Multi-user security unloading method based on backscattering auxiliary edge calculation

Abstract

The invention provides a multi-user secure unloading method based on backscatter-assisted edge calculation. The method comprises the following steps: acquiring task unloading parameters of each user, current user groups, randomized continuous wave parameters, channel parameters of a backscatter communication stage, channel parameters of an active transmission stage and channel parameters of an eavesdropper; according to the parameters, the secrecy rate of each user in the backscatter communication stage and the active transmission stage is determined; according to the secrecy rate of the backscatter communication stage and the secrecy rate of the active transmission stage, determining the total task processing delay time, the total energy consumption of the user and the energy collected by the user in the backscatter communication stage; the total energy consumption of the users and the energy collected by the users in the backscatter communication stage are used as constraints, the total task processing delay time is minimized, and randomized continuous wave parameters, task unloading parameters and user grouping are jointly optimized. By implementing the method and the device, the safety performance and the task processing efficiency in a multi-user scene are enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of task offloading, and particularly relates to a multi-user security offloading method based on backscattering assisted edge computing. BACKGROUND

[0002] The rapid development of the Internet of Things promotes the emergence of various innovative applications, such as virtual reality (VR), Internet of Vehicles (IoV), smart home, smart city, etc. These new applications lead to a substantial increase in computationally intensive tasks. Fortunately, mobile edge computing (MEC) can provide additional computing resources for Internet of Things wireless devices (users). However, the energy and signal processing capabilities of wireless devices are usually limited by the limited manufacturing cost, which cannot smoothly offload tasks. In order to cope with this challenge, wireless-powered mobile edge computing (WPMEC) has received extensive attention. WPMEC combines wireless power transmission (WPT) with mobile edge computing, allowing wireless devices to harvest energy from the radio frequency (RF) signals emitted by a specific energy source (such as a power beacon (base station)) and use the harvested energy for local computing and active transmission (active transmission), or reflect the incident signal by modulating the antenna impedance, which is also known as backscattering communication (backscattering communication).

[0003] Existing research mainly focuses on minimizing task processing delay, maximizing system energy efficiency (EE), maximizing system computing bits, etc. Some literatures maximize the computing efficiency of the worst IoT node by jointly optimizing the offloading parameters of IoT nodes, the local computing parameters of IoT nodes, the transmit power and the trajectory of the UAV, thereby improving fairness. However, the above research only considers the time division multiple access (TDMA) protocol, i.e., only a single user is allowed to offload tasks in a time slot. Considering the low spectrum resource utilization and short device battery life, some literatures propose a backscatter-assisted MEC network system based on a non-orthogonal multiple access (NOMA) communication mode to maximize the computing efficiency. In addition, a UAV-assisted backscatter MEC system is considered to cope with the challenge of intensive computing demand in hotspots. By jointly optimizing data offloading decisions and contract design, the long-term utility maximization of all hotspots is achieved while ensuring the stability of the hot energy queue. However, since the expected signal of wireless devices is usually a finite codebook input in practice, rather than a Gaussian input, the achievable rate described in the above work is not particularly accurate for backscatter-assisted WPMEC systems. Some literatures propose an innovative user cooperation scheme that integrates backscatter communication-active transmission in a cooperative WPMEC system composed of source nodes, helper nodes and hybrid access points to improve system efficiency. Considering the limited computing capacity of MEC servers, the quality of service (QoS) and energy consumption constraints of IoT nodes, two resource allocation schemes are proposed to maximize the total computing bits of all IoT nodes and the system computing efficiency, respectively. However, existing research does not consider the information leakage problem caused by the broadcast nature of wireless channels, so the security performance of the system needs to be further discussed.

[0004] Due to the weak signal processing capability of IoT devices, physical layer security (PLS) techniques that take advantage of the characteristics of wireless channels are more suitable for MEC systems than traditional encryption techniques. Some literatures study the secure offloading of NOMA-assisted vehicular edge computing networks in the presence of multiple malicious eavesdropping vehicles to ensure the security of wireless offloading from user vehicles to MEC servers. Under the computing delay constraint, the system energy consumption is minimized by jointly optimizing the transmit power, computing resource allocation, and selection of interfering vehicles in each NOMA cluster. Unmanned ground vehicles (UGVs) are used to handle intensive computing tasks for drones in areas without base stations. Some literatures maximize the average utility of UAV-ground vehicle cooperation by jointly optimizing the UAV trajectory, transmission power and CPU frequency while ensuring communication security. However, the security problem of backscatter-assisted WPMEC systems remains to be solved. Therefore, it is urgent to design a backscatter-assisted multi-user secure offloading system to ensure secure wireless offloading for multiple devices simultaneously. SUMMARY

[0005] Therefore, the present application aims to provide a multi-user secure offloading method and device based on backscatter-assisted edge computing to meet the need of providing secure wireless offloading for multiple devices at the same time.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] The present application provides a multi-user secure offloading method based on backscatter-assisted edge computing, which is applied to a system composed of a full-duplex power base station equipped with an edge computing server, multiple users and eavesdroppers. The base station transmits multi-carrier random continuous waves as a radio frequency source. The multiple users offload tasks to the edge computing server through hybrid backscatter communication and active transmission. The method comprises the following steps: obtaining the task offloading parameters of each user, the current user grouping, the random continuous wave parameters and the channel parameters of the backscatter communication stage, the channel parameters of the active transmission stage and the eavesdropper channel parameters; determining the secrecy rate of each user in the backscatter communication stage and the active transmission stage according to the current user grouping, the eavesdropper channel parameters, the channel parameters of the backscatter communication stage, the channel parameters of the active transmission stage and the random continuous wave parameters, wherein the multiple users in the same user group offload tasks at the same time in the backscatter communication stage, and the edge computing server decodes the target user signal and regards other user signals as noise; determining the total task processing delay time, the total user energy consumption and the energy collected by the user in the backscatter communication stage according to the task offloading parameters of each user, the secrecy rate of the backscatter communication stage and the secrecy rate of the active transmission stage; and minimizing the total task processing delay time with the total user energy consumption and the energy collected by the user in the backscatter communication stage as constraints, and jointly optimizing the random continuous wave parameters, the task offloading parameters and the user grouping.

[0008] The present application provides a multi-user secure offloading method based on backscatter-assisted edge computing. By constructing a system composed of a full-duplex power base station, multiple users and eavesdroppers, the base station transmits multi-carrier random continuous waves as a radio frequency source, so that the users can offload tasks through hybrid backscatter communication and active transmission. The method can obtain multiple types of parameters and determine the secrecy rate of each stage, the total task processing delay time, the total user energy consumption and the collected energy. Then, the multiple types of parameters are jointly optimized with the energy consumption and the collected energy as constraints, effectively solving the information leakage problem caused by the lack of consideration of the broadcast characteristics of the wireless channel in the prior art, realizing the simultaneous secure offloading of multiple users, improving the spectrum resource utilization rate and reducing the device energy consumption. By jointly optimizing the random continuous wave parameters, the task offloading parameters and the user grouping, the total task processing delay time of the system is significantly reduced, the security performance and task processing efficiency of the system in the multi-user scenario are enhanced, and a safe and efficient task offloading solution is provided for the intensive computing demand of Internet of Things devices.

[0009] Additional advantages, objects, and features of the application will be set forth in part by the description that follows, and in part will become apparent to those skilled in the art upon examination of the following or can be learned from practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0010] To make the objects, technical solutions and advantages of the present application clearer, the present application is described below with the help of the accompanying drawings:

[0011] Figure 1 A backscattering assisted multi-user secure offloading system in wireless powered mobile edge computing;

[0012] Figure 2 A specific example flow chart of the backscattering assisted edge computing based multi-user secure offloading method;

[0013] Figure 3 A time structure diagram of the backscattering assisted multi-user secure offloading system in wireless powered mobile edge computing;

[0014] Figure 4 A flow chart of joint optimization of the three sub-goals;

[0015] Figure 5 A diagram of the relationship between the number of devices and the total task processing delay of the system;

[0016] Figure 6 A diagram of the relationship between the amount of task data and the total task processing delay of the system;

[0017] Figure 7 A diagram of the relationship between the transmission power of the power beacon and the total task processing delay of the system. DETAILED DESCRIPTION

[0018] The technical solutions of the present application will be described below in detail with the help of the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0019] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "linking" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or it can be the internal communication of two elements, it can be wireless connection, or wired connection. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0020] In addition, the technical features involved in the different embodiments of the application described below can be combined with each other as long as there is no conflict between them.

[0021] The embodiment provides a multi-user secure offloading method based on backscattering assisted edge computing, which is applied to a system as shown in the figure. Figure 1 The system is composed of a full-duplex power base station equipped with an edge computing server, M users and an eavesdropper. The user is a wireless device with a single antenna, and the set of users can be represented as M. The base station transmits a multicarrier random continuous wave as a radio frequency source. Multiple users offload tasks to the edge computing server through hybrid backscattering communication and active transmission. The method is as shown in the figure. Figure 2 The method comprises the following steps.

[0022] S101, acquiring the task offloading parameters of each user, the current user grouping, the randomization continuous wave parameters and the channel parameters of the backscattering communication stage and the active transmission stage, and the eavesdropper channel parameters;

[0023] S102, determining the secrecy rate of each user in the backscattering communication stage and the active transmission stage according to the current user grouping, the eavesdropper channel parameters, the channel parameters of the backscattering communication stage, the channel parameters of the active transmission stage and the randomization continuous wave parameters, wherein multiple users in the same user group offload tasks simultaneously in the backscattering communication stage, the edge computing server decodes the target user signal and regards other user signals as noise.

[0024] S103, determining the total task processing delay time, the total energy consumption of users and the energy collected by users in the backscattering communication stage according to the task offloading parameters of each user, the secrecy rate of the backscattering communication stage and the secrecy rate of the active transmission stage.

[0025] S104, minimizing the total task processing delay time with the total energy consumption of users and the energy collected by users in the backscattering communication stage as constraints, and jointly optimizing the randomization continuous wave parameters, the task offloading parameters and the user grouping.

[0026] Exemplarily, to elaborate the above process, the following is the construction of each model of the above system:

[0027] The multi-carrier randomized continuous wave signal can be mathematically formulated as:

[0028]

[0029] where x u,k,i represents the weight of the i-th subcarrier of the u-th group of users in the k-th time slot. ω i = 2πf i and f i represent the angular frequency and the center frequency of the i-th subcarrier, respectively. It is assumed that the frequency interval between adjacent carriers is the same, denoted by f Δ . Therefore, the carrier frequency f C of the transmitted signal can be represented as:

[0030]

[0031] where f1and f I represent the carrier frequency of the 1st subcarrier and the carrier frequency of the I-th subcarrier, respectively, and I represents the total number of subcarriers.

[0032] In existing research, multi-user backscatter communication mainly uses continuous waves with a constant value x u,k,i , however, randomized continuous waves can be used to enhance security. Assuming that the source signal x u,k is random and follows a Gaussian distribution, i.e.: represents the mean vector of the u-th group of user signals, and Φ u represents the variance vector of the u-th group of user signals. E||x u,k || 2 = P, where P represents the transmission power of the base station. The channel response vector from the base station to user m can be given by the following formula:

[0033] h PW,m = [h PW,m,1 , h PW,m,2 ,..., h PW,m,I ] (3)

[0034] where h PW,m,i represents the complex channel response from the base station to user m at the i-th carrier frequency, which can be modeled as:

[0035]

[0036] where d PW,mdenotes the distance from the base station to user m, γ denotes the attenuation coefficient, v denotes the propagation speed, g0denotes the channel power gain when the reference distance is 1 meter, g0= (λ / 4π) 2 , λ denotes the wavelength of the carrier frequency, λ = v / f C At the kth time slot, the received radio frequency signal by user m in the u-th group is denoted as:

[0037] y m,u,k = X u,k h PW,m (5)

[0038] where X u,k = Λ(x u,k ), in practice, the modulation based on backscatter uses a finite alphabet, i.e., D.

[0039] Due to the full-duplex nature of the base station, it is assumed that the channel reciprocity between the base station and the users holds. The backscattered communication signal received by the base station from the u-th group at the kth time slot can be given by:

[0040]

[0041] where U u denotes the set of users included in the u-th group, c m,u,k ∈ D denotes the kth data symbol from user m in the u-th group. n PB,u,k denotes the additive noise of the backscatter reader, denotes the thermal noise power at the reader at the base station, I I denotes an I x I identity matrix.

[0042] The channel response matrix between the base station and the users from the u-th group can be simplified as H u and then equation (6) can be changed to:

[0043] y PB,u,k = X u,k H u c u,k + n PB,u,k (7)

[0044] where c u,k denotes the sequence of symbols transmitted by the users in the u-th group at the kth time slot.

[0045] The channel response vector between the base station and the eavesdropper is:

[0046] H PE = [h PE,1 , h PE,2 ,..., h PE,I ] T (8)

[0047] where h PE,i represents the complex channel response between the base station and the eavesdropper on the i-th carrier frequency, which can be expressed as:

[0048]

[0049] where d PE represents the distance from the base station to the eavesdropper. The channel vector from user m to the eavesdropper can be expressed as:

[0050] g WE,m = [g WE,m,1 , g WE,m,2 ,..., g WE,m,I ] T (10)

[0051] Since the communication between ground nodes is susceptible to obstacles and scatterers, the channel response between user m and the eavesdropper is modeled as a Rayleigh fading channel. Therefore, g WE,m,i can be modeled as:

[0052]

[0053] where d WE,m represents the distance from user m to the eavesdropper, and ξ WE,m,i ~ CN(0, 1). Thus, the signal received by the eavesdropper in the backscatter communication phase is:

[0054]

[0055] where n EB,u,k represents a zero-mean additive Gaussian noise, The channel response matrix between users from the u-th group and the eavesdropper can be simplified as G u , then formula (12) can be changed to:

[0056] y EB,u,k = Λ(G u c u,k + H PE ) x u,k + n EB,u,k . (13)

[0057] Based on the above modeling derivation, the task offloading model and the secrecy rate of each user can be constructed, specifically:

[0058] Users offload tasks with achievable secrecy rates to ensure that the base station can successfully decode the information while an eavesdropper cannot obtain information from the received signal. When multiple users in the same group offload tasks simultaneously, interference occurs among users. In the invented system, the edge computing server decodes the signal of the target user while considering the signals of other users as noise. Therefore, the secrecy rate of a single user within a group will be derived below. In this embodiment, for performance evaluation, the secrecy rate of the backscatter communication phase is in units of bits per channel use (BPCU) and the secrecy rate of the active transmission phase is in units of bits per second. For ease of description, the subscript u representing the order of the group and the subscript k representing the time slot are omitted.

[0059] The achievable rate of each user's legitimate link and the achievable eavesdropping link rate of the user are determined according to the current user grouping, the eavesdropper channel parameters, the channel parameters of the backscatter communication phase, and the randomized continuous wave parameters as follows:

[0060] The achievable rate of the legitimate link of the nth user in each group can be derived by the following formula:

[0061]

[0062] where D represents a finite alphabet, A represents the number of symbol combinations that a single user can transmit, A = |D|, L represents the number of symbol combinations that each group of users can transmit, L = |D I |, L' represents the number of symbol combinations that other users in each group can transmit except for user n, L' = |D I-1 |, represents the mean of the transmitted symbols of all users, represents the mean of the transmitted symbols of other users except for user n, can be regarded as a variable satisfying can be regarded as a variable satisfying X = Λ(x),

[0063] The Gaussian random nature of the multi-carrier randomized continuous wave signal makes it an artificial noise that interferes with the eavesdropping link. The achievable eavesdropping link rate of user n can be represented as:

[0064]

[0065] where represents the mean vector of the user signals, represents the mean of the transmitted symbols of all users, can be regarded as a variable satisfying where can be derived​​​ where denotes the mean of the transmitted symbols by other users than user n, can be considered to satisfy a variable, where

[0066] After expressing the achievable rates of the legitimate link and the eavesdropping link as Equations (14) and (15) respectively, the secrecy rate of user n can be expressed as:

[0067] R SB,n = max(R PB,n - R EB,n , 0) (16)

[0068] However, due to the complexity of the integrals in R PB,n and R EB,n , it is difficult to obtain a closed-form expression for the secrecy rate. Approximate calculations of R PB,n and R EB,n are made using the Jensen inequality. The approximate value of the legitimate link can be expressed as:

[0069]

[0070] where

[0071] Φ denotes the variance vector of the user signals.

[0072] Similarly, the approximate value of the eavesdropping link can be expressed as:

[0073]

[0074] where

[0075] Therefore, the secrecy rate R SB can be easily approximated as:

[0076]

[0077] Based on the channel parameters of the active transmission phase, the eavesdropper channel parameters, and the randomized continuous wave parameters, the process of determining the user's active transmission rate and the eavesdropper's data rate when the eavesdropping user offloads the channel is as follows:

[0078] This embodiment assumes that the conventional radio frequency wireless communication technology is used in the active transmission phase. The active transmission rate of user m can be obtained by the following formula:

[0079]

[0080] where B denotes the channel bandwidth, p m denotes the transmit power of user m, N0denotes the power spectral density of additive white Gaussian noise (AWGN), h PW,m denotes the channel response vector from the base station to user m.

[0081] The base station interferes the eavesdropper by generating artificial noise to enhance the security of the active transmission phase. Therefore, the data rate of the eavesdropper when eavesdropping the offloaded channel of user m can be expressed as:

[0082]

[0083] where P J denotes the power of the artificial noise generated by the base station, h WE,m denotes the channel response vector from user m to the eavesdropper, h PE denotes the channel response vector between the base station and the eavesdropper.

[0084] Therefore, the achievable secrecy rate of user m in the active transmission phase can be expressed as:

[0085] R SA,m = max(R WA,m - R EA,m , 0) (22)

[0086] Next, the partial offloading scheme is considered in the proposed system, i.e., the computing task can be intentionally divided into different sub-tasks. Each sub-task can be offloaded to an edge computing server or computed locally. The computing-intensive task generated by user m is denoted as Task m = <a m , b m , D m , C m | m e M>, where a m denotes the offloading rate of user m in the backscatter communication phase, b m denotes the offloading rate of user m in the active transmission phase, D m denotes the task data size of user m, and C m denotes the computing resource. In this embodiment, a m , b m , C m , D m are taken as the task offloading parameters. Therefore, the offloaded data size of user m can be expressed as (a m + b m )D m , and the total number of CPU cycles required by user m can be given by C m D m . In this embodiment, a m , b m , Dm , C m as a task offloading parameter parameter.

[0087] The time structure of the proposed system is shown in FIG. 1, where each user performs local computation on its task within a time block T. According to the task offloading parameter of each user, the local processing delay of user m is determined as: Figure 3

[0088]

[0089] where f m represents the CPU operation frequency of user m.

[0090] In the backscatter communication phase, users in the u-th group can offload tasks simultaneously. Therefore, the task offloading delay of the u-th group can be unified as the maximum offloading delay of the users in this group. According to the task offloading parameter of each user and the secrecy rate of the backscatter communication phase, the maximum offloading delay time of each user group in the backscatter phase can be determined as:

[0091]

[0092] In the active transmission phase, each user offloads its task to the edge computing server respectively. According to the task offloading parameter of each user and the secrecy rate of the active transmission phase, the task offloading delay time of each user in the active transmission phase is determined, and the task offloading delay of user m in this phase can be represented as:

[0093]

[0094] The edge computing server will allocate its computing resource f E and the computing duration T E to process the received tasks.

[0095] To ensure the quality of service requirements, the edge computing server should at least complete all the tasks offloaded by the users, i.e.:

[0096]

[0097] Therefore, the task processing delay of the system is the maximum value between the local computation delay of each user and the task processing delay of all users, and its calculation formula is:

[0098]

[0099] where T PB represents the duration of the energy signal broadcast by the base station,

[0100] Then, the energy collection and consumption model in this system is analyzed, which is as follows:​

[0101] This embodiment uses a nonlinear energy harvesting model to describe the energy harvesting circuit of each user. The energy harvested by user m during the backscatter communication phase can be expressed as:

[0102]

[0103] Among them, a m 、b m and e m represents the nonlinear energy harvesting model parameters of user m, P represents the base station's transmit power, and I represents the total number of subcarriers. The energy consumption of each user consists of three parts: local computing energy consumption, backscatter communication phase energy consumption, and active transmission phase energy consumption. The local computing energy consumption of user m is determined by the user's local processing delay time, the user's operation frequency, and the energy efficiency coefficient, specifically:

[0104]

[0105] Among them, ε m Represents the energy efficiency coefficient of user m.

[0106] The energy consumption of user m during the backscatter communication phase can be determined based on the maximum offloading delay time of each user group during the backscatter communication phase and the constant circuit power consumption during the backscatter communication phase, which is given by the following formula:

[0107] E B,m =p B,m T B,m (30)

[0108] Among them, p B,m Represents the constant circuit power consumption during the backscatter communication phase.

[0109] The energy consumption of user m during the active transmission phase can be calculated based on the task offloading delay time of each user during the active transmission phase and the active

[0110] The constant circuit power consumption during the transmission phase is determined as:

[0111] E A,m =(p m +p A,m )T A,m (31)

[0112] Among them, p A,m represents the constant circuit power consumption during the active transmission phase, p m represents the transmit power of user m.

[0113] Finally, on the basis of the above model construction and derivation, the optimization objectives are described as follows: by jointly optimizing the user grouping, the power of the randomized continuous wave signal source, the power of the randomized continuous wave interference signal, the offloading rate of backscatter communication, the offloading rate of active transmission, the CPU computing frequency of the user, the CPU computing frequency of the edge computing server, the duration of the base station broadcast energy signal, the computing duration of the edge computing server, and the power of the artificial noise generated by the base station, the task processing delay of the system is minimized. Therefore, the optimization problem can be expressed as:

[0114]

[0115] s.t.C1: E L，m +E B,m +E A,m ≤E EH,m (32b)

[0116] C2: 0≤f m ≤f max (32c)

[0117] C3: 0≤f E ≤f Emax (32d)

[0118] C4: 0≤P J ≤P Jmax (32e)

[0119] C5:

[0120] C6:

[0121] C7: α m ,β m ,α m +β m ∈[0,1] (32h)

[0122] C8:

[0123] C9:

[0124] C 10 :

[0125] C 11 :

[0126] wherein α=[α1,...,α M ],β=[β1,...,β M ],f=[f1,...,fM ], P max and P Jmax respectively denote the maximum transmit power and the maximum interference power of PB, f max and f Emax respectively denote the maximum computing frequencies of WD and edge server.

[0127] In this problem, C1 represents the energy constraint, C2 and C3 respectively constrain the maximum CPU operation frequencies of users and edge computing servers, C4 constrains the maximum power of artificial noise generated by the base station, C5 represents the QoS constraint, C6 constrains the duration of the base station broadcasting the energy signal, C7 constrains the task offloading rate of each user, C8 and C9 constrain the mean and variance of the randomized continuous wave signal, C 10 and C 11 represent the user grouping constraint.

[0128] In order to minimize the task processing delay of the proposed backscatter-assisted multi-user secure offloading system, the randomized continuous wave setting, the offloading parameter setting and the user grouping are jointly optimized. Considering that the optimization problem is a non-convex mixed integer programming problem, there are multiple coupled optimization variables in the objective function and the constraint conditions, therefore the block coordinate descent method is adopted to decompose the original optimization objective into three sub-objects, including the first sub-object for optimizing the randomized continuous wave parameters, the second sub-object for optimizing the task offloading parameters, and the third sub-object for optimizing the user grouping.

[0129] In order to realize the joint optimization of the three sub-objects, the randomized continuous wave setting, the offloading parameter setting and the user grouping are regarded as three variable blocks in this embodiment. In the proposed algorithm, the user grouping is first performed, and then the randomized continuous wave setting and the offloading parameter setting are alternately optimized in each iteration, as shown in Figure 4 , which specifically includes:

[0130] S1, based on the user grouping and the task offloading parameters of the last time, the randomized continuous wave parameters are obtained by the SCA algorithm through repeated iterations, with the objective of maximizing the secrecy rate in the backscatter communication stage;

[0131] S2, according to the randomized continuous wave parameters obtained by the first sub-object, the task offloading parameters are solved by convex optimization with the objective of minimizing the total task processing delay time and the constraint of the total energy consumption of users being less than or equal to the backscatter collection energy;

[0132] S3, according to the channel response parameters between users, the correlation between users is determined, and the user grouping of this round is updated according to the user correlation;

[0133] S4, judging whether the number of iterations meets the requirements. If it does not meet the requirements, the users in this round in step S3 are divided into the user groups in the previous step in step S1, and steps S1-S4 are repeated until the requirements are met.

[0134] For the first sub-goal, the process of optimizing the randomized continuous wave parameters is as follows:

[0135] The randomized continuous wave setting problem can be viewed as the problem of χ and The power distribution problem among the elements in The changes only affect The value of , subproblem P1a can be expressed as:

[0136]

[0137] stC8 andC9(33b)

[0138] Since the objective function of the optimization problem is for χ and are all non-convex, so it is difficult to solve P1a directly. As an alternative, this embodiment will propose a solution based on continuous convex approximation (SCA). First, The gradient of can be expressed as:

[0139]

[0140] Among them, V i,j 、W i,j , V′ i,j,l and W′ i,j,l is defined as follows:

[0141]

[0142] in, The gradient with respect to χ can be obtained by setting and Get as follows:

[0143]

[0144] Substituting formula (36) into formula (34) yields the gradient Therefore, at the tth SCA iteration, The proxy function for χ can be expressed as:

[0145]

[0146] where χ (t) It means that the optimized χ,ν after the t-th SCA iteration is a positive constant. Therefore, The surrogate problem for χ can be formulated as:

[0147]

[0148] s.t.C8 (38b)

[0149] C 12 : χ±0 (38c)

[0150] P1b is a strictly concave function with respect to the convex constraint, and the solution χ' can be efficiently solved by CVX and other convex tools. Similarly, in the t-th SCA iteration, The surrogate function for can be expressed as:

[0151]

[0152] Therefore, The surrogate problem for can be formulated as:

[0153]

[0154] s.t.C8 (40b)

[0155] C 13 :

[0156] P1c is a strictly concave function with respect to the convex constraint, and the solution can be efficiently solved by CVX and other convex tools.

[0157] For the second sub-objective, the process of optimizing the task offloading parameters is as follows:

[0158] Given the specific grouping of users and the specific settings of the randomized continuous wave, the system's task processing delay is further reduced by solving the offloading parameter setting problem, where the sub-problem P2a can be expressed as:

[0159]

[0160] s.t.C1-C7(41b)

[0161] Since the objective function of P2a contains a min-max function, an auxiliary variable μ = T total is introduced, and P2a is rewritten as:

[0162]

[0163] C2-C7 (42c)

[0164] C 14 : TL，m” μ (42d)

[0165] C 15 :T E，m” μ (42e)

[0166] Among them, C 14 and C 15 is obtained by introducing auxiliary variables, and C′1 is obtained by replacing T with μ in C1. L,m However, P2b is still very complicated. Through simple deduction, it can be obtained that the minimum task processing delay of the system is obtained under the premise that the edge computing server executes the task at the maximum CPU computing frequency and the base station transmits artificial noise at the maximum power, that is, Therefore, P2b can be rewritten as:

[0167]

[0168] stC2, 6, and C7 43b

[0169] C″1:

[0170] C′5:

[0171] C′ 14 (1-α m -β m )C m D m” f m μ (43e)

[0172]

[0173] Among them, E′ A,m By putting P J =P Jmax Substitute into formula (21) to get. However, due to C″1 and C′ 14 Medium m and μ, P2c is still non-convex, since each user has a unique f m But they share the same μ, so they cannot be solved by introducing auxiliary variables. However, when μ is fixed, the optimization problem P2c can be simplified to P2d, which is expressed as:

[0174] P2d:A={α,β,f,T PB ,T E}(44a)

[0175] stC″1,C2,C′5,C6,C7,C′ 14 ,and C′15 (44b)

[0176] where P2dis a concave function, which can be solved efficiently by convex tools such as CVX.

[0177] For the third sub-goal, the process of optimizing user grouping is as follows:

[0178] Given the specific settings of the randomized continuous wave and offloading parameters, the task processing delay of the system is further reduced by solving the user grouping problem, where the sub-problem P3a can be expressed as:

[0179]

[0180] s.t.C 10 and C 11 (45b)

[0181] In fact, P3acan be solved by exhaustive search and replacing all grouping schemes. Unfortunately, the complexity of this method is high. The present embodiment proposes a user grouping solution based on correlation, which minimizes the overall system delay by optimizing user grouping. In this method, user grouping is based on the correlation coefficient, and the correlation coefficient between user m and user n can be calculated as:

[0182]

[0183] where θ m,n represents the correlation between user m and user n, represents the transpose matrix of h PW,m , represents the conjugate of h PW,m , and h PW,m represents the channel response vector from the base station to user m, represents the transpose matrix of h PW,n , represents the conjugate of h PW,n , and h PW,n represents the channel response vector from the base station to user n. The basic idea of the proposed method is to improve the secrecy rate of each user group in the backscatter communication phase by grouping highly correlated users into different groups.

[0184] The application provides a multi-user secure offloading method based on backscatter-assisted edge computing, a system composed of a full-duplex power base station, multiple users and eavesdroppers is constructed, a multi-carrier random continuous wave transmitted by the base station is used as a radio frequency source, the users can offload tasks through hybrid backscatter communication and active transmission, multiple types of parameters can be obtained, and the security rate of each stage, the total task processing delay time, the total energy consumption of the users and the collected energy are determined, the energy consumption and the collected energy are used as constraints to jointly optimize the multiple types of parameters, the information leakage problem caused by the fact that the prior art does not consider the broadcast characteristics of a wireless channel is effectively solved, the multi-user simultaneous secure offloading task is realized, the spectrum resource utilization is improved, the device energy consumption is reduced, the total task processing delay time of the system is significantly reduced by jointly optimizing the random continuous wave parameters, the task offloading parameters and the user grouping, and the security performance and the task processing efficiency of the system in the multi-user scenario are enhanced, and a safe and efficient task offloading solution is provided for the intensive computing demand of Internet of Things devices.

[0185] The following is a simulation example made for the present embodiment:

[0186] The application takes the total task processing delay of the backscatter-assisted mobile edge computing system as the optimization target, effectively eliminates the eavesdropping risk and improves the security performance of the system, and simultaneously realizes multi-user task offloading. The simulation scene is set in a square area of 150m*150m, there are 20 users, 1 eavesdropper and 1 base station in the scene. It is assumed that the users have a BPSK codebook input, the size of the task data generated by each user is set to 100kbits. The number of CPU cycles required for each bit of data is set to 1000 revolutions / bit.

[0187] In the proposed algorithm, the RCW setting, the offloading parameter setting and the user grouping are optimized. In order to illustrate the advantages of the multi-user secure offloading scheme proposed by the application, the following benchmark schemes are considered for performance comparison, including a no-eavesdropping scheme, a no-local-computing scheme, a no-backscatter-communication offloading scheme and a no-active-transmission offloading scheme.

[0188] The relationship between the total task processing delay of the system and the number of users is as follows: Figure 5The total system delay of all task offloading schemes increases with the number of users. For the no backscatter communication offloading scheme, the increase in the number of users means that the total delay of all users offloading tasks in the active transmission phase increases. For the proposed scheme, the no eavesdropper scheme, and the no local computation scheme, the increase in the number of users not only affects the number of user groups, but also increases the overall delay of all users offloading tasks in the active transmission phase. Since the energy required for backscatter communication is lower than that required for active transmission, the no active transmission offloading scheme can efficiently complete energy harvesting and task offloading when the number of users is small. However, the task offloading speed of backscatter communication is lower than that of active transmission, resulting in longer delay time for energy harvesting and task offloading of this scheme as the number of users increases. As can be seen from the figure, the overall system delay of the proposed scheme is very close to that of the no eavesdropper scheme, which verifies that the proposed scheme effectively improves the security of users during task offloading. At the same time, the overall system delay of the proposed scheme is always lower than that of other schemes, which reflects the feasibility and superiority of the proposed scheme.

[0189] Figure 6 The relationship between the amount of task data and the total system task processing delay is compared. The amount of task data increases from 60 kbits to 140 kbits, and the relationship between the amount of task data and the overall system delay of each scheme is close to a linear relationship. Among all the schemes, the no active transmission offloading scheme performs the worst, followed by the no backscatter communication offloading scheme. The proposed scheme and the no local computation scheme both achieve good results. As can be seen from the figure, the overall system delay of the proposed scheme is very close to that of the no eavesdropper scheme, which reflects the superiority of the proposed physical layer security method.

[0190] Figure 7 The relationship between the base station transmit power and the total system task processing delay is shown. As can be seen from the figure, the overall system delay of each offloading scheme decreases as the transmit power increases. For each scheme, the increase in transmit power not only affects the secrecy rate in the backscatter communication phase, but also increases the energy used by users for local computation and data transmission. Since the energy required for backscatter communication is lower than that required for active transmission, the no active transmission offloading scheme has lower task processing delay at low transmit power. Since the task offloading rate of active transmission is higher than that of backscatter communication, the no backscatter communication offloading scheme performs better at high transmit power. RCW cannot fully interfere with the eavesdropper at low transmit power, resulting in a slight performance gap between the proposed scheme and the no eavesdropper scheme. However, the overall system delay of the proposed scheme is very close to that of the no eavesdropper scheme, and is always lower than that of other schemes at high base station transmit power, which indicates the superiority of the proposed scheme.

[0191] The simulation result proves accuracy of comparison between the approximate value and the accurate value, and verifies that the scheme proposed in the application can significantly reduce task processing delay of the system, and efficiently implement multi-user task offloading.

[0192] Finally, it should be explained that the above preferred embodiments are only used to illustrate the technical solutions of the application but not limit the application, although the application has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the application.

Claims

1. A multi-user secure offloading method based on backscatter-assisted edge computing, characterized in that: Applied to a system consisting of a full-duplex power base station equipped with an edge computing server, multiple users, and an eavesdropper, the base station transmits a multi-carrier random continuous wave as the RF source, and multiple users offload tasks to the edge computing server through hybrid backscatter communication and active transmission, including: Obtain the task offloading parameters of each user, the current user group, the randomized continuous wave parameters, the channel parameters of the backscatter communication phase, the channel parameters of the active transmission phase, and the eavesdropper channel parameters; The confidentiality rate of each user in the backscatter communication phase and the active transmission phase is determined based on the current user group, the eavesdropper channel parameters, the channel parameters in the backscatter communication phase, the channel parameters in the active transmission phase, and the randomized continuous wave parameters. In the backscatter communication phase, multiple users in the same user group simultaneously offload tasks, and the edge computing server decodes the target user signal and treats other user signals as noise. Based on the task offloading parameters of each user, the confidentiality rate of the backscatter communication phase, and the confidentiality rate of the active transmission phase, the total task processing delay time, the total energy consumption of the user, and the energy collected by the user in the backscatter communication phase are determined; Taking the total energy consumption of users and the energy collected by users in the backscatter communication phase as constraints, the total task processing delay time is minimized, and the randomized continuous wave parameters, task offloading parameters and user grouping are jointly optimized.

2. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 1, characterized in that: The confidentiality rate of each user in the backscatter communication phase and the active transmission phase is determined based on the current user group, the eavesdropper channel parameters, the channel parameters in the backscatter communication phase, the channel parameters in the active transmission phase, and the randomized continuous wave parameters, including: Determine the achievable rate of each user's legitimate link and the user's achievable eavesdropping link rate based on the current user group, the eavesdropper's channel parameters, the channel parameters of the backscatter communication phase, and the randomized continuous wave parameters; Determine the confidentiality rate of the user in the backscatter communication phase based on the achievable rate of each user's legitimate link and the achievable eavesdropping link rate of the user; According to the channel parameters in the active transmission phase, the eavesdropper's channel parameters and the randomized continuous wave parameters, the rate of the user's active transmission phase and the data rate of the eavesdropper when the eavesdropper unloads the channel are determined; The confidentiality rate of each user in the active transmission phase is determined according to the rate of the user in the active transmission phase and the data rate of the eavesdropper when the eavesdropping user unloads the channel.

3. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 2, characterized in that: The total task processing delay is determined based on the task offloading parameters of each user, the confidentiality rate of the backscatter communication phase, and the confidentiality rate of the active transmission phase, including: Determine the user's local processing delay time based on each user's task offloading parameters; Determine the maximum offloading delay time for each user group in the backscattering phase based on the task offloading parameters of each user and the confidentiality rate of the backscattering communication phase; Determine the task offloading delay time of each user in the active transmission phase according to the task offloading parameters of each user and the confidentiality rate of the active transmission phase; According to the task offloading parameters, the time required for the edge server to complete all task offloading calculations is constrained; Constrain the duration of the base station's broadcast energy signal based on the maximum offloading delay of each user group during the backscattering phase; The total task processing delay time is determined based on the user's local processing delay time, the duration of the base station broadcast energy signal, the task offloading delay time of each user in the active transmission phase, and the time it takes for the edge server to complete all task offloading calculations.

4. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 3 is characterized in that: The total energy consumption of a user is composed of local computing energy consumption, backscattering phase energy consumption, and active transmission phase energy consumption. The total energy consumption of a user is determined by: Obtain the user's operation frequency, energy efficiency coefficient, constant circuit power consumption during the backscatter communication phase, and constant circuit power consumption during the active transmission phase; Determine the user's local computing energy consumption based on the user's local processing delay time, the user's operation frequency, and the energy efficiency coefficient; Determine the energy consumption of the backscattering phase based on the maximum offloading delay time of each user group in the backscattering phase and the constant circuit power consumption in the backscattering communication phase; The energy consumption of the active transmission phase is determined according to the task offloading delay time of each user in the active transmission phase and the constant circuit power consumption in the active transmission phase.

5. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 1, characterized in that: Taking the total energy consumption of users and the energy collected by users during the backscatter communication phase as constraints, the total task processing delay is minimized, and the randomized continuous wave parameters, task offloading parameters, and user grouping are jointly optimized, including: Based on the block coordinate descent method, the total energy consumption of users and the energy collected by users during the backscatter communication phase are constrained to minimize the total task processing delay time. The randomized continuous wave parameters, task offloading parameters, and user grouping are jointly optimized. The optimization is decomposed into three sub-goals: the first sub-goal is to optimize the randomized continuous wave parameters, the second sub-goal is to optimize the task offloading parameters, and the third sub-goal is to optimize the user grouping. The three sub-goals are collaboratively optimized in the following ways: S1, based on the last user grouping and task offloading parameters, with the goal of maximizing the confidentiality rate of the backscatter communication phase, repeatedly iterates through the SCA algorithm to obtain randomized continuous wave parameters; S2, based on the randomized continuous wave parameters obtained from the first sub-goal, with the goal of minimizing the total task processing delay time and the constraint that the total user energy consumption is less than or equal to the backscattering collection energy, solve the task offloading parameters through convex optimization; S3, determine the correlation between users based on the channel response parameters between users, and update the user grouping in this round based on the user correlation; S4, judging whether the number of iterations meets the requirements. If it does not meet the requirements, the users in this round in step S3 are divided into the user groups in the previous step in step S1, and steps S1-S4 are repeated until the requirements are met.

6. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 2, characterized in that: The confidentiality rate of the user in the backscatter communication phase is: in, Represents R SB.n The approximate value of R PB,n Approximate value of R PB,n R represents the achievable rate of the legal link of the nth user. EB,n is the achievable eavesdropping link rate of user n, R EB,n Approximate value of The confidentiality rate of the user in the active transmission phase is R SA,m : R SA,m =max(R WA,m -R EA,m ,0); Among them, R WA,m is the rate of user m during the active transmission phase, R EA,m is the data rate of the eavesdropper when the eavesdropping user m unloads the channel, B represents the channel bandwidth, p m represents the transmission power of user m, N0 represents the power spectral density of additive white Gaussian noise, P J represents the power of artificial noise generated by the base station, h PW,m represents the channel response vector from the base station to user m, h WE,m represents the channel response vector from user m to the eavesdropper, h PE represents the channel response vector from the base station to the eavesdropper.

7. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 3, characterized in that: The total task processing delay is: Among them, T L,m Indicates the local processing delay time of user m, α m represents the offloading rate of user m in the backscatter communication phase, β m represents the unloading rate of user m in the active transmission phase, f m Indicates the CPU operation frequency of user m, C m 、D m Represent the computing resources and task data size of user m respectively; T PB Indicates the duration of the base station broadcast energy signal, T E Indicates the time it takes for the edge computing server to complete all tasks offloaded by users. It represents the sum of the task offloading delay times of each user in the active transmission phase.

8. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 4, characterized in that: The user's local computing energy consumption is: Among them, ε m represents the energy efficiency coefficient of user m, f m Indicates the CPU operation frequency of user m, T L,m Local processing delay; The energy consumption in the backscattering stage is: E B,m =p B,m T B,m ; Among them, p B,m represents the constant circuit power consumption of user m during the backscatter communication phase, T B,m represents the maximum offloading delay time of user m during the backscatter communication phase; The energy consumption during the active transmission phase is: E A,m =(p m +p A,m )T A,m ; Among them, p A,m represents the constant circuit power consumption of user m during the active transmission phase, P m Indicates that T A,m represents the task offloading delay time of user m in the active transmission phase.

9. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 1, characterized in that: The energy collected by the user during the backscatter communication phase is determined by the following formula: Where I represents the total number of subcarriers, P represents, a m 、b m and e m represents the nonlinear energy harvesting model parameters of user m, h PW,m represents the channel response vector from the base station to user m, T PB Indicates the duration of the base station broadcast energy signal, T B,u Offload the maximum offload delay for tasks in group u.

10. The multi-user secure offloading method based on backscatter-assisted edge computing according to claim 5, characterized in that: Determine the correlation between users based on the channel response parameters between users, including: Among them, θ m,n represents the correlation between user m and user n, Indicates h PW,m The transposed matrix of Indicates h PW,m The conjugate of h PW,m represents the channel response vector from the base station to user m, Indicates h PW,n The transposed matrix of Indicates h PW,n The conjugate of h PW,n represents the channel response vector from the base station to user n.

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