CN112911587B - Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment - Google Patents

Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment Download PDF

Info

Publication number
CN112911587B
CN112911587B CN202110103867.XA CN202110103867A CN112911587B CN 112911587 B CN112911587 B CN 112911587B CN 202110103867 A CN202110103867 A CN 202110103867A CN 112911587 B CN112911587 B CN 112911587B
Authority
CN
China
Prior art keywords
mec
mode
calculation
unloading
task
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110103867.XA
Other languages
Chinese (zh)
Other versions
CN112911587A (en
Inventor
余雪勇
李星海
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202110103867.XA priority Critical patent/CN112911587B/en
Publication of CN112911587A publication Critical patent/CN112911587A/en
Application granted granted Critical
Publication of CN112911587B publication Critical patent/CN112911587B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a method for unloading a safe anti-eavesdropping task by utilizing a physical layer in an MEC-D2D environment. The invention relates to the technical field of computer wireless communication, and constructs a novel MEC-D2D system architecture; offloading part of the computation task to an MEC server or a D2D node closest thereto through the MEC link or the D2D link; considering that data on an unloading link is in the risk of being intercepted, a physical layer security technology is utilized to resist an eavesdropper, and if the average unloading rate is within the security transmission rate, secret communication can be realized; the invention takes the minimized system energy consumption as an objective function and takes the secrecy unloading requirement as a main constraint to plan an optimization problem. Computing task allocation, sub-channel allocation and transmission power allocation strategies are jointly optimized; the optimization problem is non-convex and is relatively complex, so an algorithm is constructed to solve the optimization problem, and the main idea is to decompose the main problem into three subproblems and solve the problems by adopting a Lagrangian dual method.

Description

Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment
Technical Field
The invention relates to the technical field of computer wireless communication, in particular to a method for safely unloading an anti-eavesdropping task by utilizing a physical layer in a Mobile Edge Computing-Device to Device (MEC-D2D) environment.
Background
The rapid development of the internet of things and smart devices in recent years has driven a large array of new applications such as virtual reality, unmanned and augmented reality. These applications are computationally intensive and delay sensitive, and typically have the characteristics of computational complexity, high energy consumption, and short response time. The limited computing resources and processing power of local devices have failed to meet their needs. Moving edge computation has recently become an effective paradigm to address the above dilemma. The MEC is an extension of cloud computing, and the problem of large overhead and high management cost of a cloud computing backhaul link is solved by putting down computing resources to a network edge side. The MEC can reduce the end-to-end time delay of mobile Service delivery, explore the inherent capability of a wireless network, and obviously improve the QoS (Quality of Service) of a user. The MEC is also a new architecture, and a new industrial chain and network ecosphere can be established based on the MEC. The most important function of MECs is computational offloading, by deploying high performance MEC servers near the edge of the wireless access network, local devices can offload some or all of the computationally intensive tasks to the MEC servers, which can significantly reduce the energy consumption and latency of the local devices.
MEC makes task processing efficient and there is a great deal of literature on MEC technology. Most of these documents minimize delay or energy consumption by optimizing task allocation or resource allocation, but rarely consider the problem of resource conflicts that may arise with servers when a large number of tasks are simultaneously requested from the MEC server. Resource conflicts can lead to increased processing time for computing tasks and, in the worst case, even to a server crash. In addition, due to the broadcast characteristic of wireless communication, the data unloaded in the process of unloading the computing task is exposed to the risk of interception, and the data often contains some important and sensitive information, so how to protect the privacy of the user becomes a big problem. If a traditional cryptography-based encryption technology is applied to an MEC architecture to ensure communication security, the MEC is a novel technology of 5G, and the 5G has a large number of nodes, which makes the complexity of key distribution and management extremely high and even difficult to implement, and the computing resources of the nodes are limited and cannot support the highly complex encryption and authentication technology. The MEC server resource conflict problem is not negligible and a method for effectively ensuring the security of the unloaded data suitable for the MEC architecture is urgently needed.
Disclosure of Invention
Aiming at the problems, the invention provides a method for unloading a physical layer security anti-eavesdropping task under an MEC-D2D environment, which introduces a D2D communication technology into a traditional MEC system to form the MEC-D2D system, and a user can unload a calculation task to an MEC server and also can unload the calculation task to a D2D node closest to the MEC server through a D2D uplink; meanwhile, a physical layer security method is used to achieve a secure transmission rate in the unloading process to guarantee the communication security.
The technical scheme of the invention is as follows: the method for unloading the security anti-eavesdropping task by utilizing the physical layer under the MEC-D2D environment comprises the following specific steps:
step (1.1), establishing an MEC-D2D system model with an eavesdropper;
step (1.2), establishing an MEC-D2D system model into a formulaic optimization problem through mathematical modeling;
step (1.3), the established optimization problem is calculated according to three different calculation modes: decomposing the local calculation mode, the MEC unloading calculation mode and the D2D unloading calculation mode into three sub-problems for subsequent solution;
step (1.4), the request equipment judges whether the calculation task can be completed by the request equipment per se within the specified time, if so, a local calculation mode is selected, and the process is ended after the calculation is completed;
if not, continuing to execute the subsequent steps;
step (1.5), when the request device judges that the calculation task is not completed by itself within the specified time, the optimization problems in the MEC unloading calculation mode and the D2D unloading calculation mode are respectively solved,
respectively obtaining the minimum energy consumption, the optimal task allocation strategy, the optimal sub-channel allocation strategy and the optimal transmitting power by calculating the gradient and applying a gradient descent method to iterate;
step (1.6), comparing the energy consumption in the MEC unloading calculation mode with the energy consumption in the D2D unloading calculation mode,
selecting a calculation mode with lower energy consumption, and unloading part of calculation tasks to an MEC server or a D2D node closest to the MEC server at a safe transmission rate;
and (1.7) returning the result after the calculation is finished, and ending the process.
Further, in step (1.1), the building of the MEC-D2D system model with the eavesdropper includes four devices: the device comprises a wireless access point, N request devices, M service devices and a malicious eavesdropper, wherein the wireless access point is integrated with an MEC server.
Further, in step (1.2), the established optimization problem specifically includes an objective function and a constraint condition; the target function is to minimize system energy consumption, and the constraint condition is safe transmission rate limit;
wherein, the objective function is:
Figure BDA0002916593450000021
in the formula (1), the reaction mixture is,
Figure BDA0002916593450000022
and
Figure BDA0002916593450000023
respectively representing the energy consumption of each RD in the local computing mode, the energy consumption of the MEC in the unloading computing mode and the energy consumption of the D2D in the unloading mode;
l, theta and p respectively represent a calculation task allocation strategy, a subchannel number allocation strategy and transmission power;
α i ，β i and gamma i Respectively representing calculation mode decision variables;
the constraint conditions include two constraint conditions:
1. RD i The task unloading rate constraint conditions in the MEC unloading calculation mode are as follows:
(L i -l i )/T≤S i (θ i,k ,p i,k )
in the formula, RD i Denotes the ith requesting device, L i And l i Respectively represent RD i T represents the processing task limit time, S i Represents RD i Safe transmission rate of theta i,k Indicate about RD i Assignment strategy for the k-th sub-channel, p i,k Represents RD i The transmission power allocated on the k-th sub-channel, in addition, S i Is theta i,k And p i,k A binary function of (a);
2. RD i The task offload rate constraints in the D2D offload computation mode are:
Figure BDA0002916593450000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002916593450000032
and represents the amount of tasks left in local computation in the D2D offload computation mode,
Figure BDA0002916593450000033
indicating RD in D2D offload computation mode i The rate of transmission of the data to be transmitted,
Figure BDA0002916593450000034
indicating RD in D2D offload computation mode i Of (2) in addition to S i Is that
Figure BDA0002916593450000035
A unitary function of (a).
Further, in the step (1.5), the optimization problems in the MEC offload computation mode and the D2D offload computation mode are respectively solved by applying a lagrange dual method to convert a non-convex optimization problem into a convex optimization problem, and the specific operation steps are as follows:
first, the lagrange multiplier λ or μ is introduced into the original optimization problem:
Figure BDA0002916593450000036
and then, carrying out iterative solution by adopting a gradient descent method, and ending the iteration when a convergence condition is met or the iteration exceeds a certain number of times, wherein the finally obtained solution is the solution of the sought optimization problem.
Further, in the step (1.6), the comparing the energy consumption in the MEC offload computation mode with the energy consumption in the D2D offload computation mode specifically means that when the energy consumption in the MEC offload computation mode is less than the energy consumption in the D2D offload computation mode, the MEC offload computation mode is selected, and part of the computation tasks are offloaded to the MEC server at a safe transmission rate, and after the computation is completed, the result is returned, so that the process is ended;
otherwise, selecting a D2D unloading calculation mode, unloading part of calculation tasks to the D2D node closest to the D2D node at a safe transmission rate, and returning the result after the calculation is finished, thereby finishing the process.
Further, few literature on MECs considers that servers can generate resource conflict (RS) problems when a large number of computing tasks are simultaneously requested from MEC servers; the RS can cause the processing delay to rise, and even cause the edge server to crash when the processing delay is serious; a D2D communication mechanism is introduced into a traditional MEC system, so that user equipment can communicate with each other, and the simple communication between an upper layer and a lower layer is expanded to the communication between the planes in the layers, and on the basis, the MEC-D2D system comprising RD, SD, AP and an edge server is established; the MEC-D2D system can reduce the calculation burden of the edge server, and the RD and the SD can cooperate with each other due to the D2D communication mechanism, so that the idle calculation resources of the SD can be fully utilized by the RD; the processing efficiency of the computing tasks and the utilization rate of computing resources are improved on the whole.
Due to the broadcasting characteristic of wireless communication, data in uplink offload links (D2D offload links and MEC offload links) in the offload process of computing tasks are at risk of being intercepted, and the data often contain important and sensitive information of users, so how to protect the privacy information of the users becomes a big problem; if a traditional cryptography-based encryption technology is applied to an MEC architecture in an internet of things environment to ensure communication security, the complexity of key distribution and management is extremely high or even difficult to achieve due to the distribution of a large number of user nodes, and the computing resources of the nodes are limited and cannot support the highly complex encryption and authentication technologies. Under the above background, compared with the traditional upper layer security technology which completely depends on the confidentiality and the computational complexity of a key, the security technology based on a physical channel aims to provide light-weight and high-security guarantee for a network and a user by utilizing the randomness and uniqueness of a wireless communication physical medium, fully utilizes channel resources of wireless transmission, and realizes high-strength unconditional security transmission without the key under the condition of not assuming the limited computational capability of an attacker; the invention adopts a physical layer security methodOver-setting the unload workload l i And
Figure BDA0002916593450000042
thereby limiting the average offload rate to the safe transfer rate S i And
Figure BDA0002916593450000041
the method can be used for resisting eavesdroppers, avoids the difficulty of the traditional encryption technology and guarantees the safety in the unloading process.
The invention has the beneficial effects that: the invention adopts a joint optimization calculation task allocation l and resource (channel resource theta and emission power resource p) allocation strategy to solve the energy consumption minimization problem E sum (ii) a Compared with a single optimization task allocation strategy or a resource allocation strategy, the combined optimization strategy can obtain lower system energy consumption because two decision variables can be optimized simultaneously; considering that the optimization problem is that non-convex (non-convex) can not be solved by directly using a convex optimization algorithm, and considering that three different calculation modes lead to more complex objective function and optimization problem and more difficult solution; the optimization problem is decomposed into three subproblems, the non-convex optimization problem is converted into the convex optimization problem by using a Lagrangian dual method, and finally the non-convex optimization problem is iteratively solved by using a gradient descent method.
Drawings
FIG. 1 is a diagram of a system model of the present invention;
FIG. 2 is a schematic flow diagram of the present invention;
FIG. 3 is a diagram of the detailed operation steps of the optimization problem solution of the present invention in the MEC offload computation mode;
FIG. 4 is a graph illustrating the impact of the total computation workload on the average system energy consumption in different computation modes in an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings:
as shown in the figure, the method for unloading the security anti-eavesdropping task by using the physical layer in the MEC-D2D environment specifically comprises the following steps:
step (1.1), establishing an MEC-D2D system model with an eavesdropper;
step (1.2), establishing an MEC-D2D system model into a formulaic optimization problem through mathematical modeling;
step (1.3), the established optimization problem is calculated according to three different calculation modes: decomposing the local computing mode, the MEC unloading computing mode and the D2D unloading computing mode into three sub-problems for subsequent solution;
step (1.4), the request equipment judges whether the calculation task can be completed by the request equipment per se within the specified time, if so, a local calculation mode is selected, and the process is ended after the calculation is completed;
if not, continuing to execute the subsequent steps;
step (1.5), when the request device judges that the calculation task is not completed by itself within the specified time, the optimization problems in the MEC unloading calculation mode and the D2D unloading calculation mode are respectively solved,
respectively obtaining the minimum energy consumption, the optimal task allocation strategy, the optimal sub-channel allocation strategy and the optimal transmitting power by calculating the gradient and applying a gradient descent method to iterate;
step (1.6), comparing the energy consumption in the MEC unloading calculation mode with the energy consumption in the D2D unloading calculation mode,
selecting a calculation mode with lower energy consumption, and unloading part of calculation tasks to an MEC server or a D2D node closest to the MEC server at a safe transmission rate;
and (1.7) returning the result after the calculation is finished, and ending the process.
Further, as depicted in FIG. 1; for step (1.1): fig. 1 shows a system model of the present invention, in which four devices are present: a wireless Access Point (AP) fused with an MEC server, N Request Devices (RD), M Service Devices (SD) and a malicious eavesdropper; note that all computation tasks are initiated by RD, SD is a pairing of RD in D2D offload mode, which is only responsible for receiving tasks offloaded to it from RD and does not initiate computation tasks; the four equipment structuresWith the MEC-D2D system in which an eavesdropper is present, the invention assumes, in view of the communication requirements, that all devices are distributed in a two-dimensional plane and have fixed coordinates, i.e. the coordinates of RD and SD are V i ＝(x i ,y i ) And V j ＝(x j ,y j ) (ii) a The coordinate of AP is V ap The coordinate of the eavesdropper is V eav (ii) a Each RD should complete the computation task within a limited time T; there are three calculation modes available for RD selection: a local computation mode, an MEC offload computation mode and a D2D offload computation mode; the invention provides that RD can only select one calculation mode; when the RD selects the two calculation modes needing unloading, the RD unloads part of calculation tasks to an MEC server or a D2D node closest to the MEC server through corresponding uplinks; it is noted that the data faces the risk of being intercepted by a malicious eavesdropper in the unloading process, and the communication safety in the unloading process can be ensured by setting the safe transmission rate by adopting a physical layer safety method;
further, in the step (1.2), the established optimization problem specifically includes an objective function and a constraint condition;
wherein, the objective function is:
Figure BDA0002916593450000051
in the formula (1), the reaction mixture is,
Figure BDA0002916593450000061
and
Figure BDA0002916593450000062
respectively representing the energy consumption of each request device in a local computing mode, the energy consumption of an MEC unloading computing mode and the energy consumption of a D2D unloading mode;
l, theta and p respectively represent a calculation task distribution strategy, a subchannel number distribution strategy and transmitting power;
α i ，β i and gamma i Representing calculation mode decision variables whose role is limitingThe request equipment can only select one calculation mode at the same time;
the constraint conditions include two constraint conditions:
1. RD i The task unloading rate constraint conditions in the MEC unloading calculation mode are as follows:
(L i -l i )/T≤S i (θ i,k ,p i,k )
in the formula, RD i Denotes the ith requesting device, L i And l i Respectively represent RD i T represents the processing task limit time, S i Represents RD i Safe transmission rate of theta i,k Indicate about RD i Assignment strategy for the k-th sub-channel, p i,k Represents RD i The transmission power allocated on the k-th sub-channel, in addition, S i Is θ i,k And p i,k A binary function of (a);
2. RD i The task offload rate constraints in the D2D offload computation mode are:
Figure BDA0002916593450000063
in the formula (I), the compound is shown in the specification,
Figure BDA0002916593450000064
and represents the amount of tasks left in local computation in the D2D offload computation mode,
Figure BDA0002916593450000065
indicating RD in D2D offload computation mode i The rate of transmission of the data to be transmitted,
Figure BDA0002916593450000066
indicating RD in D2D offload computation mode i Of (d) and, in addition, S i Is that
Figure BDA0002916593450000067
A univariate function of (c); root of herbaceous plantThe realization of secret communication in the unloading process can be ensured as long as the conditions are met according to the knowledge of the physical layer safety related documents;
for step (1.2): the objective function is
Figure BDA0002916593450000068
And
Figure BDA0002916593450000069
weighted sum of energy consumption in three calculation modes:
in particular, RD i The energy consumption when selecting the local computation mode is:
Figure BDA00029165934500000610
in the formula (2), the reaction mixture is,
Figure BDA00029165934500000611
all generated from a Central Processing Unit (CPU), ζ represents the capacitance conversion factor of the effective CPU, f i,m Represents RD i The CPU frequency required at the m-th CPU cycle, C represents the number L of CPU cycles required to calculate the 1 input bit i Represents RD i Calculating the task amount totally, wherein T represents the task processing limit time;
RD i the energy consumption for selecting the MEC offload computation mode is:
Figure BDA00029165934500000612
in the formula (3), the reaction mixture is,
Figure BDA00029165934500000613
the energy consumption is calculated locally and communication energy consumption is unloaded in the process, and the energy consumption calculated on the MEC server and the energy consumption returned by the result are not considered because the MEC server is generally considered to have strong calculation power and small calculation result; l. the i Representing the amount of tasks remaining in the local computation, using the communication bandwidth during the offloading processSub-channel allocation scheme, theta i,k Representing the assignment of the k-th sub-channel to the i-th user, p i,k Represents the power of the ith user on the kth sub-channel;
RD i the energy consumption to select the D2D offload computation mode is:
Figure BDA0002916593450000071
in the formula (4), the reaction mixture is,
Figure BDA0002916593450000072
the method comprises the steps of local calculation energy consumption, unloading energy consumption for communication generation and D2D node calculation energy consumption;
Figure BDA0002916593450000073
indicating RD in D2D offload mode i The amount of tasks left on the local computation,
Figure BDA0002916593450000074
represents RD i To SD j Transmit power when unloaded;
next, the main constraint condition is to limit the transmission rate according to the knowledge related to the physical layer security method; the offload computation mode for MEC is:
(L i -l i )/T≤S i (θ i,k ,p i,k ) (5)
Figure BDA0002916593450000075
in formula (6), S i (θ i,k ,p i,k ) RD under finger MEC uninstalling mode i Is a safe transfer rate of theta i,k And p i,k Function of (A), B sub Is the sub-channel bandwidth, h i,k And g i,k Are each RD i MEC uplink channel gain and eavesdropping link channel gain, h, on the k-th sub-channel i,k And g i,k All conform to
Figure BDA0002916593450000076
A path loss model wherein d i May be RD i The distance from the AP can also be RD i Distance from the eavesdropper, the two distances being defined by the previously set coordinates V of RD on the two-dimensional plane i ＝(x i ,y i ) The coordinate of AP is V ap The coordinate of the eavesdropper is V eav The calculation is carried out to obtain the result,
Figure BDA0002916593450000077
is d 0 Path loss coefficient under the condition of =1 meter; (6) The meaning of the formula is the difference value of the MEC uplink channel capacity and the interception link channel capacity, and the communication safety can be ensured as long as the average unloading rate is lower than the safe transmission rate according to the safety related knowledge of the physical layer;
the computation mode for D2D offload is:
Figure BDA0002916593450000078
Figure BDA0002916593450000079
in the formula (8), the reaction mixture is,
Figure BDA00029165934500000710
refer to RD under D2D offload mode i About a safe transmission rate of
Figure BDA00029165934500000711
As a function of (a) or (b),
Figure BDA0002916593450000081
and
Figure BDA0002916593450000082
are each RD i D2D uplink channel gain and eavesdropping link channel gain on the kth subchannel; its meaning withSimilar in MEC offload computation mode;
in summary, the optimization problem of the present invention is expressed as:
Figure BDA0002916593450000083
s.t.
Figure BDA0002916593450000084
Figure BDA0002916593450000085
Figure BDA0002916593450000086
Figure BDA0002916593450000087
Figure BDA0002916593450000088
Figure BDA0002916593450000089
wherein alpha is i ，β i And gamma i Is a variable that can only take values of 0 or 1, and this limits RD due to the constraint (9 a) i Only one of the three calculation modes can be selected;
further, for step (1.3): the established optimization problem is decomposed into three sub-problems, namely energy consumption of a local computing mode
Figure BDA00029165934500000810
Can be directly calculated, but the MEC unloads a calculation moduleEnergy consumption of formula
Figure BDA00029165934500000811
And energy consumption of D2D offload computing mode
Figure BDA00029165934500000812
The method is a non-convex optimization problem, converts the problem into a Lagrangian dual problem and iteratively solves the Lagrangian dual problem by a gradient descent method;
wherein, the objective functions of the three sub-problems are respectively:
Figure BDA00029165934500000813
Figure BDA00029165934500000814
Figure BDA00029165934500000815
the three formulas represent energy consumption under three calculation modes; the formula (10) can be directly calculated, and the two subsequent subproblems of the formula (11) and the formula (12) can be solved according to the solution in the step (1.5).
Further, for step (1.4): the method comprises the steps that firstly, according to a given total calculation task amount and the calculation force of a CPU of the RD, the RD calculates out execution time, if the execution time exceeds a limit time T, an MEC unloading calculation mode or a D2D unloading calculation mode is selected, and otherwise, a local calculation mode is selected;
further, in step (1.5), the solving of the optimization problem in the MEC offload computation mode and the D2D offload computation mode respectively is to convert the non-convex optimization problem into a convex optimization problem by applying a lagrange dual method. The solution of the problem under the MEC uninstall calculation mode is similar to that under the D2D uninstall calculation mode, here, taking the MEC uninstall calculation mode as an example, the specific operation steps are as follows:
first, introduce the lagrange multiplier λ or μ into the original optimization problem:
Figure BDA0002916593450000091
thus, the non-convex original optimization problem is converted into a convex Lagrange dual problem, then a gradient descent method is adopted for iterative solution, when a convergence condition is met or iteration exceeds a certain number of times, the iteration is finished, and finally the obtained solution is the solution of the sought optimization problem; the flow chart of the operation steps is shown in FIG. 3;
the principle is as follows: from the conversion into Lagrangian dual problem (D1)
Figure BDA0002916593450000092
And assign the task to the strategy l i Sub-channel allocation strategy theta i,k And a transmit power setting strategy p i,k The three decision variables are all expressed by Lagrange multipliers and converted into a univariate lambda optimization problem; considering the strong duality of the problem, the convex optimization problem must be solved after the transformation, and then the invention considers solving the problem by a gradient descent method. Then, initializing and setting the sub-channel bandwidth B sub The task processing limits parameters such as time T and sets initial values of step length omega and lambda or mu;
then, the gradient G of the dual problem is solved, and the value of lambda is iteratively updated by applying a gradient descent method until a convergence condition is met (the absolute value of the difference value of the dual functions of two times is less than a threshold | f (lambda is less than the absolute value of the difference value of the dual functions of two times) i+1 )-f(λ i ) | ≦ ε or the number of iterations t exceeds 100). The obtained lambda value is the optimal value
Figure BDA0002916593450000093
Reuse of
Figure BDA0002916593450000094
Substitute for Hui
Figure BDA0002916593450000095
And
Figure BDA0002916593450000096
then, the three decision variables are replaced back to the objective function in the original optimization problem (P1) to obtain the minimum energy consumption;
specifically, the method comprises the following steps: the sub-problems in MEC offload computation mode are:
Figure BDA0002916593450000097
Figure BDA0002916593450000098
Figure BDA0002916593450000099
Figure BDA00029165934500000910
Figure BDA00029165934500000911
equation (14) jointly optimizes three decision variables: compute task allocation policy l i Sub-channel number allocation strategy theta i,k And a transmission power p i (ii) a Since the problem is non-convex and can not directly apply convex optimization method, the invention converts the problem into convex optimization problem by some means, and then applies Lagrange transformation to the objective function and the safety transmission constraint condition and introduces Lagrange multiplier lambda i The following objective function becomes:
Figure BDA0002916593450000101
thus, a dual function is obtained:
Figure BDA0002916593450000102
then the dual problem is:
Figure BDA0002916593450000103
the dual problem has strong dual, namely, is a convex optimization problem, and then a convex optimization method can be adopted to solve the dual problem;
next, to obtain f (λ) i ) Is required to use the lagrange multiplier λ i Represents 3 decision variables l i ， p i K and theta i,k ；
By discarding irrelevant constraints
Figure BDA0002916593450000104
The problem can be broken down into two sub-problems:
Figure BDA0002916593450000105
Figure BDA0002916593450000106
Figure BDA0002916593450000107
where equation (18) corresponds to each RD, where equation (19) corresponds to each sub-channel, and it is paired with l for the ith sub-problem in (3) i Solving for a first derivative and making it zero yields the equation:
Figure BDA0002916593450000108
in the formula (19), RD alone is apparently obtained
Figure BDA0002916593450000109
Since equation (19) has two parameters i and k, the invention, in order to solve for k fixed, for the kth sub-problem it traverses all RD's to select the RD that minimizes the objective function value of equation (19); for a fixed k when RD i When active, i.e. theta i,k =1, the present invention rewrites the objective function of equation (19) to:
Figure BDA0002916593450000111
for the above formula regarding p i,k Taking the derivative and making it zero yields the following equation:
Figure BDA0002916593450000112
when h is generated i,k ≤g i,k ，log 2 (1+h i,k p i,k )-log 2 (1+g i,k p i,k ) ≦ 0, then
Figure BDA0002916593450000113
So p is i,k (λ i )＝0；
Wherein, for
Figure BDA0002916593450000114
Is provided with
Figure BDA0002916593450000115
In this case, the sequence numbers of the active users are:
Figure BDA0002916593450000116
then the subchannel allocation strategy is:
Figure BDA0002916593450000117
after derivation, 3 decision variables are represented by lagrange multiplier λ:
Figure BDA0002916593450000118
Figure BDA0002916593450000121
Figure BDA0002916593450000122
will l i (λ i )，p i,k (λ i ) And theta i,k (λ i ) The generation back to the dual function can obtain f (lambda) i ) (ii) a Then iteratively adjusting the value of λ using a gradient descent method, wherein f (λ) i ) The gradient of (a) is:
G i ＝(L i -l i,k (λ i ))-TS i (θ i,k (λ i ),p i,k (λ i )) (28)
the resulting lambda i The value is obtained, and the value is used for replacing the original objective function to obtain the solution of the original optimization problem;
specifically, the method comprises the following steps: the sub-problem in the D2D offload computation mode is:
Figure BDA0002916593450000123
Figure BDA0002916593450000124
Figure BDA0002916593450000125
Figure BDA0002916593450000126
the solution is similar to that in the MEC offload computation mode, taking the Lagrangian multiplier μ i The original advantages described above were introduced, thereby converting to the following form:
Figure BDA0002916593450000127
after the solution, the two decision variables are respectively
Figure BDA0002916593450000128
Figure BDA0002916593450000131
f D (μ i ) About mu i The gradient of (a) is:
Figure BDA0002916593450000132
iterative adjustment of mu by gradient descent method i Value of (d), resulting in μ i The value is obtained, and the value is used for replacing the original objective function to obtain the solution of the original optimization problem;
further, in the step (1.6), the comparing the energy consumption in the MEC offload computation mode with the energy consumption in the D2D offload computation mode specifically means that when the energy consumption in the MEC offload computation mode is less than the energy consumption in the D2D offload computation mode, the MEC offload computation mode is selected, and part of the computation tasks are offloaded to the MEC server at a safe transmission rate, and after the computation is completed, the result is returned, so that the process is ended;
otherwise, selecting a D2D unloading calculation mode, unloading part of calculation tasks to the D2D node closest to the D2D node at a safe transmission rate, and returning the result after the calculation is finished, thereby finishing the process.
Specifically, the following steps: as shown in fig. 4, fig. 4 shows a total of 5 schemes: in the case of an eavesdropper, the proposed: 1), secure MEC-D2D offload mode, 2), local computation mode, 3), simulation results of traditional MEC offload computation mode, while also checking simulation results of non-eavesdropper case 4), MEC offload mode and 5), MEC-D2D offload mode and referring to them.
The parameter settings for the simulation are as follows: the number of Requesting Devices (RD) N =4, the number of subchannels K =64; for the rayleigh fading channel model:
Figure BDA0002916593450000133
Figure BDA0002916593450000134
represents the standard distance d 0 A path loss coefficient under the condition of =1 meter and is set to-30dB i Is shown as RD i Distance from AP or RD i Distance from the eavesdropper, η represents the model index and is set to 3.7 accordingly; CPU energy conversion coefficient ζ =10 -28 Joule (J)/cycle, the number of CPU cycles required to calculate 1 bit is C =10 3 cycles/bit,
Setting a system bandwidth B to be 0.3125MHZ, noise power spectral density to be-105 dBm/HZ, and setting a channel power gain estimation error tau between the RD and an eavesdropper to be 10%; the total task amount to be processed by each RD is uniformly set to L, the limit time T =0.2s, and the distance from the RD to the AP is uniformly set to 20 meters;
FIG. 4 illustrates the variation of the average system energy consumption with the computation workload L for each RD, with each RD being set at a distance of 20 meters from the eavesdropper; it can be observed from the figure that when L is small (e.g., L.ltoreq.3X10 5 bits), the 5 schemes shown in the figure, namely a local computing mode, an MEC unloading computing mode, an eavesdropper-free MEC unloading computing mode, an MEC-D2D unloading computing mode and an eavesdropper-free MEC-D2D unloading computing mode, have similar variation trends and are not significant along with the variation of L(ii) a This is because when the amount of computing tasks is small, the local computing mode can be fully competent; on the other hand, when L is large (e.g., L.gtoreq.4X 10) 5 bits), it can be observed that in the presence of an eavesdropper, the performance of the MEC-D2D scheme proposed by the present invention is significantly improved compared to the local computation mode, and is also better than the conventional MEC mode, and the data shows that the average power consumption of the MEC-D2D scheme is reduced by 87.56% and 65.33% respectively at L =1Mbits compared to the latter two reference schemes. On one hand, the method shows the superiority of the combination of MEC unloading and D2D unloading, and D2D can effectively assist the MEC when the MEC computing resources are in shortage, and on the other hand, the method shows the superiority of the strategy of joint optimization computing task allocation and resource allocation (channel resources and transmission power resources); it is noted that the secret MEC and secret MEC-D2D scheme with the presence of an eavesdropper consumes more energy than the non-eavesdropper MEC and MEC-D2D scheme, because RD needs to pay more transmission power to guarantee the secure transmission rate S to the expected value to combat the eavesdropper; but the data show that the energy consumption of MEC-D2D solution with an eavesdropper at L =1Mbits is also only 0.467 times increased compared to the case without an eavesdropper.
In summary, the invention introduces a D2D unloading strategy in the traditional MEC system, and establishes an MEC-D2D system; the MEC-D2D system can reduce the burden of the MEC server and can enhance the cooperation among user equipment so that idle computing resources can be fully utilized; the method comprises the following steps of considering the existence of an eavesdropper in an MEC-D2D framework for the first time, and adopting a physical layer security method to resist the eavesdropper; and a joint optimization calculation task allocation and resource (channel resource and transmission power resource) allocation strategy is adopted to solve the problem of energy consumption minimization. Considering that the optimization problem is non-convex and relatively complex, and the solution is relatively difficult, the optimization problem is decomposed into three subproblems and is solved by adopting a Lagrangian dual method.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the invention; other variations are also possible within the scope of the invention; thus, by way of example, and not limitation, alternative configurations of embodiments of the invention may be considered consistent with the teachings of the present invention; accordingly, the embodiments of the invention are not limited to the embodiments explicitly described and depicted.

Claims (3)

  1. The method for unloading the security anti-eavesdropping task by utilizing the physical layer under the MEC-D2D environment is characterized by comprising the following specific steps of:
    step (1.1), establishing an MEC-D2D system model with an eavesdropper;
    step (1.2), establishing an MEC-D2D system model into a formulaic optimization problem through mathematical modeling;
    the established optimization problem specifically comprises an objective function and a constraint condition;
    wherein, the objective function is:
    Figure FDA0003907135220000011
    in the formula (1), the reaction mixture is,
    Figure FDA0003907135220000012
    and
    Figure FDA0003907135220000013
    respectively representing the energy consumption of each request device in a local computing mode, the energy consumption of an MEC unloading computing mode and the energy consumption of a D2D unloading mode;
    l, theta and p respectively represent a calculation task allocation strategy, a subchannel number allocation strategy and transmission power;
    α i ，β i and gamma i Respectively representing the decision variables of the calculation mode;
    the constraint conditions specifically include:
    1. RD i The task unloading rate constraint conditions in the MEC unloading calculation mode are as follows:
    (L i -l i )/T≤S i (θ i,k ,p i,k )
    RD i denotes the ith requesting device, L i And l i Respectively represent RD i T represents the processing task limit time, S i Represents RD i Safe transmission rate of theta i,k Indicate about RD i Assignment strategy for the k-th sub-channel, p i,k Represents RD i The transmission power allocated on the k-th sub-channel, in addition, S i Is theta i,k And p i,k A binary function of (a);
    2. RD i The task offload rate constraints in the D2D offload computation mode are:
    Figure FDA0003907135220000014
    in the formula (I), the compound is shown in the specification,
    Figure FDA0003907135220000015
    representing the amount of tasks left in local computation in the D2D offload computation mode,
    Figure FDA0003907135220000016
    indicating RD in D2D offload computation mode i The rate of the secure transmission of (a),
    Figure FDA0003907135220000017
    indicating RD in D2D offload computation mode i Of (2) in addition to S i Is that
    Figure FDA0003907135220000018
    A univariate function of (c);
    step (1.3), the established optimization problem is calculated according to three different calculation modes: decomposing the local calculation mode, the MEC unloading calculation mode and the D2D unloading calculation mode into three sub-problems for subsequent solution;
    step (1.4), the request equipment judges whether the calculation task can be completed by the request equipment per se within the specified time, if so, a local calculation mode is selected, and the process is ended after the calculation is completed;
    if not, continuing to execute the subsequent steps;
    step (1.5), when the request device judges that the calculation task is not completed by itself within the specified time, the optimization problems in the MEC unloading calculation mode and the D2D unloading calculation mode are respectively solved,
    calculating gradient, and iterating by using a gradient descent method to respectively obtain minimum energy consumption, an optimal task allocation strategy, an optimal sub-channel allocation strategy and optimal transmitting power;
    the respectively solving of the optimization problems in the MEC offload computation mode and the D2D offload computation mode is to convert the non-convex optimization problem into the convex optimization problem by applying a lagrange dual method, the solutions of the problems in the MEC offload computation mode and the D2D offload computation mode are the same, taking the MEC offload computation mode as an example, the specific operation steps are as follows:
    first, the lagrange multiplier λ or μ is introduced into the original optimization problem:
    Figure FDA0003907135220000021
    then, iterative solution is carried out by adopting a gradient descent method, iteration is finished when a convergence condition is met or the iteration exceeds a certain number of times, and a finally obtained solution is the solution of the sought optimization problem;
    step (1.6), comparing the energy consumption in the MEC unloading calculation mode with the energy consumption in the D2D unloading calculation mode,
    selecting a calculation mode with lower energy consumption, and unloading part of calculation tasks to an MEC server or a D2D node closest to the MEC server at a safe transmission rate;
    and (1.7) returning the result after the calculation is finished, and ending the process.
  2. 2. The method for offloading a security anti-eavesdropping task using a physical layer under the MEC-D2D environment according to claim 1,
    in step (1.1), the establishment of the MEC-D2D system model with the eavesdropper includes four devices: the mobile communication system comprises a wireless access point fusing an MEC server, N request devices, M service devices and a malicious eavesdropper.
  3. 3. The method for offloading a security anti-eavesdropping task using a physical layer under the MEC-D2D environment according to claim 1,
    in the step (1.6), the comparing the energy consumption in the MEC offload computation mode with the energy consumption in the D2D offload computation mode specifically means that when the energy consumption in the MEC offload computation mode is less than the energy consumption in the D2D offload computation mode, the MEC offload computation mode is selected, and part of the computation tasks are offloaded to the MEC server at a safe transmission rate, and after the computation is completed, the result is returned, so that the process is ended;
    and otherwise, selecting a D2D unloading calculation mode, unloading part of calculation tasks to the D2D node closest to the D2D node at a safe transmission rate, and returning the result after calculation is finished so as to finish the process.
CN202110103867.XA 2021-01-26 2021-01-26 Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment Active CN112911587B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110103867.XA CN112911587B (en) 2021-01-26 2021-01-26 Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110103867.XA CN112911587B (en) 2021-01-26 2021-01-26 Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment

Publications (2)

Publication Number Publication Date
CN112911587A CN112911587A (en) 2021-06-04
CN112911587B true CN112911587B (en) 2023-02-28

Family

ID=76120049

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110103867.XA Active CN112911587B (en) 2021-01-26 2021-01-26 Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment

Country Status (1)

Country Link
CN (1) CN112911587B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113626107B (en) * 2021-08-20 2024-03-26 中南大学 Mobile computing unloading method, system and storage medium
CN113784340B (en) * 2021-09-15 2023-03-14 云南大学 Secret unloading rate optimization method and system
CN115086316B (en) * 2022-06-13 2023-03-14 西安电子科技大学 Safety and resource allocation method for computing offload in joint optimization vehicle edge network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108934002A (en) * 2018-07-18 2018-12-04 广东工业大学 A kind of task unloading algorithm based on D2D communication cooperation
CN111447619A (en) * 2020-03-12 2020-07-24 重庆邮电大学 Joint task unloading and resource allocation method in mobile edge computing network
CN112000481A (en) * 2020-08-25 2020-11-27 东北大学秦皇岛分校 Task unloading method for maximizing computing capacity of D2D-MEC system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111132077B (en) * 2020-02-25 2021-07-20 华南理工大学 Multi-access edge computing task unloading method based on D2D in Internet of vehicles environment

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108934002A (en) * 2018-07-18 2018-12-04 广东工业大学 A kind of task unloading algorithm based on D2D communication cooperation
CN111447619A (en) * 2020-03-12 2020-07-24 重庆邮电大学 Joint task unloading and resource allocation method in mobile edge computing network
CN112000481A (en) * 2020-08-25 2020-11-27 东北大学秦皇岛分校 Task unloading method for maximizing computing capacity of D2D-MEC system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Resource Allocation in Information-Centric Wireless Networking With D2D-Enabled MEC: A Deep Reinforcement Learning Approach;D. Wang, H. Qin, B. Song, X. Du and M. Guizani;《in IEEE Access, vol. 7, pp. 114935-114944, 2019, doi: 10.1109/ACCESS.2019.2935545.》;20190815;全文 *
基于边缘计算的新型任务卸载与资源分配策略;薛建彬等;《计算机工程与科学》;20200615(第06期);全文 *

Also Published As

Publication number Publication date
CN112911587A (en) 2021-06-04

Similar Documents

Publication Publication Date Title
CN112911587B (en) Method for safely unloading anti-eavesdropping task by using physical layer under MEC-D2D environment
Saleem et al. Latency minimization for D2D-enabled partial computation offloading in mobile edge computing
Sun et al. Joint offloading and computation energy efficiency maximization in a mobile edge computing system
Chen et al. IRS-aided wireless powered MEC systems: TDMA or NOMA for computation offloading?
CN109413724B (en) MEC-based task unloading and resource allocation scheme
CN109639377B (en) Spectrum resource management method based on deep reinforcement learning
Zhou et al. Offloading optimization for low-latency secure mobile edge computing systems
CN112104494A (en) Task security unloading strategy determination method based on air-ground cooperative edge computing network
CN110856259A (en) Resource allocation and offloading method for adaptive data block size in mobile edge computing environment
Kim et al. Joint optimization of signal design and resource allocation in wireless D2D edge computing
CN114665937B (en) Design method and device of multi-input multi-output transceiver
Jiang et al. Research on new edge computing network architecture and task offloading strategy for Internet of Things
Feng et al. Latency-aware offloading for mobile edge computing networks
CN112788764A (en) Method and system for task unloading and resource allocation of NOMA ultra-dense network
Salh et al. Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G
Wang et al. Delay minimization for secure NOMA mobile-edge computing
Lakew et al. Adaptive partial offloading and resource harmonization in wireless edge computing-assisted ioe networks
Gupta et al. Lifetime maximization in mobile edge computing networks
Maraqa et al. Energy-efficient optimization of multi-user noma-assisted cooperative thz-simo mec systems
Yang et al. Secure resource allocation in mobile edge computing systems
Zeng et al. Joint Communication and Computation Cooperation in Wireless Powered Mobile Edge Computing Networks with NOMA
Xu et al. Sum-rate optimization for irs-aided d2d communication underlaying cellular networks
Huang et al. User cooperation for NOMA-based mobile edge computing
Hadi et al. Joint resource allocation, user clustering and 3-d location optimization in multi-uav-enabled mobile edge computing
Wang Energy-efficient computational offloading for secure NOMA-enabled mobile edge computing networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant