Online sensor dynamic replacement method and device for wireless sensor network
By employing an online sensor dynamic replacement method that combines time and event-triggered conditions to perform energy priority and performance optimization, the adaptability of wireless sensor networks under dynamic changes and malicious attacks is addressed. This enables rapid response and efficient recovery, thereby improving system observability and network lifetime.
Patent Information
- Application Number
- CN202511698874.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-19
- Publication Date
- 2026-02-13
AI Technical Summary
Existing wireless sensor networks are poorly adaptable and slow to respond to dynamic changes and malicious attacks. They lack a comprehensive decision-making mechanism, which leads to a decrease in system estimation accuracy and loss of observability. Furthermore, they are not capable of recovering from malicious attacks.
An online sensor dynamic replacement method is adopted. By monitoring system performance indicators and sensor status, and combining time-triggered and event-triggered conditions, the principle of energy priority and performance optimization is implemented to select replacement sensors to form a new active set that meets system constraints, thereby achieving second-level response and rapid recovery.
It achieves second-level response to deception attacks, keeps performance degradation within 25%, extends network lifetime by 53.8%, has low computational complexity, improves energy efficiency, increases attack detection rate by 10%, and improves performance degradation by 44.4%.
Smart Images
Figure CN121531315A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of wireless sensor network management and control technology, and particularly relates to an online sensor dynamic replacement method and device of a wireless sensor network. BACKGROUND
[0002] After the wireless sensor network is actually deployed, the running environment thereof is usually dynamic and uncertain. The sensor nodes can be disabled due to battery depletion; the wireless communication link can be interrupted or the performance thereof can be degraded due to interference or malicious attacks (such as denial-of-service attacks, deception attacks); and the demand of the monitoring task can also change. These factors require the WSN system to have the ability of online dynamic optimization, and to be able to adjust the sensor configuration thereof at runtime to maintain the required estimation performance.
[0003] The traditional sensor selection method is mostly offline design, that is, a fixed sensor set is calculated once and used throughout the network life cycle. This method cannot adapt to the above dynamic changes, and when part of the nodes are disabled or the performance thereof is degraded, the system estimation accuracy will be sharply decreased, and even the system can completely lose observability. A simple solution is to periodically re-execute the offline selection algorithm. However, frequently restarting the calculation of the complex offline algorithm will generate huge computational overhead, which is not suitable for the resource-limited sensor network nodes, especially the central coordinator. In addition, when facing fast dynamic changes such as deception attacks, the periodic strategy cannot respond in time.
[0004] The dynamic replacement method in the prior art usually only considers a single trigger condition (such as energy depletion or performance degradation), and lacks a comprehensive decision mechanism. At the same time, the traditional method often does not take the security threat (such as deception attack) as the core trigger condition, resulting in that the system cannot respond in time and has poor recovery ability when facing malicious attacks. SUMMARY
[0005] In order to solve the problems of poor adaptability, untimely response, lack of comprehensive decision mechanism and insufficient recovery ability when facing malicious attacks in the prior art, the primary purpose of the present application is to provide an online sensor dynamic replacement method of a wireless sensor network which can respond in seconds and quickly recover when attacked.
[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: an online sensor dynamic replacement method of a wireless sensor network, which comprises the following sequential steps:
[0007] (1) monitoring the system performance indicators of the current active sensor set and the state of the sensors in the current active sensor set; judging whether the preset double trigger condition is met, the double trigger condition comprising a time trigger condition and an event trigger condition;
[0008] (2) if any one of the time trigger condition and the event trigger condition is met, it is judged whether the energy priority principle is met, if the result of the judgment is yes, it indicates that there is a sensor in the current active sensor set that meets the energy priority principle, the sensor is determined as the candidate sensor to be replaced, if the result of the judgment is no, it is judged whether the performance optimization principle is met, if the performance optimization principle is met, the candidate sensor to be replaced is identified from the current active sensor set;
[0009] (3) all inactive sensors are traversed, a replacement sensor is searched from the inactive sensors, so that after the candidate sensor to be replaced is replaced by the replacement sensor, the system performance index of the new active sensor set formed under the premise of meeting the system constraint condition is larger.
[0010] In step (1), the time trigger condition is that a preset periodic check moment is reached, the event trigger condition is that the system performance index is lower than a preset safety threshold, and the system performance index drop in the event trigger condition is caused by a malicious network attack, the malicious network attack includes a deception attack and a denial of service attack.
[0011] In step (2), the energy priority principle specifically refers to that the current active sensor set is traversed, if it is found that there is a sensor whose residual energy is lower than a preset energy threshold, the sensor is determined as the candidate sensor to be replaced, and the performance optimization principle specifically refers to that if the candidate sensor to be replaced is not determined through the energy priority principle, the contribution loss of each sensor in the current active sensor set to the system performance is evaluated by calculation, and the sensor with the largest contribution loss is determined as the candidate sensor to be replaced.
[0012] In step (3), the system constraint condition refers to that the total energy consumption of the new active sensor set after replacement does not exceed the total energy budget, the new active sensor set refers to the sensor set formed after the candidate sensor to be replaced is replaced by the replacement sensor, and the system performance index is the minimum eigenvalue λmin of the observability Gramian matrix.
[0013] The contribution loss is evaluated by the system performance index, and the greater the drop amplitude of the system performance index, the greater the contribution loss.
[0014] Another object of the application is to provide an electronic device comprising:
[0015] a processor; and
[0016] a memory, in which computer program instructions are stored, the computer program instructions, when executed by the processor, cause the processor to execute the online sensor dynamic replacement method of the wireless sensor network as described above.
[0017] The application further provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions enable a processor to execute the online sensor dynamic replacement method of the wireless sensor network when the processor runs.
[0018] From the above technical solution, the application has the following advantages: first, high toughness and rapid recovery: through the event triggering mechanism, the application can respond to security threats such as spoofing attacks within seconds. Experiments show that when subjected to spoofing attacks, the application can control the performance degradation within 25% and quickly recover within an average of 1.2 cycles, while the static strategy is completely ineffective and the frequent restart strategy requires 5.2 cycles; second, energy intelligent balancing and life extension: through the energy priority replacement mechanism, the application effectively prevents the "energy hole" problem; experiments show that the system integrated with the online strategy of the application can achieve a complete 200-cycle network life, which is 53.8% longer than the static strategy, and the energy efficiency reaches 0.87; third, efficient computing and online feasibility: the replacement strategy of the application is locally optimized, with a computational complexity of O(N·n³), which is much lower than the O(N²·n³) of global reselection, and is very suitable for online real-time operation on resource-limited central coordinators; experiments show that the average single execution overhead is only 15ms / cycle, which is 46.4% lower than the frequent restart strategy; fourth, comprehensive performance guarantee: the application achieves an attack detection rate of 95%, which is 10 percentage points higher than the frequent restart strategy, and the maximum performance degradation is controlled within 25%, which is 44.4% better than the frequent restart strategy. BRIEF DESCRIPTION OF DRAWINGS
[0019] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION
[0020] As shown in Figure 1 An online sensor dynamic replacement method of a wireless sensor network, the method comprising the following sequential steps:
[0021] (1) Monitor the system performance indicators of the current active sensor set and the state of the sensors in the current active sensor set; determine whether the preset double-trigger condition is met, the double-trigger condition including a time-trigger condition and an event-trigger condition; the innovation of the application lies in the organic combination of the two triggering conditions, which ensures the periodic maintenance of the system and responds to sudden performance degradation (such as spoofing attacks) in time.
[0022] (2) If any of the time trigger condition and the event trigger condition is met, it is determined whether the energy priority principle is met. If the result of the determination is yes, it indicates that there is a sensor in the current active sensor set that meets the energy priority principle, and the sensor is determined as the candidate sensor to be replaced. This mechanism ensures that the system gives priority to the energy crisis and effectively prevents network partition and "energy hole" problems. If the result of the determination is no, it is determined whether the performance optimization principle is met. If the performance optimization principle is met, the candidate sensor to be replaced is identified from the current active sensor set. This mechanism can intelligently identify the "performance bottleneck" in the system when there is no urgent energy problem, and continuously optimize the estimation accuracy.
[0023] For the energy crisis, the replacement is performed even if the performance is not immediately improved to ensure network survivability. For the performance optimization, a clear performance improvement is required.
[0024] (3) All inactive sensors are traversed to find a replacement sensor, so that after the candidate sensor to be replaced is replaced by the replacement sensor, the new active sensor set formed under the premise of meeting the system constraint condition has a larger system performance index.
[0025] In step (1), the time trigger condition is reaching a preset periodic check time, and the event trigger condition is that the system performance index is lower than a preset safety threshold. The decrease in the system performance index in the event trigger condition is caused by a malicious network attack, and the malicious network attack includes a deception attack and a denial of service attack.
[0026] In step (2), the energy priority principle specifically refers to traversing the current active sensor set, and if it is found that the remaining energy of a sensor is lower than a preset energy threshold, the sensor is determined as the candidate sensor to be replaced. The performance optimization principle specifically refers to that if the candidate sensor to be replaced is not determined through the energy priority principle, the contribution loss of each sensor in the current active sensor set to the system performance is calculated and evaluated, and the sensor with the largest contribution loss is determined as the candidate sensor to be replaced.
[0027] In step (3), the system constraint condition refers to that the total energy consumption of the new active sensor set after replacement does not exceed the total energy budget. The new active sensor set refers to the sensor set formed after the candidate sensor to be replaced is replaced by the replacement sensor. The system performance index is the minimum eigenvalue λmin of the observability Gramian matrix.
[0028] The contribution loss is evaluated by the system performance index. The greater the decrease in the system performance index, the greater the contribution loss.
[0029] In summary, the application can respond to security threats such as spoofing attacks in seconds, experiments show that when subjected to spoofing attacks, the application can control the performance degradation within 25%, and recover quickly within an average of 1.2 cycles, while the static strategy is completely ineffective, and the frequent restart strategy also needs 5.2 cycles: through the energy priority replacement mechanism, the application effectively prevents the "energy hole" problem; experiments show that the system integrated with the online strategy of the application can realize the complete network life of 200 cycles, which is 53.8% longer than the static strategy life, and the energy efficiency reaches 0.87; the replacement strategy of the application is a local optimization, and the computational complexity is O(N·n³), which is much lower than the global reselection of O(N²·n³), and is very suitable for online real-time operation on resource-limited central coordinators; experiments show that its single execution overhead is only 15ms / cycle on average, which is reduced by 46.4% compared with the frequent restart strategy; the application achieves 95% in attack detection rate, which is improved by 10 percentage points compared with the frequent restart strategy, and the maximum performance degradation is controlled within 25%, which is improved by 44.4% compared with the frequent restart strategy. N represents the total number of sensor nodes in the wireless sensor network, which is a parameter describing the size of the network, for example, in a network deployed with 100 sensors, N = 100. n represents the dimension of the estimated system state vector, which is a parameter describing the complexity of the problem itself, for example, in target tracking, the state vector may contain the position and speed of the target, such as [x, y, vx, vy], then n = 4.
[0030] The basic principles, main features and advantages of the application are shown and described above. Those skilled in the art should understand that the application is not limited by the above examples, and the above examples and descriptions in the specification are only the principles of the application. Without departing from the spirit and scope of the application, various changes and improvements can be made to the application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection claimed by the application is defined by the appended claims and their equivalents.
Claims
1. A method for online dynamic sensor replacement in a wireless sensor network, characterized in that: The method includes the following steps in sequence: (1) Monitor the system performance indicators of the currently active sensor set and the status of the sensors in the currently active sensor set; determine whether the preset dual triggering conditions are met, the dual triggering conditions include time triggering conditions and event triggering conditions; (2) If either the time trigger condition or the event trigger condition is met, it is determined whether the energy priority principle is met. If the result is yes, it means that there is a sensor in the current active sensor set that meets the energy priority principle, and the sensor is determined as a candidate sensor to be replaced. If the judgment result is negative, then determine whether the performance optimization principle is met. If the performance optimization principle is met, then identify the candidate sensor to be replaced from the currently active sensor set. (3) Traverse all inactive sensors and find alternative sensors among the inactive sensors so that the new set of active sensors formed after replacing the candidate sensor to be replaced with the alternative sensor increases the system performance index under the premise of satisfying the system constraints.
2. The online sensor dynamic replacement method for wireless sensor networks according to claim 1, characterized in that: In step (1), the time trigger condition is reaching a preset periodic check time; the event trigger condition is that the system performance index is lower than a preset security threshold, and the decline in the system performance index in the event trigger condition is caused by a malicious network attack, including deception attacks and denial-of-service attacks.
3. The online sensor dynamic replacement method for wireless sensor networks according to claim 1, characterized in that: In step (2), the energy priority principle specifically means: traversing the current set of active sensors, if a sensor is found to have remaining energy lower than a preset energy threshold, then that sensor is identified as a candidate sensor to be replaced; the performance optimization principle specifically means: if a candidate sensor to be replaced is not identified through the energy priority principle, then the contribution loss of each sensor in the current set of active sensors to the system performance is calculated and evaluated, and the sensor with the largest contribution loss is identified as a candidate sensor to be replaced.
4. The online sensor dynamic replacement method for wireless sensor networks according to claim 1, characterized in that: In step (3), the system constraint condition means that the total energy consumption of the new set of active sensors after replacement does not exceed the total energy budget; the new set of active sensors means the set of sensors formed after replacing the candidate sensors to be replaced with alternative sensors; the system performance index is the minimum eigenvalue λmin of the observability Gramian matrix.
5. The online sensor dynamic replacement method for wireless sensor networks according to claim 3, characterized in that: The contribution loss is evaluated using system performance metrics; the greater the decline in system performance metrics, the greater the contribution loss.
6. An electronic device, comprising: processor; as well as A memory storing computer program instructions that, when executed by the processor, cause the processor to perform the online sensor dynamic replacement method for a wireless sensor network as described in any one of claims 1-5.
7. A computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform an online sensor dynamic replacement method for a wireless sensor network as described in any one of claims 1-5.