CN117939572B - Electric power Internet of things terminal access method - Google Patents
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Abstract
The invention discloses a terminal access method of an electric power internet of things, which comprises the steps of firstly, determining an initial candidate set of a channel and edge sink node combination according to access load reliability constraint; secondly, providing an improved heuristic algorithm on the basis of a genetic algorithm and a particle swarm optimization algorithm; and finally, by introducing cross mutation operation in a genetic algorithm into a particle swarm algorithm, determining the combination of the best channel and the edge sink node matched with each type of access load, and minimizing the average processing time delay of all load accesses under the constraint of access load reliability. The invention can reduce the average processing time delay of the load while guaranteeing the reliability of the access load, and effectively improves the efficiency of the access of the electric power Internet of things terminal.
Description
Technical Field
The invention relates to a terminal access method of an electric power internet of things, and belongs to the field of the internet of things.
Background
The access efficiency of the power internet of things terminal and the processing efficiency of the access load depend on a trusted execution environment, in the power internet of things solution related to service quality, the availability, reliability and other attributes have high priority, and faults and errors are major threats facing low-power consumption power internet of things terminal equipment and edge aggregation nodes, and the faults and errors can influence the operation of the whole power internet of things system. In order to prevent complete failure of the system or degradation of quality of service, an effective fault tolerant system is needed that helps to detect and recover from faults during operation, thereby reducing the negative impact of the faults. Meanwhile, for the electric power internet of things, the service experience of the terminal equipment can be improved through rapid processing of the access load. Therefore, the method has great practical significance in paying attention to reliable access of the electric power Internet of things terminal and guaranteeing the processing time delay of the electric power Internet of things terminal.
In the electric power internet of things, sudden conditions caused by power failure, hardware failure, network failure or service failure and the like occur. Therefore, fault tolerance of load access and handling becomes very important in order to improve its reliability. For the application of the electric power Internet of things, which involves the multistage transmission of the edge sink node and the main sink node in the communication process, the density and the randomness of the access load of the terminal equipment are increased, and the difficulty of reliable communication and processing is increased. On the one hand, if the reliability of the load access provided by the electric power internet of things is low, the load processing quality is degraded or the access is interrupted. On the other hand, if the reliability is pursued uniformly, the resource allocation redundancy occurs again, and the access load continues to wait, which causes a problem of high processing delay. Therefore, in designing the power internet of things communication system, both time delay and reliability are factors to be considered.
From the above analysis, it is needed to design an access method for an electric power internet of things terminal, which uses limited network resources to improve the reliability of an access load on the premise of guaranteeing time delay.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention provides the electric power internet of things terminal access method, which improves the reliability of access load by jointly considering the reliability of channel transmission, the processing reliability of the edge aggregation node and the overall reliability of the network system.
The technical scheme is as follows: in order to solve the technical problems, the method for accessing the electric power internet of things terminal comprises the steps of firstly, determining an initial candidate set of a channel and edge aggregation node combination according to access load reliability constraint; secondly, providing an improved heuristic algorithm on the basis of a genetic algorithm and a particle swarm optimization algorithm; and finally, by introducing cross mutation operation in a genetic algorithm into a particle swarm algorithm, determining the combination of the best channel matched with each type of access load and the edge sink node, and minimizing the average processing time delay of all loads under the constraint of access load reliability. The method specifically comprises the following steps:
S1: determining a reliability model, wherein the reliability model comprises the channel transmission reliability of the electric power Internet of things terminal and the edge sink node, the processing reliability of the edge sink node and the overall reliability of the electric power Internet of things;
s2: access load model update: when a terminal generates a new load and needs to be connected to the electric power internet of things, the new load is firstly transmitted to an edge sink node to generate a first part of wireless transmission delay, and a main sink node sends signaling through a centralized controller to transmit processing copies of the same access load to different edge sink nodes;
S3: and (5) updating an access load processing model: obtaining access load processing time delay according to the average arrival rate, the processing capacity and the processing capacity of the edge aggregation node of the access load and the M/M/1 queuing model;
S4: edge aggregation node selection: selecting an initial channel and edge aggregation node set meeting the requirements according to the access load reliability requirements;
S5: outputting a scheduling result: and outputting a particle swarm with the best global fitness, namely a cooperative edge aggregation node, by using an improved genetic particle swarm algorithm according to the access load reliability requirement, the access load processing size and the access load delay tolerance, and transmitting a copy of the access load to the node for processing to finish scheduling.
Further, the step S1 specifically includes the following steps:
S11: channel transmission reliability : The method comprises the steps that a load of an access node of a terminal is transmitted to an edge aggregation node through a wireless channel, the edge aggregation node transmits the access load to a main aggregation node, then the main aggregation node distributes processing copies of the same access load on different edge aggregation nodes, and when one edge aggregation node fails or performance is reduced, the processing of the access load is completed by selecting other edge aggregation nodes;
S12: edge aggregation node processing reliability : Evaluating the processing reliability of the edge aggregation node according to the average maintenance time and the fault interval time of the edge aggregation node;
S13: overall system reliability : The channel transmission reliability of the edge sink node and the processing reliability of the edge sink node are required to meet the access load reliability requirement; wherein, transmission reliability/>The method comprises the following steps:
At the position of In/>Terminal sets representing access loads, e.g. including smart meters, power distribution equipment, data acquisition and analysis devices,/>Representing a set of load task types, e.g. comprising power data acquisition, power data analysis, power data visualization,/>Representing a set of transmission channels,/>Representing transmission channel/>Is an input value; /(I)When the channel is selected,/>; Otherwise,/>; Processing reliability is/>：
Wherein,Representing a collection of edge aggregation nodes,/>The average maintenance time and the fault interval time of the edge aggregation node are related and are input values; /(I)When the edge aggregation node is selected, the edge aggregation node,; Otherwise,/>；
Thereby obtaining the overall reliability of the systemIs that
。
Further, the step S2 specifically includes the following steps:
s21: assume terminal access Type of load, all subject to mean value/>Is provided with an access load/>, in advanceBy channel/>Transmission rate to edge sink node/>Wherein d represents what number of accessed terminals, and the access load size is/>The wireless transmission delay is calculated by adopting an M/M/1 queuing model, and the uplink transmission time of the access load is obtained as follows:
S22: transmitting the processing copies of the same access load to different edge sink nodes for processing, transmitting each processing copy by different wireless channels, and calculating the total wireless transmission time delay ，/>In the multiple copy transmission, the maximum value of the uplink transmission delay is used as the transmission delay of the load request,
。
Further, the step S3 specifically includes the following steps:
s31: assume an access load Arrival servers obey poisson distribution with average arrival rate/>Assume that the access load of each electric power internet of things terminal is/>，/>Representing that edge aggregation node provides access load/>The processing resource is provided, the processing time of the power terminal accessing to the load is/>, and the processing resource is input valueAnd obeys the exponential distribution, and the M/M/1 queuing model is used for processing the load at each edge sink node so as to obtain the access load/>Average completion delay on edge aggregation node, access load/>The average completion time delay at the edge aggregation node is formulated as:
Wherein, ；
The service rate of the edge aggregation node is larger than the arrival rate of the access load, namely:
；
the total average processing delay can be expressed as:
Handling access loads Total delay/>Expressed as:
。
Further, the step S4 specifically includes:
Step S4.1: from a collection In accordance with the access load type/>The channel with reliability requirements is put into an initial candidate channel set, which is denoted/>；
Step 4.2: limiting the maximum number of channels toI.e. the minimum number of channels that meet the overall reliability of the network, and will be able to handle the access load type/>The edge aggregation node of (1) is put into an initial candidate node set, and the set is used for/>Representation, limiting maximum edge aggregation node to/>I.e. the minimum number of edge aggregation nodes meeting the overall reliability requirement of the network,/>Given as input, the minimum number of edge aggregation nodes required to characterize the overall reliability of the network, i.e., set/>Least/>There is an edge aggregation node, defining a collection/>Representing a set of initial candidate edge sink nodes and channel combinations, wherein "/>"Represents Cartesian product operator, each element in the set represents a combination of a channel and an edge sink node,/>The elements in (a) define a weight function/>：
Under the four constraint conditions, the weight coefficient is optimized to realize reliable transmission by using less power Internet of things wireless network resources as much as possible, and the optimal is obtainedIf the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability do not meet the requirements, deleting the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability, sorting the rest elements from small to large, and taking the front/>The elements are used as final candidate sets to obtain processed candidate sets/>And takes this as an input to step S5, where x is determined according to actual demand,/>I.e. x is equal to or greater than the limit maximum number of channels/>And limiting the maximum edge aggregation node to/>The maximum of the two.
Further, the step S5 specifically includes the following steps:
S51: initializing a population: each individual in the population represents a feasible solution to the access load allocation problem, and each individual is represented by a vector in an algorithm on the assumption that each terminal device has only one type of access load, each dimension of the vector represents an allocation strategy of the access load of one terminal device, and each particle corresponds to each edge aggregation node;
Particles The fitness function of (2) is expressed as:
Wherein, Accessing load for transmission and processing ]Is a total delay of (1);
in initializing a population, the diversity of the population is defined as:
Wherein L is expressed as the population particle number in the genetic particle swarm optimization algorithm, L is equivalent to x in step S42, namely the set The number of elements in/>Indicating the current particle at the/>Value of dimension,/>Represents all particles/>Average value of dimensions, threshold/>Is a standard for measuring population diversity, is set according to requirements, if/>Greater than threshold/>The particles are retained in the population, otherwise randomly from the candidate set/>Randomly selecting a particle to be added into the population, repeating the process, and keeping the size of the population as/>；
S52: based on the initialized population, wherein the location of each particle represents a solution to the problem, whereinThe individual particles will record several pieces of information: particle current position/>Each particle has its own best fitness value/>Its corresponding fitness value is/>Location of global historical best fitness value/>The corresponding fitness value is/>Meanwhile, cross mutation operation in a genetic algorithm is introduced, so that the particle determines the mutation direction and amplitude according to the self optimal solution, the optimal solution of the sub-population and the evolution speed of the individual;
Velocity of particles Representing the direction and speed of movement of the particles in the search space, the updated formula is expressed as:
Wherein, Called inertial factor,/>，/>And/>Called acceleration constant, where/>Individual learning factor representing particles,/>Social learning factor representing particles,/>And/>Let 2, random number/>And/>The value range of (2) is/>The location update formula can be expressed as:
Wherein, The cross formula for particles, representing rounding, is:
Wherein, Cross probability for each generation of particles;
s53: and carrying out mutation operation on the particles, wherein the particle mutation formula is as follows:
Wherein, Representing random number with value range of/>Calculating individual fitness value after the particle mutation operation is completed, adding the individual fitness value into the population, updating global historical optimal fitness according to the fitness value updated by each particle, and performing loop iteration until the maximum iteration frequency/>Or the difference of fitness of all particles in the population after two previous and subsequent iterations is less than/>At this point, the cycle is ended and a population of particles/>, which best suits globally, is outputAnd combining the edge sink node and the transmission channel as a cooperative access load. S51, each particle corresponds to each edge aggregation node, which processes the total delay/>, of the access loadThe method reflects the quality of the current allocation strategy, and finally, a particle population which is stable and enables the global fitness to be optimal is screened through a particle swarm algorithm and is used as an edge aggregation node for cooperatively completing the access load, namely, copies of the access load are distributed to particles contained in the population after the optimization of the particle swarm algorithm for processing. In addition, the main sink node mainly affects the transmission delay between the terminal and the edge sink node, that is, in S21, the bandwidths and the transmission rates of different main sink nodes all have differences, so that the performance of the edge sink node is indirectly affected.
In the present invention, div corresponds to the standard deviation of particle distribution, and the probability density function of normal distribution is referenced, so that the generated population has higher stability in diversity, and the threshold value。
The beneficial effects are that: aiming at the problem of poor processing stability of the terminal access load in the electric power Internet of things, the invention provides the terminal reliable access method for time delay guarantee, which can reduce the average processing time delay of the load while guaranteeing the reliability of the access load, and effectively improve the access efficiency of the electric power terminal equipment.
Drawings
FIG. 1 is a scene model diagram of the present invention;
FIG. 2 is a flow chart of access to an electric power Internet of things terminal according to the invention;
fig. 3 is a flowchart of a particle swarm optimization algorithm according to the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 1 to 3, the method for accessing the electric power internet of things terminal according to the invention specifically comprises the following steps:
Step 1: determining a reliability model comprising channel transmission reliability of an electric power Internet of things terminal and an edge sink node Processing reliability/>, of edge aggregation nodeAnd overall reliability/>, of the electric power internet of thingsThe method specifically comprises the following steps:
S11: channel transmission reliability : The method comprises the steps that a load of an access node of a terminal is transmitted to an edge aggregation node through a wireless channel, the edge aggregation node transmits the access load to a main aggregation node, then the main aggregation node distributes processing copies of the same access load on different edge aggregation nodes, and when one edge aggregation node fails or performance is reduced, the processing of the access load is completed by selecting other edge aggregation nodes;
S12: edge aggregation node processing reliability : Evaluating the processing reliability of the edge aggregation node according to the average maintenance time and the fault interval time of the edge aggregation node;
S13: overall system reliability : The channel transmission reliability of the edge sink node and the processing reliability of the edge sink node are required to meet the access load reliability requirement; wherein, transmission reliability/>The method comprises the following steps:
At the position of In/>Terminal set representing access load,/>Representing a set of load task types,/>Representing a set of transmission channels,/>Representing transmission channel/>Is an input value; /(I)When the channel is selected,/>; Otherwise,/>; Processing reliability/>The method comprises the following steps:
Wherein, Representing a collection of edge aggregation nodes,/>The average maintenance time and the fault interval time of the edge aggregation node are related and are input values; /(I)When the edge aggregation node is selected, the edge aggregation node,; Otherwise,/>；
And further the integral reliability of the single edge aggregation node system can be obtainedIs that
。
Step 2: access load model update: when a terminal generates a new load and needs to be connected to the electric power internet of things, the new load is firstly transmitted to an edge sink node to generate a first part of wireless transmission delay, then in order to improve the fault tolerance and reliability of the connected load, a main sink node sends signaling through a centralized controller to transmit processing copies of the same connected load to different edge sink nodes, and the method specifically comprises the following steps:
s21: assume terminal access Type of load, all subject to mean value/>Is provided with an access load/>, in advanceBy channel/>Transmission rate to edge sink node/>Wherein d represents what number of accessed terminals, and the access load size is/>The wireless transmission delay is calculated by adopting an M/M/1 queuing model, and the uplink transmission time of the access load is obtained as follows:
S22: transmitting the processing copies of the same access load to different edge sink nodes for processing, transmitting each processing copy by different wireless channels, and calculating the total wireless transmission time delay ，/>In the multiple copy transmission, the maximum value of the uplink transmission delay is used as the transmission delay of the load request,
。
Step 2.3: in an electric power internet of things wireless transmission system, in order to maintain the transmission stability of a communication system and ensure that an access load in a queue can be processed within a specified time, meanwhile, the maximum utilization rate of queue resources is ensured, the access quantity in a channel queue needs to be controlled, the increase of transmission delay and even the loss of perceived data caused by excessive load backlog are avoided, and the service rate of the queue needs to be controlled to be larger than the arrival rate of the access load.
Step 3: and (5) updating an access load processing model: s31: assume an access loadArrival servers obey poisson distribution with average arrival rate/>Assume that the access load of each electric power internet of things terminal is/>，/>Representing that edge aggregation node provides access load/>The provided processing resource is input value, and the power terminal is connected to load/>The treatment time of (2) isAnd obeys the exponential distribution, and the M/M/1 queuing model is used for processing the load at each edge sink node so as to obtain the access load/>Average completion delay on edge aggregation node, access load/>The average completion time delay at the edge aggregation node is formulated as:
Wherein, ；
The service rate of the edge aggregation node is larger than the arrival rate of the access load, namely:
；
the total average processing delay can be expressed as:
Handling access loads Total delay/>Expressed as:
。
Step 4: edge aggregation node selection: selecting an initial channel and edge aggregation node set meeting the requirements according to the access load reliability requirements, and specifically comprising the following steps:
Step S4.1: from a collection In accordance with the access load type/>The channel with reliability requirements is put into an initial candidate channel set, which is denoted/>；
Step 4.2: limiting the maximum number of channels toI.e. the minimum number of channels that meet the overall reliability of the network, and will be able to handle the access load type/>The edge aggregation node of (1) is put into an initial candidate node set, and the set is used for/>Representation, limiting maximum edge aggregation node to/>I.e. the minimum number of edge aggregation nodes meeting the overall reliability requirement of the network,/>Given as input, the minimum number of edge aggregation nodes required to characterize the overall reliability of the network, i.e., set/>At least there areEdge aggregation node, define collection/>Representing a set of initial candidate edge sink nodes and channel combinations, wherein "/>"Represents Cartesian product operator, each element in the set represents a combination of a channel and an edge sink node,/>The elements in (a) define a weight function/>：
Under the four constraint conditions, the weight coefficient is optimized to realize reliable transmission by using less power Internet of things wireless network resources as much as possible, and the optimal is obtainedIf the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability do not meet the requirements, deleting the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability, sorting the rest elements from small to large, and taking the front/>The elements are used as final candidate sets to obtain processed candidate sets/>And takes this as an input to step S5.
Step 5: outputting a scheduling result: and outputting the average completion time delay of the access load by using the improved genetic particle swarm algorithm according to the access load reliability requirement, the access load processing size and the access load time delay tolerance time delay. The method specifically comprises the following steps:
S51: initializing a population: each individual in the population represents a feasible solution to the access load allocation problem, and each individual is represented by a vector in an algorithm on the assumption that each terminal device has only one type of access load, each dimension of the vector represents an allocation strategy of the access load of one terminal device, and each particle corresponds to each edge aggregation node;
Particles The fitness function of (2) is expressed as:
Wherein, Accessing load for transmission and processing ]Is a total delay of (1);
in initializing a population, the diversity of the population is defined as:
Wherein, Expressed as the population particle count in the genetic particle swarm optimization algorithm, L corresponds to x in step S42, i.e., set/>The number of elements in/>Indicating the current particle at the/>Value of dimension,/>Represents all particles/>Average value of dimensions, threshold/>Is a standard for measuring population diversity, is set according to requirements, if/>Greater than threshold/>The particles are retained in the population, otherwise randomly from the candidate set/>Randomly selecting a particle to be added into the population, repeating the process, and keeping the size of the population as/>；
S52: based on the initialized population, wherein the location of each particle represents a solution to the problem, whereinThe individual particles will record several pieces of information: particle current position/>Each particle has its own best fitness value/>Its corresponding fitness value is/>Location of global historical best fitness value/>The corresponding fitness value is/>Meanwhile, cross mutation operation in a genetic algorithm is introduced, so that the particle determines the mutation direction and amplitude according to the self optimal solution, the optimal solution of the sub-population and the evolution speed of the individual;
Velocity of particles Representing the direction and speed of movement of the particles in the search space, the updated formula is expressed as:
Wherein, Called inertial factor,/>，/>And/>Called acceleration constant, where/>Individual learning factor representing particles,/>Social learning factor representing particles,/>And/>Let 2, random number/>And/>The value range of (2) is/>The location update formula can be expressed as:
Wherein, The cross formula for particles, representing rounding, is:
Wherein, Cross probability for each generation of particles;
s53: and carrying out mutation operation on the particles, wherein the particle mutation formula is as follows:
Wherein, Representing random number with value range of/>Calculating individual fitness value after the particle mutation operation is completed, adding the individual fitness value into the population, updating global historical optimal fitness according to the fitness value updated by each particle, and performing loop iteration until the maximum iteration frequency/>Or the difference of fitness of all particles in the population after two previous and subsequent iterations is less than/>At this point, the cycle is ended and a population of particles/>, which best suits globally, is outputAnd combining the edge sink node and the transmission channel as a cooperative access load.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (1)
1. The electric power internet of things terminal access method is characterized by comprising the following steps of:
S1: determining a reliability model, wherein the reliability model comprises the channel transmission reliability of the electric power Internet of things terminal and the edge sink node, the processing reliability of the edge sink node and the overall reliability of the electric power Internet of things;
s2: access load model update: when a terminal generates a new load and needs to be connected to the electric power internet of things, the new load is firstly transmitted to an edge sink node to generate a first part of wireless transmission delay, and a main sink node sends signaling through a centralized controller to transmit processing copies of the same access load to different edge sink nodes;
S3: and (5) updating an access load processing model: obtaining access load processing time delay according to the average arrival rate, the processing capacity and the processing capacity of the edge aggregation node of the access load and the M/M/1 queuing model;
S4: edge aggregation node selection: selecting an initial channel and edge aggregation node set meeting the requirements according to the access load reliability requirements;
s5: outputting a scheduling result: according to the access load reliability requirement, the access load processing size and the access load delay tolerance, using an improved genetic particle swarm algorithm to output a particle swarm with the best global fitness, namely a cooperative edge aggregation node, and transmitting a copy of the access load to the node for processing to finish scheduling; the step S1 specifically comprises the following steps:
S11: channel transmission reliability : The method comprises the steps that a load of an access node of a terminal is transmitted to an edge aggregation node through a wireless channel, the edge aggregation node transmits the access load to a main aggregation node, then the main aggregation node distributes processing copies of the same access load on different edge aggregation nodes, and when one edge aggregation node fails or performance is reduced, the processing of the access load is completed by selecting other edge aggregation nodes;
S12: edge aggregation node processing reliability : Evaluating the processing reliability of the edge aggregation node according to the average maintenance time and the fault interval time of the edge aggregation node;
S13: overall system reliability : The channel transmission reliability of the edge sink node and the processing reliability of the edge sink node are required to meet the access load reliability requirement; wherein, transmission reliability/>The method comprises the following steps:
At the position of In/>Terminal set representing access load,/>Representing a set of load task types,/>Representing a set of transmission channels,/>Representing transmission channel/>Is an input value; When the channel is selected,/> ; Otherwise,/>; Processing reliability/>The method comprises the following steps:
Wherein, Representing a collection of edge aggregation nodes,/>The average maintenance time and the fault interval time of the edge aggregation node are related and are input values; /(I)When the edge aggregation node is selected, the edge aggregation node,; Otherwise,/>；
And further the integral reliability of the single edge aggregation node system can be obtainedIs that
; The step S2 specifically includes the following steps:
s21: assume terminal access Type of load, all subject to mean value/>Is provided with an access load in advanceBy channel/>Transmission rate to edge sink node/>Wherein d represents what number of accessed terminals, and the access load size is/>The wireless transmission delay is calculated by adopting an M/M/1 queuing model, and the uplink transmission time of the access load is obtained as follows:
S22: transmitting the processing copies of the same access load to different edge sink nodes for processing, transmitting each processing copy by different wireless channels, and calculating the total wireless transmission time delay ，/>In the multiple copy transmission, the maximum value of the uplink transmission delay is used as the transmission delay of the load request,
; The step S3 specifically includes:
s31: assume an access load Arrival servers obey poisson distribution with average arrival rate/>Assume that the access load of each electric power internet of things terminal is/>，/>Representing that edge aggregation node provides access load/>The provided processing resource is input value, and the power terminal is connected to load/>The processing time of (2) is/>And obeys the exponential distribution, and the M/M/1 queuing model is used for processing the load at each edge sink node so as to obtain the access load/>Average completion delay on edge aggregation node, access load/>The average completion time delay at the edge aggregation node is formulated as:
Wherein, ；
The service rate of the edge aggregation node is larger than the arrival rate of the access load, namely:
；
the total average processing delay is expressed as:
Handling access loads Total delay/>Expressed as:
; the step S4 specifically includes:
Step S41: from a collection In accordance with the access load type/>The channel with reliability requirements is put into an initial candidate channel set, which is denoted/>；
Step 42: limiting the maximum number of channels toI.e. the minimum number of channels that meet the overall reliability of the network, and will handle the access load type/>The edge aggregation node of (1) is put into an initial candidate node set, and the set is used for/>Representation, limiting maximum edge aggregation node to/>I.e. the minimum number of edge aggregation nodes meeting the overall reliability requirement of the network,/>Given as input, the minimum number of edge aggregation nodes required to characterize the overall reliability of the network, i.e., set/>At least/>Edge aggregation node, define collection/>Representing a set of initial candidate edge sink nodes and channel combinations, wherein "/>"Represents Cartesian product operator, each element in the set represents a combination of a channel and an edge sink node,/>The elements in (a) define a weight function/>：
Under the four constraint conditions, the weight coefficient is optimized to realize reliable transmission by using less power Internet of things wireless network resources as much as possible, and the optimal is obtainedIf the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability do not meet the requirements, deleting the channel transmission reliability, the processing reliability of the edge aggregation node and the overall system reliability, and remaining elements according to/>The values are ordered from small to large, and the front/>, after the ordering, is takenThe elements are used as final candidate sets to obtain processed candidate sets/>And takes this as an input to step S5; the step S5 specifically includes:
S51: initializing a population: each individual in the population represents one possible solution to the access load allocation problem, assuming that each terminal device has only one type of access load, each individual is represented in the algorithm by a vector, and each dimension of the vector represents an allocation policy of the access load of one terminal device;
Particles The fitness function of (2) is expressed as:
Wherein, Accessing load for transmission and processing ]Is a total delay of (1);
in initializing a population, the diversity of the population is defined as:
Wherein, Expressed as the population particle number in the genetic particle swarm optimization algorithm, L corresponds to x in step S42, i.e. the setThe number of elements in/>Indicating the current particle at the/>Value of dimension,/>Represents all particles/>Average value of dimensions, threshold/>Is a standard for measuring population diversity, is set according to requirements, if/>Greater than threshold/>The particles are retained in the population, otherwise randomly from the candidate set/>Randomly selecting a particle to be added into the population, repeating the process, and keeping the size of the population as/>；
S52: based on the initialized population, wherein the location of each particle represents a solution to the problem, whereinThe individual particles will record several pieces of information: particle current position/>Each particle has its own best fitness value/>Its corresponding fitness value is/>Location of global historical best fitness value/>The corresponding fitness value is/>Meanwhile, cross mutation operation in a genetic algorithm is introduced, so that the particle determines the mutation direction and amplitude according to the self optimal solution, the optimal solution of the sub-population and the evolution speed of the individual;
Velocity of particles Representing the direction and speed of movement of the particles in the search space, the updated formula is expressed as:
Wherein, Called inertial factor,/>，/>And/>Called acceleration constant, where/>Individual learning factor representing particles,/>Social learning factor representing particles,/>And/>Let 2, random number/>And/>The range of the values is as followsThe location update formula is expressed as:
Wherein, The cross formula for particles, representing rounding, is:
Wherein, Cross probability for each generation of particles;
s53: and carrying out mutation operation on the particles, wherein the particle mutation formula is as follows:
Wherein, Representing random number with value range of/>Calculating individual fitness value after the particle mutation operation is completed, adding the individual fitness value into the population, updating global historical optimal fitness according to the fitness value updated by each particle, and performing loop iteration until the maximum iteration frequency/>Or the difference of fitness of all particles in the population after two previous and subsequent iterations is less than/>At this point, the cycle is ended and a population of particles/>, which best suits globally, is outputAnd combining the edge sink node and the transmission channel as a cooperative access load.
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