CN116684483B - Method for distributing communication resources of edge internet of things proxy and related products - Google Patents
Method for distributing communication resources of edge internet of things proxy and related products Download PDFInfo
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- CN116684483B CN116684483B CN202310967835.3A CN202310967835A CN116684483B CN 116684483 B CN116684483 B CN 116684483B CN 202310967835 A CN202310967835 A CN 202310967835A CN 116684483 B CN116684483 B CN 116684483B
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Abstract
The application discloses a method for distributing communication resources of an edge internet of things proxy and related products, which can be applied to the technical field of the internet of things, and the method comprises the following steps: acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; determining satisfaction degree of the edge internet of things proxy on data transmission delay; determining the weight of the edge internet of things agent in the transmission network based on the influence factors; establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction; and solving a communication network resource allocation model of the edge internet of things agent by using a genetic algorithm and a water injection algorithm, and realizing the allocation of the communication resources of the edge internet of things agent. Therefore, the resource allocation model of the edge Internet of things proxy communication network is established and solved based on the weight and the satisfaction degree, so that the allocation of communication resources is realized, and the instantaneity of the edge Internet of things proxy service transmission is improved.
Description
Technical Field
The application relates to the technical field of the Internet of things, in particular to a method for distributing communication resources of an edge Internet of things proxy and related products.
Background
The intelligent Internet of things system is a source of data acquisition of the electric power Internet of things and a foundation for realizing various applications, is a necessary substance foundation for realizing energy intercommunication and sharing mutual aid, and has important significance for accelerating the promotion of the landing of strategic targets in the electric power industry. The system mainly comprises an Internet of things management platform, an edge Internet of things agent and various types of terminal standardized access.
The edge internet of things agent refers to a device or a component for carrying out unified access, data analysis and real-time calculation on various intelligent sensors and intelligent service terminals. The edge internet of things agent and the internet of things management platform are in bidirectional interconnection and are deployed on the edge side, so that on-site integrated sharing, regional autonomy and cloud edge cooperative business processing of cross-professional data are realized. The existing method for distributing the communication resources of the edge internet of things proxy is relatively fixed, cannot meet the transmission requirement of the current edge internet of things proxy service differentiation, and causes the problems of service transmission lag and lack of certain instantaneity.
Therefore, how to improve the instantaneity of the edge internet of things proxy service transmission is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
Based on the problems, the application provides a method for distributing communication resources of an edge Internet of things proxy and related products, and establishes an edge Internet of things proxy communication network resource distribution model based on weight and satisfaction and solves the problem that the edge Internet of things proxy service transmission in the prior art lacks a certain instantaneity.
In a first aspect, the present application provides a method for allocating communication resources by using an edge internet of things proxy, including:
acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; the influencing factors include: influence factors of the transmission data amount category, influence factors of the transmission delay requirement category, influence factors of the residual buffer space category and influence factors of the data criticality category;
determining satisfaction degree of the edge internet of things agent for data transmission delay;
determining the weight of the edge internet of things agent in a transmission network based on the influence factors;
establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree;
and solving the communication network resource allocation model of the edge internet of things agent by using a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things agent.
Optionally, the determining the satisfaction degree of the edge internet of things agent on the data transmission delay includes:
calculating a utility value of the edge internet of things agent based on a utility function;
determining satisfaction degree of the edge internet of things agent for data transmission delay according to the utility value;
the utility function is:
；
Wherein m is the total number of edge internet of things agents, U k Utility value, t, representing edge proxy k k Andand respectively representing the actual transmission delay and the transmission delay requirement of the edge internet-of-things proxy k.
Optionally, the determining the weight of the edge internet of things agent in the transport network based on the influencing factor includes:
respectively determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent;
carrying out dimensionless treatment on the influence factors of each category to obtain dimensionless forms corresponding to the influence factors of each category;
combining the weight of the influence factors of each class on the bandwidth resource demand degree of the edge internet of things agent and the non-dimensionalized form corresponding to the influence factors of each class, and determining the weight of the edge internet of things agent in a transmission network by using an overall weight algorithm;
the overall weight algorithm is as follows:
；
wherein omega k Weights in the transport network for edge internet of things agents,and->Weight and corresponding dimensionless form of influence factors of transmission data amount category on bandwidth resource demand degree of edge internet of things agent k>And->Edge object respectively influenced by transmission delay requirement category The weight and corresponding dimensionless form of the influence of the extent of bandwidth resource demand of the co-agent k,/->And->Weight and corresponding dimensionless form of influence factors of residual buffer space categories on bandwidth resource demand degree of edge internet-of-things agent k>And->The weight and the corresponding dimensionless form of the influence factors of the data critical degree category on the bandwidth resource demand degree of the edge internet-of-things agent k are respectively.
Optionally, the determining the weight of the influence factors of each category on the bandwidth resource requirement degree of the edge internet of things agent includes:
combining influence factors of all categories, and constructing an initial resource allocation matrix by adopting an expert scoring method;
and determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent based on the initial resource allocation matrix.
Optionally, the performing dimensionless processing on the influence factors of each category to obtain a dimensionless form corresponding to the influence factors of each category includes:
determining the expression of each category of influencing factors;
combining the expression of the influence factors of the residual cache space category and the expression of the influence factors of the data critical degree category, classifying the influence factors of the residual cache space category and the influence factors of the data critical degree category according to the level, and determining the expression of the residual cache space level and the data critical degree level;
And obtaining a dimensionless form corresponding to each type of influence factors based on the expression of the influence factors of the transmission data amount type, the expression of the influence factors of the transmission delay requirement type, the expression of the residual buffer space level and the expression of the data criticality level.
Optionally, before the establishing the edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, the method further includes:
carrying out consistency check on the weight ratio of the influence factors of each category on the bandwidth resource demand degree by using a consistency check algorithm;
the consistency check algorithm is as follows:
；
wherein alpha represents the number of influencing factors of each category, R I Represents a random consistency index, lambda max For maximum characteristic value, C R Is a consistency ratio;
when said C R <And 0.1, considering the consistency degree to be within the allowable range, otherwise, adjusting the initial resource allocation matrix, and redetermining the weight of the influence factors of each category on the bandwidth resource demand degree.
Optionally, the establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree includes:
establishing a subcarrier and power allocation model of the edge internet of things proxy communication network with the maximization of the weighted utility sum as a target based on the weight and the satisfaction;
The subcarrier and power distribution model of the edge internet of things proxy communication network is as follows:
；
wherein the method comprises the steps ofTo weight the utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing utility value of edge proxy k, N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge internet of things agent k,for the total transmit power on the s-th subcarrier of the edge proxy k +.>And representing the power resources allocated to the s-th sub-carrier allocated to the edge Internet of things agent k, wherein m is the total number of the edge Internet of things agents.
In a second aspect, the present application provides an apparatus for allocating communication resources of an edge internet of things proxy, including:
the acquisition module is used for acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; the influencing factors include: influence factors of the transmission data amount category, influence factors of the transmission delay requirement category, influence factors of the residual buffer space category and influence factors of the data criticality category;
the first determining module is used for determining the satisfaction degree of the edge internet of things agent on the data transmission delay;
the second determining module is used for determining the weight of the edge internet of things agent in the transmission network based on the influence factors;
The establishing module is used for establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree;
and the distribution module is used for solving the communication network resource distribution model of the edge internet of things agent by utilizing a genetic algorithm and a water injection algorithm, and realizing the distribution of the communication resources of the edge internet of things agent.
In a third aspect, the present application provides an apparatus for edge internet of things proxy communication resource allocation, including:
a memory for storing a computer program;
a processor for implementing the steps of the method for edge internet of things proxy communication resource allocation as claimed in any one of the preceding claims when executing the computer program.
In a fourth aspect, the present application provides a readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of edge-to-internet-of-things proxy communication resource allocation as claimed in any preceding claim.
Compared with the prior art, the application has the following advantages that:
the method comprises the steps of firstly obtaining influence factors of the edge internet of things agent on the bandwidth demand degree. Among these, influencing factors include: the method comprises the following steps of influencing factors of the transmission data quantity category, influencing factors of the transmission delay requirement category, influencing factors of the residual buffer space category and influencing factors of the data criticality category. And then determining the satisfaction degree of the edge internet of things agent for the data transmission delay, and determining the weight of the edge internet of things agent in the transmission network based on the influence factors. And finally, establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, and solving the edge internet of things proxy communication network resource allocation model by utilizing a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things proxy. Therefore, the resource allocation model of the edge Internet of things proxy communication network is established and solved based on the weight and the satisfaction degree, so that the allocation of communication resources is realized, and the instantaneity of the edge Internet of things proxy service transmission is improved.
Drawings
FIG. 1 is a flow chart of a method for allocating communication resources of an edge Internet of things proxy according to the present application;
fig. 2 is a schematic diagram of a channel resource and power resource allocation result provided in the present application;
FIG. 3 is a schematic diagram showing a comparison between a weighted transmission rate and a variation of the number of the agents of the edge-based Internet of things according to the present application;
fig. 4 is a schematic structural diagram of an apparatus for allocating communication resources of an edge internet of things proxy according to the present application.
Detailed Description
As described above, the existing method for allocating communication resources by the edge internet of things proxy easily causes a lack of instantaneity in service transmission. Specifically, after more than ten years of development, the application of the electric power internet of things has a certain foundation, various terminals (sets) such as intelligent electric meters are connected with the terminals, the daily increment of collected data exceeds 60TB, and a large number of service types with complex types are transmitted through the edge internet of things agent. The edge self-caching capability is limited, and as the edge node needs to perceive and transmit mass data, if the current node caching space is insufficient, the system local congestion can be caused, the system transmission efficiency is reduced, the loss of data packets can be caused, and the efficiency of the electric power Internet of things is reduced, so that the performance of the system is influenced. Therefore, the existing relatively fixed edge internet of things proxy communication resource allocation method cannot meet the transmission requirement of the current edge internet of things proxy service differentiation, so that service transmission is delayed, and the problem of certain instantaneity is lacking.
In order to solve the above problems, the present application provides a method for allocating communication resources by an edge internet of things proxy, which includes: firstly, obtaining influence factors of an edge internet of things agent on the bandwidth demand degree. Among these, influencing factors include: the method comprises the following steps of influencing factors of the transmission data quantity category, influencing factors of the transmission delay requirement category, influencing factors of the residual buffer space category and influencing factors of the data criticality category. And then determining the satisfaction degree of the edge internet of things agent for the data transmission delay, and determining the weight of the edge internet of things agent in the transmission network based on the influence factors. And finally, establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, and solving the edge internet of things proxy communication network resource allocation model by utilizing a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things proxy.
Therefore, the resource allocation model of the edge Internet of things proxy communication network is established and solved based on the weight and the satisfaction degree, so that the allocation of communication resources is realized, and the instantaneity of the edge Internet of things proxy service transmission is improved.
It should be noted that the method for allocating communication resources by the edge internet of things proxy and the related products provided by the application can be applied to the technical field of the internet of things. The foregoing is merely exemplary, and the application fields of the method for allocating communication resources by using the edge internet of things proxy and the related products provided by the present application are not limited.
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. 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.
Fig. 1 is a flowchart of a method for allocating communication resources of an edge internet of things proxy according to an embodiment of the present application. Referring to fig. 1, the method for allocating communication resources by using an edge internet of things proxy according to the present application may include:
s101: acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; the influencing factors include: the method comprises the following steps of influencing factors of the transmission data quantity category, influencing factors of the transmission delay requirement category, influencing factors of the residual buffer space category and influencing factors of the data criticality category.
In practical application, an intelligent Internet of things system is built, an electric power Internet of things covering all links of an energy power system is built, the ubiquitous interconnection of a power supply side, a power grid side, a client side and a supply chain can be promoted to the outside, the deep perception of the states of the power grid and the client is realized, various resources are converged to participate in system regulation, coordination and interaction of the charge storage of the source network are promoted, the energy autonomy of a region is supported, and the optimal configuration resources, the safety guarantee and the intelligent interaction capability of the power grid are improved; the method can push the sensing layer resource sharing and the data source in the pair, realize source end data fusion and service real-time on-line, and aggregate various data for sharing application. The intelligent physical system mainly comprises an Internet of things management platform, an edge Internet of things agent and various types of terminal standardized access. The edge internet of things proxy communication resources are required to be reasonably allocated according to the transmission requirements of the current edge internet of things proxy service differentiation. Therefore, factors affecting the bandwidth demand level of the edge internet of things agent first need to be obtained before allocation. The method mainly obtains the influencing factors of the transmission data quantity category, the influencing factors of the transmission delay requirement category, the influencing factors of the residual buffer space category and the influencing factors of the data critical degree category.
S102: and determining the satisfaction degree of the edge internet of things agent on the data transmission delay.
In practical application, a single edge internet of things proxy has a time delay requirement on data transmission, namely, the time requirement that edge internet of things proxy service is transmitted to an internet of things management platform by the edge internet of things proxy. In order to make the communication resource allocation more reasonable, the satisfaction degree of the edge internet of things agent on the data transmission time delay needs to be determined. Specifically, the utility function can be determined by combining the influence factors of the transmission delay requirement category.
In addition, since the ways of determining the satisfaction of the data transmission delay are not the same, the present application can be described in terms of one possible way of determination.
In one case, it is directed to how the satisfaction is determined. Correspondingly, S102: the method for determining the satisfaction degree of the edge internet of things agent on the data transmission delay specifically comprises the following steps:
calculating a utility value of the edge internet of things agent based on a utility function;
determining satisfaction degree of the edge internet of things agent for data transmission delay according to the utility value;
the utility function is:
；
wherein m is the total number of edge internet of things agents, U k Utility value, t, representing edge proxy k k Andand respectively representing the actual transmission delay and the transmission delay requirement of the edge internet-of-things proxy k.
In practical application, the satisfaction degree of the single edge internet of things agent on the time delay can be determined by utilizing the influence factors of the transmission time delay requirement category and the utility function. Specifically, the utility function may be expressed as:
；
wherein m is the total number of the edge internet of things agents, U k Utility value, t, representing edge proxy k k Andthe actual transmission delay and the transmission delay requirement of the edge internet of things proxy k are respectively represented.
S103: and determining the weight of the edge internet of things agent in the transmission network based on the influence factors.
In practical application, when the communication resource is allocated to the edge internet of things proxy, only the satisfaction degree of the edge internet of things proxy to the data transmission delay is considered to lack certain rationality. Because the edge internet of things agents exist in the sensing layer, the service processed on each edge internet of things agent is different and has a light and heavy urgency. Therefore, the application also needs to combine the influencing factors corresponding to each edge internet of things agent to determine the weight of the edge internet of things agent in the transmission network, so that the device for distributing the communication resources of the edge internet of things agent distributes the resources for the edge internet of things agent with high weight preferentially.
In addition, since the ways of determining the weights of the edge internet of things agents in the transmission network are not the same, the present application can be described in terms of one possible determination.
In one case, it is directed to how the weights are determined. Accordingly, S103: determining the weight of the edge internet of things agent in a transmission network based on the influence factors specifically comprises the following steps:
respectively determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent;
carrying out dimensionless treatment on the influence factors of each category to obtain dimensionless forms corresponding to the influence factors of each category;
combining the weight of the influence factors of each class on the bandwidth resource demand degree of the edge internet of things agent and the non-dimensionalized form corresponding to the influence factors of each class, and determining the weight of the edge internet of things agent in a transmission network by using an overall weight algorithm;
the overall weight algorithm is as follows:
；
wherein omega k Weights in the transport network for edge internet of things agents,and->Weight and corresponding dimensionless form of influence factors of transmission data amount category on bandwidth resource demand degree of edge internet of things agent k >And->Weight and corresponding dimensionless form of influence factors of transmission delay requirement category on bandwidth resource demand degree of edge internet-of-things proxy k>And->Weight and corresponding dimensionless form of influence factors of residual buffer space categories on bandwidth resource demand degree of edge internet-of-things agent k>And->The weight and the corresponding dimensionless form of the influence factors of the data critical degree category on the bandwidth resource demand degree of the edge internet-of-things agent k are respectively.
In practical application, when the edge internet of things proxy service is transmitted in real time, the demand degree of the transmission bandwidth is different due to the difference of the edge characteristics of the transmission service and the current buffering capacity of the edge internet of things proxy. Firstly, calculating the weight of each category of influence factors by using an analytic hierarchy process, namely respectively determining the weight of the influence of each category of influence factors on the bandwidth resource demand degree of the edge internet-of-things agent. And then carrying out dimensionless processing on the influence factors of each category, and carrying out dimensionality removal on the influence factors of the transmission data size category, the influence factors of the transmission delay requirement category, the residual buffer space level and the data critical degree level to obtain a corresponding dimensionless form. And finally, calculating the weight of each edge internet of things agent in the transmission network by combining the whole weight algorithm. The overall weight algorithm is as follows:
；
Wherein omega k Weights in the transport network for edge internet of things agents,and->Weight and corresponding dimensionless form of influence factors of transmission data amount category on bandwidth resource demand degree of edge internet of things agent k>And->Weight and corresponding dimensionless form of influence factors of transmission delay requirement category on bandwidth resource demand degree of edge internet-of-things proxy k>And->Respectively remain asWeight and corresponding dimensionless form of influence factors of redundant cache space class on bandwidth resource demand degree of edge Internet of things agent k>And->The weight and the corresponding dimensionless form of the influence factors of the data critical degree category on the bandwidth resource demand degree of the edge internet-of-things agent k are respectively.
In addition, since the ways of determining the weight of the influence factors of each category on the bandwidth resource requirement degree of the edge internet of things agent are different, the application can be described in terms of a possible determination way.
In one case, it is directed to how the weights are determined. Correspondingly, the determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet of things agent includes:
Combining influence factors of all categories, and constructing an initial resource allocation matrix by adopting an expert scoring method;
and determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent based on the initial resource allocation matrix.
In practical application, the importance degree of the influence factors of each category is ordered by adopting an expert scoring method, and the initial resource allocation matrix is constructed as follows:
；
in the matrix A, the influence factors of the transmission data amount type are marked as 1, the influence factors of the transmission delay requirement type are marked as 2, the influence factors of the residual buffer space type are marked as 3, and the influence factors of the data critical degree type are marked as 4.Representing the bandwidth of an edge-to-internet-of-things proxy compared with the impact factors i and j of different classesThe relative weight of the demand level impact. For example, the processing steps may be performed,、/>and +.>The relative weight of the influence factors of the transmission data amount category and the influence factors of the transmission delay requirement category, the influence factors of the residual buffer space category and the influence factors of the data critical degree category are respectively compared; />、/>And +.>The relative weight of the influence factors of the transmission delay requirement category, the influence factors of the transmission data volume category, the influence factors of the residual buffer space category and the influence factors of the data critical degree category, which are respectively the influence on the bandwidth requirement degree of the edge internet of things proxy; / >、/>And +.>The relative weight of the influence factors of the residual buffer space categories on the bandwidth requirement level of the edge-based internet of things agent is respectively compared with the influence factors of the transmission data amount categories, the influence factors of the transmission delay requirement categories and the influence factors of the data critical level categories; />、/>And +.>And respectively comparing the influence factors of the data critical degree category with the influence factors of the transmission data quantity category, the influence factors of the transmission delay requirement category and the influence factors of the residual buffer space category, and relatively weighting the influence on the bandwidth requirement degree of the edge internet of things proxy. And then combining the matrix A to determine the weight coefficient. Let the eigenvalue of matrix be lambda, its corresponding eigenvector be eta, according to matrix theory, the largest eigenvalue of matrix can be calculated as lambda max And obtains a feature vector eta= (W) corresponding to the feature vector eta= (W) 1 ,W 2 ,W 3 ,W 4 ) Normalizing the feature vector to obtain +.>Wherein W is 1 、W 2 、W 3 W is provided 4 The method comprises the steps of determining the characteristic vector corresponding to the influence factors of the transmission data quantity type, the influence factors of the transmission delay requirement type, the influence factors of the residual buffer space type and the influence factors of the data criticality type. />、/>、/>And +.>The method comprises the steps of respectively determining the influence factors of the transmission data quantity type, the influence factors of the transmission delay requirement type, the influence factors of the residual buffer space type and the weight of the influence factors of the data critical degree type on the influence of the bandwidth resource requirement degree.
In addition, since the manner of non-dimensional processing is not the same, the present application can be described in terms of one possible processing manner.
In one case, it is directed to how the dimensionless treatment is performed. Correspondingly, the dimensionless processing is performed on the influence factors of each category to obtain a dimensionless form corresponding to the influence factors of each category, which comprises the following steps:
determining the expression of each category of influencing factors;
combining the expression of the influence factors of the residual cache space category and the expression of the influence factors of the data critical degree category, classifying the influence factors of the residual cache space category and the influence factors of the data critical degree category according to the level, and determining the expression of the residual cache space level and the data critical degree level;
and obtaining a dimensionless form corresponding to each type of influence factors based on the expression of the influence factors of the transmission data amount type, the expression of the influence factors of the transmission delay requirement type, the expression of the residual buffer space level and the expression of the data criticality level.
In practical application, firstly, it is assumed that m edge internet of things agents are provided, and the data size carried by each edge internet of things agent is as follows The transmission delay requirement is->The residual buffer space isThe data critical degree is +.>I.e. an expression of each type of influencing factor is determined. The residual buffer space and the data critical degree are classified according to the grades to obtain the grade of the residual buffer spaceAnd data criticality level->I.e. determining the remainderThe expression of the spatial hierarchy and the data criticality hierarchy is cached. And then obtaining a dimensionless form corresponding to the influence factors of each category based on the expression of the influence factors of the transmission data amount category, the expression of the influence factors of the transmission delay requirement category, the expression of the residual buffer space level and the expression of the data criticality level. The concrete representation is as follows:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,、/>、/>and +.>The expression corresponding to the influence factors of the transmission data amount type, the influence factors of the transmission delay requirement type, the residual buffer space level and the data criticality level of the edge internet of things agent k respectively, wherein m is the total number of the edge internet of things agents>、/>、/>And +.>The expressions of the influence factors of the transmission data quantity category, the influence factors of the transmission delay requirement category, the residual buffer space level and the data criticality level of the edge internet of things proxy k respectively >And->The data size of the edge internet of things agent k and the edge internet of things agent z are respectively +.>And->Transmission delay requirements of edge internet of things proxy k and edge internet of things proxy z, respectively, +.>And->The remaining cache space levels for edge Internet of things agent k and edge Internet of things agent z, respectively, +.>And->The data criticality level of the edge internet of things agent k and the edge internet of things agent z are respectively.
In addition, since the ways of verifying the rationality of weights are not the same, the present application can be described in terms of one possible way of verification.
In one case, it is directed to how to verify the rationality of the resulting weights. Correspondingly, before the edge internet of things proxy communication network resource allocation model is established based on the weight and the satisfaction degree, the method further comprises the following steps:
carrying out consistency check on the weight ratio of the influence factors of each category on the bandwidth resource demand degree by using a consistency check algorithm;
the consistency check algorithm is as follows:
；
wherein alpha represents the number of influencing factors of each category, R I Representing random oneIndex of sex, lambda max For maximum characteristic value, C R Is a consistency ratio;
when said C R <And 0.1, considering the consistency degree to be within the allowable range, otherwise, adjusting the initial resource allocation matrix, and redetermining the weight of the influence factors of each category on the bandwidth resource demand degree.
In practical applications, the weight ratio may be subjected to consistency check. In combination with the consistency check algorithm, consistency ratios are utilized to determine consistency of weight ratios. Specifically, the consistency check algorithm is:
；
wherein alpha represents the number of influencing factors of each category, R I Represents a random consistency index, lambda max For maximum characteristic value, C R Is a consistency ratio. When the consistency ratio C R <And 0.1, considering the consistency degree to be within the allowable range, otherwise, adjusting the initial resource allocation matrix according to the actual situation, and redetermining the weight of the influence factors of each category on the bandwidth resource demand degree.
S104: and establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree.
In practical application, the satisfaction degree of the edge internet of things agent on the data transmission delay and the weight of the edge internet of things agent in the transmission network are mainly considered for the communication resource allocation of the single edge internet of things agent. The application can take the satisfaction degree of the edge internet of things agent on the data transmission delay and the weight of the edge internet of things agent in the transmission network as conditions, establish an edge internet of things agent communication network resource allocation model, and reasonably allocate the edge internet of things agent communication resources by solving the model.
In addition, since the mode of establishing the resource allocation model of the edge internet of things proxy communication network is different, the application can be described with respect to one possible establishment mode.
In one case, the edge-to-thing proxy communication network resource allocation model is built for how to build. Correspondingly, S104: establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, wherein the method specifically comprises the following steps:
establishing a subcarrier and power allocation model of the edge internet of things proxy communication network with the maximization of the weighted utility sum as a target based on the weight and the satisfaction;
the subcarrier and power distribution model of the edge internet of things proxy communication network is as follows:
；
wherein the method comprises the steps ofTo weight the utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing utility value of edge proxy k, N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge internet of things agent k,for the total transmit power on the s-th subcarrier of the edge proxy k +.>And representing the power resources allocated to the s-th sub-carrier allocated to the edge Internet of things agent k, wherein m is the total number of the edge Internet of things agents.
In practical applications, the allocation of communication resources mainly takes into account the allocation of subcarriers and power. Therefore, on the premise of meeting the requirement of the edge internet of things proxy for differential transmission delay, the weighted utility sum of the system users is maximized The subcarrier and power allocation model of the edge internet of things proxy communication network is established as follows: />
；
Wherein the method comprises the steps ofTo weight the utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing utility value of edge proxy k, N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge internet of things agent k,for the total transmit power on the s-th subcarrier of the edge proxy k +.>And representing the power resources allocated to the s-th sub-carrier allocated to the edge Internet of things agent k, wherein m is the total number of the edge Internet of things agents.
S105: and solving the communication network resource allocation model of the edge internet of things agent by using a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things agent.
In practical application, specific numerical values are substituted, and a subcarrier and power distribution model of the edge internet of things agent communication network is solved by utilizing a genetic algorithm and a water injection algorithm, so that the distribution of communication resources of the edge internet of things agent is realized.
In addition, simulation analysis can be performed on the resource allocation result of the edge internet of things proxy communication network based on simulation software.
Specifically, the situation is assumed to have the data transmission quantity, the transmission delay requirement, the residual buffer space and the data critical degree corresponding to the edge internet of things proxy 1, the edge internet of things proxy 2 and the edge internet of things proxy 3 of 2.8Mbit, 150ms, 35% and general respectively; 2.3Mbit, 100ms, 30% and severe; 1.9Mbit, 70ms, 15% and critical. The distribution result is obtained through the algorithm provided by the application. Fig. 2 is a schematic diagram of a channel resource and power resource allocation result provided in the present application. Referring to fig. 2, the number of subcarriers allocated to the edge internet of things proxy 1, the edge internet of things proxy 2 and the edge internet of things proxy 3 is 41, 61 and 90, which correspond to the transmitting power, and meet the requirement of allocating bandwidth resources for the edge internet of things proxy according to the different demands of the edge internet of things proxy on the communication resources. Then, in order to verify the effectiveness of the simulation algorithm, the application can simulate and compare the dynamic average allocation algorithm and the Shen algorithm with the algorithm. Fig. 3 is a schematic diagram showing comparison between a weighted transmission rate and the number of the edge-based internet of things agents according to the present application. With the increase of the number of the edge internet of things agents, as the bandwidth resources which can be allocated to a single edge internet of things agent are reduced, the transmission rate is reduced, but compared with a dynamic average allocation algorithm and a Shen algorithm, the frequency resources with better channel quality are dynamically allocated to each edge internet of things agent as required by using the algorithm to consider the difference of the edge side service transmission requirements, so that service sensitive users can be allocated to more bandwidth resources on the premise of meeting the edge side service transmission delay requirements, and the weighted transmission rate sum of the system users is improved. It should be noted that the algorithms herein refer to genetic algorithms and water filling algorithms.
In summary, the application first obtains the influence factor of the edge internet of things agent on the bandwidth demand degree. Among these, influencing factors include: the method comprises the following steps of influencing factors of the transmission data quantity category, influencing factors of the transmission delay requirement category, influencing factors of the residual buffer space category and influencing factors of the data criticality category. And then determining the satisfaction degree of the edge internet of things agent for the data transmission delay, and determining the weight of the edge internet of things agent in the transmission network based on the influence factors. And finally, establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, and solving the edge internet of things proxy communication network resource allocation model by utilizing a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things proxy. Therefore, the resource allocation model of the edge Internet of things proxy communication network is established and solved based on the weight and the satisfaction degree, so that the allocation of communication resources is realized, and the instantaneity of the edge Internet of things proxy service transmission is improved.
Based on the method for allocating the communication resources of the edge internet of things proxy provided by the embodiment, the application also provides a device for allocating the communication resources of the edge internet of things proxy. The device for allocating communication resources of the edge internet of things proxy is described below with reference to the embodiments and the drawings, respectively.
Fig. 4 is a schematic structural diagram of an apparatus for allocating communication resources of an edge internet of things proxy according to an embodiment of the present application. Referring to fig. 4, an apparatus 200 for allocating communication resources by an edge internet of things proxy according to an embodiment of the present application includes:
an obtaining module 201, configured to obtain an influence factor of an edge internet of things agent on a bandwidth demand level; the influencing factors include: influence factors of the transmission data amount category, influence factors of the transmission delay requirement category, influence factors of the residual buffer space category and influence factors of the data criticality category;
a first determining module 202, configured to determine satisfaction degree of the edge internet of things proxy to the data transmission delay;
a second determining module 203, configured to determine a weight of the edge internet of things agent in a transport network based on the influencing factor;
an establishing module 204, configured to establish an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree;
and the allocation module 205 is configured to solve the edge internet of things agent communication network resource allocation model by using a genetic algorithm and a water injection algorithm, so as to implement allocation of communication resources of the edge internet of things agent.
As an embodiment, for how to determine the satisfaction degree of the edge internet of things proxy for the data transmission delay, the first determining module 202 is specifically configured to:
Calculating a utility value of the edge internet of things agent based on a utility function;
determining satisfaction degree of the edge internet of things agent for data transmission delay according to the utility value;
the utility function is:
；
wherein m is the total number of edge internet of things agents, U k Utility value, t, representing edge proxy k k Andand respectively representing the actual transmission delay and the transmission delay requirement of the edge internet-of-things proxy k.
As an embodiment, for how to determine the weight of the edge internet of things agent in the transport network based on the influencing factors, the second determining module 203 specifically includes: the system comprises a first determining sub-module, a processing module and a second determining sub-module;
the first determining submodule is used for respectively determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent;
the processing module is used for carrying out dimensionless processing on each type of influence factors to obtain dimensionless forms corresponding to each type of influence factors;
the second processing sub-module is used for combining the weight of the influence factors of each class on the bandwidth resource demand degree of the edge internet-of-things agent and the dimensionless form corresponding to the influence factors of each class, and determining the weight of the edge internet-of-things agent in a transmission network by utilizing an integral weight algorithm;
The overall weight algorithm is as follows:
；
wherein omega k Weights in the transport network for edge internet of things agents,and->Weight and corresponding dimensionless form of influence factors of transmission data amount category on bandwidth resource demand degree of edge internet of things agent k>And->Weight and corresponding dimensionless form of influence factors of transmission delay requirement category on bandwidth resource demand degree of edge internet-of-things proxy k>And->Weight and corresponding dimensionless form of influence factors of residual buffer space categories on bandwidth resource demand degree of edge internet-of-things agent k>And->The weight and the corresponding dimensionless form of the influence factors of the data critical degree category on the bandwidth resource demand degree of the edge internet-of-things agent k are respectively.
As an implementation manner, for how to determine the weight of the influence factor of each category on the influence of the bandwidth resource requirement degree of the edge internet of things agent, the first determining submodule is specifically configured to:
combining influence factors of all categories, and constructing an initial resource allocation matrix by adopting an expert scoring method;
and determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent based on the initial resource allocation matrix.
As an implementation manner, aiming at how to perform dimensionless processing on the influence factors of each category, a dimensionless form corresponding to the influence factors of each category is obtained, and the processing module is specifically configured to:
determining the expression of each category of influencing factors;
combining the expression of the influence factors of the residual cache space category and the expression of the influence factors of the data critical degree category, classifying the influence factors of the residual cache space category and the influence factors of the data critical degree category according to the level, and determining the expression of the residual cache space level and the data critical degree level;
and obtaining a dimensionless form corresponding to each type of influence factors based on the expression of the influence factors of the transmission data amount type, the expression of the influence factors of the transmission delay requirement type, the expression of the residual buffer space level and the expression of the data criticality level.
As an embodiment, the apparatus 200 for allocating communication resources by the edge internet of things proxy further includes: a checking module;
the inspection module is used for carrying out consistency inspection on the weight ratio of the influence factors of each category on the bandwidth resource demand degree by utilizing a consistency inspection algorithm;
The consistency check algorithm is as follows:
；
wherein alpha represents the number of influencing factors of each category, R I Represents a random consistency index, lambda max For maximum characteristic value, C R Is a consistency ratio;
when said C R <And 0.1, considering the consistency degree to be within the allowable range, otherwise, adjusting the initial resource allocation matrix, and redetermining the weight of the influence factors of each category on the bandwidth resource demand degree.
As an embodiment, the establishing module 204 is specifically configured to:
establishing a subcarrier and power allocation model of the edge internet of things proxy communication network with the maximization of the weighted utility sum as a target based on the weight and the satisfaction;
the subcarrier and power distribution model of the edge internet of things proxy communication network is as follows:
；
wherein the method comprises the steps ofTo weight the utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing utility value of edge proxy k, N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge internet of things agent k,for the total transmit power on the s-th subcarrier of the edge proxy k +.>And representing the power resources allocated to the s-th sub-carrier allocated to the edge Internet of things agent k, wherein m is the total number of the edge Internet of things agents.
In summary, the application first obtains the influence factor of the edge internet of things agent on the bandwidth demand degree. Among these, influencing factors include: the method comprises the following steps of influencing factors of the transmission data quantity category, influencing factors of the transmission delay requirement category, influencing factors of the residual buffer space category and influencing factors of the data criticality category. And then determining the satisfaction degree of the edge internet of things agent for the data transmission delay, and determining the weight of the edge internet of things agent in the transmission network based on the influence factors. And finally, establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree, and solving the edge internet of things proxy communication network resource allocation model by utilizing a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things proxy. Therefore, the resource allocation model of the edge Internet of things proxy communication network is established and solved based on the weight and the satisfaction degree, so that the allocation of communication resources is realized, and the instantaneity of the edge Internet of things proxy service transmission is improved.
In addition, the application also provides equipment for distributing the communication resources of the edge internet of things agent, which comprises the following steps: a memory for storing a computer program; a processor for implementing the steps of the method for edge internet of things proxy communication resource allocation as claimed in any one of the preceding claims when executing the computer program.
In addition, the application also provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program realizes the steps of the method for allocating communication resources of the edge internet of things proxy according to any one of the above steps when being executed by a processor.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for edge internet of things proxy communication resource allocation, the method comprising:
acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; the influencing factors include: influence factors of the transmission data amount category, influence factors of the transmission delay requirement category, influence factors of the residual buffer space category and influence factors of the data criticality category;
Determining satisfaction degree of the edge internet of things agent for data transmission delay;
determining the weight of the edge internet of things agent in a transmission network based on the influence factors;
establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree;
solving a communication network resource allocation model of the edge internet of things agent by using a genetic algorithm and a water injection algorithm to realize the allocation of the communication resources of the edge internet of things agent;
the determining the satisfaction degree of the edge internet of things agent on the data transmission delay comprises the following steps:
calculating a utility value of the edge internet of things agent based on a utility function;
determining satisfaction degree of the edge internet of things agent for data transmission delay according to the utility value;
the utility function is:
;
wherein m is the total number of edge internet of things agents, U k Utility value, t, representing edge proxy k k Andrespectively representing the actual transmission delay and the transmission delay requirement of the edge internet-of-things proxy k;
the establishing an edge internet of things proxy communication network resource allocation model based on the weights and the satisfaction degree comprises the following steps:
establishing a subcarrier and power allocation model of the edge internet of things proxy communication network with the maximization of the weighted utility sum as a target based on the weight and the satisfaction;
The subcarrier and power distribution model of the edge internet of things proxy communication network is as follows:
;
wherein the method comprises the steps ofTo weight the utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing utility value of edge proxy k, N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge-connected agent k,/->For the total transmit power on the s-th subcarrier of the edge proxy k +.>Representing edge objectsAnd the power resource allocated to the s-th subcarrier allocated to the joint proxy k, and m is the total number of the edge thing joint proxies.
2. The method of claim 1, wherein determining the weight of the edge internet of things agent in a transport network based on the influencing factors comprises:
respectively determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent;
carrying out dimensionless treatment on the influence factors of each category to obtain dimensionless forms corresponding to the influence factors of each category;
combining the weight of the influence factors of each class on the bandwidth resource demand degree of the edge internet of things agent and the non-dimensionalized form corresponding to the influence factors of each class, and determining the weight of the edge internet of things agent in a transmission network by using an overall weight algorithm;
The overall weight algorithm is as follows:
;
wherein omega k Weights in the transport network for edge internet of things agents,and->Weight and corresponding dimensionless form of influence factors of transmission data amount category on bandwidth resource demand degree of edge internet of things agent k>Andweights respectively influencing the bandwidth resource demand degree of the edge internet of things proxy k by influencing factors of transmission delay requirement typesAnd the corresponding dimensionless form, +.>And->Weight and corresponding dimensionless form of influence factors of residual buffer space categories on bandwidth resource demand degree of edge internet-of-things agent k>And->The weight and the corresponding dimensionless form of the influence factors of the data critical degree category on the bandwidth resource demand degree of the edge internet-of-things agent k are respectively.
3. The method according to claim 2, wherein the determining the weight of the influence of each category of influence factors on the bandwidth resource requirement level of the edge internet of things agent includes:
combining influence factors of all categories, and constructing an initial resource allocation matrix by adopting an expert scoring method;
and determining the weight of the influence factors of each category on the bandwidth resource demand degree of the edge internet-of-things agent based on the initial resource allocation matrix.
4. The method according to claim 2, wherein the performing dimensionless processing on the influence factors of each category to obtain a dimensionless form corresponding to the influence factors of each category includes:
determining the expression of each category of influencing factors;
combining the expression of the influence factors of the residual cache space category and the expression of the influence factors of the data critical degree category, classifying the influence factors of the residual cache space category and the influence factors of the data critical degree category according to the level, and determining the expression of the residual cache space level and the data critical degree level;
and obtaining a dimensionless form corresponding to each type of influence factors based on the expression of the influence factors of the transmission data amount type, the expression of the influence factors of the transmission delay requirement type, the expression of the residual buffer space level and the expression of the data criticality level.
5. The method of claim 3, wherein prior to said establishing an edge internet of things proxy communication network resource allocation model based on said weights and said satisfaction, further comprising:
carrying out consistency check on the weight ratio of the influence factors of each category on the bandwidth resource demand degree by using a consistency check algorithm;
The consistency check algorithm is as follows:
;
wherein alpha represents the number of influencing factors of each category, R I Represents a random consistency index, lambda max For maximum characteristic value, C R Is a consistency ratio;
when said C R <And 0.1, considering the consistency degree to be within the allowable range, otherwise, adjusting the initial resource allocation matrix, and redetermining the weight of the influence factors of each category on the bandwidth resource demand degree.
6. An apparatus for edge internet of things proxy communication resource allocation, comprising:
the acquisition module is used for acquiring influence factors of the edge internet of things agent on the bandwidth demand degree; the influencing factors include: influence factors of the transmission data amount category, influence factors of the transmission delay requirement category, influence factors of the residual buffer space category and influence factors of the data criticality category;
the first determining module is used for determining the satisfaction degree of the edge internet of things agent on the data transmission delay;
the second determining module is used for determining the weight of the edge internet of things agent in the transmission network based on the influence factors;
the establishing module is used for establishing an edge internet of things proxy communication network resource allocation model based on the weight and the satisfaction degree;
The distribution module is used for solving the communication network resource distribution model of the edge internet of things agent by utilizing a genetic algorithm and a water injection algorithm, and realizing the distribution of the communication resources of the edge internet of things agent;
the first determining module is specifically configured to calculate a utility value of the edge internet of things agent based on a utility function;
determining satisfaction degree of the edge internet of things agent for data transmission delay according to the utility value;
the utility function is:
;
wherein m is the total number of edge internet of things agents, U k Utility value, t, representing edge proxy k k Andrespectively representing the actual transmission delay and the transmission delay requirement of the edge internet-of-things proxy k;
the establishing module is specifically configured to establish a subcarrier and power allocation model of the edge internet of things proxy communication network with the maximization of the weighted utility sum as a target based on the weight and the satisfaction;
the subcarrier and power distribution model of the edge internet of things proxy communication network is as follows:
;
where is the weighted utility sum, ω k For the weight of the edge internet of things agent k in the transmission network, U k Representing the utility of edge proxy kValue N is total number of sub-carriers, N k Representing the total number of sub-carriers allocated to edge internet of things agent k, For the total transmit power on the s-th subcarrier of the edge proxy k +.>And representing the power resources allocated to the s-th sub-carrier allocated to the edge Internet of things agent k, wherein m is the total number of the edge Internet of things agents.
7. An apparatus for edge internet of things proxy communication resource allocation, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of edge internet of things proxy communication resource allocation as claimed in any one of claims 1 to 5 when executing said computer program.
8. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the method of edge-to-internet-of-things proxy communication resource allocation according to any of claims 1 to 5.
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