Block secure transmission method in block chain enabled multi-antenna unmanned aerial vehicle network

Abstract

The invention discloses a block security transmission method in a block chain enabled multi-antenna unmanned aerial vehicle network, and the method comprises the steps: building a block chain enabled multi-antenna unmanned aerial vehicle communication model, introducing an external friendly interference unmanned aerial vehicle, and reducing the channel quality of an eavesdropper through transmitting an interference signal, thereby improving the communication security. And meanwhile, the energy consumption limitation of the unmanned aerial vehicle is considered, and the optimal block size is found by optimizing the block chain performance. In order to solve the non-convexity problem caused by the optimization target, the overall optimization problem is divided into three sub-problems of unmanned aerial vehicle trajectory optimization, beam forming optimization and block length optimization. For the sub-problems, a first-order approximation method is adopted to convert the sub-problems into a convex optimization problem, and an alternating iteration algorithm based on a continuous convex approximation (SCA) technology is introduced for solving. Through multi-round iteration, the method can effectively improve the communication security and system performance of the task unmanned aerial vehicle in a complex environment.

Description

Technical Field

[0001] The present invention relates to a method for secure block transmission in a multi - antenna UAV network empowered by blockchain. Background Art

[0002] With the rapid development of UAV communication technology, its significant advantages such as low cost, high flexibility, and easy deployment have enabled UAVs to be widely used in the fields of wireless communication and networks. UAVs can be quickly deployed as temporary base stations in disaster relief to restore communication; provide flexible coverage in remote areas to make up for the lack of infrastructure; and serve as relay nodes in agriculture to support the Internet of Things and precision agriculture. It is expected that by 2025, the UAV market will reach $125 billion, creating more than 100,000 jobs and driving the development of the industrial chain. However, the open characteristics of UAV networks and the high sensitivity of transmitted data also pose severe security challenges. The wireless communication links of UAVs are vulnerable to eavesdropping, interference, and forgery attacks. At the same time, its nodes may be maliciously hijacked or exploited, resulting in data leakage or network paralysis.

[0003] In the application scenario of the combination of UAVs and blockchain technology, blockchain, with its characteristics of decentralization, encrypted storage, and data immutability, provides strong security guarantees for data storage and transmission in UAV communication systems. The distributed ledger technology of blockchain can ensure that the data generated by UAVs (such as flight logs, sensor data, or mission records) is not tampered with during transmission and storage. At the same time, automated and trusted task execution is achieved through smart contracts. However, although blockchain technology can effectively prevent data tampering, there are still certain security risks in the process of block publishing and consensus. For example, malicious nodes may disrupt the normal operation of the network by eavesdropping on block transmission information or launching a Sybil attack, which will not only lead to task failure but also may cause serious security hazards. In addition, although traditional encryption technologies can enhance the security of communication to a certain extent, their high demand for computing power and resources makes it difficult to be efficiently applied in resource - constrained UAV communication networks. The computing power, storage space, and energy supply of UAVs are usually limited and difficult to support complex encryption algorithms and frequent key update operations. Most studies only focus on the security performance of the physical layer of UAV communication and do not consider that the messages within the block may also be eavesdropped during the message transmission process. And the length of the block will affect the performance of the blockchain and the secure transmission performance. Therefore, it is necessary to consider the secure transmission performance of the blockchain communication system and the performance of the blockchain.

[0004] In previous research work, only the secure block transmission in a single - antenna UAV network empowered by blockchain was considered, without considering multi - antenna UAVs and the issue of UAV energy consumption. Therefore, the secure block transmission in a multi - antenna UAV network still needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for secure block transmission in a blockchain-empowered multi-antenna UAV network in view of the deficiencies in the above-mentioned prior art. Aiming at the deficiencies of existing research, the block size parameter is optimized, and a joint optimization method of UAV trajectory, energy consumption, beamforming and block length is adopted to improve the effectiveness and security of the multi-antenna UAV communication system.

[0006] To achieve the above object, the present invention provides the following solution: A method for secure block transmission in a blockchain-empowered multi-antenna UAV network, which reduces the quality of the eavesdropping channel by using an external friendly interfering UAV to transmit interference signals, equips the UAV with a uniform linear array to implement beamforming, optimizes the energy consumption of the UAV at the same time, and finds the optimal block size by optimizing the blockchain performance. The non-convex optimization problem is decomposed into three sub-problems: UAV trajectory optimization, beamforming optimization and block length optimization; the approximate optimal solution of the joint optimization problem is obtained by alternately solving the three sub-problems to obtain better secure communication performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A blockchain-empowered multi-antenna UAV communication system.

[0008] Figure 2 Comparison of the maximum secrecy energy efficiency (MSEE) for different schemes.

[0009] Figure 3 Comparison of the secrecy energy efficiency for different numbers of users. DETAILED DESCRIPTION OF THE INVENTION

[0010] The following combines the attached Figure 1 , and specifically describes the technical solution of the present invention.

[0011] The present invention is a method for secure block transmission in a blockchain-empowered multi-antenna UAV network, which reduces the quality of the eavesdropping channel by using an external friendly interfering UAV to transmit interference signals, equips the UAV with a uniform linear array to implement beamforming, optimizes the energy consumption of the UAV at the same time, and finds the optimal block size by optimizing the blockchain performance. The non-convex optimization problem is decomposed into three sub-problems: UAV trajectory optimization, beamforming optimization and block length optimization; the approximate optimal solution of the joint optimization problem is obtained by alternately solving the three sub-problems to obtain better secure communication performance.

[0012] Step 1: Establish a blockchain-enabled multi-antenna UAV communication model, considering the downlink transmission scenario. The mission UAV conducts secure communication with multiple legitimate ground users. There is an eavesdropper attempting to eavesdrop on the communication process. A jamming UAV is used to transmit jamming signals to the eavesdropper to reduce the quality of the eavesdropping channel. At the same time, the energy consumption problem of the UAV is also considered;

[0013] Step 2: Utilize the position constraint, beamforming constraint, energy consumption constraint, and block length constraint of the UAV to establish an optimization problem that maximizes the secrecy energy efficiency of legitimate users;

[0014] Step 3: Introduce slack variables to reformulate the optimization problem;

[0015] Step 4: Fix the beamforming and block length to obtain the UAV position optimization sub-problem and transform it into an equivalent convex optimization problem;

[0016] Step 5: Fix the UAV beamforming and position to obtain the block length optimization sub-problem and transform it into an equivalent convex optimization problem;

[0017] Step 6: Fix the UAV position and block length to obtain the UAV beamforming optimization sub-problem and transform it into an equivalent convex optimization problem;

[0018] Step 7: Use an alternating iterative algorithm based on the successive convex approximation technique to iteratively solve these sub-problems, thereby obtaining an approximate optimal solution to the joint optimization problem.

[0019] In the blockchain-enabled multi-antenna UAV communication model in Step 1, the communication between the UAV and the ground nodes can be modeled as a Rayleigh fading model:

[0020]

[0021] where, g u,g is the channel coefficient between UAV u and ground node g, and g represents that the ground node includes ground user m and eavesdropper e; δ u,g is the channel gain after Rayleigh fading, and the channel gain of each time slot changes randomly. The path loss exponent α is usually between 2 and 4; d u,g is the instantaneous distance between node u and node g, that is, the distance from UAV u to ground node g at time slot τ, which can be expressed as: ‖q u [τ] - q g ‖.

[0022] At time τ, the achievable rate (bps / Hz) from UAV B to the m-th ground receiver can be expressed as:

[0023]

[0024] Among them, δ B,m is the channel gain after Rayleigh fading, and the channel gain of each time slot changes randomly; similarly, δ J,m is the channel gain after Rayleigh fading between the UAV J and the ground receiver m; σ 2 is the noise power of additive white Gaussian noise (AWGN).

[0025] Similarly, the rate (bps / Hz) that the eavesdropper e can achieve can be expressed as:

[0026]

[0027] The selection of the block size involves two key factors: the block generation time and the transmission delay time. Larger blocks can accommodate more transactions, thus reducing the block generation frequency. However, due to the increased data volume, their transmission time will also be correspondingly extended. On the contrary, smaller blocks can shorten the transmission time of a single block, but may lead to an increase in the overall block generation time because more blocks are needed to process all the transactions to be confirmed. Therefore, reasonably optimizing the block size is crucial for improving system performance.

[0028] The time taken to create a block is:

[0029]

[0030] where S mem is the size of the memory pool, l is the size of the block, then represents the number of blocks; T overhead represents other additional overhead times; S t represents the size of the transaction, then l / S t represents the transactions included in a block; Merkle t represents the number of transactions in a Merkle tree, usually set to a fixed value; T merkle represents the time required to create the Merkle tree.

[0031] For the time of block transmission delay, it can be expressed as:

[0032]

[0033] T p is the processing time, R is the bandwidth of the node, and M is the number of ground users. It is assumed that R is equal for each node in the blockchain.

[0034] The overall objective function regarding the block size can be expressed as:

[0035] F(l) = ρT d+(1 - ρ)T g . (6)

[0036] Assign corresponding weights to these two factors, where the weight of the delay is set as ρ, and the weight of the block generation time is (1 - ρ). When ρ increases, the weight of the block generation time decreases accordingly. That is, a larger weight value will shorten the transmission delay but at the same time increase the block generation time.

[0037] Generally speaking, in our system, the energy consumption can be mainly divided into three parts: First is the energy consumption related to communication; second is the propulsion energy consumption, which is used to ensure that the drone can maintain its flight altitude and support its maneuverability; finally is the energy consumption during the blockchain communication process. It should be emphasized that in practical applications, the energy consumption related to communication is much lower than the propulsion energy consumption, usually differing by three orders of magnitude, so it can be ignored in this article. The propulsion energy consumption E p [τ] of the rotor drone at time slot τ can be modeled as

[0038]

[0039] where the horizontal flight speed of the drone is U tip represents the tip speed of the rotor blade, P 0 、P 1 、v 0 represent the blade section power coefficient, induced power coefficient, and the average induced speed of the drone in hover respectively; s’ represents the rotor solidity; d’ represents the fuselage drag coefficient; A represents the rotor disk area; ρ represents the air density.

[0040] For the energy consumption part of the blockchain, it includes the energy consumption for block generation and transmission energy consumption.

[0041] Energy consumption for generating a block:

[0042] Egen[τ] = Pgen × Tgen[τ], (8)

[0043] where, P gen : the average computing power during the block generation process, T gen : the time required to generate a block.

[0044] Transmission energy consumption of the block:

[0045] E trans [τ] = P trans × Y trans [τ], (9)

[0046] where, P trans : the transmission power when transmitting the block, Ttrans : The time required for the transmission block.

[0047] Total energy consumption of the blockchain:

[0048] E block [τ] = E gen [τ] + E trans [τ], (10)

[0049] Total energy consumption:

[0050] E total [τ] = E block [τ] + E p [τ]. (11)

[0051] In step two, using the position constraint, beamforming constraint, energy consumption constraint, and block length constraint of the unmanned aerial vehicle (UAV), an optimization problem (P1) of maximizing the secrecy energy efficiency of legitimate users is established;

[0052]

[0053] ‖q u [τ + 1] - q u [τ]‖ ≤ L, (12b)

[0054] ‖q B [τ] - q J [τ]‖ ≥ d min , (12c)

[0055] ‖w B ‖ 2 ≤ P B , (12d)

[0056] ‖w J ‖ 2 ≤ P J , (12e)

[0057] 0 ≤ l ≤ l max . (12f)

[0058] Among them, (12b) indicates that the distance the UAV moves between adjacent time slots cannot exceed the maximum allowable flight distance L, and (12c) indicates the minimum safety distance between UAV B and UAV J. (12d) and (12e) are the constraints of UAV beamforming, and (12f) indicates the block length constraint.

[0059] In step three, slack variables μ g [τ], Ψ g [τ], The optimization problem (P2) is reformulated as:

[0060]

[0061] s.t. (12b)(12c)(12d)(12e)(12f),(13b)

[0062]

[0063] In problem (P2), constraint (13c) transforms the problem into a more tractable form. Constraints (13d) and (13e) ensure the smooth execution of the subsequent iterative algorithm. In addition, when constraints (13c) and (13d) hold with equality, it can be ensured that problems (P1) and (P2) are equivalent, and by decreasing μ g [τ] and Ψ g [τ] can effectively improve the secrecy rate.

[0064] In step four, by fixing the beamforming and block length, the UAV position optimization sub-problem is obtained and transformed into an equivalent convex optimization problem.

[0065] First, optimize the position of UAV B while keeping the beamforming of UAV B unchanged, the positions and beamformings of UAV J unchanged, and the block length unchanged. Thus, the corresponding sub-problem can be written as (P3.1):

[0066]

[0067] s.t. (12b)(12c)(13c)(13d)(13e),(14b)

[0068] Problem (P3.1) still belongs to a non-convex optimization problem because the objective function (14a) is non-concave, and at the same time, constraints (13c) and (13d) are also non-convex. Next, the focus will be on the equivalent transformation of problem (P3.1) to reconstruct it into a convex optimization problem. Define

[0069] For it can be reformulated as:

[0070]

[0071] Introduce the slack variable g ∈ {m,e}, and constraint (13c) can be rewritten as:

[0072]

[0073] where,

[0074]

[0075] For the numerator of formula (14a), we observe that is about ‖q B [τ] - q m ‖ 2 and μ e [τ] is a convex function. Therefore, we use the first-order Taylor expansion to obtain the lower bound of, and the specific expansion is as follows:

[0076]

[0077] For the denominator, introduce a slack variable z B [τ].

[0078]

[0079] is equivalent to

[0080]

[0081] The original optimization problem can be equivalently transformed into the following problem P(3.2):

[0082]

[0083] s.t.(12b)(12c)(13e)(16a)(16b),(19b)

[0084]

[0085] where,

[0086] (P3.2) is a fractional optimization problem with a linear numerator, a convex denominator, and multiple convex constraints, which can be further solved by the DinkelBach algorithm.

[0087] Next, optimize the position of UAV J, keep the beamforming of UAV J unchanged, while keeping the position and beamforming of UAV B unchanged and the block length unchanged. Therefore, the corresponding sub-problem can be written as (P4.1):

[0088]

[0089] s.t.(12b)(12c)(13c)(13d)(13e),(20b)

[0090] For problem (P4.1) is non-convex because (20a) is non-concave and constraints (13c) and (13d) are non-convex. Define can be re-expressed as:

[0091]

[0092] Since the second term of formula (21) is a non-concave function, by performing a first-order Taylor expansion on we have

[0093]

[0094] Similarly, for the denominator, we introduce a slack variable z J [τ].

[0095]

[0096] which is equivalent to

[0097]

[0098] The original optimization problem can be equivalently transformed into the following problem P(4.2):

[0099]

[0100] s.t. (12b)(12c)(13e)(16a)(16b),(24b)

[0101]

[0102] where

[0103] (P4.2) is a fractional optimization problem with a linear numerator, a convex denominator, and multiple convex constraints, which can be further solved by the DinkelBach algorithm.

[0104] In step five, the position of the UAV and beamforming are fixed to obtain a sub-problem for block length optimization, and it is transformed into an equivalent convex optimization problem. The corresponding sub-problem can be written as (P5.1):

[0105]

[0106] 0 ≤ l ≤ l max , (25b)

[0107] where, defining the objective function can be rewritten as

[0108]

[0109] This is a concave function with respect to the block length l, so it can be directly solved using the CVX tool.

[0110] In Step 6, fix the position of the UAV and the block length to obtain the sub - problem of UAV beamforming optimization and transform it into an equivalent convex optimization problem;

[0111] First, optimize the beamforming \(w\) of UAV B B , and the corresponding sub - problem can be written as (P6.1)

[0112]

[0113] s.t. (12d)(12e)(13c)(13d)(13e),(27b)

[0114] Define It can be reformulated as:

[0115]

[0116] First, perform Taylor expansion on \(|\rho B,m [\tau]w B,m [\tau]|\) at \(w 2 to obtain: B,m (i) We have:

[0117]

[0118] where,

[0119]

[0120] Therefore, the first - order approximation technique can be used to construct a lower bound of , which can be expressed as (31)

[0121]

[0122] After the above transformation, (P6.1Z) can be rewritten as (P6.2):

[0123]

[0124] s.t. (12d)(12e)(13c)(13d)(13e),(32b)

[0125] Next, optimize the beamforming \(w\) of UAV J J , and the corresponding sub - problem can be written as (P7.1):

[0126]

[0127] s.t. (12d)(12e)(13c)(13d)(13e),(33b) Define It can be reformulated as:

[0128]

[0129] First, for |ρ B,m [τ]w B,m [τ]| 2 Performing a Taylor expansion at w B,m (i) yields:

[0130]

[0131] where,

[0132]

[0133] Therefore, the first-order approximation technique can be used to construct a lower bound, which can be expressed as (37):

[0134]

[0135] After the above transformation, (P7.1) can be rewritten as (P7.2):

[0136]

[0137] s.t. (12d)(12e)(13c)(13d)(13e), (38b)

[0138] After such an approximation, the sub-problem (P7.2) has been transformed into a standard convex optimization problem, which can be optimally solved by CVX.

[0139] In Step 7, an alternating iterative algorithm based on the successive convex approximation technique is used to iteratively solve these sub-problems, thereby obtaining an approximately optimal solution to the joint optimization problem. The specific steps are as follows:

[0140] First, initialize the iteration count i = 0 and select a feasible point

[0141] Then perform the iteration, calculate U(i), and update the iteration count i ← i + 1;

[0142] Solve the sub-problem (P5.1) by CVX, and then update

[0143] For a given L (i) , solve the sub-problems (P3.2) and (P4.2) by CVX respectively,

[0144] Update

[0145] For a given Solve sub-problems (P6.2) and (P7.2) respectively by CVX, and update

[0146]

[0147] At the new feasible point Calculate U(i);

[0148] Finally, judge the convergence degree according to ‖U(i) - U(i - 1)‖ ≤ ∈.

[0149] The effectiveness of the proposed alternating optimization algorithm is verified by the experimental results. In this algorithm, we jointly consider the performance of secure transmission and the performance of the blockchain. The algorithm jointly optimizes the trajectory of the UAV, the block length, and beamforming (JTBW). To prove the effectiveness of the proposed algorithm, we consider the following benchmark schemes: FB: fixed block length, different numbers of users.

[0150] In Figure 2 we respectively plot the MSEE performance of the JTBW method and the method with a fixed block length (FB) under different numbers of iterations, aiming to verify the convergence speed and steady-state performance of the algorithm proposed in this paper, and highlight its advantages compared with the comparative algorithms. It can be seen from the results in the figure that as the number of iterations increases, the MSEE performance of both methods increases rapidly and gradually tends to be stable, reflecting the good convergence characteristics of the algorithm. At the same number of iterations, the MSEE of JTBW is about 11% higher than that of FB, and the performance of the JTBW method proposed in this study is significantly better than that of the FB method.

[0151] Figure 3 shows the variation trend of MSEE with the number of iterations under different numbers of users M. It can be observed that as the number of users M increases, the value of MSEE increases significantly. Specifically, when the number of users increases from M = 3 to M = 6 and M = 9, the final convergence value of MSEE increases accordingly. This indicates that as the number of users increases, the system needs to process more data information, and the optimization complexity increases, resulting in an increase in the overall value of MSEE.

[0152] The above is the specific implementation process of the present invention. Under the constraints of the mobility of the UAV, the transmission power, the energy consumption, the beamforming, and the block length, by jointly optimizing the performance of the blockchain and the secure transmission performance of the block, it can be seen that this method can improve the communication efficiency and security performance of the mission UAV.

Claims

1. A method for secure transmission of blocks in a blockchain-enabled multi-antenna drone network, comprising the following steps: Step 1: Establish a multi-antenna UAV communication model enabled by blockchain, considering the downlink transmission scenario. The mission UAV communicates confidentially with multiple legitimate users on the ground. There are eavesdroppers trying to eavesdrop on the communication process. Interference UAVs are used to transmit interference signals to the eavesdroppers to reduce the quality of the eavesdropping channel. At the same time, the energy consumption of UAVs is also considered; Step 2: Using the UAV's position constraints, beamforming constraints, energy consumption constraints, and block length constraints, establish an optimization problem to maximize the confidentiality energy efficiency of legitimate users; Step 3: Introduce slack variables and reformulate the optimization problem; Step 4: Fix the beamforming and block length, obtain the UAV position optimization subproblem, and transform it into an equivalent convex optimization problem; Step 5: Fix the UAV beamforming and position, obtain the sub-problem of block length optimization, and transform it into an equivalent convex optimization problem; Step 6: Fix the position of the UAV and the block length, obtain the sub-problem of UAV beamforming optimization, and transform it into an equivalent convex optimization problem; Step 7: Use an alternating iterative algorithm based on successive convex approximation technology to iteratively solve these sub-problems to obtain an approximate optimal solution to the joint optimization problem.

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