Block chain assisted mobile edge computing secure unloading method for wireless communication network

Abstract

The invention discloses a wireless communication network-oriented block chain-assisted mobile edge computing secure unloading method, which comprises the following steps of: constructing a user task unloading model in a wireless communication network; the method comprises the following steps: deploying a distributed account book system among a plurality of mobile edge computing servers, constructing node capability parameters based on residual computing resources and residual communication bandwidths of the servers, and electing a consensus master node in a next period to perform consensus communication; performing joint optimization on unloading decision information, bandwidth allocation and computing resource allocation based on a user task unloading model and a consensus mechanism; and completing calculation task processing according to a joint optimization result and returning to the user terminal. The invention relates to the technical field of wireless communication, and the method comprises the steps: introducing a distributed account book between mobile edge computing servers, and electing a consensus master node based on a consensus mechanism; while the security of wireless data transmission is ensured, efficient unloading of the calculation task is realized, and the method is suitable for a new generation wireless communication network scene.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to a secure offloading method for blockchain-assisted mobile edge computing for wireless communication networks. Background Technology

[0002] With the rapid development of mobile communication technology and the widespread adoption of emerging applications such as the Internet of Things (IoT), augmented reality, autonomous driving, and the Industrial Internet, terminal devices have an increasingly urgent need for low-latency, high-reliability, and energy-efficient computing services. However, limited by size, power consumption, and computing power, many smart terminals struggle to independently complete complex computing tasks. To address this, Mobile Edge Computing (MEC) has emerged—by offloading computing, storage, and network resources to the network edge, MEC can provide users with localized computing offloading services, significantly reducing transmission latency and core network load, and improving user experience.

[0003] In a typical MEC architecture, user terminals can offload some or all of their computing tasks to one or more edge servers for processing. To optimize system performance, existing research typically focuses on joint optimization of task offloading decisions, wireless resource allocation (such as bandwidth), and computing resource scheduling, with objectives including minimizing task completion latency, system energy consumption, or maximizing quality of service. However, such optimizations often rely on a centralized controller or the assumption that all parties involved are completely trustworthy, which faces significant challenges in real-world open, heterogeneous, and dynamically changing wireless environments.

[0004] First, in collaborative scenarios involving multiple users and multiple MEC servers, task unloading and resource allocation involve multi-party collaboration. Without a trusted mechanism, malicious or faulty nodes may report false resource statuses (such as remaining computing power or channel quality), causing optimization results to deviate from the true optimal level, and even triggering service denial or resource preemption. Second, if critical information during task execution (such as unloading requests, resource allocation schemes, and task status) is recorded solely by the central node, it is easily susceptible to single points of failure or tampering, making it difficult to meet the auditability and data integrity requirements of high-security scenarios (such as connected vehicles and industrial control). Furthermore, when traditional consensus mechanisms (such as PoW and PBFT) are directly applied to resource-constrained edge environments, they suffer from high communication overhead, high response latency, and excessive energy consumption, making it difficult to support the real-time requirements of MEC task scheduling.

[0005] In recent years, blockchain technology, due to its decentralized, immutable, and traceable characteristics, has been explored for enhancing the security and trustworthiness of edge computing. Existing solutions have attempted to introduce blockchain into MEC systems to record task transactions or verify node identities. However, most works still treat blockchain as an independent evidence storage layer, failing to deeply couple it with resource-aware task offloading decisions. Furthermore, consensus mechanism designs often ignore the heterogeneity of edge nodes and dynamic load changes, leading to low consensus efficiency and becoming a system bottleneck.

[0006] Therefore, proposing a method that can both ensure the security and reliability of the task unloading process and efficiently coordinate multiple MEC servers for resource optimization is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of the above problems, this invention is proposed to provide a blockchain-assisted mobile edge computing security offloading method for wireless communication networks that overcomes or at least partially solves the above problems. By introducing a distributed ledger among mobile edge computing servers and electing a consensus master node based on a consensus mechanism, this invention achieves efficient offloading of computing tasks while ensuring the security of wireless data transmission, and is suitable for next-generation wireless communication network scenarios.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] This invention provides a method for secure offloading of blockchain-assisted mobile edge computing for wireless communication networks, comprising the following steps: S1. In a wireless communication network, a user task offloading model is constructed based on task offloading behavior; the task offloading behavior includes: the user terminal offloading the computing task to be processed to multiple mobile edge computing servers through a wireless link. S2. Deploy a distributed ledger system among the multiple mobile edge computing servers. Each server acts as a ledger node to jointly maintain the distributed ledger. When a task unloading request is received, the unloading request information, wireless transmission status information, resource allocation results, and task execution status are encapsulated into a transaction record. The consensus master node packages the transaction into a block, and after consensus verification, it is written into the distributed ledger for storage in a chain structure. S3. Based on the remaining computing resources and remaining communication bandwidth of each server after completing the current task, construct node capability parameters, and elect the consensus master node for the next cycle for consensus communication according to the node capability parameters. S4. Based on the user task offloading model and consensus mechanism, under the conditions of satisfying wireless communication quality and latency constraints and computing resource limitations, jointly optimize user offloading decision information, server bandwidth allocation and computing resource allocation. S5. Complete the computation task processing based on the joint optimization results, and return the processing results to the corresponding user terminal via wireless link.

[0010] Further, in step S1, during the task unloading behavior, the baseband signal received by the mobile edge computing server It can be expressed by the formula:

[0011] In the task unloading behavior, the user terminal The uplink transmission rate is expressed by the formula:

[0012] in, Indicates user terminal Uplink transmit power, Indicates user terminal Channel coefficients between the mobile edge computing server and the mobile edge computing server Indicates user terminal The signal sent, This represents additive white Gaussian noise; This represents the bandwidth resources allocated by the mobile edge computing server to the user terminal. Indicates noise power.

[0013] Furthermore, in step S1, the user task offloading model is used to characterize the quantitative relationship between transmission delay, computation delay, energy consumption, and resource consumption; it includes a task offloading transmission delay sub-model, a task computation delay sub-model, a user-side transmission energy consumption sub-model, a server-side computation energy consumption sub-model, a server-side total energy consumption sub-model, a user-side total energy consumption model, and a task completion delay sub-model.

[0014] Furthermore, the task offloading transmission delay sub-model is expressed by the following formula:

[0015] The task computation delay sub-model is expressed by the following formula:

[0016] The user-side transmission energy consumption sub-model is expressed by the following formula:

[0017] The server-side computing energy consumption sub-model is expressed by the following formula:

[0018] The total energy consumption sub-model on the server side is expressed by the following formula:

[0019] The user-side total energy consumption sub-model is expressed by the following formula:

[0020] The task completion delay sub-model is expressed by the following formula:

[0021] in, Indicates user terminal Task offloading transmission latency, superscript tx For transmission, an abbreviation is used. Indicates user terminal The amount of task data; Indicates the server-side user terminal Task computation latency, superscript cmp For calculation, an abbreviation is used. Indicates user terminal The computational load of the task Indicates user-side user terminal Transmission energy consumption, Indicates user-side user terminal Energy consumption coefficient; Indicates server-side server Computational energy consumption, Indicates server Calculate the energy consumption coefficient. Indicates allocation to the server Computing resources; Indicates server-side server Total energy consumption; Indicates user-side user terminal Total energy consumption; Indicates user terminal Task completion delay.

[0022] Furthermore, in step S3, the node capability parameters are expressed by the following formula:

[0023] in, Indicates server The remaining computing resources Indicates server The remaining communication bandwidth, superscript res The remaining abbreviations are represented by... and Representing blocks b The generation interval and block size.

[0024] Furthermore, in step S3, a consensus master node for the next cycle is elected based on the node capability parameters for consensus communication, specifically including: At the end of each block generation cycle, each server participates in the dynamic election of the consensus master node based on its currently calculated node capability parameters. A probability selection mechanism based on node capability parameters is adopted to calculate the probability that each server will be selected as the consensus master node in the next cycle; The server with the highest probability is broadcast as the election result to all ledger nodes before the start of the new block cycle, and a new round of consensus communication process is initiated.

[0025] Furthermore, the joint optimization described in step S4 specifically includes: S41. Initialize local decision variables and global coordination variables; the local decision variables include user terminal task offloading ratio, server bandwidth allocation vector, and computing resource allocation vector; the global coordination variables include global consistency variables used to characterize system-level consistency constraints, which are uniformly generated and broadcast by the consensus master node in the distributed ledger system in each iteration. S42. In each iteration, each mobile edge computing server solves the local optimization subproblem based on the global coordination variables recorded in the current block and the latest local decision variable summary information obtained synchronously from other ledger nodes, so as to update its own local decision variables. S43. Each server submits its updated local decision variables to the distributed ledger through transactions; the consensus master node collects the local decision variables of all participating nodes, performs aggregation operations to generate new global consistency variables, and encapsulates them into a consensus message and writes them into a new block; after consensus verification, the block is broadcast to the entire network to complete global state synchronization. S44. Iteratively execute the "local update-consensus aggregation-global synchronization" process until the update relationship between the global consistency variable and the local decision variable reaches the termination condition; then stop the iteration and output the corresponding unloading decision result, bandwidth allocation scheme and computing resource configuration.

[0026] Furthermore, in step S4, the conditions for satisfying wireless communication quality and latency constraints as well as computing resource limitations specifically include: The end-to-end completion latency of each user task shall not exceed the preset maximum tolerable latency; The allocated bandwidth and uplink transmission rate of the channel conditions meet the minimum reliability requirements; The total computing resources allocated to all tasks by each mobile edge computing server shall not exceed the currently available computing power; The total uplink bandwidth allocated to all users shall not exceed the total available bandwidth.

[0027] Furthermore, in step S42, the local decision variable is expressed by the formula:

[0028] in, Indicates server m In the t Local decision variables in +1 iterations t Indicates the number of iterations. Indicates server m The local cost function constructed in the current iteration based on the local task state and resource constraints. Indicates server m The corresponding coordinating variables, Indicates the first t In the next iteration, the server m The corresponding coordinating variables.

[0029] Furthermore, in step S44, the update relationship between the global coordination variable and the local decision variable is represented by the coordination variable, and is expressed by the formula:

[0030]

[0031] in, Indicates the first t In +1 iteration, the server m The corresponding coordinating variables, This represents the penalty parameter in the iterative update. Indicates the first t Globally consistent variables in +1 iterations M This indicates the total number of mobile edge computing servers participating in the parallel processing of the current user task.

[0032] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a blockchain-assisted mobile edge computing security offloading method for wireless communication networks, which has the following beneficial effects: I. Synergistic Improvement of Security and Performance By using blockchain distributed ledger to reliably record the entire offloading process, it effectively resists eavesdropping and spoofing attacks and ensures data security. At the same time, it deeply integrates blockchain consensus mechanism with resource scheduling, avoiding the problem of excessive overhead introduced by security mechanisms in traditional solutions, and achieving a balance between security and offloading efficiency.

[0033] II. Dynamic Lightweight Consensus Mechanism The consensus role is dynamically selected based on the real-time remaining computing and communication resources of the edge server, enabling nodes with sufficient resources to undertake core consensus tasks. Combined with a differentiated bandwidth allocation strategy, consensus latency and communication overhead are significantly reduced, improving the system's adaptability in dynamic edge environments.

[0034] III. Integrated Optimization of Resources and Security A joint optimization model is constructed that couples the state of blockchain consensus resources with task offloading scheduling. Under the premise of satisfying communication and computing constraints, the offloading decision, bandwidth allocation and computing resource allocation are optimized in a coordinated manner to achieve the comprehensive goal of reducing energy consumption and meeting latency requirements at the system level.

[0035] This invention enables secure, efficient, and reliable task offloading in complex wireless environments, providing an effective solution for next-generation edge computing networks. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0037] Figure 1 This is a flowchart of a blockchain-assisted mobile edge computing security offloading method for wireless communication networks provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the urban intelligent vehicle network architecture provided in an embodiment of the present invention. Detailed Implementation

[0038] 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.

[0039] This invention discloses a secure offloading method for blockchain-assisted mobile edge computing for wireless communication networks, referring to... Figure 1 As shown, it includes the following steps: S1. In a wireless communication network, a user task offloading model is constructed based on task offloading behavior; the task offloading behavior includes: the user terminal offloading the computing task to be processed to multiple mobile edge computing servers through a wireless link. S2. Deploy a distributed ledger system among the multiple mobile edge computing servers. Each server acts as a ledger node to jointly maintain the distributed ledger. When a task unloading request is received, the unloading request information, wireless transmission status information, resource allocation results, and task execution status are encapsulated into a transaction record. The consensus master node packages the transaction into a block, and after consensus verification, it is written into the distributed ledger for storage in a chain structure. S3. Based on the remaining computing resources and remaining communication bandwidth of each server after completing the current task, construct node capability parameters, and elect the consensus master node for the next cycle for consensus communication according to the node capability parameters. S4. Based on the user task offloading model and consensus mechanism, under the conditions of satisfying wireless communication quality and latency constraints and computing resource limitations, jointly optimize user offloading decision information, server bandwidth allocation and computing resource allocation. S5. Complete the computation task processing based on the joint optimization results, and return the processing results to the corresponding user terminal via wireless link.

[0040] This embodiment applies to an autonomous driving cooperative perception system in an urban intelligent vehicle-to-everything (V2X) environment. In this scenario, multiple autonomous vehicles, acting as user terminals, travel on urban main roads and need to process high-dimensional perception data from LiDAR and cameras in real time, such as target detection and path prediction, to achieve millisecond-level decision-making. Since the onboard computing unit has limited computing power and is energy-sensitive, in this embodiment, each vehicle offloads its perception tasks to multiple mobile edge computing servers deployed on roadside units via a 5G NR wireless link, in proportion to the total tasks.

[0041] These MEC servers are managed by different operators or traffic management departments, lacking natural trust among them. To ensure the security and fairness of task scheduling, the MEC servers jointly construct a lightweight blockchain network: when a vehicle initiates an unloading request, each MEC node calculates its node capability parameters based on its remaining CPU resources and available backhaul bandwidth, and dynamically elects the consensus master node for this round; subsequently, under the constraints of task end-to-end latency ≤ 50ms, total uplink bandwidth ≤ 100 MHz, and the upper limit of each MEC's ​​computing power, the system determines the optimal unloading ratio, bandwidth allocation, and computing resource configuration through distributed iterative optimization; all decision-making processes and execution states are written to blocks in the form of transactions to ensure immutability.

[0042] Ultimately, vehicles achieve reliable environmental perception results with low latency and high energy efficiency. Meanwhile, the traffic management center can conduct post-event audits of the task execution process based on the blockchain ledger, effectively preventing malicious MEC nodes from falsely reporting resources or denying service, and significantly improving the security and robustness of the intelligent transportation system.

[0043] In the constructed autonomous driving cooperative perception system, refer to Figure 2 The diagram shown illustrates the urban intelligent vehicle network architecture of this embodiment.

[0044] Figure 2 The components and their interactions are as follows: Ground users, represented by blue mobile phone icons, are autonomous vehicles that initiate tasks by offloading high-load perception tasks (such as image recognition and path planning) to nearby mobile edge computing servers via wireless links at a preset ratio. The offloading signal is indicated by a black dashed arrow.

[0045] Mobile edge computing servers, represented by yellow drone icons, are deployed along roads or in the air. They possess computing and communication capabilities, receiving and processing user unloading tasks. These servers collectively form a distributed ledger network, where each node is both a computing service provider and a blockchain ledger node.

[0046] The blockchain system, represented by a chain of blocks, is maintained by all MEC servers and used to record key information such as task unloading requests, resource allocation results, execution status, and computation results. Each transaction is encapsulated as a block and written into the chain structure through a consensus mechanism, ensuring the immutability and traceability of data throughout its entire lifecycle.

[0047] Smart contracts, represented by a green file icon, are deployed on the blockchain and automatically execute resource scheduling strategies and incentive mechanisms. For example, when an MEC node successfully completes a task, the smart contract can trigger payment or reputation updates, ensuring fairness in collaboration.

[0048] Eavesdropping signals emitted by attackers are indicated by red dashed arrows: these represent malicious interference or eavesdropping behavior from themselves or unauthorized servers, such as forging task requests, tampering with resource status, or stealing sensitive data. Figure 2 This signal highlights the security threats facing the system, and the blockchain mechanism is a key protective measure against such attacks—preventing single-point tampering and identity impersonation through decentralized storage and consensus verification.

[0049] The implementation process of this embodiment is described in detail below.

[0050] First, following step S1, a user task offloading model is constructed in the wireless communication network based on task offloading behavior.

[0051] The wireless communication network environment in this embodiment includes multiple user terminals (ground users), base stations, and several mobile edge computing servers. The connection relationships between user terminals, base stations, and mobile edge computing servers represent wireless links and backhaul links, respectively.

[0052] The user terminal is used to generate computing tasks to be processed and upload task data to the base station via a wireless link; however, the user terminal does not participate in the maintenance of the distributed ledger and exists only as a task initiation node.

[0053] The base station is used to provide wireless access services to user terminals and is responsible for the wireless communication connection between user terminals and mobile edge computing servers. The base station forwards the task data uploaded by the user terminal to the corresponding mobile edge computing server and sends the calculation results returned by the mobile edge computing server to the user terminal.

[0054] Next, following step S2, a distributed ledger system is deployed among multiple mobile edge computing servers. These servers are positioned at the edge of the wireless network to receive computing tasks offloaded by user terminals and execute corresponding computational processing. The multiple mobile edge computing servers are interconnected via backhaul links, forming a distributed ledger network. Each mobile edge computing server acts as a ledger node, jointly maintaining the distributed ledger to record task offload requests, resource allocation results, and task execution status information.

[0055] During operation, the user terminal offloads the computing task to at least one mobile edge computing server through the base station. After completing the task computing, the mobile edge computing server returns the computing result to the corresponding user terminal through a wireless link. At the same time, key information in the task offloading and computing process is written into a distributed ledger to achieve trusted management of the task offloading process.

[0056] In this embodiment, ground users send computing task data to be unloaded to the mobile edge computing server via a wireless link. Based on this, a user task unloading model is constructed to characterize the quantitative relationship between transmission latency, computing latency, energy consumption, and resource consumption; specifically, it includes task unloading transmission latency, task computing latency, user-side transmission energy consumption, server-side computing energy consumption, server-side total energy consumption, user-side total energy consumption, and task completion latency.

[0057] When a ground user sends task data to the mobile edge computing server, the baseband signal received by the server can be represented as:

[0058] in, Indicates user terminal Uplink transmit power, Indicates user terminal Channel coefficients between the mobile edge computing server and the mobile edge computing server Indicates user terminal The signal sent, This represents additive white Gaussian noise. In this embodiment, , This represents a set of ground users or user terminals.

[0059] Based on the above signal model, the uplink transmission rate for ground users can be calculated as follows:

[0060] Where B represents the bandwidth resources allocated by the mobile edge computing server to the user terminal. This represents the noise power. This uplink transmission rate is used to calculate the task offload delay.

[0061] When a ground user offloads a task to a mobile edge computing server, the transmission latency can be expressed as:

[0062] in, Indicates the amount of data for the task.

[0063] The computation latency of the mobile edge computing server for offloading tasks can be expressed as:

[0064] in, Indicates user terminal The computational load of the task Indicates allocation to the server Computing resources.

[0065] Ground users The energy consumption generated during task transmission is:

[0066] in, Indicates user-side user terminal The energy consumption coefficient is used to characterize the power amplifier efficiency / circuit loss, etc., and its value can be preset by the equipment parameters. Indicates user uplink transmit power, Indicates user terminal The task offloading transmission delay, its superscript tx This is an abbreviation for transmission.

[0067] The energy consumption generated by the server during task computation is:

[0068] in, Indicates server The calculated energy consumption coefficient can be used to characterize the chip's energy consumption characteristics / effective switching capacitor, etc., and its value can be preset. Indicates allocation to the server Computing resources Indicates the server-side user terminal The task computation latency, its superscript cmp This is an abbreviation for calculation.

[0069] The total energy consumption on the server side and the total energy consumption on the user side are respectively expressed as follows:

[0070]

[0071] in, Indicates server-side server Total energy consumption Indicates user-side user terminal Total energy consumption.

[0072] Ground users The unloading completion delay is expressed by the formula:

[0073] In this embodiment, the distributed ledger system consists of a ledger network composed only of mobile edge computing servers, with each server interconnected as a ledger node via a backhaul link; user terminals do not participate in ledger maintenance, but only submit task offloading requests to the ledger nodes.

[0074] Upon receiving a user's task unloading request, the ledger node encapsulates the unloading request information, wireless transmission status information, resource allocation results, and computing status information into a transaction record. The consensus master node then packages these transaction records into a block. After the block is confirmed as valid through the consensus mechanism, it is written into the distributed ledger and linked with the previous block to achieve an immutable record of task unloading, bandwidth and computing resource allocation, and task execution status. The user terminal is only responsible for generating and submitting the task unloading request; the maintenance of the distributed ledger, block generation, and consensus confirmation are handled by the mobile edge computing server.

[0075] The transaction record in this embodiment includes at least a task identifier, an unload request summary, a wireless transmission status summary, a bandwidth allocation result summary, a computing resource allocation result summary, and a task execution status summary.

[0076] In this embodiment, the consensus master node collects transaction records and packages them into blocks within a preset block generation cycle. Each block contains a block header and a block body. The block header includes the hash value and timestamp of the previous block, used for connection to the previous blockchain; the block body contains a set of transaction records. After consensus confirmation, the block is written into the distributed ledger and broadcast synchronously to all ledger nodes, thereby achieving reliable recording and traceability of the unloading and resource allocation processes.

[0077] After completing the user task calculation, each server elects the consensus master node for the next cycle and conducts consensus communication in step S3.

[0078] Based on the remaining computing resources and communication bandwidth of each server after completing its current task, node capability parameters are constructed, and the server... m The node capability parameters are expressed by the formula:

[0079] in, Indicates server The remaining computing resources Indicates server The remaining communication bandwidth, its superscript res The remaining abbreviations are represented by... and Representing blocks b The generation interval and block size.

[0080] Based on node capability parameters, compute server m The probability of becoming the consensus master node in the next cycle is calculated using the following formula:

[0081]

[0082] in, Indicates server m The probability of becoming the consensus master node in the next cycle. This indicates the highest probability of becoming the consensus master node in the next cycle.

[0083] To reduce communication overhead during consensus communication, this embodiment employs differentiated bandwidth allocation for state confirmation information and block synchronization information. m Communication bandwidth for:

[0084] in, Indicates server m The communication bandwidth used for consensus confirmation information, its superscript ack This is an abbreviation for consensus confirmation information. Indicates server m The communication bandwidth used for block synchronization information, its superscript blk This is an abbreviation for block synchronization information.

[0085] server m As a consensus node, it reaches the node n The communication rate between them can be expressed by the formula:

[0086] in, Indicates server m Transmission power, This represents the equivalent interference power generated by other wireless nodes or background communication during consensus communication. Represents consensus nodes m With nodes n Channel coefficients between; Represents consensus nodes m With nodes n The communication rate between them.

[0087] In this embodiment, to evaluate the communication overhead of a follower node, the average of its communication rates with other consensus nodes is used. As an equivalent communication rate:

[0088] in, M This indicates the total number of mobile edge computing servers participating in the parallel processing of the current user task, i.e., the total number of nodes.

[0089] Next, following step S4, decentralized joint optimization will be carried out.

[0090] This embodiment, based on the user task offloading model constructed in step S1, the distributed ledger deployed in step S2, and the consensus mechanism in step S3, jointly optimizes user offloading decision information, server bandwidth allocation, and computing resource allocation under the conditions of satisfying wireless communication quality and latency constraints and computing resource limitations.

[0091] To achieve joint optimization of offloading decisions, bandwidth allocation, and computing resource allocation, while avoiding the high computational complexity and single point of failure risk associated with centralized solutions, this embodiment employs a distributed iterative update method based on a consensus mechanism. This allows each mobile edge computing server to process local information in parallel without relying on a central controller and achieve global coordination through lightweight consensus interaction. The specific steps are as follows: (1) Parameter initialization.

[0092] Each server initializes the optimization and coordination variables for the entire system based on the received task unloading requests and its local resource status. These variables include: Local decision variables include, for example, the offload ratio (offload decision) for each user terminal to each mobile edge computing server, the bandwidth ratio allocated to that user-server link (bandwidth allocation), and the amount of computing resources allocated by the server for that task (computing resource allocation). These variables are initialized by each mobile edge computing server based on local information such as local channel status and remaining resources.

[0093] Global Coordination Variables: This embodiment introduces a set of dual variables (e.g., Lagrange multipliers) to coordinate the local decisions of each server in subsequent iterations, so that they satisfy global constraints (such as total bandwidth limit, upper limit of total computing power of servers) and approach the global optimization goal.

[0094] (2) Each mobile edge computing server updates local variables in parallel.

[0095] Each mobile edge computing server updates its local decision variables in parallel and independently, based on the currently acquired global coordination variables and public information from other servers (such as the decision values ​​from the previous round). This step aims to minimize or maximize a local optimization subproblem that includes local objectives (such as local energy consumption and processing latency) and penalty terms (composed of coordination variables and global constraints).

[0096] Specifically, the m-th mobile edge computing server in the iteration t After the wheel t The local variable update relationship in the +1 iteration can be expressed by the following formula:

[0097] in, Indicates server m In the t Local decision variables in +1 iterations t Indicates the number of iterations; Indicates server m In the current iteration, a local cost function is constructed based on the local task state and resource constraints to characterize the combined cost of task offloading, bandwidth allocation, and computational resource allocation. Indicates server m The corresponding coordinating variables, Indicates the first t In the next iteration, the server m The corresponding coordinating variables.

[0098] (3) Calculate the global variables based on the consistency constraints.

[0099] After all servers have completed their local updates, new global coordination variables need to be calculated. In this embodiment, each server broadcasts the updated local decision variables to other nodes in the ledger network; the consensus master node collects the information reported by each node, calculates the global consistency variables, and writes the coordination instructions into a new block through blockchain transactions; after the block is confirmed, all nodes synchronously update their local copies to ensure that subsequent iterations are based on a consistent global state.

[0100] Globally consistent variables are expressed by the following formula:

[0101] in, Indicates the first t Globally consistent variables in +1 iterations M This indicates the total number of mobile edge computing servers participating in the parallel processing of the current user task.

[0102] (4) Termination condition judgment and result output.

[0103] This embodiment iteratively executes the "local update-consensus aggregation-global synchronization" process until the update relationship between the global consistency variable and the local decision variable reaches the termination condition.

[0104] The termination conditions of this embodiment include: Convergence condition: In two consecutive iterations, the update relationships of all local decision variables and / or global coordination variables are less than a preset threshold; expressed by the formula:

[0105] in, Indicates the first t In +1 iteration, the server m The corresponding coordinating variables, This represents the penalty parameter during iterative updates.

[0106] Maximum number of iterations: The number of iterations reaches the preset maximum limit.

[0107] If the termination condition is met, the iteration stops, and the optimization result of the last round is output as the final joint optimization scheme, including the optimal offloading decision, bandwidth allocation, and computing resource allocation. Otherwise, the updated global coordination variables are broadcast to all mobile edge computing servers, and then the process returns to step S2 to start a new round of iteration.

[0108] Finally, step S5 is executed. Based on the joint optimization results obtained in step S4, each mobile edge computing server collaboratively executes the task computation and returns the processing results to the corresponding user terminal via wireless link, completing the secure offloading closed loop. Among them, the key status information during the task execution process and the summary of the final computation result are encapsulated as transaction records and written to the blockchain by the consensus master node, realizing full lifecycle traceability and tamper-proofing from task submission, resource scheduling, execution to result feedback.

[0109] This embodiment not only ensures the authenticity and traceability of data throughout the entire process from perception to decision-making through blockchain, effectively defending against attacks such as false information injection and data tampering; more importantly, through dynamic consensus and joint resource optimization, it enables the intelligent and secure scheduling of limited and fluctuating edge computing and communication resources. This maximizes task processing speed while ensuring security, gaining valuable time for emergency command. This is something that traditional solutions relying solely on encryption or static resource allocation cannot achieve.

[0110] This embodiment first constructs a lightweight distributed ledger system among multiple mobile edge computing servers, storing key data such as task unloading requests, resource allocation schemes, execution status, and calculation results on the blockchain in the form of transactions. This achieves immutability, traceability, and auditability throughout the entire lifecycle of tasks, effectively preventing malicious nodes from forging resource status or tampering with scheduling decisions, and significantly improving system security and credibility.

[0111] Secondly, by introducing capability parameters based on the remaining computing and communication resources of nodes, a consensus master node is dynamically elected, so that the consensus process matches the real-time load of the edge server, reducing consensus overhead, improving response efficiency, and avoiding the performance bottleneck of the traditional fixed master node mechanism under high load.

[0112] Furthermore, user uninstallation decisions, bandwidth allocation, and computing resource allocation are jointly optimized in a decentralized manner. Each edge server solves local sub-problems in parallel and achieves global coordination through a blockchain consensus mechanism. While ensuring task latency and communication quality constraints, this effectively reduces the total system energy consumption and improves resource utilization efficiency.

[0113] Finally, user terminals only need to submit task requests and do not need to participate in consensus or ledger maintenance, which reduces the burden on terminals and is suitable for resource-constrained IoT devices.

[0114] It achieves an organic balance between security, energy efficiency, and scalability, making it suitable for future wireless edge application scenarios with stringent requirements for low latency, high reliability, and strong security, such as the Internet of Vehicles and the Industrial Internet.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0116] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A secure offloading method for blockchain-assisted mobile edge computing for wireless communication networks, characterized in that, Includes the following steps: S1. In a wireless communication network, a user task offloading model is constructed based on task offloading behavior; The task unloading behavior includes: the user terminal unloading the computing task to be processed to multiple mobile edge computing servers via a wireless link; S2. Deploy a distributed ledger system among the multiple mobile edge computing servers. Each server acts as a ledger node to jointly maintain the distributed ledger. When a task unloading request is received, the unloading request information, wireless transmission status information, resource allocation results, and task execution status are encapsulated into a transaction record. The consensus master node packages the transaction into a block, and after consensus verification, it is written into the distributed ledger for storage in a chain structure. S3. Based on the remaining computing resources and remaining communication bandwidth of each server after completing the current task, construct node capability parameters, and elect the consensus master node for the next cycle for consensus communication according to the node capability parameters. S4. Based on the user task offloading model and consensus mechanism, under the conditions of satisfying wireless communication quality and latency constraints and computing resource limitations, jointly optimize user offloading decision information, server bandwidth allocation and computing resource allocation. S5. Complete the computation task processing based on the joint optimization results, and return the processing results to the corresponding user terminal via wireless link.

2. The method as described in claim 1, characterized in that, Step S1, during the task unloading behavior, the baseband signal received by the mobile edge computing server. It can be expressed by the formula: In the task unloading behavior, the user terminal The uplink transmission rate is expressed by the formula: in, Indicates user terminal Uplink transmit power, Indicates user terminal Channel coefficients between the mobile edge computing server and the mobile edge computing server Indicates user terminal The signal sent, B represents additive white Gaussian noise; B represents the bandwidth resources allocated by the mobile edge computing server to the user terminal. Indicates noise power.

3. The method as described in claim 2, characterized in that, In step S1, the user task offloading model is used to characterize the quantitative relationship between transmission latency, computation latency, energy consumption, and resource consumption. It includes a task unloading transmission delay sub-model, a task computation delay sub-model, a user-side transmission energy consumption sub-model, a server-side computation energy consumption model, a server-side total energy consumption model, a user-side total energy consumption model, and a task completion delay sub-model.

4. The method as described in claim 3, characterized in that, The task offloading transmission delay sub-model is expressed by the following formula: The task computation delay sub-model is expressed by the following formula: The user-side transmission energy consumption sub-model is expressed by the following formula: The server-side computing energy consumption sub-model is expressed by the following formula: The total energy consumption sub-model on the server side is expressed by the following formula: The user-side total energy consumption sub-model is expressed by the following formula: The task completion delay sub-model is expressed by the following formula: in, Indicates user terminal Task offloading transmission latency, superscript tx For transmission, an abbreviation is used. Indicates user terminal The amount of task data; Indicates the server-side user terminal Task computation latency, superscript cmp For calculation, an abbreviation is used. Indicates user terminal The computational load of the task Indicates user-side user terminal Transmission energy consumption, Indicates user-side user terminal Energy consumption coefficient; Indicates server-side server Computational energy consumption, Indicates server Calculate the energy consumption coefficient. Indicates allocation to the server Computing resources; Indicates server-side server Total energy consumption; Indicates user-side user terminal Total energy consumption; Indicates user terminal Task completion delay.

5. The method as described in claim 1, characterized in that, In step S3, the node capability parameters are expressed by the following formula: in, Indicates server The remaining computing resources Indicates server The remaining communication bandwidth, superscript res The remaining abbreviations are represented by... and Representing blocks b The generation interval and block size.

6. The method as described in claim 5, characterized in that, In step S3, a consensus master node for the next cycle is elected based on the node capability parameters for consensus communication, specifically including: At the end of each block generation cycle, each server participates in the dynamic election of the consensus master node based on its currently calculated node capability parameters. A probability selection mechanism based on node capability parameters is adopted to calculate the probability that each server will be selected as the consensus master node in the next cycle; The server with the highest probability is broadcast as the election result to all ledger nodes before the start of the new block cycle, and a new round of consensus communication process is initiated.

7. The method as described in claim 1, characterized in that, The joint optimization described in step S4 specifically includes: S41. Initialize local decision variables and global coordination variables; the local decision variables include user terminal task offloading ratio, server bandwidth allocation vector, and computing resource allocation vector; the global coordination variables include global consistency variables used to characterize system-level consistency constraints, which are uniformly generated and broadcast by the consensus master node in the distributed ledger system in each iteration. S42. In each iteration, each mobile edge computing server solves the local optimization subproblem based on the global coordination variables recorded in the current block and the latest local decision variable summary information obtained synchronously from other ledger nodes, so as to update its own local decision variables. S43. Each server submits its updated local decision variables to the distributed ledger through transactions; the consensus master node collects the local decision variables of all participating nodes, performs aggregation operations to generate new global consistency variables, and encapsulates them into a consensus message and writes them into a new block; after consensus verification, the block is broadcast to the entire network to complete global state synchronization. S44. Iteratively execute the "local update-consensus aggregation-global synchronization" process until the update relationship between the global consistency variable and the local decision variable reaches the termination condition; then stop the iteration and output the corresponding unloading decision result, bandwidth allocation scheme and computing resource configuration.

8. The method as described in claim 1, characterized in that, In step S4, the conditions for satisfying wireless communication quality and latency constraints as well as computing resource limitations specifically include: The end-to-end completion latency of each user task shall not exceed the preset maximum tolerable latency; The allocated bandwidth and uplink transmission rate of the channel conditions meet the minimum reliability requirements; The total computing resources allocated to all tasks by each mobile edge computing server shall not exceed the currently available computing power; The total uplink bandwidth allocated to all users shall not exceed the total available bandwidth.

9. The method as described in claim 7, characterized in that, In step S42, the local decision variable is expressed by the formula: in, Indicates server m In the t Local decision variables in +1 iterations t Indicates the number of iterations. Indicates server m The local cost function constructed in the current iteration based on the local task state and resource constraints. Indicates server m The corresponding coordinating variables, Indicates the first t In the next iteration, the server m The corresponding coordinating variables.

10. The method as described in claim 9, characterized in that, In step S44, the update relationship between the global coordination variable and the local decision variable is represented by the coordination variable, and is expressed by the formula: in, Indicates the first t In +1 iteration, the server m The corresponding coordinating variables, This represents the penalty parameter in the iterative update. Indicates the first t Globally consistent variables in +1 iterations M This indicates the total number of mobile edge computing servers participating in the parallel processing of the current user task.

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