credibility.

5. The wireless sensor network system according to claim 4, characterized in that: The real-time credibility is calculated as follows T : ； in, T The value range of is [0,1], X ={ x 1, x 2, … , x n }, represents the data feature vector of the real-time data, x i Indicates i The data characteristics of the data items, H ={ h 1, h 2, … , h n }, represents the historical credibility vector of historical data, h i Indicates i The historical credibility of each data item, ω i Indicates i The weight of the data item, n Indicates the number of data items in the real-time data.

6. The wireless sensor network system according to claim 4, characterized in that: The authentication failure rate fluctuation coefficient is calculated according to the following formula F : F = σ Fobs / F avg ； in, σ Fobs represents the standard deviation of the number of authentication failures within a predetermined time period, F avg Indicates the average number of authentication failures within a predetermined time period.

7. The wireless sensor network system according to claim 4, characterized in that: The safety risk index is calculated according to the following formula S : S = g ( T , I )= α ·(1- T )+ β · I ； in, S The value range of is [0,1], T Indicates real-time credibility, I represents the channel interference index during the real-time data transmission process, α and β Represents the weight coefficient.

8. The wireless sensor network system according to claim 4, characterized in that: The comprehensive risk assessment index is calculated as follows R : R = h ( T , S , F )= λ 1· S + λ 2· F ·(1- T )； in, R The value range of is [0,1], T Indicates real-time credibility, S represents the security risk index, F Indicates the authentication failure rate fluctuation coefficient, λ 1 and λ 2 represents the weight coefficient.

9. The wireless sensor network system according to claim 4, characterized in that: The central server is also used to execute preset security protection measures when the comprehensive risk assessment index is greater than a preset assessment index threshold, or the security risk index is greater than a preset risk index threshold. The security protection measures include: the central server controls the authentication server to update the key.

10. The wireless sensor network system according to any one of claims 1 to 9, characterized in that: The sensor node is also used to pre-process the real-time data, including performing denoising, de-duplication, compression and encryption processing on the real-time data in sequence.

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