Unmanned aerial vehicle ad hoc network trusted routing control method and system, electronic equipment and storage medium

Abstract

The invention discloses an unmanned aerial vehicle ad hoc network trusted routing control method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the local monitoring of a neighbor node through an unmanned aerial vehicle node, generating a disable proof for the neighbor node with a local trust index falling from a preset threshold value or having a data tampering behavior, and transmitting the disable proof to a trust anchor point; calling an on-chain global reputation value of the reporter according to the information of the reporter, and if the on-chain global reputation value is higher than a preset reputation threshold value, executing a global reputation deduction operation on the reporter; calling a historical accumulated effective report number of the reported person according to the information of the reported person, and if the historical accumulated effective report number reaches a preset consensus threshold value, executing a global reputation deduction operation on the reported person; and if the updated global reputation value of the reported person is lower than a malicious node judgment threshold value, marking the node state of the reported person as malicious and triggering an on-chain alarm event. Through the setting, framing attacks of malicious nodes are resisted, and legal nodes are prevented from being mistakenly isolated.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) self-organizing network security technology, and in particular to a trusted routing control method, system, electronic device and storage medium for UAV self-organizing networks. Background Technology

[0002] The application of drone swarms is becoming increasingly widespread in fields such as military reconnaissance, emergency communications, and logistics delivery. As a typical highly dynamic mobile ad hoc network, drone ad hoc networks are characterized by topology transients, open communication channels, and limited node computing and energy resources. Network security is therefore crucial for stable network operation. Existing mainstream FANET routing protocols are mostly based on extensions of on-demand routing protocols such as AODV. These protocols are designed with the premise of "all nodes are trustworthy," and inherently lack effective oversight of node forwarding behavior, making the network highly vulnerable to malicious attacks such as internal node black holes / grey holes and data tampering.

[0003] To defend against malicious internal attacks, the industry uses the Watchdog local reputation monitoring mechanism to detect node forwarding behavior locally. However, this mechanism suffers from a serious "subjectivity silo" problem. The monitoring results of a single node lack global credibility, and malicious nodes can spread false reputation evaluations through "framing attacks," which can easily lead to the collapse of the trust system. Therefore, a global consensus mechanism is urgently needed to achieve a unified judgment of node reputation.

[0004] In recent years, blockchain technology has been attempted to be used for storing reputation records in drone self-organizing networks due to its immutable and decentralized characteristics. However, its traditional consensus mechanism has high computational overhead and high ledger storage load, far exceeding the resource carrying capacity of drone edge nodes, making it impossible to deploy directly. On the other hand, relying solely on local monitoring cannot solve the global credibility problem of trust evaluation. Existing technologies have always been unable to balance the lightweight nature of local monitoring with the credibility of global trust consensus on resource-constrained drone edge nodes, which has become a key technical challenge restricting the construction of a trust system for drone self-organizing networks. Summary of the Invention

[0005] To address the shortcomings mentioned above, this application provides a trusted routing control method, system, electronic device, and storage medium for unmanned aerial vehicle (UAV) self-organizing networks.

[0006] Firstly, to achieve the above objectives, this application provides a trusted routing control method for unmanned aerial vehicle (UAV) self-organizing networks, comprising: The drone node performs local monitoring of its neighboring nodes. For neighboring nodes whose local trust index falls below a preset threshold or which have engaged in data tampering, a malicious certificate is generated and sent to the trust anchor. The malicious certificate includes information about the whistleblower and the reported party. Based on the whistleblower's information, retrieve the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than a preset reputation threshold, perform a global reputation deduction operation on the reported party. Based on the reported party's information, retrieve the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches a preset consensus threshold, perform a global reputation deduction operation on the reported party. The global reputation value of the reported party is updated by performing a global reputation deduction operation. If the updated global reputation value of the reported party is lower than the malicious node determination threshold, the node status of the reported party is marked as malicious and an on-chain alarm event is triggered.

[0007] Preferably, the drone node performs local monitoring of its neighboring nodes, and generates malicious evidence and sends it to the trust anchor for neighboring nodes whose local trust index falls below a preset threshold or which exhibit data tampering behavior, including: The drone node performs local monitoring through the Watchdog bypass monitoring mechanism to obtain the forwarding behavior data of the neighboring nodes. If the forwarded data packets in the forwarding behavior data are inconsistent with the original packets, then there is data tampering behavior. Based on the forwarding behavior data, the local trust index of the neighbor node is obtained, and the formula for the local trust index is as follows: ; In the formula: The local trust index of the neighboring node; The local trust index of the neighboring node in the previous period; A score is given to the forwarding behavior of the neighboring nodes; Based on the neighboring nodes whose local trust index falls below a preset threshold or who have engaged in data tampering, a malicious proof is generated and sent to the trust anchor.

[0008] Preferably, the calculation formula for the score of the neighbor node forwarding behavior is as follows: ; ; ; In the formula: The number of data packets successfully forwarded by the neighboring node detected by the drone node; The number of data packets forwarded by the neighboring nodes detected by the drone node; A marker indicating that tampering has occurred.

[0009] Preferably, the step of obtaining the local trust index of the neighbor node and verifying the local trust index includes: The drone node performs local monitoring of its neighboring nodes to obtain the signal strength of the drone node and the received signal strength of the neighboring nodes. Based on the signal strength of the drone node, the rate of change of the drone node is obtained. If the rate of change is greater than the attenuation threshold, the verification fails. The absolute difference between the signal strength of the drone node and the received signal strength of the neighboring node is calculated. If the absolute difference exceeds the signal tolerance threshold, the verification fails. The neighboring nodes that fail the verification are determined to have no fraudulent behavior and are set not to be exempted. Based on the received signal strength of the neighboring node that has passed verification, the corresponding exemption coefficient is calculated, and the local trust index that has passed verification is obtained based on the exemption coefficient.

[0010] Preferably, the generation of malicious evidence and its sending to a trust anchor point, wherein the trust anchor point verifies the malicious evidence, wherein the malicious evidence includes an attacker identifier, a whistleblower identifier, a timestamp, an evidence hash, a digital signature, and a crime evidence snapshot, wherein the crime evidence snapshot is a hash snapshot of a tampered data packet or a count record of unforwarded data packets; The verification includes whether the whistleblower's signature is valid and whether the evidence of wrongdoing is old, unprocessed evidence.

[0011] Preferably, the global reputation deduction operation on the reported party includes: The penalty value of the global reputation deduction operation is a non-linear function of the number of historical violations of the reported individual, and the penalty value increases as the number of historical violations increases.

[0012] Preferably, the step of marking the node status of the reported party as malicious and triggering an on-chain alert event further includes: The trust anchor will add the identifier of the reported malicious node to the network-wide blacklist and broadcast the blacklist information to all nodes in the drone self-organizing network.

[0013] Secondly, this application also provides a trusted routing control system for unmanned aerial vehicle (UAV) self-organizing networks, comprising: The data acquisition module is used by drone nodes to perform local monitoring of their neighboring nodes. The processing module is used to generate a malicious proof and send it to the trust anchor for neighboring nodes whose local trust index falls below a preset threshold or which have engaged in data tampering. The malicious proof includes information about the whistleblower and the reported party. Based on the whistleblower's information, the module retrieves the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than a preset reputation threshold, the module performs a global reputation deduction operation on the reported party. Based on the reported party's information, the module retrieves the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches a preset consensus threshold, the module performs a global reputation deduction operation on the reported party. The execution module is used to update the global reputation value of the reported party by performing a global reputation deduction operation. If the updated global reputation value of the reported party is lower than the malicious node determination threshold, the node status of the reported party is marked as malicious and an on-chain alarm event is triggered.

[0014] Thirdly, this application also provides an electronic device, including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit performs the above-described method.

[0015] Fourthly, this application also provides a storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the above-described method.

[0016] Compared with the prior art, the beneficial effects of this application are as follows: This application uses a dual approach, combining the whistleblower's global reputation value on the blockchain with the number of valid reports accumulated by the reported party throughout history. This ensures that valid reports from high-reputation nodes are processed quickly, while requiring reports from low-reputation nodes to be endorsed by multiple parties before triggering penalties. This fundamentally resists malicious attacks and prevents legitimate nodes from being wrongly isolated, significantly improving the reliability and security of the drone self-organizing network trust system. Attached Figure Description

[0017] Figure 1 This is a flowchart of the trusted routing control method for UAV self-organizing networks proposed in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] This application provides a network architecture for trusted routing in a drone self-organizing network. The network architecture includes a monitoring terminal, i.e., a drone node, which is responsible for performing lightweight monitoring, routing calculation and data forwarding, and a trust anchor, i.e., a ground control station, which is responsible for maintaining the full blockchain ledger and executing smart contracts.

[0020] To prevent external attackers from impersonating others or launching replay attacks, the network employs a permission-based access system, which includes: Before joining the network, drone nodes must register with a trusted anchor point to obtain a unique public-private key pair and digital certificate; All routing control messages and critical data packets must carry the sender's digital signature and timestamp. Before processing a message, a node verifies the signature's validity and the timestamp's freshness. Messages with failed signature verification or expired certificates are considered external attacks and are discarded directly at the link layer.

[0021] The network architecture control method includes: The drone node performs local monitoring of its neighboring nodes. For neighboring nodes whose local trust index falls below a preset threshold or who have engaged in data tampering, it generates a malicious proof and sends it to the trust anchor. The malicious proof includes information about the whistleblower and the reported party. Specifically, for nodes that have passed identity authentication, a Watchdog mechanism is used for monitoring. The monitoring process includes: After forwarding data to node B, node A starts promiscuous listening mode and waits for a preset window T. wait Does the internal listening node B forward the data to the next hop C as intended? For the newly added neighbor node j, its TI local (0) is TI local (0)=TI threshold (Route admission threshold, for example, 0.6).

[0022] Nodes calculate a behavioral score based on the forwarding success rate (FR) and data integrity (DI) of their neighbors using a sliding window. ; ; ; In the formula: The number of data packets successfully forwarded by neighboring nodes detected by the drone node; The number of data packets that the drone node detects being forwarded by neighboring nodes; A marker indicating that tampering has occurred; Update the final value using the exponentially weighted moving average formula. To avoid drastic fluctuations in scores due to occasional congestion of the wireless channel, the formula is as follows: ; In the formula: This represents the local trust index of neighboring nodes. This represents the local trust index of neighboring nodes in the previous period. Scoring the forwarding behavior of neighboring nodes; For weights.

[0023] Furthermore, if α is large (e.g., 0.8): the trust value updates slowly, has high stability, and strong anti-interference capabilities (suitable for scenarios with harsh channel environments); if α is small (e.g., 0.3): the trust value updates quickly, reacting rapidly to malicious behavior, but is prone to misjudgment. Therefore, when Eval(t)... <TI local (t-1), take a smaller α (e.g., 0.4); when Eval(t)≥TI local (t-1), take a larger α (such as 0.8); in order to achieve the characteristic of slow trust establishment and fast trust collapse.

[0024] Furthermore, by conducting local monitoring of its neighboring nodes, the signal strength of the drone node and the received signal strength of its neighboring nodes can be obtained. Based on the signal strength of the drone node, the rate of change of the drone node is obtained. If the rate of change is greater than the attenuation threshold, the verification fails. The absolute difference between the signal strength of the drone node and the received signal strength of the neighboring node is calculated. If the absolute difference exceeds the signal tolerance threshold, the verification fails. Neighboring nodes that fail verification are deemed not to have engaged in fraudulent activities and are therefore not exempt from exemption. Based on the received signal strength of the neighboring nodes that have passed verification, the corresponding exemption coefficient is calculated, and the local trust index that has passed verification is obtained based on the exemption coefficient. The formula for the exemption coefficient is as follows: ; In the formula: The signal strength of the drone node; The safety signal strength threshold; This is the critical signal strength threshold.

[0025] The final formula for the local trust index is as follows: ; ; .

[0026] Based on the whistleblower's information, retrieve the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than the preset reputation threshold, perform a global reputation deduction operation on the reported party. Based on the reported party's information, retrieve the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches the preset consensus threshold, perform a global reputation deduction operation on the reported party. Specifically, the trust anchor (ground base station) acts as a blockchain gateway, verifying malicious proofs before packaging blocks. Malicious proofs include attacker identifiers, whistleblower identifiers, timestamps, evidence hashes, digital signatures, and evidence snapshots. Evidence snapshots are hash snapshots of tampered data packets or count records of unforwarded data packets. Verification includes verifying the whistleblower's signature and whether the malicious proof is old, unprocessed evidence. Only malicious proofs that pass verification enter the transaction pool to be packaged and are subsequently written into the blockchain ledger. To prevent malicious nodes from using the malicious proof mechanism to launch false accusations, the trust anchor introduces a reputation-weighted verification mechanism when processing malicious proofs. Only when the whistleblower's global reputation value is higher than a preset reputation threshold will their submitted malicious proof be processed immediately; otherwise, the malicious proof will be stored in a pending pool until k malicious proofs targeting the same objective are collected from different neighboring nodes (i.e., k-neighbor consensus, e.g., k=2), at which point the penalty logic is triggered.

[0027] Furthermore, the reputation value of the whistleblower at the previous block height and the number of valid whistleblower reports in the history of the reported party are obtained from the blockchain ledger. If the whistleblower's reputation value at the previous block height is higher than a preset reputation threshold or the cumulative number of valid whistleblower reports in the history reaches a preset consensus threshold, a global reputation deduction operation is performed on the reported party; otherwise, the reported party is placed in a pending pool. The global reputation deduction operation on the reported party includes: The penalty value for global reputation deduction is a non-linear function of the reported individual's historical violations; the penalty value increases with the increase in the number of historical violations. The formula for this non-linear function is shown below: ; In the formula: Penalty is the penalty value; The base penalty score is a preset fixed constant; Target.History_Count is the historical cumulative number of valid reports against the reported node.

[0028] The global reputation value of the reported party is updated based on the global reputation deduction operation performed by the reported party. If the updated global reputation value of the reported party is lower than the malicious node judgment threshold, the node status of the reported party will be marked as malicious and an on-chain alarm event will be triggered. The trust anchor will add the identifier of the reported malicious node to the network-wide blacklist and broadcast the blacklist information to all nodes in the drone ad hoc network.

[0029] In the implementation of this application, a lightweight Bloom filter is used as the local storage medium for the blacklist in order to achieve millisecond-level route interception on drone nodes with extremely limited computing resources.

[0030] The drone maintains an efficient bit array in its local memory. Compared to traditional hash tables or linked lists, Bloom filters can perform route request invalidity checks in constant time complexity, with extremely low space consumption (tens of thousands of nodes can be accommodated in KB-level memory), greatly reducing the memory pressure on embedded devices. When an Emergency_Blacklist_Update frame is received from the ground station, the node directly maps the newly added malicious ID into the filter, achieving immediate threat blocking.

[0031] To ensure the timeliness of the blacklist and clean up outdated data, a global state synchronization frame is introduced. When a significant change occurs in the threat landscape of the network (such as a large number of nodes regaining credit), the ground station broadcasts a full blacklist data frame containing the latest version number (Version ID). Upon receiving this frame, nodes directly reset their local filters and load the latest state. This mechanism avoids complex single-point deletion operations and ensures consistency between local and cloud data.

[0032] To address the inherent probabilistic false positives of Bloom filters and potential state conflicts between local observation data and the global blacklist, a conflict arbitration mechanism combining default blocking and cloud-based authorization is established. When a routing request hits the global blacklist but local monitoring indicates the node has good reputation, a data consistency conflict is identified. Based on the principle of security priority, the global blacklist's determination is adopted first, and the current routing request is immediately discarded to avoid potential risks. Simultaneously, an asynchronous state verification command is sent to the ground-based trusted anchor. Forwarding service to the target node is only restored in subsequent communications after receiving confirmation of the false positive from authoritative cloud feedback and adding it to the local whitelist. This ensures the authority and accuracy of the blacklist mechanism while tolerating a very small number of false positive interruptions.

[0033] Secondly, this application also provides a trusted routing control system for unmanned aerial vehicle (UAV) self-organizing networks, comprising: The data acquisition module is used by drone nodes to perform local monitoring of their neighboring nodes. The processing module is used to generate malicious proofs and send them to the trust anchor for neighboring nodes whose local trust index falls below a preset threshold or who have engaged in data tampering. The malicious proofs include information about the whistleblower and the reported party. Based on the whistleblower's information, the module retrieves the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than a preset reputation threshold, a global reputation deduction operation is performed on the reported party. Based on the reported party's information, the module retrieves the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches a preset consensus threshold, a global reputation deduction operation is performed on the reported party. The execution module is used to update the global reputation value of the reported party based on the global reputation deduction operation performed on the reported party. If the updated global reputation value of the reported party is lower than the malicious node judgment threshold, the node status of the reported party will be marked as malicious and an on-chain alarm event will be triggered.

[0034] This application also provides an electronic device, including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit performs the above-described method.

[0035] This application also provides a storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the above-described method.

[0036] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A trusted routing control method for unmanned aerial vehicle (UAV) self-organizing networks, characterized in that, include: The drone node performs local monitoring of its neighboring nodes. For neighboring nodes whose local trust index falls below a preset threshold or which have engaged in data tampering, a malicious certificate is generated and sent to the trust anchor. The malicious certificate includes information about the whistleblower and the reported party. Based on the whistleblower's information, retrieve the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than a preset reputation threshold, perform a global reputation deduction operation on the reported party. Based on the reported party's information, retrieve the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches a preset consensus threshold, perform a global reputation deduction operation on the reported party. The global reputation value of the reported party is updated by performing a global reputation deduction operation. If the updated global reputation value of the reported party is lower than the malicious node determination threshold, the node status of the reported party is marked as malicious and an on-chain alarm event is triggered.

2. The trusted routing control method for UAV self-organizing networks according to claim 1, characterized in that, The drone node performs local monitoring of its neighboring nodes. For neighboring nodes whose local trust index falls below a preset threshold or exhibits data tampering behavior, it generates malicious evidence and sends it to the trust anchor point, including: The drone node performs local monitoring through the Watchdog bypass monitoring mechanism to obtain the forwarding behavior data of the neighboring nodes. If the forwarded data packets in the forwarding behavior data are inconsistent with the original packets, then there is data tampering behavior. Based on the forwarding behavior data, the local trust index of the neighbor node is obtained, and the formula for the local trust index is as follows: ； In the formula: The local trust index of the neighboring node; The local trust index of the neighboring node in the previous period; A score is given to the forwarding behavior of the neighboring nodes; Based on the neighboring nodes whose local trust index falls below a preset threshold or who have engaged in data tampering, a malicious proof is generated and sent to the trust anchor.

3. The trusted routing control method for UAV self-organizing networks according to claim 2, characterized in that, The formula for calculating the score of the neighbor node forwarding behavior is as follows: ； ； ； In the formula: The number of data packets successfully forwarded by the neighboring node detected by the drone node; The number of data packets forwarded by the neighboring nodes detected by the drone node; A marker indicating that tampering has occurred.

4. The trusted routing control method for UAV self-organizing networks according to claim 3, characterized in that, The process involves obtaining the local trust index of the neighboring node and verifying the local trust index, including: The drone node performs local monitoring of its neighboring nodes to obtain the signal strength of the drone node and the received signal strength of the neighboring nodes. Based on the signal strength of the drone node, the rate of change of the drone node is obtained. If the rate of change is greater than the attenuation threshold, the verification fails. The absolute difference between the signal strength of the drone node and the received signal strength of the neighboring node is calculated. If the absolute difference exceeds the signal tolerance threshold, the verification fails. The neighboring nodes that fail the verification are determined to have no fraudulent behavior and are set not to be exempted. Based on the received signal strength of the neighboring node that has passed verification, the corresponding exemption coefficient is calculated, and the local trust index that has passed verification is obtained based on the exemption coefficient.

5. The trusted routing control method for UAV self-organizing networks according to claim 4, characterized in that, The generation of malicious evidence is sent to a trust anchor, which verifies the malicious evidence. The malicious evidence includes an attacker identifier, a whistleblower identifier, a timestamp, an evidence hash, a digital signature, and a crime evidence snapshot. The crime evidence snapshot is a hash snapshot of a tampered data packet or a count record of unforwarded data packets. The verification includes whether the whistleblower's signature is valid and whether the evidence of wrongdoing is old, unprocessed evidence.

6. The trusted routing control method for UAV self-organizing networks according to claim 5, characterized in that, The process of deducting global reputation points from the reported individual includes: The penalty value of the global reputation deduction operation is a non-linear function of the number of historical violations of the reported individual, and the penalty value increases as the number of historical violations increases.

7. The trusted routing control method for UAV self-organizing networks according to claim 6, characterized in that, The step of marking the node status of the reported party as malicious and triggering an on-chain alert event further includes: The trust anchor will add the identifier of the reported malicious node to the network-wide blacklist and broadcast the blacklist information to all nodes in the drone self-organizing network.

8. A trusted routing control system for unmanned aerial vehicle (UAV) self-organizing networks, characterized in that, include: The data acquisition module is used by drone nodes to perform local monitoring of their neighboring nodes. The processing module is used to generate a malicious proof and send it to the trust anchor for neighboring nodes whose local trust index falls below a preset threshold or which have engaged in data tampering. The malicious proof includes information about the whistleblower and the reported party. Based on the whistleblower's information, the module retrieves the whistleblower's on-chain global reputation value. If the on-chain global reputation value is higher than a preset reputation threshold, the module performs a global reputation deduction operation on the reported party. Based on the reported party's information, the module retrieves the reported party's historical cumulative valid whistleblower count. If the historical cumulative valid whistleblower count reaches a preset consensus threshold, the module performs a global reputation deduction operation on the reported party. The execution module is used to update the global reputation value of the reported party by performing a global reputation deduction operation. If the updated global reputation value of the reported party is lower than the malicious node determination threshold, the node status of the reported party is marked as malicious and an on-chain alarm event is triggered.

9. An electronic device, characterized in that, The method includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the method described in any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the method described in any one of claims 1 to 7.

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