CN116709391B

CN116709391B - Ultra-dense network combined resource allocation and energy efficiency type safe calculation unloading optimization method

Info

Publication number: CN116709391B
Application number: CN202310978112.3A
Authority: CN
Inventors: 周天清; 傅炎炎; 聂学方; 李轩
Original assignee: East China Jiaotong University
Current assignee: East China Jiaotong University
Priority date: 2023-08-02
Filing date: 2023-08-02
Publication date: 2023-10-20
Anticipated expiration: 2043-08-02
Also published as: CN116709391A

Abstract

The invention discloses an ultra-dense network combined resource allocation and energy efficiency type safe computing unloading optimization method, which comprises the following steps: acquiring network basic information of an ultra-dense Internet of things network, constructing a network system, and constructing an optimization problem of multi-step safe calculation unloading under the constraint of the network system; obtaining an initial solution according to the optimization problem, defining the initial solution as an initial population, carrying out coarse-granularity search on the initial population to obtain a target population, and outputting codes of all whale individuals; initializing the positions of particles in the particle swarm by using the codes, carrying out fine granularity search by using an improved self-adaptive particle swarm algorithm, and updating the positions of the particles in the particle swarm to obtain the global optimal particle positions; and performing unloading optimization configuration according to the position of the global optimal particles by combining the improved whale algorithm and the particle swarm algorithm to form the improved whale particle swarm algorithm. The invention has the advantages of executing multi-step calculation unloading, meeting the total cost and delay constraint of security vulnerabilities and realizing the aim of minimizing the energy consumption of the whole network.

Description

Ultra-dense network combined resource allocation and energy efficiency type safe calculation unloading optimization method

Technical Field

The invention relates to the technical field of wireless communication, in particular to an ultra-dense network combined resource allocation and energy efficiency type safe computing unloading optimization method.

Background

With the rapid development of mobile internet of things (Internet of Things, ioT), new applications of smart home, virtual reality, augmented reality, and autopilot have grown. Although the computing power of the user terminal device (IoT mobile device, IMD, abbreviated as user) has been a qualitative leap, these delay-sensitive and computation-intensive tasks cannot be fully supported due to limited CPU computing resources and battery capacity. To extend the IMD lifetime and alleviate IMD computing resource limitations, the advent of mobile edge computing (Mobile Edge Computing, MEC) can provide significant computing resources for IMDs at the network edge, thereby handling delay-sensitive tasks in time and effectively solving computationally intensive tasks.

To further shorten the distance of the user from the computing center, ultra-dense heterogeneous networks (UDHNs) are combined with MECs. In UDHNs, both macro base stations (Macro Base Station, MBS) and a large number of small base stations (Small Base Station, SBS) deploy edge computing servers. In the computational offload model where security is considered, although the transmission delay from the IMD to the base station is reduced, a lot of energy is still incurred.

Accordingly, efforts are made to study joint security computing offloading, user (terminal device) association, and resource allocation to reduce overall network power consumption, and how to extend IMD and SBS standby times, while meeting limited network resource constraints. Furthermore, it should be noted that secure computation offload refers to the need for cryptographic transmission of the IMD's computing tasks if performed at the edge server. And the user (terminal device) association determines the specific selection between the IMD and the base station.

Disclosure of Invention

In view of this, the embodiment of the invention provides an ultra-dense network joint resource allocation and energy efficiency type safe computing and unloading optimization method, so as to solve the problem of algorithm convergence speed and performance reduction caused by lack of research on new problems generated during computing and unloading in the prior art.

The first aspect of the embodiment of the invention provides an ultra-dense network joint resource allocation and energy efficiency type safe computing unloading optimization method, which comprises the following steps:

step S1: acquiring basic information of an ultra-dense Internet of things network, constructing a network system according to the basic information, wherein the network system comprises a communication model, a calculation model, a security model and a multi-task model, constructing an optimization problem under the constraint of the network system, namely combining resource allocation, power control and user association while meeting the total cost and delay constraint of security vulnerabilities, and executing multi-step security calculation unloading to achieve the aim of minimizing the energy consumption of the whole network;

Step S2: carrying out coarse-granularity search by adopting an improved whale algorithm IWOA according to the optimization problem, wherein the method specifically comprises the steps of initializing a population and determining historical optimal whale individuals; then in the shrink wrapping stage, adaptive nonlinear weighting factors are adopted and />For updating the position of whale individuals, and for the spiral bubble network attack and hunting phase; in the spiral bubble network attack stage, inertia weight coefficient is utilized +.>The method is used for adaptively adjusting the spiral amplitude, so as to avoid sinking into local optimum; in the hunting stage, the improved whale algorithm searching space is expanded by adopting the inverse cumulative distribution function of the cauchy to perform long tail mutation operation on whales, so that the global searching capability of whales is improved, and the phenomenon of sinking into local optimum is avoided; then repeatedly executing shrink wrapping, spiral bubble network attack, searching for the updates of prey and historical optimal whale individuals in sequence until the maximum iteration sequence is reached, and finally outputting codes of all whale individuals in the target population;

step S3: initializing the positions of particles in a particle swarm by using codes of all whale individuals in a target population, carrying out fine granularity search by using an improved self-adaptive particle swarm algorithm (IPSO), and updating the positions of the particles in the particle swarm to obtain the positions of global optimal particles;

Step S4: the improved whale particle swarm algorithm WPSO is formed by combining the improved whale algorithm IWOA and the improved self-adaptive particle swarm algorithm IPSO, and finally, the joint resource allocation and the energy efficiency type safe calculation unloading optimization configuration are executed according to the position of the global optimal particle.

In summary, according to the ultra-dense network combined resource allocation and energy efficiency type safe computing unloading optimization method, coarse granularity searching is performed by utilizing the improved whale algorithm IWA, fine granularity searching is performed by utilizing the improved self-adaptive particle swarm IPSO algorithm, and the problems of premature convergence and easy sinking into local optimum respectively existing in solving problems of the traditional whale algorithm WOA and the particle swarm optimization PSO algorithm are effectively avoided by combining the improved whale algorithm IWA with the improved self-adaptive particle swarm algorithm IPSO to form the improved whale particle swarm algorithm WPSO. The method meets the requirements of the user on transmitting power, computing resources and delay constraint, and simultaneously achieves the aims of minimizing energy consumption and safety cost. Compared with other existing methods, the method provided by the invention can realize the communication with lower energy consumption and higher safety and reliability. Specifically, an optimization problem is constructed according to network basic information of an ultra-dense network, the optimization problem is subjected to preliminary calculation to obtain an initial solution of the optimization problem, the initial solution of the optimization problem is used as a whale population, an improved whale population is adopted to perform coarse-grained search to obtain a target population, and codes of all whale individuals in the target population are output; initializing the positions of particles in the particle swarm by taking the target swarm as the particle swarm and using codes of all whale individuals in the target swarm, and updating the positions of the particles in the particle swarm by using an improved self-adaptive particle swarm algorithm to obtain the positions of global optimal particles; and executing safe calculation unloading and resource optimization configuration according to the position of the global optimal particle. Aiming at the ultra-dense multi-user and multi-task internet of things network, the invention combines user association, safe calculation unloading and resource allocation to minimize the total network energy consumption under the constraint of IMDs time delay and security vulnerability total cost. The method can well achieve the aims of minimizing energy consumption and safety cost.

Further, step S1 includes:

the optimization problem is constructed according to the following formula:

wherein, assuming that the local execution and the computation offload are performed simultaneously,representing the total time of the computing task>Representing the total energy consumption of the computing task, IMD->Representing user terminal equipment +.>SBS stands for small base station, MBS stands for macro base station, BS stands for base station, SBS has +.>The set of SBS is denoted +.>The index of MBS is given by 0,representing the set of all base stations, the users have at most +.>Index set of usersAt most, each user has +>Each of the independent tasksThe relatively independent task index set of the user is +.>The method comprises the steps of carrying out a first treatment on the surface of the Encryption algorithm has->The minimum and maximum robustness algorithms are expressed as and />The index set is marked->；/>Representing the user offloading decision, thenIndex set representing offloading decisions, +.>Indicating IMD->With SBS->Associated, otherwise, < >>；/>Indicating IMD->Is>Whether the individual task selects an encryption algorithm +.>ThenIndex set representing security decision variables of a task, +.>Indicating IMD->Is>Encryption algorithm of personal task and security protection level>Associated, otherwise, < >>Representing IMD->Is>The security protection level of each task is 1; / >For IMD->Uplink transmit power of (2), then +.>Indicating IMD->An index set of uplink transmit powers of (a); />Indicating IMD->Executive gaugeCalculating the amount of data offloaded, IMD->Index set of data amount offloaded to BS +.>；/>Data volume offloaded to MBS for SBS, +.>Data volume index set indicating SBS offloaded to MBS, < >>Taking a value approaching zero; the band division factor is-> and />Indicating IMD->Is a security vulnerability total cost of (1); />Indicating IMD->The total time of the calculation task of (a) cannot exceed the maximum allowable time delay of the task execution +.>；/>Indicating IMD->Is->Cannot exceedIMD/>Is +.>；/> and />Indicating that an IMD can only be associated with one base station; /> and />Indicating IMD->Is>A task can only be associated with one secure protection encryption algorithm; />Indicating IMD->Is>And upper bound->；/>Indicating when IMD->With SBSWhen associated, this IMD can add tasks->Unload->Bit to SBS->Upper treatment, then SBS->Can receive +.>Part of the individual task->Bit offloading to MBS; /> and />Greater than or equal to->But less than or equal to IMDTask of (1)>Data size +.>At the same time, a->Greater than or equal to->；/>Representing a band division factor Lower bound of->And an upper bound of 1.

According to the technical scheme, the optimization problem is built on the basis of a communication model, a calculation model and a security model in the ultra-dense network system, under the constraint of high total cost of security vulnerabilities and strict time delay, a task association matrix, a distribution matrix of user calculation resources, a distribution matrix of base station calculation resources and a user emission power set are all used as reference indexes of the optimization problem, the problem of single reference index in the prior art is solved, safer calculation and unloading can be achieved, calculation and unloading with lower energy consumption are achieved, and a brand new optimization problem is built.

Further, step S2 includes:

step S21: initializing maximum iteration order using modified whale algorithmAnd index the current iteration +>Set to 1;

step S22: individual whale individualsPopulation->Completing whale individual codes for each whale individual; firstly, defining an objective function as an fitness value of whale individual, and optimizing parametersEncoded as +.>, wherein ,/>，Representing individual whale->Middle and IMD->An associated BS index; />，/>Representing whale individualsMiddle and IMD->An associated security encryption algorithm level; / >，/>Representing individual whale->Middle IMD->Is set to the transmission power of (a); />，/>Representing individual whale->Middle IMD->Data volume offloaded onto an associated SBS; wherein (1)>，/>Representing individual whale->Middle IMD->The amount of data offloaded onto the associated MBS; wherein->Representing individual whale->Is a frequency band division factor of (a). />Representing a collection of virtual IMDs. Its length is->Any virtual IMD indicator is easily translated into a real IMD and task indicator. Instead, the indices of any IMD and task are easily converted to virtual IMD indices.

Step S23: initializing a whale population and constructing whale individuals in the whale population according to the following formulaIs a fitness function of:

wherein ,representing individual whale->Fitness function value of->Indicating IMD->Penalty factor of latency constraint of ∈1->Indicating IMD->Penalty factor of security vulnerability total cost constraint, +.>Representing the total energy consumption of the computing task,representing the total time of the computing task>Maximum allowable delay for task execution, +.>For IMD->Is>For IMD->Is a maximum allowable security vulnerability total cost;

calculating fitness values of all whale individuals in the whale population by using a fitness function, and taking the whale individual with the highest fitness value as a historical optimal whale individual;

Step S24: judging the current iteration indexWhether or not it is equal to or less than the maximum iteration order->If the current iteration index +>Less than or equal to the maximum iteration order->Then the whale population is subjected to the operations of searching for prey, contracting surrounding and attacking the spiral bubble net to obtain a target population, if the current iteration index +.>Greater than maximum iteration order->And outputting codes of all whale individuals in the target population.

As can be seen from the above technical scheme,

further, step S2 includes:

step S241: the position of each whale is a feasible solution, and a plurality of whale individuals continuously update the position in the solution space until a globally optimal solution is obtained. Herein, a single whale individual。

Whales can identify the location of the prey and then fully enclose them. Thus, all whales are agents seeking prey. In conventional WOA, the best agent is currently assumed to be the target game and all whales update their position on the target game in an iterative process. To ensure and accelerate global convergence of WOA, we replace the current optimal agent with a historical optimal agent. The former is the whale individual with the highest fitness (function) value in all whale individuals of the current iteration, and the latter is the whale individual with the highest fitness (function) value in all whale individuals of the previous iteration and the current iteration. Mathematically, whale individuals Surrounding the prey location +.>The behavior of (2) can be expressed as

wherein ,representation pair->Function of rounding down, ++>Is->Absolute value of (2);for the location of the historically optimal whale individual,for globally optimal whale individualsIs a position of (2);

and />For the adaptive nonlinear weight value, updating the position of the whale individual; />To take the value of +.>Random numbers in between; />Is an iteration index; />Is the maximum iterative order; />Representing a prey location; />And->All are random numbers, and the value is +.>Between them;

step S242: the spiral bubble mesh attack of whales involves simultaneous constrictive wrapping and spiral movement with equal probability. By performing these operations, the new location of any agent will be between its current location and the location of the historically optimal agent. This means that optimization problems can be found with a bubble network attack. To simulate the spiral motion of whales, any whale individualIs>The spiral equation between may be:

wherein ,for the location of the historically optimal whale individual,for the global optimal position of whale individuals, < +.>For adaptively adjusting the spiral amplitude, avoiding falling into local optimum, < >>Is used for adaptively adjusting the spiral amplitude, avoiding sinking into local optimum and can be formed by

wherein Is a random number, and takes the value of +.>Between (I)>Is an inertial weight coefficient, +.>For iterative index, ++>Is the maximum iterative order;

step S243: in looking for the prey of traditional WOA, whales were forced to move towards random whales. By doing so, the search space of the improved whale algorithm may be expanded. But its global search capability may depend greatly on the choice of random whales, easily sinking into local optima. To solve this problem, the inverse cumulative distribution function of cauchy can be used for mutation manipulation of whale because the tail of the whale algorithm is long. In light of this, this function was used to formulate a search for whale prey. Mathematically, any whale individualThe hunting behavior of (1) can be expressed as:

wherein ,for the global optimum of the position of the whale individual, weight +.>For adaptively adjusting the size of the mutation;

step S244: calculating the fitness value of a whale individual in the mutated whale population by using a fitness function, taking the whale individual with the highest fitness value as a current optimal whale individual, judging whether the fitness value of the current optimal whale individual is higher than a historical optimal fitness value, and if the fitness value of the current optimal whale individual is higher than the historical optimal fitness value, replacing the historical optimal whale individual by the current optimal whale individual;

Step S245: index the current iterationThe value of (2) is increased by 1.

As can be seen from the above technical scheme,

further, step S3 includes:

step S31: initializing a maximum iteration order of an improved adaptive particle swarm algorithmAnd index the current iteration +>Set to 1;

step S32: taking all whale individuals in the target population as particles of improved self-adaptive particle swarm algorithm, for any whale individualSetting upIn order of particle->Historical optimal positions of the secondary iterations; order of principle

and />In +.>Index set of historical optimal position of the secondary iteration;for particle at->Optimal position of the next iteration, let

And +.>For improved adaptive particle swarm +.>An index set of optimal positions for the secondary iterations; taking the particles with the history optimal positions as history optimal particles, taking the particles with the global history optimal positions as global optimal particles, wherein the global optimal particles are formed by whale individuals +.>Obtaining particles in an optimal position;

step S33: calculating fitness values of all particles in the history optimal position by using a fitness function, taking the particle with the highest fitness value in the history optimal position as the global optimal particle, and initially Global optimization particleIs a position of (2);

step S34: judging the current iteration indexWhether or not it is equal to or less than the maximum iteration order->If the current iteration indexLess than or equal to the maximum iteration order->Updating the speed and the position of the whale individual, and updating the speed and the position of the globally optimal particles according to the speed and the position of the whale individual;

step S35: calculating the fitness of all particles at the historical optimal position by using a fitness function, and taking the particle with the highest fitness value of all particles at the historical optimal position as the global optimal particle;

step S36: index the current iterationThe value of (2) is increased by 1.

As can be seen from the above technical scheme,

further, step S34 includes:

wherein, any whale individualThe inertial weight of (2) is represented by the formula>Update-> and />Maximum inertial weight and minimum inertial weight, respectively, +.>For iterative index, ++>For maximum iteration order, +.>Representing a combination of particles in common,in order of particle->The historical optimal position for the next iteration,for particle group->Optimal position of the iteration->And->Respectively representAnd->In->The speed of the number of iterations is such that,and->Respectively indicate->And->In->The position of the iteration, ++>And->Respectively representAnd- >In->Speed of the second iteration, +.>Representing individual whale->In the first placeInertial weights of the secondary iterations; /> and />Respectively representing a learning factor and a social factor,and->Are random numbers;

step S342: after updating the particle velocity, any whale individualThe location may be updated by:

wherein ,representation pair->A function of the down-rounding,and->Respectively representAnd->In->The position of the next iteration;

step S343: updating globally optimal particles according to the following formulaIs a speed of (2):

/>

wherein ,respectively represent global optimum particlesFirst->Speed of the second iteration, +.>Respectively represent global optimum particle->First->Speed of the second iteration, +.>Is->Scaling factor at multiple iterations, +.>Is a constant coefficient +.>，/>And->Are random numbers;

step S344: updating globally optimal particles according to the following formulaIs defined by the position of:

wherein ,respectively represent global optimum particle->First->The position of the iteration is that in order to make the improved self-adaptive particle swarm algorithm to randomly search the surrounding area of the global optimal position, the global optimal particle position is made to be +.>ThenScale factor->May be updated by the following rules: />

Wherein the scale factorFor driving the improved adaptive particle swarm algorithm to search for feasible solutions around globally optimal particles, +. >Representing the number of consecutive successes,/->Representing the number of consecutive failures>And->Representing a threshold parameter; furthermore, if->Displaying failure, otherwise suggesting that the search is successful;

step S345: and calculating the fitness of all the particles by using a fitness function, judging whether the current fitness is higher than the fitness when the particles are positioned at the historical optimal position, and taking the current position of the particles as the historical optimal position if the current fitness is higher than the fitness when the particles are positioned at the historical optimal position.

Further, step S4 includes:

according to the improved adaptive particle swarm in step S32The index set of the optimal position of the secondary iteration restores the position of the global optimal particle into the original optimized parameter solutionForm of (c);

and according to the obtained solution, user task unloading, cryptographic algorithm selection, user computing resource allocation, base station resource allocation and user power control are executed.

A second aspect of an embodiment of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the ultra-dense network joint resource allocation and energy efficiency type secure computing offload optimization method provided in the first aspect when the computer program is executed.

A third aspect of the embodiments of the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the ultra-dense network joint resource allocation and energy efficient secure computing offload optimization method provided in the first aspect.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of an implementation of an ultra-dense network joint resource allocation and energy efficiency type secure computing offload optimization method provided by an embodiment of the present invention;

FIG. 2 is a diagram illustrating the effect of the number of network users on total energy consumption according to the present invention;

FIG. 3 is a diagram illustrating the influence of the number of network users on the fitness function value according to the present invention;

FIG. 4 is a diagram illustrating the effect of maximum transmit power of a user on total energy consumption according to the present invention;

FIG. 5 is a diagram illustrating the effect of maximum transmit power of a user on fitness function values according to the present invention;

FIG. 6 is a diagram illustrating the convergence of the IWOA algorithm and the IPSO algorithm in accordance with the present application.

Detailed Description

The following detailed description of embodiments of the application, with reference to the accompanying drawings, is illustrative of the embodiments described herein, and it is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application.

The terms first, second, third and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprising," "including," and "having," and any variations thereof, are intended to cover an exclusive inclusion. For example, a series of steps or elements may be included, or alternatively, steps or elements not listed or, alternatively, other steps or elements inherent to such process, method, article, or apparatus may be included.

Referring to fig. 1, fig. 1 shows a flowchart of an implementation of an ultra-dense network joint resource allocation and energy efficiency type secure computing offload optimization method according to an embodiment of the present application.

The application scene of the invention is an ultra-dense network, firstly, network basic information of the ultra-dense network is acquired, then, a communication model, a calculation model and a safety model in the network model are built step by step, and an optimization problem is built on the basis of the communication model, the calculation model and the safety model in the network model, wherein the optimization problem is a weighted sum minimization problem of standardized total energy consumption and total safety cost.

Specifically, step S11: firstly, obtaining index sets of U users asConsider an MBS and +.>SBS, wherein the set of SBS is denoted +.>The index of MBS is given by 0. />Representing the set of all base stations. The index set of tasks for each user is，/>Index set of personal cryptographic algorithm->Sets of their security levels, wherein ,/>Representing cryptographic algorithm->It is assumed that all SBSs are connected to the MBS via wired links and that any IMD has a computationally intensive and delay sensitive application to execute within the security vulnerabilities and for a certain period of time and introduces an effective interference management mechanism to eliminate network interference. Specifically, the whole frequency band is- >Cut into-> and />Two parts, for MBS and SBS respectively. Then, band->Is cut into sub-bandsThese sub-bands have the same bandwidth and are respectively allocated to SBSs; the frequency band of each SBS is equally allocated to its associated users. Frequency band->、/> and />The width of (2) is +.>，/> and />. wherein />Is a band division factor. Thus, inter-layer interference can be eliminated, intra-layer interference can be eliminated, and intra-cell interference can be completely avoided. Although the spectrum utilization of such interference management mechanisms is low, the frequency bands allocated to IMDs should be sufficient, since in ultra-dense internet of things networks, each SBS typically serves at most one IMD.

Step S12: the communication model is constructed based on the network basic information, and first, since the amount of data of the calculation results downloaded from any BS is very small, the time taken to download them is negligible. That is, we only need to focus on upstream transmissions. When IMD (in-mold digital)With SBS->Associated from IMD->To SBS->Uplink signal-to-interference-and-noise ratio (SINR) of (a) is

wherein ,indicating IMD->Is set to the transmission power of (a); />Indicating IMD->With SBS->Channel gain between; />Representing noise power; />Indicating the interference power.

Under the above interference management mechanism, IMD To SBS->Upstream data rate->From the bottomThe formula is given:

wherein Representation and SBS->The number of associated IMDs; />Representation and SBS->The bandwidth of any IMD associated.

If IMD (in-mold digital)Associated with MBS, then from IMD->The uplink signal-to-interference-and-noise ratio (SINR) to MBS may be given by:

wherein ,indicating IMD->Channel gain with MBS, < >>For IMD->Is provided.

Similarly, from IMDUplink data rate to MBS +.>Can be given by:

wherein ,representing the number of IMDs associated with MBS; />Representing the bandwidth of any IMD associated with MBS, < >>Representing the uplink signal-to-interference-and-noise ratio.

Step S13: constructing a computational model based on the communication model, assuming that any IMD has a function ofAn application program composed of individual tasks. Furthermore, the +.>The individual tasks can be expressed as +.>, wherein />Indicating IMD->Is>Data volume of individual calculation tasks, +.>Is used forCalculate one-bit task->Is a CPU cycle number of (c). Furthermore, any IMD->Is not allowed to exceed the maximum delay +.>. In this context, we consider that the computational offload process may include the following two steps. First step, IMD->Is>Part of the individual tasks is offloaded to SBS +. >And (5) processing. Second step, the first stepOffloading of individual tasks to SBS->Is offloaded to MBS for processing.

When IMD (in-mold digital)With BS->When associated, the number of the kth tasks processed locally is +.>For processing and BS->Associated IMD->Local execution of the kth task of (c)Line time->Given by the formula:

wherein ,indicating IMD->Computing power allocated to kth task, < >>Indicating IMD->Is>Whether the individual task selects an encryption algorithm +.>，/>Calculating time for encryption->The amount of data offloaded for performing the calculation.

When IMD (in-mold digital)With BS->Associated, local computing energy consumption for performing the remainder of the kth taskCan be given by:

wherein ,is an effective switched capacitance depending on the chip architecture, < >>And the energy consumption for encryption and decryption.

When IMD (in-mold digital)The time taken for such an operation to complete its kth task using a two-step computational offload includes seven parts. Specifically, the first part is the uplink transmission time from the uploading task to the SBS; the second part is the execution time of the task in SBS; the third part is the uplink transmission time from the uploading task to the MBS; fourth is the execution time of MBS tasks; fifth is decryption delay on SBS; the sixth is the encryption delay on SBS; the last one is the decryption delay on MBS. Thus, under two-step computational offloading, when IMD +. >With SBS->When associated, time for completing its kth task +.>Can be given by:

/>

wherein ,representing the cable backhaul rate of SBS to MBS; />Is SBS->Assigned to IMD->The computing power of the kth task; />Is MBS allocated to IMD->Computing power of kth task, for example>In order to decrypt the calculated time period,the amount of data offloaded to the macro base station for the small base station; the first, second, third and fourth items on the right side of the equation are respectively the uplink transmission time from the uploading task to the SBS, the execution time of the task in the SBS and the uplink transmission time from the uploading task to the MBS, and the execution time of the task on the MBS. The last three terms are the decryption delay on SBS, the encryption delay on SBS and the MBS decryption delay.

When IMD (in-mold digital)Energy consumption for such operations when employing one-step computational offloading to complete its kth taskCan be given by:

the first term on the right side of the above formula is the energy consumption for uploading the task to the MBS, the second term is the execution energy consumption of the task on the MBS, and the third term is the MBS decryption energy consumption.

Step S14: constructing a safety model: first, security protection levelRepresenting the robustness of the specified cryptographic algorithm. Related management fees for encryption algorithms>The method comprises two parts of time and energy cost: encryption and decryption calculation time requirements are respectively as follows (in CPU cycles/bit) and +.>Expressed in units of CPU cycles/bit, the encryption and decryption energy consumption（10 ^-7 J/bit）。

Including security protection levelsThe cost of security vulnerabilities that may occur for a task of (a) is expressed as:

wherein The expected security level of the task is defined.

IMDThe total cost of security vulnerabilities is:

wherein ,for IMD->Task of (1)>Cost generated when security protection fails, +.>Indicating IMD->Is the first of (2)Whether the individual task selects an encryption algorithm +.>When IMD->Task of (1)>Select security protection level->When (I)>The method comprises the steps of carrying out a first treatment on the surface of the And the other is 0./>

According to this model, there is a security breach cost for tasks that do not reach the intended security protection. Otherwise, this cost is zero. The security vulnerability cost represents the vulnerability of the offload task to malicious attacks or eavesdropping if the intended protection is not achieved.

Step S15: constructing a multi-task model: to satisfy a practical implementation, we assume that all computing tasks are performed sequentially. That is, for any IMDOnly when it is front->The completion of the individual tasksAt the time of formation, it is->The individual tasks can be performed. Furthermore, we assume that the local execution and the computation offload are performed simultaneously. Thus, IMD->Total time to complete its calculation tasks +. >Is the maximum of the local execution and computation offload times, which can be given by:

wherein ,for the total time of local execution, +.>Total time for performing multi-step secure computation offload,/->Representing the total time of the computing task, i.e., taking the maximum of both the local execution and the multi-step secure computing offload time.

IMDTotal energy consumption for completing its calculation tasks>Is the sum of locally executed and multi-step secure computation offload energy consumption, which can be given by:

wherein ,representing the total energy consumption of the computing task>Representing the energy consumption of the local execution,representing the power consumption to perform multi-step secure computation offload.

Step S16: the optimization problem is constructed according to the following formula:

wherein ,representing the total time of the computing task, i.e. taking the maximum of the local execution and the computing offload time, i.e. computing offload time maximum +.>Neither can the maximum delay constraint be exceeded>。/>Representing the total energy consumption of the computing task, i.e. the sum of the energy consumption of the local execution and the computing offload. SBS stands for small cell, MBS stands for macro cell, BS stands for base station, i.e. comprising macro cell and small cell. SBS at most->The set of SBS is denoted +.>The index of MBS is given by 0, +.>Representing the set of all base stations, IMD- >Representing user terminal equipment +.>，/>Representing IMD (user terminal device) set +.>，/>Representing the relatively independent tasks of each user, expressed as. Encryption algorithm has->The algorithm with the least and the greatest robustness is denoted by +.>Andthe index set is marked->, wherein />Is an index of security level.Representing a collection of virtual IMDs. It has a length ofAny virtual IMD indicator is easily translated into a real IMD and task indicator. Instead, the indices of any IMD and task are easily converted to virtual IMD indices. />Indicating an offloading decision-making,indicating IMD->With SBS->Associated, otherwise, < >>；/>Security decision variables representing tasks, +.>Indicating IMD->Is>Encryption algorithm of personal task and security protection level>Associated, otherwise, < >>Indicating IMD->Is>The security protection level of each task is 1; />For IMD->Uplink transmit power of (a)Let IMD->Uplink transmit power index set of +.>；/>Indicating IMD->Performing a calculation of the amount of data offloaded, IMD->Index set of data amount offloaded to BS；/>For the amount of data that SBS offloads to MBS,data volume index set indicating SBS offloaded to MBS, < >>Take a value approaching zero, e.g. +.>To avoid dividing by 0; the band division factor is- > and />Indicating IMD->Is a security vulnerability total cost of (1); />Indicating IMD->Is not allowed to exceed the maximum allowable delay +.>；/>Indicating IMD->Is->Cannot exceed IMD>Maximum allowable security hole total cost->；/> and />Indicating that an IMD can only be associated with one base station; /> and />Indicating IMD->Is>A task can only be associated with one secure protection encryption algorithm;gives IMD->Is>And upper bound->；/>Indicating when IMD->With SBS->When associated, this IMD can associate the task +.>Unload->Bit to SBS->Upper treatment, then SBS->Can receive +.>Part of the individual task->Bits are offloaded to the MBS. Obviously (I)> and />Greater than or equal to->But less than or equal to IMD>Task of (1)>Data size +.>At the same time, a->Greater than or equal to->；/>Gives the lower bound of the band division factor +.>And an upper bound of 1.

Step S2: carrying out coarse-granularity search by adopting an improved whale algorithm IWOA according to the optimization problem, wherein the method specifically comprises the steps of initializing a population and determining historical optimal whale individuals; then in the shrink wrapping stage, adaptive nonlinear weighting factors are adopted and />For updating the position of whale individuals, and for the spiral bubble network attack and hunting phase; then in the spiral bubble network attack stage, inertia weight coefficient is used +. >The method is used for adaptively adjusting the spiral amplitude, so as to avoid sinking into local optimum; in the hunting search stage, the search space of the algorithm is expanded by adopting the reverse cumulative distribution function of the cauchy to perform long tail mutation operation on whales, so that the global searching capability of whales is improved, and the phenomenon of sinking into local optimum is avoided; then repeatedly executing shrink wrapping, spiral bubble net attack, searching hunting and updating of historical optimal whale individuals in sequence,and finally outputting codes of all whale individuals in the target population until the maximum iteration sequence is reached.

It should be noted that, for the optimization problem, the embodiment obtains an initial solution of the optimization problem by using the improved whale algorithm, and then uses the improved adaptive particle swarm algorithm proposed in the embodiment to perform continuous iterative optimization on the initial solution to obtain an optimal solution of the optimization problem, that is, a solution with the minimum sum of the standardized total energy consumption and the standardized total safety cost is obtained.

Specifically, step S21: initializing maximum iteration order using modified whale algorithmAnd index the current iteration +>Set to 1;

step S22: individual whale individualsPopulation->Completing whale individual codes for each whale individual; firstly, defining an objective function as an fitness value of whale individuals, and then optimizing parameters +. >Encoded as +.>, wherein ,/>，/>Representing individual whale->Middle and IMD->An associated BS index; />，/>Representing individual whale->Middle and IMD->An associated security encryption algorithm level; />，/>Representing individual whale->Middle IMD->Is set to the transmission power of (a);，/>representing individual whale->Middle IMD->Data volume offloaded onto an associated SBS;，/>representing individual whale->Middle IMD->The amount of data offloaded onto the associated MBS; wherein->Representing individual whale->Is a frequency band division factor of (2); />Represents a set of virtual IMDs, length +.>；

wherein ,representing individual whale->Fitness function value of->Indicating IMD->Penalty factor of latency constraint of ∈1->Indicating IMD->Penalty factor of security vulnerability total cost constraint, +.>Representing the total energy consumption of the computing task,representing the total time of the computing task>Maximum allowable delay for task execution time, < >>For IMD->Is>For IMD->Is a maximum allowable security vulnerability total cost;

Step S24: judging the current iteration indexWhether or not it is equal to or less than the maximum iteration order->If the current iteration index +>Less than or equal to the maximum iteration order->Searching for prey, shrink wrapping and spiral bubble mesh attack on whale population to obtain target population, if currently overlappedIndex of substitution->Greater than maximum iteration order->And outputting codes of all whale individuals in the target population.

As can be seen from the above technical scheme,

further, step S2 includes:

Whales can identify the location of the prey and then fully enclose them. Thus, all whales are agents seeking prey. In conventional WOA, the best agent is currently assumed to be the target prey, and all whales update their positions to it in an iterative process. To ensure and accelerate global convergence of WOA, we replace the current optimal agent with a historical optimal agent. The former refers to the whale individual with the highest fitness (function) value among all individuals in the current iteration, and the latter refers to the whale individual with the highest fitness (function) value among all individuals in the last iteration and the current iteration. Mathematically, whale individuals Surrounding the prey location +.>The behavior of (2) can be expressed as:

wherein ,representation pair->Function of rounding down, ++>Is->Absolute value of (2); />Representing a prey location; />And->All are random numbers, and the value is +.>Between them; />For the position of historically optimal whale individuals, +.>The position of the global optimal whale individual;

and />For the adaptive nonlinear weight value, updating the position of the whale individual; />To take the value of +.>Random numbers in between; />Is an iteration index; />Is the maximum iterative order;

step S242: the spiral bubble net attack of whales involves simultaneous constrictive and spiral movements, any whale individualIs>The spiral equation between: />

wherein ,for the location of the historically optimal whale individual,for the global optimal position of whale individuals, < +.>Is used for adaptively adjusting the spiral amplitude, avoiding sinking into local optimum and can be formed by

step S243: any whale individualThe hunting behavior of (1) is expressed as:

Step S245: index the current iterationThe value of (2) is increased by 1.

Step S3: initializing the positions of particles in the particle swarm by using codes of all whale individuals in the target population, carrying out fine granularity search by using an improved self-adaptive particle swarm algorithm (IPSO), and updating the positions of the particles in the particle swarm to obtain the positions of the global optimal particles.

step S32: taking all whale individuals in the target population as particles of improved self-adaptive particle swarm algorithm for whale individuals，In order of particle->Historical optimal positions of the secondary iterations; order of principle

And +.>For improved adaptive particle swarm +.>An index set of optimal positions for the secondary iterations; taking the particles with the history optimal positions as history optimal particles, taking the particles with the global history optimal positions as global optimal particles, wherein the global optimal particles are formed by whale individuals +.>Obtaining optimal particles;

step S33: calculating fitness values of all particles in the history optimal position by using a fitness function, taking the particle with the highest fitness value in the history optimal position as a global optimal particle, and initializing the global optimal particle Is a position of (2);

step S34: judging the current iteration indexWhether or not it is equal to or less than the maximum iteration order->If the current iteration index +>Less than or equal to the maximum iteration order->Updating the speed and the position of the whale individual, and updating the speed and the position of the globally optimal particles according to the speed and the position of the whale individual;

step S36: index the current iterationThe value of (2) is increased by 1.

As can be seen from the above technical scheme,

further, step S34 includes:

step S341: updating individual whales according to the following formulaIs a speed of (2):

wherein, any whale individualThe inertial weight of (2) is represented by the formula>Update-> and />Maximum inertial weight and minimum inertial weight, respectively, +.>For iterative index, ++>For maximum iteration order, +.>Representing a combination of particles in common,in order of particle->The historical optimal position for the next iteration,for particle group->Optimal position of the iteration->And->Respectively representAnd->In->The speed of the number of iterations is such that,and->Respectively indicate->And->In->The position of the iteration, ++ >And->Respectively indicate->And->In->Speed of the second iteration, +.>Representing individual whale->In the first placeInertial weights of the secondary iterations; /> and />Respectively representing a learning factor and a social factor,and->Are random numbers;

step S342: at the time of updating particlesAfter speed, any whale individualThe location may be updated by:

wherein ,respectively represent global optimum particle->First->Speed of the second iteration, +.>Respectively represent global optimum particle->First->Speed of the second iteration, +.>Is->Scaling factor at multiple iterations, +.>Is a constant coefficient +.>，/>And->Are random numbers;

wherein ,respectively represent global optimum particle->First->The position of the iteration is that in order to make the improved self-adaptive particle swarm algorithm to randomly search the surrounding area of the global optimal position, the global optimal particle position is made to be +.>ThenScale factor->May be updated by the following rules:

According to the improved adaptive particle swarm in step S32The index set of the optimal position of the secondary iteration restores the position of the global optimal particle into the form of an original optimization parameter solution; and according to the obtained solution, user task unloading, cryptographic algorithm selection, user computing resource allocation, base station resource allocation and user power control are executed.

The effect of the embodiment of the invention can be further illustrated by simulation.

The simulation conditions were set as: the base stations and the users are randomly distributed in a macro cell (macro base station coverage area) with the radius of 500 m; consider 1 macro base station, 25 small base stations, with 10 tasks per user; the system bandwidth is 20MHz, the maximum transmitting power of a user is 23dBm, the maximum computing power of the user is 1-2 GHz, and the maximum computing power of a base station is 2.5GHz;6 kinds of cryptographic algorithms, the CPU cycle number required for encrypting one bit data is [100 200 250 300 350 1050 ]]The cycles/bit, the CPU cycle number required for decrypting one bit data is [90 280 350 300 400 1700 ]]cycle/bit, addThe consumption of the data with dense bit is [2.5296 5.0425 6.837 7.8528 8.7073 26.3643 ]]*1e ^-7 J/bit; the data size of each task is 2.56KB, the cut-off time delay is 0.1-0.5 s, and the required CPU cycle number is 20 Mycles; the weight parameter in the optimized objective function is 0.5; the energy coefficient of the user is 10 ^-24 Base station energy coefficient 10 ^-26 The method comprises the steps of carrying out a first treatment on the surface of the The cost is generated by the failure of the task security protection, and the task security coefficient is {5,6}.

Fig. 2 is a schematic diagram of the present invention to disclose the effect of the number of network users on the total energy consumption. Small base station deployment density in a single macrocell networkAnd transmit power of all users in macrocell +. >Other conditions were unchanged, the total energy consumption of the three algorithms, namely, network user number in a single macrocell varied from 15 to 35, iwoa, ipso, and modified whale particle swarm algorithm (WPSO), all increased as the number of network users increased. In addition, the total energy consumption of the WPSO algorithm provided by the invention is always lower than that of the other two algorithms.

Fig. 3 is a schematic diagram showing the influence of the number of network users on the fitness function value according to the present invention. Small base station deployment density in a single macrocell networkAnd transmit power of all users in macrocell +.>Under the condition that other conditions are unchanged, the fitness function values of the three algorithms, namely the number of network users in a single macro cell is changed from 15 to 35, the number of IWOA, the IPSO and the WPSO, are reduced along with the increase of the number of network users, but the fitness function value of the WPSO algorithm provided by the invention is always superior to the fitness function values of the other two algorithms.

Fig. 4 is a schematic diagram showing the effect of maximum transmit power of a user on total energy consumption according to the present invention. Small base station deployment density in a single macrocell networkAnd deployment Density of user terminal Equipment in macrocell ∈>And other conditions are unchanged, as the user transmission power increases, the total energy consumption of the WPSO algorithm is always lower than that of the other two algorithms due to the stronger searching capability.

Fig. 5 is a schematic diagram showing the influence of the maximum transmit power of the user on the fitness function value according to the present invention. Small base station deployment density in a single macrocell networkAnd deployment Density of user terminal Equipment in macrocell ∈>Under the condition that other conditions are unchanged, along with the increase of the user transmitting power, the fitness function value of the WPSO algorithm provided by the invention is obviously superior to the fitness function values of the other two algorithms, the side surface reflection WPSO algorithm has stronger searching capability, and the solution precision is higher. />

Fig. 6 (a) and fig. 6 (b) disclose the convergence of the IWOA algorithm and the IPSO algorithm of the present invention, respectively, as shown in the figure, the IWOA algorithm can converge rapidly, but easily falls into a locally optimal solution, and iteration is continued by using the IPSO on the basis of the IWOA algorithm, so it can be known that the WPSO algorithm formed by such hierarchical iteration can find a better solution.

The invention further provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for joint resource allocation and energy efficient secure computing offload optimization for ultra-dense internet of things networks. The processor executes the computer program to implement the steps in the embodiments of the above-mentioned ultra-dense network joint resource allocation and energy efficiency type safe computing unloading optimization method, such as S1 to S4 shown in fig. 1.

Embodiments of the present invention also provide a computer readable storage medium storing a computer program which, when executed by a processor, is operable to:

It will be apparent that the described embodiments are only some, but not all, embodiments of the application. Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application for the embodiment. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. The ultra-dense network combined resource allocation and energy efficiency type safe computing unloading optimization method is characterized by comprising the following steps of:

step S1: acquiring basic information of an ultra-dense internet of things network, constructing a network system according to the basic information, wherein the network system comprises a communication model, a calculation model, a security model and a multi-task model, constructing an optimization problem under the constraint of the network system, namely combining resource allocation, power control and user association while meeting the total cost and delay constraint of security vulnerabilities, executing multi-step security calculation and unloading, realizing the aim of minimizing the energy consumption of the whole network, and constructing the optimization problem according to the following formula:

wherein ,representing the total time of the computing task>Representing the total energy consumption of the computing task, IMD->Representing user terminal equipment +.>SBS stands for small base station, MBS stands for macro base station, BS stands for base station, SBS has +. >The set of SBS's is denoted asThe index of MBS is given by 0, +.>Representing the set of all base stations, the users have at most +.>The index set of the user +.>At most, each user has +>Each independently is at willThe task index set of each user is +.>Encryption algorithm has->The algorithm with the least and the greatest robustness is denoted by +.> and />The index set is marked->，/>Representing the user's offloading decision-making,index set representing offloading decisions, +.>Indicating IMD->With SBS->Associated, otherwise, < >>；/>Indicating IMD->Is>Whether the individual task selects an encryption algorithm +.>，Index set representing security decision variables of a task, +.>Indicating IMD->Is>Encryption algorithm of personal task and security protection level>Associated, otherwise, < >>Indicating IMD->Is>The security protection level of each task is 1; />For IMD->Uplink transmit power of (2), then +.>Indicating IMD->An index set of uplink transmit powers of (a); />Indicating IMD->Performing a calculation of the amount of data offloaded, IMD->Index set of data amount offloaded to BS +.>；/>For the data volume offloaded from SBS to MBS +.>Data volume index set indicating SBS offloaded to MBS, < >>Taking a value approaching zero; the band division factor is- > and />Indicating IMD->Is a security vulnerability total cost of (1); />Indicating IMD->The total time of the calculation task of (a) cannot exceed the maximum allowable time delay of the task execution +.>；/>Indicating IMD->Is->Cannot exceed IMD>Is +.>；/> and />Indicating that an IMD can only be associated with one base station; /> and />Indicating IMD->Is>A task can only be associated with one secure protection encryption algorithm; />Indicating IMD->Is>And upper bound->；/>Indicating when IMD->With SBS->When associated, IMD will task->Unload->Bit to SBS->Upper treatment, then SBS->Receive->Part of the individual task->Bit offloading to MBS; /> and />Greater than or equal to->But less than or equal to IMD>Task of (1)>Data size +.>，/>Greater than or equal to->；/>Lower bound representing band division factor +.>And an upper bound 1;

step S2: carrying out coarse-granularity search by adopting an improved whale algorithm IWOA according to the optimization problem, wherein the method specifically comprises the steps of initializing a population and determining historical optimal whale individuals; then in the shrink wrapping stage, adaptive nonlinear weighting factors are adopted and />For updating the position of whale individuals, and for the spiral bubble network attack and hunting phase; in the spiral bubble network attack stage, inertia weight coefficient is utilized +. >The method is used for adaptively adjusting the spiral amplitude, so as to avoid sinking into local optimum; in the hunting stage, the improved whale algorithm searching space is expanded by adopting the inverse cumulative distribution function of the cauchy to perform long tail mutation operation on whales, so that the global searching capability of whales is improved, and the phenomenon of sinking into local optimum is avoided; then repeatedly executing shrink wrapping, spiral bubble network attack, searching for the updates of prey and historical optimal whale individuals in sequence until the maximum iteration sequence is reached, and finally outputting codes of all whale individuals in the target population;

2. The ultra-dense network joint resource allocation and energy-efficient secure computing offload optimization method of claim 1, wherein step S2 comprises:

step S22: individual whale individualsPopulation->Each whale individual is finishedCoding whale individuals; firstly, defining an objective function as an fitness value of whale individuals, and then optimizing parameters +.>Encoded as +.>, wherein ,/>，/>Representing individual whale->Middle and IMD->An associated BS index; />，/>Representing individual whale->Middle and IMD->An associated security encryption algorithm level; />，/>Representing individual whale->Middle IMD->Is set to the transmission power of (a);，/>representing individual whale->Middle IMD->Data volume offloaded onto an associated SBS;，/>representing individual whale->Middle IMD->The amount of data offloaded onto the associated MBS; wherein->Representing individual whale->Is a frequency band division factor of (2); />Represents a set of virtual IMDs, length +.>；

Step S23: initialization ofWhale population, and whale individuals in the whale population were constructed according to the following formulaIs a fitness function of:

wherein ,representing individual whale->Fitness function value of->Indicating IMD->Penalty factor of latency constraint of ∈1->Indicating IMD->Penalty factor of security vulnerability total cost constraint, +.>Representing the total energy consumption of the computing task>Representing the total time of the computing task >Maximum allowable delay for task execution, +.>For IMD->Is added to the total cost of the security breach,for IMD->Is a maximum allowable security vulnerability total cost;

3. The method for optimizing ultra-dense network joint resource allocation and energy-efficient secure computing offload of claim 2, wherein step S24 comprises:

step S241: the position of each whale is a feasible solution, and a plurality of whale individuals continuously update the positions in the solution space until the global optimal solution is obtained, and each whale individual；

Whale individualSurrounding the prey location +.>The behavior of (2) is expressed as:

wherein ,representation pair->Function of rounding down, ++ >Is->Absolute value of (2); />Representing a prey location;for the location of the historically optimal whale individual,the position of the global optimal whale individual;

and />For the adaptive nonlinear weight value, updating the position of the whale individual; />To take the value of +.>Random numbers in between; />Is an iteration index; />Is the maximum iterative order; />And->All are random numbers, and the value is +.>Between them;

step S242: the spiral bubble net attack of whales involves simultaneous constrictive and spiral movements, any whale individualIs>The spiral equation between:

wherein Is a random number, and takes the value of +.>Between (I)>Is an inertial weight coefficient; />Is an iteration index; />Is the maximum iterative order;

step S243: any whale individualThe hunting behavior of (1) is expressed as:

Step S245: index the current iterationThe value of (2) is increased by 1.

4. The method for optimizing resource allocation and energy-efficient secure computing offload for ultra-dense networks according to claim 3, wherein said step S3 comprises:

step S32: taking all whale individuals in the target population as particles of improved self-adaptive particle swarm algorithm for whale individuals，/>In order of particle->Historical optimal position of the next iteration, let

Andfor improved adaptive particle swarm +.>An index set of optimal positions for the secondary iterations; taking the particles with the history optimal positions as history optimal particles, taking the particles with the global history optimal positions as global optimal particles, wherein the global optimal particles are formed by whale individuals +.>Obtaining particles in an optimal position;

step (a)S36: index the current iterationThe value of (2) is increased by 1.

5. The method for optimizing resource allocation and energy efficient secure computing offload for ultra-dense networks according to claim 4, wherein said step S34 comprises:

wherein, any whale individualThe inertial weight of (2) is represented by the formula>Update (F)> and />Maximum inertial weight and minimum inertial weight, respectively, +.>For iterative index, ++>For maximum iteration order, +.>Representing a combination of particles in common,is the particle in the firstThe historical optimal position for the next iteration,for particle group->Optimal position of the iteration- >And->Respectively representAnd->In->The speed of the number of iterations is such that,and->Respectively indicate->And->In->The position of the iteration, ++>And->Respectively representAnd->In->Speed of the second iteration, +.>Representing individual whale->In the first placeInertial weights of the secondary iterations; /> and />Respectively representing a learning factor and a social factor,and->Are random numbers;

wherein ,respectively represent global optimum particle->First->Speed of the second iteration, +.>Respectively represent global optimum particle->First->Speed of the second iteration, +.>Is->At the time of iterationScale factor (F)>Is a constant coefficient +.>，/>And->Are random numbers;

wherein ,respectively represent global optimum particle->First->The position of the iteration is that in order to make the improved self-adaptive particle swarm algorithm to randomly search the surrounding area of the global optimal position, the global optimal particle position is set as/>Then Scale factor->May be updated by the following rules:

wherein the scale factorFor driving the improved adaptive particle swarm algorithm to search for feasible solutions around globally optimal particles, +.>Representing the number of consecutive successes,/->Representing the number of consecutive failures>And->Representing a threshold parameter; in addition, ifDisplaying failure, otherwise suggesting that the search is successful;

6. The method for ultra-dense network joint resource allocation and energy efficient secure computing offload optimization of claim 5, wherein step S4 comprises:

according to the improved adaptive particle swarm in step S32The index set of the optimal position of the secondary iteration restores the position of the global optimal particle into the form of an original optimization parameter solution;

7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when the computer program is executed.

8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 6.