Robust communication resource allocation method for multi-IRS-assisted USV security data unloading

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a robust communication resource allocation method for multi-IRS auxiliary USV security data unloading. The method comprises the following steps: constructing a multi-IRS-assisted USV-UAV network data unloading system model; calculating a USV average information age based on a multi-IRS-assisted USV-UAV network data unloading system model; according to the USV average information age, constructing a minimum average information age optimization problem by taking a CUAV calculation time delay constraint, an edge server resource constraint, a service scheduling constraint, a UIRS trajectory constraint, a safety rate constraint, a phase parameter constraint and an error range constraint as constraint conditions; solving a minimum average information age optimization problem to obtain a communication resource allocation scheme; according to the invention, communication security can be guaranteed, system information age can be effectively reduced, and data timeliness and system robustness in a multi-task continuous scene can be improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a robust communication resource allocation method for multi-IRS-assisted USV secure data offloading. Background Technology

[0002] With the rapid development of IoT technology and the continuous growth of the marine economy, applications such as marine environmental monitoring, maritime rescue, and maritime patrol are placing higher demands on data collection, transmission, and processing capabilities. Traditional maritime operations mainly rely on manual labor, which suffers from low efficiency, high risk, and slow response times, making it difficult to meet the real-time and reliability requirements of complex marine environments.

[0003] Unmanned surface vehicles (USVs) can carry various sensors to perceive and collect information about the surrounding water surface and underwater environment. They offer advantages such as flexible deployment and low operating costs, and have been widely used in surveying, search and rescue, and patrol scenarios. However, limited by their navigation range and communication and computing capabilities, USVs are typically suitable for data collection and operational tasks within a small area of ​​the water surface. Unmanned aerial vehicles (UAVs), on the other hand, are widely used in emergency communication, disaster relief, and environmental monitoring due to their high mobility, wide coverage, and convenient deployment, making them suitable for large-scale aerial operations. Therefore, the collaborative work of USVs and UAVs can achieve joint acquisition of multi-dimensional data from the air and sea surface, helping to improve the comprehensiveness of the system's perception of the marine environment and possessing significant engineering application value.

[0004] However, in real-world maritime applications, USVs and UAVs typically need to continuously collect multi-source environmental information, with tasks characterized by continuous generation, concurrent arrival, and significant priority differences. Existing research often models the data offloading process as a single or periodic task, failing to capture the timeliness degradation caused by data accumulation in the system under multi-task scenarios. This approach cannot effectively reflect the urgent need for real-time data in emergency rescue and on-site monitoring scenarios. Furthermore, the open maritime wireless communication environment makes data vulnerable to security threats such as illegal eavesdropping during transmission, especially in applications involving rescue information, monitoring data, and sensitive environmental information, where communication security issues are particularly prominent. However, most existing USV-UAV collaborative offloading studies assume secure and reliable communication links, failing to adequately consider data offloading security issues in the presence of eavesdropping users. Simultaneously, in the complex electromagnetic environment and dynamic topology at sea, terminals struggle to accurately obtain precise channel state information from eavesdropping links, and the location of eavesdroppers and channel parameters are often uncertain. Some existing studies model based on perfect channel state information, which differs from real-world application scenarios and lacks systematic research on secure data offloading under conditions of channel uncertainty. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a robust communication resource allocation method for multi-IRS-assisted USV secure data offloading, the method comprising:

[0006] S1: Construct a USV-UAV network data offloading system model with multiple IRS assistance;

[0007] S2: Calculate the average information age of USVs based on a USV-UAV network data offloading system model assisted by multiple IRSs;

[0008] S3: Based on the average information age of USV, construct an optimization problem to minimize the average information age with constraints such as CUAV calculation latency, edge server resource constraints, service scheduling constraints, UIRS trajectory constraints, security rate constraints, phase parameter constraints, and error range constraints.

[0009] S4: Solve the optimization problem of minimizing the average information age to obtain the communication resource allocation scheme.

[0010] A preferred multi-IRS-assisted USV-UAV network data offloading system model includes: M USVs, L UAVs (Unmanned Aerial Vehicles) equipped with IRS providing reflective links, one eavesdropping user Eve, one surveillance UAV (Unmanned Aerial Vehicle), and one base station equipped with a MEC server; the IRS is composed of... A linear uniform array of reflective elements with total internal reflection; a fixed UIRS height; USVs and CUAVs offload all tasks to the MEC server for computation; M USVs are divided into L USV clusters, each cluster is served by a corresponding UIRS, and within a USV cluster, only one USV can offload data in a time slot.

[0011] Preferably, the formula for calculating the average information age of USV is expressed as follows:

[0012]

[0013]

[0014] in, This indicates the average age of USV information. Indicates the number of USVs. Indicates the number of time slots. Indicates the first A set of USV clusters Indicates the number of USV clusters. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. This indicates the data packet indicator variable for the m-th USV within time slot t. Indicates whether the m-th USV is used by the first USV in time slot t. IRS scheduling. This represents the system time of the m-th USV task within time slot t. This represents the information age of the m-th USV at time slot t.

[0015] Furthermore, the data packet indicator variable is represented as:

[0016]

[0017] in, This indicates the data packet indicator variable for the m-th USV in time slot t+1. This represents the task indicator variable for the m-th USV within time slot t. This represents the computation delay of the m-th USV. Indicates the length of a time slot. This represents the task indicator variable for the m-th USV in time slot t+1.

[0018] Preferably, the CUAV computation delay constraint is expressed as follows:

[0019]

[0020]

[0021] in, This represents the computational delay of the CUAV terminal within time slot t. Indicates the first The amount of data that a CUAV terminal under a USV cluster needs to compute within time slot t. Indicates the first The computing resources required for a CUAV terminal under a USV cluster to complete a task within time slot t. Indicates the transmission rate of CUAV at time slot t. This represents the computing resources allocated to CUAV in time slot t. This indicates the maximum computational latency allowed by CUAV.

[0022] Preferably, the UIRS trajectory constraint is expressed as:

[0023]

[0024]

[0025]

[0026] in, Indicates the first The position of each UIRS in the (t+1)th time slot Indicates the first The position of each UIRS in time slot t Indicates the first The position of each UIRS in time slot t Indicates the length of a time slot. This indicates the maximum flight speed of UIRS. Indicates the number of UIRS. Indicates the number of time slots. This represents the minimum distance between UIRS. Indicates the first The x-coordinate of the position of each UIRS in time slot t Indicates the first The ordinate of the position of each UIRS in time slot t Indicates the first The position and height of each UIRS in time slot t.

[0027] Preferably, the safe rate constraint is expressed as:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] in, This indicates the transmission rate from the USV to the base station. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This indicates the transmission rate from the USV to the eavesdropping user. Indicates the minimum safe transmission rate. Indicates the number of USVs. This indicates the transmission rate from CUAV to the base station. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link. This indicates the transmission rate from CUAV to the eavesdropper. Indicates whether the m-th USV is used by the first USV in time slot t. UIRS scheduling Indicates system bandwidth. This represents the transmit power of the m-th USV. This represents the channel gain of the direct link between the m-th USV and the base station. This represents the concatenated channel from the USV to the BS in the t-th time slot. Indicates noise power. This represents the direct link from the m-th USV in time slot t to the eavesdropper. This represents the cascaded channel from the m-th USV in time slot t to the eavesdropper. This indicates the transmit power of CUAV. This represents the channel gain between CUAV and the base station in time slot t. This represents the channel gain between CUAV and the eavesdropper in time slot t.

[0035] Preferably, the error range constraint is expressed as follows:

[0036]

[0037]

[0038]

[0039]

[0040] in, Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper concatenated channel. This represents the channel gain estimation error between the m-th USV and the eavesdropper's direct link. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. The set of uncertainties representing the channel gain of the CUAV and the eavesdropper's direct link. This represents the estimated channel gain of the USV and the eavesdropper concatenated channel. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the estimated channel gain of the direct link between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This represents the estimated channel gain of the direct link between CUAV and the eavesdropper. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link.

[0041] The preferred optimization problem, minimizing the average information age, is expressed as:

[0042]

[0043] in, Indicates the number of USVs. Indicates the number of time slots. Indicates the first A set of USV clusters Indicates the number of USV clusters. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. Indicates whether the m-th USV is used by the first USV in time slot t. UIRS scheduling Indicates the first The reflection matrix of UIRS in time slot t, Indicates the first The position of each UIRS in time slot t Indicates the first The first UIRS Phase angle of each reflecting element Indicates the number of reflective elements in the IRS. Indicates the first The number of USVs in a set of USV clusters Indicates the first A set of USV clusters Indicates the first The position of each UIRS in the (t+1)th time slot Indicates the first The position of each UIRS in time slot t Indicates the length of a time slot. This indicates the maximum flight speed of UIRS. This represents the minimum distance between UIRS. This indicates the transmission rate from the USV to the base station. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the estimated channel gain of the direct link between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This indicates the transmission rate from the USV to the eavesdropping user. Indicates the minimum transmission rate. This indicates the transmission rate from CUAV to the base station. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link. This indicates the transmission rate from CUAV to the eavesdropper; Indicates the first The computing resources allocated to terminal i by the time-slot edge server This represents the computing resources of the edge server. It represents CUAV, Indicates USV; This represents the computational delay of the CUAV terminal within time slot t. This indicates the maximum allowable computational latency for CUAV. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper concatenated channel. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the set of uncertainties in the channel gain of the CUAV and the eavesdropper's direct link.

[0044] The beneficial effects of this invention are as follows:

[0045] This invention comprehensively utilizes the aerial data acquisition capabilities of the CUAV and the near-shore inspection capabilities of the USV, combined with the enhancement effect of the IRS on the wireless propagation environment, to construct a three-dimensional and efficient data acquisition and transmission system capable of adapting to maritime application scenarios with multiple tasks occurring continuously. In the presence of eavesdropping users, security rate constraints are introduced to ensure the security of the data offloading process.

[0046] Furthermore, this invention aims to minimize the average information age of USVs and jointly optimizes communication, computing, and service scheduling under multiple resource constraints. This effectively reduces the system's information age, improves the real-time performance and processing efficiency of offloaded data, and thus enhances the system's responsiveness to unexpected events. While ensuring communication security, this method also enhances the system's robustness and stability in complex marine environments, contributing to improved utilization efficiency of edge computing resources.

[0047] This invention is applicable to various scenarios such as maritime rescue, maritime patrol, marine environmental monitoring, emergency response to maritime accidents, and nearshore resource exploration, and has strong engineering practicality and promotional value. Attached Figure Description

[0048] Figure 1 This is a flowchart of the robust communication resource allocation method for multi-IRS assisted USV secure data offloading in this invention;

[0049] Figure 2 This is a schematic diagram of the multi-IRS-assisted USV-UAV network data offloading system model in this invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] To address the challenge of simultaneously ensuring data timeliness and communication security during data offloading in existing USV-UAV networks, this invention proposes a robust communication resource allocation method for USV secure data offloading assisted by multiple IRSs. This method aims to meet the real-time and reliability requirements of data transmission in time-sensitive application scenarios such as maritime emergency rescue and operational monitoring. Figure 1 As shown, the method includes the following:

[0052] S1: Construct a USV-UAV network data offloading system model with multiple IRS assistance.

[0053] like Figure 2 As shown, a multi-IRS-assisted USV-UAV network data offloading system model is constructed, including: One USV, A UIRS (Underlying Microsystems Reflection System) with an IRS providing a reflection link, an eavesdropping device for user Eve, a surveillance drone CUAV (Uniformly Accessible Vehicle), and a base station equipped with a MEC (Multi-access Edge Computing) server; the USV and CUAV continuously collect data, but due to their limited computing resources, performing all computations locally would lead to a rapid increase in the age of the information. Therefore, the USV and CUAV can choose to offload all tasks to the edge server via the UIRS for computation.

[0054] Assuming the IRS is... If a linear uniform array of n reflective elements is formed, and the reflection is total internal reflection, then the nth element... The reflection matrix of each IRS is represented as follows: . In the formula: Indicates the first The first UIRS The phase angle of each reflecting element must satisfy... To ensure all USVs receive better service, UIRS need to continuously adjust their positions. This invention assumes all UIRS are at a fixed height. Flight. Establish a Cartesian three-dimensional coordinate system, and represent the coordinates of USV, base station, eavesdropper, UIRS, and CUAV as follows: , , , , .

[0055] To avoid interference between devices, different USV clusters communicate using Orthogonal Frequency Division Multiple Access (OFDMA), which divides the total bandwidth into L sub-bandwidths, each allocated to a corresponding USV cluster. Assume the UIRS service time is divided into... If there are 1 time slot, then the length of each time slot is 1. A single UIRS provides service to USVs only within a certain range. Multiple UIRSs need to ensure that all USVs can receive service; therefore, a binary variable is introduced. Indicates whether the m-th USV is in time slot t. Each UIRS serves. This indicates that the m-th USV is generated by the first USV in time slot t. Served by a UIRS, otherwise To ensure link quality within limited bandwidth resources, it is stipulated that only one USV can offload data per time slot within a USV cluster. Therefore, the relationship between binary variables and scheduling can be constrained. express.

[0056] To reduce the complexity of problem planning, the K-means clustering algorithm can be applied to divide the USV devices into L USV clusters, with each cluster being served by a corresponding UIRS. This represents the set of USV devices in the USV cluster to which the m-th USV belongs, and the number of USVs in each cluster is . Therefore, the relationship between the binary variable and the scheduling can be simplified as follows:

[0057]

[0058] In this invention, considering that terminal tasks are frequently generated and indivisible, continuing to perform calculations locally would not only increase the terminal's computational burden but also require complex optimizations regarding offloading ratios and local scheduling. Therefore, this invention, while ensuring task integrity, chooses to upload all tasks to an edge server for unified processing to reduce the terminal's computational pressure. Furthermore, since the computation results are typically small, the time for result transmission is usually ignored; therefore, the task completion time consists of data upload time and edge server computation time. Define variables. This is used to distinguish between CUAV and USV. When This indicates the CUAV terminal. The time slot indicates the USV terminal. The task is in the time slot. The time required to complete the calculation is expressed as:

[0059]

[0060] In the formula: the first term on the right-hand side represents the time required for data upload, and the second term represents the time required for edge server computation. Indicates the method for unloading terminal tasks. Indicates the uninstallation method for CUAV. Indicates the method of unloading the USV; Indicates the first The amount of data that each time-slot terminal needs to calculate. Indicates that the terminal has completed Calculate the number of CPU cycles required. This indicates that the terminal has completed the task. Required computing resources; Indicates the first The computing resources allocated to terminal i by the edge server in each time slot; since the amount of data in the calculation result is often much smaller than the amount of data in the calculation, the time it takes for the calculation result to be transmitted back to the terminal is ignored.

[0061] S2: Calculate the average information age of USVs based on the USV-UAV network data offloading system model with multi-IRS assistance.

[0062] To characterize the freshness of transmitted information, information age is used as a performance metric. Since the channel conditions between the CUAV and the base station are better than those between the UAV and the base station, this invention focuses on optimizing the information age of the USV; the UAV only needs to meet computational latency requirements. It is assumed that the task of the m-th USV is probabilistically... Reaching the edge server, i.e. Among them, binary variables This indicates whether the USV generates a task in time slot t. This indicates that a new task has been generated; otherwise, it indicates that no new task has been generated. The process of generating tasks for all USVs is independent.

[0063] Introducing variables This represents the system time of a task. If a new task is generated in the current time slot, it means the new task can replace the old task, and the task's system time is the latest time, which is calculated from 0. Otherwise, the task's system time will increase as time progresses.

[0064]

[0065] Introducing binary variables This indicates whether the m-th USV has available data packets in the queue of time slot t. Since the latest data packet carries the latest information, only the latest data is retained in the queue, meaning the queue length is 1. If the task of the USV in the previous time slot was successfully computed, and no new task is generated in the current time slot, then in the current time slot... If a new computational task is generated in the current time slot, then ,otherwise This includes: (1) There were data packets in the previous time slot but the calculation failed. (1) If no new data packets are generated in the current time slot, the value remains 1; (2) If no data packets were generated in the previous time slot. If there are no data packets in the current time slot, the value remains 0; that is:

[0066]

[0067] The above expressions are combined to represent an indicator variable for the available data packets in each time slot:

[0068]

[0069] If a data packet exists within time slot t, and the data is successfully computed within that time slot, then the terminal's information age decreases when time slot t ends; otherwise, the information age increases over time. The information age expression for the m-th USV can be written as:

[0070]

[0071] The above expression can be rearranged as follows:

[0072]

[0073] In the set The above expression can be further written as:

[0074]

[0075] Therefore, the average information age of all USVs in the system can be expressed as:

[0076]

[0077] S3: Based on the average information age of USV, construct an optimization problem to minimize the average information age with constraints such as CUAV computation latency, edge server resource constraints, service scheduling constraints, UIRS trajectory constraints, security rate constraints, phase parameter constraints, and error range constraints.

[0078] Define USV and the first The UIRS, the The UIRS and base station, the first The channel gain between UIRS and the eavesdropper, CUAV and the base station, and CUAV and the eavesdropper is... , , , , In the time slot Within the network, the links between CUAV and the base station, and between CUAV and the eavesdropper, are point-to-point line-of-sight links. The channel gain is distance-dependent, expressed as... , In the formula: This represents the channel power gain at a reference distance of 1m. Indicates the distance between CUAV and the base station. This represents the path loss exponent. Based on the channel gain expression above, the transmission rate from the CUAV to the base station and the eavesdropper can be determined as follows: , In the formula: Indicates system bandwidth. Indicates the drone's transmission power. Indicates noise power.

[0079] The CUAV computation delay constraint is:

[0080]

[0081] Edge server resource constraints:

[0082]

[0083]

[0084] Service scheduling constraints:

[0085]

[0086] UIRS trajectory constraints:

[0087] No. A UIRS in a time slot The position can be represented as The positional change of the drone within each time slot is negligible. If the drone's maximum flight speed is... Then the flight trajectory of the drone should satisfy Furthermore, to avoid collisions between different drones, it is also necessary to meet the following requirements. .

[0088] Safety rate constraints:

[0089] There is an obstacle blocking the path between the USV and the base station, and the channel consists of a portion of direct links and a portion of reflected links. Therefore, the distance between the m-th USV and the L-th UIRS is... ,therefore .in, This represents the channel power gain at a reference distance of 1m. This represents the path loss index. The LOS component between USV and UIRS is represented as , This represents the cosine of the angle of arrival of the UIRS. Similarly, it can be seen that... , .in, and Indicates the first The distance from the UIRS to the base station and the eavesdropper , , indicating the first The LOS component between the UIRS and the base station and the eavesdropper, and , This represents the cosine value of the departure angle of UIRS.

[0090] In the time slot Within this context, the cascaded channel from USV to BS can be defined as follows: Then the transmission rate from USV to the base station can be expressed as: Similarly, the cascaded channel from the USV to the eavesdropper can be represented as... The transmission rate from the USV to the eavesdropping user can then be expressed as: ,in This represents the direct link from the m-th USV to the eavesdropper.

[0091] Since the location of eavesdroppers is often difficult to obtain accurately, the link associated with the eavesdropper is established as an imperfect CSI (Channel Sounding System). Based on the channel modeling described above, an imperfect CSI error model for the channel is considered. Specifically, the channel associated with the eavesdropper is re-represented using a spherically bounded uncertainty model as follows:

[0092]

[0093]

[0094]

[0095] in, Let represent the channel gain estimate of the concatenated channel between the m-th USV and the eavesdropper. This represents the estimated channel gain of the direct link between the USV and the eavesdropper. This represents the estimated channel gain of the direct link between CUAV and the eavesdropper. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the estimated channel gain of the direct link between the USV and the eavesdropper. The estimation error represents the channel gain estimation error of the CUAV and the eavesdropper's direct link. The m-th element represents the set of uncertainties in the channel gain of the USV and the eavesdropper concatenated channel. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the set of uncertainties in the channel gain of the CUAV and the eavesdropper's direct link.

[0096] Therefore, the security rate should be such that it does not fall below the minimum secure transmission threshold even under the worst-case eavesdropping conditions, that is:

[0097]

[0098]

[0099] Phase parameter constraints:

[0100]

[0101] Error range constraints:

[0102]

[0103] Under the premise of ensuring the computational delay constraint of the CUAV mission, the optimization objective is to minimize the average information age of the unmanned surface vessel, thereby ensuring the timeliness of the data. Therefore, the optimization problem of minimizing the average information age during the data unloading process can be expressed as:

[0104]

[0105] in, Indicates the number of USVs. Indicates the number of time slots. Indicates the first A set of USV clusters Indicates the number of USV clusters. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. Indicates whether the m-th USV is used by the first USV in time slot t. UIRS scheduling Indicates the first The reflection matrix of UIRS in time slot t, Indicates the first The position of each UIRS in time slot t Indicates the first The first UIRS Phase angle of each reflecting element Indicates the number of reflective elements in the IRS. Indicates the first The number of USVs in a set of USV clusters Indicates the first A set of USV clusters Indicates the first The position of each UIRS in the (t+1)th time slot Indicates the first The position of each UIRS in time slot t Indicates the length of a time slot. This indicates the maximum flight speed of UIRS. This represents the minimum distance between UIRS. This indicates the transmission rate from the USV to the base station. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the USV and the eavesdropper concatenated channel. This represents the estimated channel gain of the direct link between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the direct link between the USV and the eavesdropper. This indicates the transmission rate from the USV to the eavesdropping user. Indicates the minimum transmission rate. This indicates the transmission rate from CUAV to the base station. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link. This indicates the transmission rate from CUAV to the eavesdropper; Indicates the first The computing resources allocated to terminal i by the time-slot edge server This represents the computing resources of the edge server. It represents CUAV, Indicates USV; This represents the computational delay of the CUAV terminal within time slot t. This indicates the maximum allowable computational latency for CUAV. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper concatenated channel. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the set of uncertainties in the channel gain of the CUAV and the eavesdropper's direct link.

[0106] S4: Solve the optimization problem of minimizing the average information age to obtain the communication resource allocation scheme.

[0107] (1) To address the channel uncertainty in the eavesdropping link, an imperfect channel state information modeling method is adopted to describe the uncertainty of the eavesdropping channel as a bounded set of uncertainties, and the security rate constraint is converted into a deterministic robust constraint through the uncertainty equivalent transformation method.

[0108] (2) Since the optimization problem has non-convexity and coupling relationship between UIRS flight trajectory, IRS reflection matrix, service scheduling variables and edge server computing resource allocation variables, the alternating optimization method is adopted to decompose the original optimization problem into multiple sub-problems and solve them iteratively. These sub-problems include: under the condition of fixed IRS reflection matrix and UIRS flight trajectory, jointly optimizing the service scheduling variables between USV and UIRS and the computing resource allocation of edge servers; under the condition of fixed service scheduling variables, edge server computing resource allocation and UIRS flight trajectory, optimizing the IRS reflection matrix; and under the condition of fixed service scheduling variables, edge server computing resource allocation and IRS reflection matrix, optimizing the UIRS flight trajectory.

[0109] (3) For the non-convex objective function and non-convex constraints in the above sub-problems, the non-convex optimization problem is transformed into a convex optimization problem by using continuous convex approximation, semi-positive definite relaxation or equivalent convexification methods.

[0110] (4) Use convex optimization tools to iteratively solve each convex optimization subproblem until convergence, and obtain the UIRS flight trajectory, IRS reflection matrix, service scheduling variables and edge server computing resource allocation scheme, thereby obtaining the secure and robust communication resource allocation result that minimizes the average information age of USV.

[0111] In summary, addressing the shortcomings of existing research that fails to adequately consider the collaborative data acquisition and offloading tasks performed by USVs and UAVs, and that neglects data timeliness and communication security in scenarios with continuous task generation, this invention constructs a USV-UAV collaborative communication and computing model suitable for large-scale, multi-task scenarios. This method, based on the introduction of multiple IRSs to assist USVs in data offloading, fully considers the collaborative effect of camera-equipped UAVs performing aerial surveillance and computing tasks. Through joint optimization of IRS reflection matrices, UIRS trajectories, edge computing resources, and service scheduling relationships between USVs and IRSs, a robust optimization model is established under the uncertain condition that eavesdropper channel state information is difficult to obtain accurately. This model aims to minimize the average information age of USVs, comprehensively satisfying constraints such as communication security rate constraints, UAV computing latency constraints, and server resource constraints. The resource allocation method proposed in this invention can effectively reduce system information age while ensuring communication security, improving data timeliness and system robustness in multi-task continuous scenarios. It is suitable for application scenarios with high real-time and security requirements, such as maritime emergency rescue and monitoring inspection.

[0112] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A robust communication resource allocation method for multi-IRS-assisted USV secure data offloading, characterized in that, include: S1: Construct a USV-UAV network data offloading system model with multiple IRS assistance; S2: Calculate the average information age of USVs based on a USV-UAV network data offloading system model assisted by multiple IRSs; S3: Based on the average information age of USV, construct an optimization problem to minimize the average information age with constraints such as CUAV calculation latency, edge server resource constraints, service scheduling constraints, UIRS trajectory constraints, security rate constraints, phase parameter constraints, and error range constraints. S4: Solve the optimization problem of minimizing the average information age to obtain the communication resource allocation scheme.

2. The robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The multi-IRS-assisted USV-UAV network data offloading system model includes: M USVs, L UAVs (Unmanned Aerial Vehicles) equipped with IRS providing reflective links, one eavesdropping user Eve, one surveillance UAV (Unmanned Aerial Vehicle), and one base station equipped with a MEC server; the IRS is composed of... A linear uniform array of reflective elements with total internal reflection; a fixed UIRS height; USVs and CUAVs offload all tasks to the MEC server for computation; M USVs are divided into L USV clusters, each cluster is served by a corresponding UIRS, and within a USV cluster, only one USV can offload data in a time slot.

3. The robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The formula for calculating the average information age of USV is expressed as follows: ； ； in, This indicates the average age of USV information. Indicates the number of USVs. Indicates the number of time slots. Indicates the first A set of USV clusters Indicates the number of USV clusters. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. This indicates the data packet indicator variable for the m-th USV within time slot t. Indicates whether the m-th USV is used by the first USV in time slot t. IRS scheduling. This represents the system time of the m-th USV task within time slot t. This represents the information age of the m-th USV at time slot t.

4. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 3, characterized in that, The data packet indicator variable is represented as: ； in, This indicates the data packet indicator variable for the m-th USV in time slot t+1. This represents the task indicator variable for the m-th USV within time slot t. This represents the computation delay of the m-th USV. Indicates the length of a time slot. This represents the task indicator variable for the m-th USV in time slot t+1.

5. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The CUAV computation delay constraint is expressed as: ； ； in, This represents the computational delay of the CUAV terminal within time slot t. Indicates the first The amount of data that a CUAV terminal under a USV cluster needs to compute within time slot t. Indicates the first The computing resources required for a CUAV terminal under a USV cluster to complete a task within time slot t. Indicates the transmission rate of CUAV in time slot t. This represents the computing resources allocated to CUAV in time slot t. This indicates the maximum computational latency allowed by CUAV.

6. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, UIRS trajectory constraints are represented as follows: ； ； ； in, Indicates the first The position of each UIRS in the (t+1)th time slot Indicates the first The position of each UIRS in time slot t Indicates the first The position of each UIRS in time slot t Indicates the length of a time slot. This indicates the maximum flight speed of UIRS. Indicates the number of UIRS. Indicates the number of time slots. This represents the minimum distance between UIRS. Indicates the first The x-coordinate of the position of each UIRS in time slot t Indicates the first The ordinate of the position of each UIRS in time slot t Indicates the first The position and height of each UIRS in time slot t.

7. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The safe rate constraint is expressed as: ； ； ； ； ； ； in, This indicates the transmission rate from the USV to the base station. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This indicates the transmission rate from the USV to the eavesdropping user. Indicates the minimum safe transmission rate. Indicates the number of USVs. This indicates the transmission rate from CUAV to the base station. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link. This indicates the transmission rate from CUAV to the eavesdropper. Indicates whether the m-th USV is used by the first USV in time slot t. UIRS scheduling Indicates system bandwidth. This represents the transmit power of the m-th USV. This represents the channel gain of the direct link between the m-th USV and the base station. This represents the concatenated channel from the USV to the BS in the t-th time slot. Indicates noise power. This represents the direct link from the m-th USV in time slot t to the eavesdropper. This represents the cascaded channel from the m-th USV in time slot t to the eavesdropper. This indicates the transmit power of CUAV. This represents the channel gain between CUAV and the base station in time slot t. This represents the channel gain between CUAV and the eavesdropper in time slot t.

8. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The error range constraint is expressed as: ； ； ； ； in, Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper concatenated channel. This represents the channel gain estimation error between the m-th USV and the eavesdropper's direct link. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. The set of uncertainties representing the channel gain of the CUAV and the eavesdropper's direct link. This represents the estimated channel gain of the USV and the eavesdropper concatenated channel. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the estimated channel gain of the direct link between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This represents the estimated channel gain of the direct link between CUAV and the eavesdropper. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link.

9. A robust communication resource allocation method for multi-IRS assisted USV secure data offloading according to claim 1, characterized in that, The optimization problem of minimizing the average information age is expressed as: ； in, Indicates the number of USVs. Indicates the number of time slots. Indicates the first A set of USV clusters Indicates the number of USV clusters. Indicates the first The information age of the m-th USV in a USV cluster within time slot t. Indicates whether the m-th USV is used by the first USV in time slot t. UIRS scheduling Indicates the first The reflection matrix of UIRS in time slot t, Indicates the first The position of each UIRS in time slot t Indicates the first The first UIRS Phase angle of each reflecting element Indicates the number of reflective elements in the IRS. Indicates the first The number of USVs in a set of USV clusters Indicates the first A set of USV clusters Indicates the first The position of each UIRS in the (t+1)th time slot Indicates the first The position of each UIRS in time slot t Indicates the length of a time slot. This indicates the maximum flight speed of UIRS. This represents the minimum distance between UIRS. This indicates the transmission rate from the USV to the base station. Let represent the channel gain estimation error of the concatenated channel between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error for the m-th USV and the eavesdropper concatenated channel. This represents the estimated channel gain of the direct link between the m-th USV and the eavesdropper. This represents the maximum value of the channel gain estimation error between the m-th USV and the eavesdropper's direct link. This indicates the transmission rate from the USV to the eavesdropping user. Indicates the minimum transmission rate. This indicates the transmission rate from CUAV to the base station. This represents the channel gain estimation error between CUAV and the eavesdropper's direct link. This represents the maximum value of the channel gain estimation error for CUAV and the eavesdropper's direct link. This indicates the transmission rate from CUAV to the eavesdropper; Indicates the first The computing resources allocated to terminal i by the time-slot edge server This represents the computing resources of the edge server. It represents CUAV, Indicates USV; This represents the computational delay of the CUAV terminal within time slot t. This indicates the maximum allowable computational latency for CUAV. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper concatenated channel. Let represent the set of uncertainties in the channel gain of the m-th USV and the eavesdropper's direct link channel. This represents the set of uncertainties in the channel gain of the CUAV and the eavesdropper's direct link.

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