Method and device for evaluating and optimizing security of nodes of Internet of Things

Abstract

The invention provides a communication and security optimization method and device for Internet of Things nodes, and the method belongs to the field of Internet of Things communication, and comprises the steps: dividing a region of interest into a plurality of subareas, placing a plurality of Internet of Things nodes in each subarea, determining an initial position and transmitting power of each Internet of Things node in the network and a security level and a risk coefficient of a sensor in the Internet of Things node; calculating the coverage overlapping degree of the detection areas among the plurality of Internet of Things nodes and free spaces detected in the Internet of Things nodes; constructing a target optimization function, and carrying out iterative optimization on deployment, detection coverage and positions of nodes of the Internet of Things; and according to the hardware security basis, the software security measure, the physical protection and the processing capability calculation comprehensive security scoring index of the calculation Internet of Things node, evaluating and optimizing the security of the calculation Internet of Things node. According to the scheme, the deployment, communication security and credibility of the Internet of Things nodes are comprehensively optimized, and the overall performance and security of the network are improved.

Description

Technical Field

[0001] The present invention belongs to the field of Internet of Things communications, and in particular relates to a method and device for security assessment and optimization of Internet of Things nodes. Background Art

[0002] Internet of Things technology has been widely used in various fields such as smart cities, industrial monitoring, and environmental testing. A large number of sensor nodes and actuators are deployed in various complex environments, forming a huge Internet of Things system. Parameters such as the deployment location, detection coverage, and transmission power of the node directly affect the performance and security of the Internet of Things system. A reasonable node deployment scheme can not only improve the monitoring efficiency of the system, but also enhance the anti-interference ability and security protection of the system. Limitations of existing node deployment optimization methods. Existing node deployment optimization methods mainly focus on the optimization of single indicators such as coverage, energy consumption, and communication performance. There is still a lack of consideration of key factors such as system security and reliability. The importance of sensor security in nodes. Various types of sensors are integrated inside the Internet of Things node, and their security directly affects the security of the entire system. However, the existing optimization methods do not fully consider the security level and system risk of the sensor itself. Difficulty of optimization in complex scenarios. In the actual deployment environment, the node distribution is complex, there is overlap between nodes, and the security of sensors is also uneven. The combined influence of these factors makes the node deployment optimization problem complex and diverse, and more intelligent optimization methods are needed. Summary of the invention

[0003] In view of this, the present invention provides a method for security assessment and optimization of an Internet of Things node, comprising the following steps:

[0004] Step 1: Divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, and determine the initial position, transmission power, and security level and risk factor of each IoT node in the network;

[0005] Step 2, calculating the coverage overlap degree of the detection coverage overlap area between the multiple IoT nodes and the free space detected in the multiple IoT nodes;

[0006] Step 3: constructing a target optimization function based on the coverage overlap and free space, and iteratively optimizing the deployment, detection coverage area, and location of the multiple IoT nodes;

[0007] Step 4: Evaluate and optimize the security of the IoT node based on the security level and risk factor of the sensors in the IoT node, the node position after iterative optimization, and the hardware security foundation, software security measures, physical protection, and processing capacity of the IoT node to calculate the comprehensive security score index.

[0008] In particular, in step 2, the coverage overlap degree of the detection coverage overlap area is expressed by the following expression:

[0009]

[0010] Among them A i and A j They represent IoT nodes v i and neighbor node v j The total detection coverage area, O ij Represents the IoT node v i and neighbor node v j The coverage overlap area detected between nodes v i and neighbor node v j The ratio of the overlapping detection coverage areas to the larger coverage area;

[0011] The free space detected in the IoT node is expressed as follows:

[0012]

[0013] Among them, C i is node v i The space occupied by its own size, R ij is node v i With neighbor node v j The actual distance between ij It is an IoT node v i With neighbor node v j The expected detection coverage overlap distance between them; It reflects the impact of the distance between nodes on the free space.

[0014] In particular, the objective optimization function in step 3 is expressed as follows:

[0015]

[0016] represents the optimization goal of the coverage overlap, the coefficient α controls the weight of this part, and p i and p j The nodes v i and neighbor node v j The transmission power, n represents the total number of sensors;

[0017] represents the optimization goal of the node free coverage space, the coefficient β controls the weight of this part, w i and h i The nodes vi The width and height, q i is node v i The weight coefficient of

[0018] It represents the optimization goal of the node position deviation from the expected distribution. The coefficient γ controls the weight of this part. (x i ,y i ) represents the optimized node v i The actual coordinate position of , W and H represent the width and height of the entire network area respectively.

[0019] In particular, in step 4, the comprehensive safety score index is expressed as follows:

[0020]

[0021] Where S represents the comprehensive security score, HSM represents the contribution of the hardware security module, T represents the contribution of the trusted execution environment to the comprehensive security score, δ1 represents the weight coefficient of HSM and T, which measures their importance to the comprehensive security score; B represents the contribution of secure boot to the comprehensive security score, P represents the contribution of physical seamless protection to the comprehensive security score, λ is its control parameter, and the physical seamless protection at least includes an anti-tampering shell, δ2 represents the weight of physical protection on the comprehensive security score, δ3 represents the parameter of the degree of influence of data transmission efficiency, D(x i ,y i ) indicates that according to the optimized node v i The contribution of communication reliability obtained from the coordinate position of the node to the comprehensive security score, E represents parameters related to energy efficiency and data processing capability, and δ4 represents the weight of the node's own security feature factor in the comprehensive security score; F i represents the detection sensitivity of the i-th sensor, n represents the total number of sensors, κ represents the parameter for adjusting the degree of sensor protection, and s i represents the safety level of the i-th sensor, r i represents the risk factor of the i-th sensor.

[0022] In particular, the security level reflects the security protection capabilities of the sensor, including the anti-tampering and anti-destruction capabilities of the sensor hardware, the reinforcement and security update mechanism of the sensor software, the access control of the sensor input and output interfaces, and the sensor data encryption and integrity verification mechanism.

[0023] In particular, the risk factor reflects the potential risk level of the sensor in the system, including: the criticality of the data detected by the sensor to the system security, the impact on the system when the sensor is attacked or fails, the importance and exposure of the sensor location, and the type of attack the sensor may suffer and its risk level.

[0024] The present invention also discloses a communication and security optimization device for an Internet of Things node, the device comprising:

[0025] The initialization module is used to divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, and determine the initial position, transmission power, and security level and risk factor of sensors in each IoT node in the network;

[0026] A detection area detection module, used to calculate the coverage overlap degree of the detection coverage overlap area between the multiple IoT nodes and the free space detected within the multiple IoT nodes;

[0027] An iterative optimization module, configured to construct a target optimization function based on the coverage overlap and free space, and iteratively optimize the deployment area, detection coverage, and location of the plurality of IoT nodes;

[0028] The security assessment and optimization module is used to assess and optimize the security of the IoT node based on the security level and risk factor of the sensors in the IoT node, the node position after iterative optimization, and the hardware security foundation, software security measures, physical protection, and processing power of the IoT node to calculate the comprehensive security score index.

[0029] Through the technical solution of the present invention, the following technical effects can be achieved:

[0030] 1. Improve the security and efficiency of IoT node deployment: The present invention takes into account factors such as node deployment, detection coverage and location into optimization considerations by constructing a comprehensive objective function.

[0031] 2. Using iterative optimization algorithms, we can find the best balance between security and efficiency and improve the performance of the entire IoT system.

[0032] 3. Enhance the flexibility and adaptability of node deployment: The present invention fully considers the idle coverage space of the node and optimizes the node deployment position to improve the deployment flexibility. At the same time, by dynamically adjusting the detection coverage of the node, it can better adapt to changes in the monitoring environment.

[0033] 4. Improve the overall reliability of the Internet of Things system: This invention combines the security level and risk factor of the sensors in the nodes, optimizes the node deployment plan, and improves the overall reliability of the system. By rationally allocating node and sensor resources, the risk of the system being attacked or failing is reduced.

[0034] 5. Reduce the cost of IoT system deployment and maintenance: The automated optimization method proposed in this invention can significantly reduce the workload of manual debugging and maintenance. Through the automatic search of the optimization algorithm, the optimal node deployment solution can be found more quickly, reducing the cost of system deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the communication and security optimization process for IoT nodes proposed by the present invention;

[0036] Figure 2 Schematic diagram of the communication and security optimization device for Internet of Things nodes proposed by the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0038] The present invention provides a method for security assessment and optimization of IoT nodes, such as Figure 1 As shown, the following steps are included:

[0039] Step 1: Divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, determine the initial position, transmission power, and security level and risk factor of each IoT node in the network; Divide the area of ​​interest into multiple partitions: Divide the entire deployment area of ​​interest into multiple smaller sub-areas or partitions, so that the deployment of nodes in the entire area can be better managed and optimized. Place multiple IoT nodes in each partition, and deploy a certain number of IoT nodes in each sub-area or partition. Determine the initial position coordinates of each node, set the initial transmission power of the node, and set the initial transmission power parameters for each IoT node. The transmission power determines the coverage of the node; Determine the security level and risk factor of the sensor in the node, the security level reflects the security protection capability of the sensor, including the anti-tampering and anti-destruction capabilities of the sensor hardware, the reinforcement and security update mechanism of the sensor software, the access control of the sensor input and output interface, and the sensor data encryption and integrity verification mechanism.

[0040] The risk factor reflects the potential risk level of the sensor in the system, including: the criticality of the data detected by the sensor to the system security, the impact on the system when the sensor is attacked or fails, the importance and exposure of the sensor location, and the type of attack the sensor may suffer and its risk level.

[0041] Step 2, calculating the coverage overlap degree of the detection coverage overlap area between the multiple IoT nodes and the free space detected in the multiple IoT nodes; in step 2, the coverage overlap degree of the detection area is expressed by the following expression:

[0042]

[0043] Among them A i and A j They represent IoT nodes v i and neighbor node v j The total detection coverage area, O ij Represents the IoT node v i and neighbor node v j The coverage overlap area detected between nodes v i and neighbor node v j The ratio of the overlapping detection coverage areas to the larger coverage area;

[0044] The free space detected in the IoT node is expressed as follows:

[0045]

[0046] Among them, C i is node v i The space occupied by its own size, R ij is node v i With neighbor node v j The actual distance between ij It is an IoT node v i With neighbor node v j The expected detection coverage overlap distance between them; It reflects the impact of the distance between nodes on the free space.

[0047] Step 3: constructing a target optimization function based on the coverage overlap and free space, and iteratively optimizing the deployment, detection coverage area, and location of the multiple IoT nodes;

[0048] The objective optimization function in step 3 is expressed as follows:

[0049]

[0050] represents the optimization goal of the coverage overlap, the coefficient α controls the weight of this part, and p i and p j The nodes v i and v j The transmission power;

[0051] represents the optimization goal of the node free coverage space, the coefficient β controls the weight of this part, w i and h i The nodes vi The width and height, q i is node v i The weight coefficient of n represents the total number of sensors;

[0052] It represents the optimization goal of the node position deviation from the expected distribution. The coefficient γ controls the weight of this part. (x i ,y i ) represents the optimized node v i The actual coordinate position of , W and H represent the width and height of the entire network area respectively.

[0053] Iterative optimization process, initialization: set the initial node deployment, coverage and location scheme. Evaluate the objective function: calculate the comprehensive objective function value under the current scheme. Update the scheme: update the node deployment, coverage and location according to the optimization algorithm. Check the convergence condition: if the objective function value is less than the set threshold, or the maximum number of iterations is reached, the optimization ends; otherwise, continue the iteration.

[0054] Constraint processing: During the optimization process, some constraints need to be considered, such as node number restrictions, coverage requirements, etc. These constraints can be integrated into the objective function, or processed using methods such as penalty functions. Parallel computing optimization: Since a large number of iterative calculations are required, parallel computing can be considered to accelerate the optimization process. Use technologies such as GPU or distributed computing to improve the computational efficiency of the algorithm. Dynamically adjust parameters: During the iteration process, the parameters of the optimization algorithm, such as learning rate, crossover probability, etc., can be dynamically adjusted. According to the convergence of the algorithm, the parameters are dynamically adjusted to improve the optimization effect.

[0055] Step 4: Based on the security level and risk factor, the node position after iterative optimization is evaluated and optimized according to the hardware security foundation, software security measures, physical protection, and processing capacity of the IoT node to calculate the comprehensive security score index.

[0056] In step 4, the comprehensive safety score index is expressed as follows:

[0057]

[0058] Where S represents the comprehensive security score, HSM represents the contribution of the hardware security module, T represents the contribution of the trusted execution environment to the comprehensive security score, δ1 represents the weight coefficient of HSM and T, which measures their importance to the comprehensive security score; B represents the contribution of secure boot to the comprehensive security score, P represents the contribution of physical seamless protection to the comprehensive security score, λ is its control parameter, and the physical seamless protection at least includes an anti-tampering shell, δ2 represents the weight of physical protection on the comprehensive security score, δ3 represents the parameter of the degree of influence of data transmission efficiency, D(x i ,y i ) indicates that according to the optimized node v i The contribution of communication reliability to the comprehensive security score is obtained by the coordinate position of the node, E represents the parameters related to energy efficiency and data processing capability, δ4 represents the weight of the node’s own security feature factor in the comprehensive security score; F i represents the detection sensitivity of the i-th sensor, n represents the total number of sensors, κ represents the parameter for adjusting the degree of sensor protection, and s i represents the safety level of the i-th sensor, r i Represents the risk factor of the i-th sensor. This formula integrates multiple aspects such as hardware security foundation (hardware security module and trusted execution environment), software security measures (secure boot, data encryption), physical protection (protective housing and sensors), and capabilities (processing capabilities). Through nonlinear processing and parameter adjustment, it comprehensively evaluates the comprehensive security performance of IoT devices. According to actual needs, the weight of each parameter can be adjusted to emphasize the importance of different security factors.

[0059] The present invention also discloses a communication and security optimization device for an Internet of Things node, such as Figure 2 As shown, the device comprises:

[0060] The initialization module is used to divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, determine the initial position, transmission power of each IoT node in the network, and the security level and risk factor of the sensors in the IoT node; divide the area of ​​interest into multiple partitions: divide the entire deployment area of ​​interest into multiple smaller sub-areas or partitions, so that the deployment of nodes in the entire area can be better managed and optimized. Place multiple IoT nodes in each partition, and deploy a certain number of IoT nodes in each sub-area or partition. Determine the initial position coordinates of each node, set the initial transmission power of the node, and set the initial transmission power parameters for each IoT node. The transmission power determines the coverage of the node; determine the security level and risk factor of the sensor in the node, and the security level reflects the security protection capability of the sensor, including the anti-tampering and anti-destruction capabilities of the sensor hardware, the reinforcement and security update mechanism of the sensor software, the access control of the sensor input and output interface, and the sensor data encryption and integrity verification mechanism.

[0061] The risk factor reflects the potential risk level of the sensor in the system, including: the criticality of the data detected by the sensor to the system security, the impact on the system when the sensor is attacked or fails, the importance and exposure of the sensor location, and the type of attack the sensor may suffer and its risk level.

[0062] The detection area detection module is used to calculate the coverage overlap degree of the detection areas between the multiple IoT nodes and the free space detected in the IoT nodes; in the detection area detection module, the coverage overlap degree of the detection area is expressed by the following expression:

[0063]

[0064] Among them A i and A j They represent IoT nodes v i and neighbor node v j The total detection coverage area, O ij Represents the IoT node v i and neighbor node v j The coverage overlap area detected between nodes v i and neighbor node v j The ratio of the overlapping detection coverage areas to the larger coverage area;

[0065] The free space detected in the IoT node is expressed as follows:

[0066]

[0067] Among them, C i is the neighbor node vi The space occupied by its own size, R ij is node v i With neighbor node v j The actual distance between ij It is an IoT node v i With neighbor node v j The expected detection coverage overlap distance between them; It reflects the impact of the distance between nodes on the free space.

[0068] An iterative optimization module is used to construct a target optimization function based on the coverage overlap degree and the free space, and iteratively optimize the deployment, detection coverage area, and location of the multiple IoT nodes; the target optimization function in the iterative optimization module is expressed as follows:

[0069]

[0070] represents the optimization goal of the coverage overlap, the coefficient α controls the weight of this part, and p i and p j The nodes v i and v j The transmission power;

[0071] represents the optimization goal of the node free coverage space, the coefficient β controls the weight of this part, w i and h i The nodes v i The width and height, q i is node v i The weight coefficient of

[0072] It represents the optimization goal of the node position deviation from the expected distribution. The coefficient γ controls the weight of this part. (x i ,y i ) represents the optimized node v i The actual coordinate position of , W and H represent the width and height of the entire network area respectively.

[0073] Iterative optimization process, initialization: set the initial node deployment, coverage and location scheme. Evaluate the objective function: calculate the comprehensive objective function value under the current scheme. Update the scheme: update the node deployment, coverage and location according to the optimization algorithm. Check the convergence condition: if the objective function value is less than the set threshold, or the maximum number of iterations is reached, the optimization ends; otherwise, continue the iteration.

[0074] Constraint processing: During the optimization process, some constraints need to be considered, such as node number restrictions, coverage requirements, etc. These constraints can be integrated into the objective function, or processed using methods such as penalty functions. Parallel computing optimization: Since a large number of iterative calculations are required, parallel computing can be considered to accelerate the optimization process. Use technologies such as GPU or distributed computing to improve the computational efficiency of the algorithm. Dynamically adjust parameters: During the iteration process, the parameters of the optimization algorithm, such as learning rate, crossover probability, etc., can be dynamically adjusted. According to the convergence of the algorithm, the parameters are dynamically adjusted to improve the optimization effect.

[0075] The security assessment and optimization module is used to evaluate and optimize the security of the IoT node based on the security level and risk factor, and to iteratively optimize the node position according to the hardware security foundation, software security measures, physical protection, and processing capacity of the IoT node to calculate the comprehensive security score index.

[0076] In the safety assessment and optimization module, the comprehensive safety score index is expressed as follows:

[0077]

[0078] Where S represents the comprehensive security score, HSM represents the contribution of the hardware security module, T represents the contribution of the trusted execution environment to the comprehensive security score, δ1 represents the weight coefficient of HSM and T, which measures their importance to the comprehensive security score; B represents the contribution of secure boot to the comprehensive security score, P represents the contribution of physical seamless protection to the comprehensive security score, λ is its control parameter, and the physical seamless protection at least includes an anti-tampering shell, δ2 represents the weight of physical protection on the comprehensive security score, δ3 represents the parameter of the degree of influence of data transmission efficiency, D(x i ,y i ) indicates that according to the optimized node v i The contribution of communication reliability to the comprehensive security score is obtained by the coordinate position of the node, E represents the parameters related to energy efficiency and data processing capability, δ4 represents the weight of the node’s own security feature factor in the comprehensive security score; F i represents the detection sensitivity of the i-th sensor, n represents the total number of sensors, κ represents the parameter for adjusting the degree of sensor protection, and s i represents the safety level of the i-th sensor, r i represents the risk factor of the i-th sensor.

[0079] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0080] It is obvious to those skilled in the art that the embodiments of the present invention are not limited to the details of the above exemplary embodiments, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or basic features of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the embodiments of the present invention is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the embodiments of the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules or devices stated in the system, device or terminal claims can also be implemented by the same unit, module or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0081] Finally, it should be noted that the above implementation modes are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred implementation modes, those skilled in the art should understand that the technical solutions of the embodiments of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for security assessment and optimization of Internet of Things nodes, characterized in that: The following steps are involved: Step 1: Divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, and determine the initial position, transmission power, and security level and risk factor of each IoT node in the network; Step 2, calculating the coverage overlap degree of the detection coverage overlap area between the multiple IoT nodes and the free space detected in the multiple IoT nodes; Step 3: constructing a target optimization function based on the coverage overlap and free space, and iteratively optimizing the deployment, detection coverage area, and location of the multiple IoT nodes; Step 4: Evaluate and optimize the security of the IoT node based on the security level and risk factor of the sensors in the IoT node, the node position after iterative optimization, and the hardware security foundation, software security measures, physical protection, and processing capacity of the IoT node to calculate the comprehensive security score index.

2. The method for security assessment and optimization of Internet of Things nodes according to claim 1, characterized in that: In step 2, the coverage overlap degree of the detection coverage overlap area is expressed by the following formula: Among them A i and A j They represent IoT nodes v i and neighbor node v j The total detection coverage area, O ij Represents the IoT node v i and neighbor node v j The coverage overlap area detected between nodes v i and neighbor node v j The ratio of the overlapping detection coverage areas to the larger coverage area; IoT Nodev i The free space detected is expressed as follows: Among them, C i is node v i The space occupied by its own size, R ij is node v i With neighbor node v j The actual distance between ij It is an IoT node v i With neighbor node v j The expected detection coverage overlap distance between them; It reflects the impact of the distance between nodes on the free space.

3. The method for security assessment and optimization of Internet of Things nodes according to claim 2, characterized in that: The objective optimization function in step 3 is expressed as follows: represents the optimization goal of the coverage overlap, the coefficient α controls the weight of this part, and p i and p j The nodes v i and neighbor node v j The transmission power, n represents the total number of sensors; represents the optimization goal of the node free coverage space, the coefficient β controls the weight of this part, w i and h i The nodes v i The width and height, q i is node v i The weight coefficient of It indicates the optimization goal of the node position deviation from the expected distribution. The coefficient γ controls the weight of this part. (x i ,y i ) represents the optimized node v i The coordinate position of the network area is represented by W and H, respectively.

4. The method for security assessment and optimization of Internet of Things nodes according to claim 3, characterized in that: In step 4, the comprehensive safety score index is expressed as follows: Where S represents the comprehensive security score, HSM represents the contribution of the hardware security module, T represents the contribution of the trusted execution environment to the comprehensive security score, δ1 represents the weight coefficient of HSM and T, which measures their importance to the comprehensive security score; B represents the contribution of secure boot to the comprehensive security score, P represents the contribution of physical seamless protection to the comprehensive security score, λ is its control parameter, and the physical seamless protection at least includes an anti-tampering shell, δ2 represents the weight of physical protection on the comprehensive security score, δ3 represents the parameter of the degree of influence of data transmission efficiency, D(x i ,y i ) indicates that according to the optimized node v i The contribution of communication reliability obtained from the coordinate position of the node to the comprehensive security score, E represents parameters related to energy efficiency and data processing capability, and δ4 represents the weight of the node's own security feature factor in the comprehensive security score; F i represents the detection sensitivity of the i-th sensor, n represents the total number of sensors, κ represents the parameter for adjusting the degree of sensor protection, and s i represents the safety level of the i-th sensor, r i represents the risk factor of the i-th sensor.

5. The method for security assessment and optimization of Internet of Things nodes according to claim 4, characterized in that: The security level reflects the security protection capabilities of the sensor, including the anti-tampering and anti-destruction capabilities of the sensor hardware, the reinforcement and security update mechanism of the sensor software, the access control of the sensor input and output interfaces, and the sensor data encryption and integrity verification mechanism.

6. The method for security assessment and optimization of Internet of Things nodes according to claim 5, characterized in that: The risk factor reflects the potential risk level of the sensor in the system, including: the criticality of the data detected by the sensor to the system security, the impact on the system when the sensor is attacked or fails, the importance and exposure of the sensor location, and the type of attack the sensor may suffer and its risk level.

7. A device for security assessment and optimization of Internet of Things nodes, characterized in that: The device includes: The initialization module is used to divide the area of ​​interest in the network into multiple partitions, place multiple IoT nodes in each partition, and determine the initial position, transmission power, and security level and risk factor of sensors in each IoT node in the network; A detection area detection module, used to calculate the coverage overlap degree of the detection coverage overlap area between the multiple IoT nodes and the free space detected within the multiple IoT nodes; An iterative optimization module, configured to construct a target optimization function based on the coverage overlap and free space, and iteratively optimize the deployment area, detection coverage, and location of the plurality of IoT nodes; The security assessment and optimization module is used to evaluate and optimize the security of the IoT node based on the security level and risk factor of the sensors in the IoT node, the node position after iterative optimization, and the hardware security foundation, software security measures, physical protection, and processing capacity of the IoT node to calculate the comprehensive security score index.

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