CN115002783B - Industrial Internet of things resource dynamic allocation method based on network slicing - Google Patents
Industrial Internet of things resource dynamic allocation method based on network slicing Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/10—Dynamic resource partitioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0893—Assignment of logical groups to network elements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
- H04W28/20—Negotiating bandwidth
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a dynamic allocation method of industrial Internet of things resources based on network slicing, which utilizes the computing power and service provided by MEC nodes serving as relays, and on the premise of guaranteeing the large-scale connection requirement of mMTC slices and the real-time uploading of bottom equipment data, the uploaded large-scale data are timely computed and processed on the edge side according to the requirements, so that the bandwidth occupation of the data uploaded to a cloud is effectively reduced, the released bandwidth resources are provided for URLLC slices with higher time delay requirements, the data uploading time delay is reduced, the bandwidth utilization rate in the network is improved, and meanwhile, the computing power consumption of mMTC equipment for data processing at the MEC nodes is optimized, so that the QoS requirements of low-time delay service and mass connection service in the industry are met.
Description
Technical Field
The invention relates to the technical field of differentiated service communication in industrial scenes, in particular to a dynamic allocation method for industrial Internet of things resources based on network slicing.
Background
Network slicing is a key technology of the present high-speed wireless network, and can divide a physical network existing in reality into a plurality of virtual networks which are independent of each other and have different types, allocate corresponding network functions and network resources for the virtual networks according to QoS (quality of service) requirements of different services, such as time delay, bandwidth size and the like, so as to achieve the aim of providing customized services for differentiated services. According to the service classification in the application scenario, the network slice can be divided into three major classes, including large connection requirements mctc (mass machine class communication), ultra low latency requirements URLLC (ultra reliable low latency communication) and large capacity requirements eMBB (large bandwidth communication). To achieve the specific functionality of different network slices, this challenge can be addressed by fused edge computing techniques.
Edge computing is an open platform that merges network, computing, storage, application core capabilities at the network edge side physically near the data source, providing a computing model of edge intelligence services nearby. The Internet of things is vigorously developed in various fields, and the era of the Internet of things gradually progresses.
In the Internet of things era, with the development of services, a large number of devices are connected into a network, the devices belong to different fields and have differentiated QoS requirements, for example, in the field of industrial Internet of things, tasks such as automatic device motion control and fault alarm are required to perform real-time reliable data transmission, and network time delay is required to reach millisecond level; and the tasks such as industrial measurement and control, process sensing and the like require large-scale and low-power-consumption transmission.
With the development of the internet of things, almost all electronic devices can be connected to the internet and generate massive data, and transmitting such massive data from the local to the cloud is a huge challenge for network bandwidth. Therefore, the network slicing technology is utilized to slice the different QoS requirement services, and the computing and storage services of the network slicing technology are utilized to dynamically adjust the network bandwidth resource allocation among the slices through the fusion edge computing technology.
Disclosure of Invention
The invention aims to solve the technical problem of providing a dynamic allocation method of industrial Internet of things resources based on network slicing, which meets the QoS requirements of two machine communication services of URLLC and mMTC, and dynamically allocates the limited bandwidth resources in the network between the two services according to the dynamic QoS requirements of the two machine communication services so as to maximize the limited bandwidth resource utilization rate in the network.
In order to solve the technical problems, the invention adopts the following technical scheme: a dynamic allocation method of industrial Internet of things resources based on network slicing comprises the following steps:
step S1, building a three-layer bidirectional communication network architecture: the three-layer bidirectional communication network architecture comprises a comprehensive management terminal, a cloud center, and URLLC slices and mMTC slices which are mutually independent and isolated and are established by different QoS requirements, wherein each URLLC slice comprises U URLLC devices and K FAPs; the mMTC slice comprises M mMTC devices, N subcarriers and one MEC node;
step S2, U URLLC device sets are denoted as p= {1,2,3., U }, K FAP sets are denoted as q= {1,2,3., K }, wherein U epsilon P and K epsilon Q, calculating an uploading rate R when the U-th URLLC device selects the K-th FAP for data transmission u，k Thereby calculating the data uploading time delay t of each URLLC device u ；
Step S3, M mstc device sets are denoted as t= {1,2,3., M }, N subcarrier sets are denoted as y= {1,2,3., N, where mεT, nεY, calculate the data upload rate R of each mMTC device on any sub-carrier N m，n Thereby calculating the calculated energy consumption E of each mMTC device in the MEC node through the nth subcarrier m And get the total calculated energy consumption sigma m∈M E m ；
S4, in the mMTC slice, establishing a bandwidth release model by utilizing the computing capability of the MEC node;
step S5, after the computation processing of the MEC node, the subcarrier bandwidth born and utilized by each mMTC device is as followsThereby obtaining the data uploading rate R of each mMTC device at the moment * m，n ；
Step S6, under the condition that the total bandwidth of the system is limited, according to the service requirements sent by the URLLC slice and the mMTC slice to the integrated management terminal respectively, dynamically adjusting the bandwidth resources between the URLLC slice and the mMTC slice according to the requirements;
and S7, respectively giving an optimized objective function according to QoS requirements of the URLLC slice and the mMTC slice, converting a resource allocation problem formed between the URLLC slice and the mMTC slice into a layered game problem, and obtaining a game balancing solution by researching the game.
The technical scheme of the invention is further improved as follows: in the step S2, when the u-th URLLC device selects the k-th FAP for data transmission, the upload rate R u，k Expressed as:
wherein sigma 2 Is the noise power, B uk 、p uk 、h uk Respectively selecting the bandwidth total amount, the transmission power and the channel gain of the kth FAP for the nth URLLC equipment when the data is uploaded;
the data upload rate expression for each URLLC device is:
R u (x uk )＝x uk R u，k ，
wherein x is defined as uk E {0,1} is a Boolean variable, x is the value x when the u-th URLLC device selects the k-th FAP for data transmission uk =1, otherwise x uk ＝0；
Data upload delay t for each URLLC device u The expression is:
wherein s is u The size of the data content is uploaded for the URLLC device.
The technical scheme of the invention is further improved as follows: the data uploading rate R of each mctc device on any subcarrier n in step S3 m，n The expression of (2) is:
wherein B is n For the bandwidth of each subcarrier, p mn And h mn Respectively the transmission power and the channel gain of the mth mMTC device when the data is uploaded on any subcarrier n;
the data uploading rate expression of the mth mMTC device is as follows:
R m (x mn )＝x mn R m，n ，
wherein x is defined as mn E {0,1} is a Boolean variable, x mn =1 indicates that the mth mtc device is a flag variable allocated on any nth subcarrier, otherwise x mn ＝0；
Each mMTC device calculates energy consumption E in MEC node through nth subcarrier m The expression is:
E m ＝x mn s m c m αf i 2
s m representing the size of data volume that the mth mMTC device needs to upload, c m Expressed as computational complexity per bit of data, α is an adjustment parameter of the computational power of the MEC node, f i 2 Computing power for the MEC node.
The technical scheme of the invention is further improved as follows: the expression of the bandwidth release model in the step S4 is as follows:
wherein B is nM And for the bandwidth released by each mMTC device after being processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth.
The technical scheme of the invention is further improved as follows: the data uploading rate R of each mctc device after calculation processing of the MEC node in step S5 * m，n The expression is:
the technical scheme of the invention is further improved as follows: in the step S7, the optimization objective of the URLLC slice is to minimize the data uploading delay, and the expression of the corresponding optimization objective function is as follows:
the constraint indicates that the delay of URLLC equipment in data uploading through the selected FAP must not exceed the maximum delay which the URLLC equipment can tolerate
On the premise that the optimization target of mMTC slicing is to ensure the real-time uploading of equipment data, the calculation power consumption of the equipment for data processing at MEC nodes is minimized, and the expression of the corresponding optimization objective function is as follows:
constraint (b 1) indicates that the data processing energy consumption of MEC node must not exceed the maximum energy consumption tolerated by the systemConstraint (b 2) indicates that the transmission rate of the uploaded data of each mMTC device after being processed by MEC node data must not be lower than the lowest tolerable transmission rate +.>
The technical scheme of the invention is further improved as follows: in step S7, the interaction between the URLLC slice and the emtc slice is modeled as a stack game, the URLLC slice is used as a follower, the emtc slice is used as a leader, the URLLC slice used as the follower determines the amount of released bandwidth obtained from the emtc slice to minimize the data uploading delay, the emtc slice makes a decision according to the data uploading delay request sent by the URLLC slice, and determines the computing capacity of the uploaded data in the MEC node while optimizing the computing power consumption on the premise that the data uploading rate of each device in the emtc slice is higher than the minimum rate limit of the emtc slice, and the Qos requirements of the URLLC slice and the emtc slice are balanced under the game balancing solution by researching the game between the URLLC slice and the emtc slice.
By adopting the technical scheme, the invention has the following technical progress:
1. the invention breaks through a single technical route of the traditional mobile communication facing the 'large bandwidth communication', utilizes a network slicing technology and an edge computing technology, formulates a resource allocation scheme between 'mass machine type communication' and 'ultra-reliable low-delay communication' business facing the vertical industry in the present industrial high-speed wireless network such as 5G, reduces the economic cost and improves the resource utilization rate;
2. according to the invention, the computing capability and service provided by the MEC node serving as a relay are utilized, on the premise that the large-scale connection requirement of the mMTC slice and the real-time uploading of the data of the bottom equipment are ensured, the uploaded large-scale data are timely computed and processed on the edge side as required, the bandwidth occupation of the data uploaded to the cloud is effectively reduced, the released bandwidth resource is provided for the URLLC slice with higher time delay requirement, the data uploading time delay is reduced, the bandwidth utilization rate in the network is improved, and meanwhile, the computing power consumption of the mMTC equipment for data processing at the MEC node is optimized, so that the QoS requirements of low-time delay service and mass connection service in industry are met.
Drawings
FIG. 1 is a schematic diagram of a three-layer bi-directional communication network slice architecture constructed in accordance with the present invention;
fig. 2 is a flow chart of bandwidth resource allocation in accordance with the present invention.
Detailed Description
The invention is further illustrated by the following examples:
the invention relates to a dynamic allocation method of resources among network slices of a joint edge computing technology, which aims at the differentiated QoS requirements of all slices in an industrial scene under the condition of limited system bandwidth resources, meets the service requirements of users and improves the economic benefits of the network through dynamic virtual resource allocation.
The invention is further described below with reference to the accompanying drawings:
step S1, building a three-layer bidirectional communication network architecture: as shown in fig. 1, the three-layer bidirectional communication network architecture comprises a comprehensive management terminal, a cloud center, and URLLC slices and mctc slices which are established by different QoS requirements and are mutually independent and isolated, wherein each URLLC slice comprises U URLLC devices and K FAPs, and the situation of higher time delay caused by directly uploading data to the comprehensive management terminal in a long distance by the devices is improved by sinking the FAPs with buffering and computing capabilities to the network edge and deploying the FAPs close to the bottom device. The mMTC slice comprises M mMTC devices, N subcarriers and one MEC node, the MEC node is used for compressing the ultra-high bandwidth occupation required by large-scale device uploading data, the MEC node is used as a data forwarding relay, only a small amount of data is uploaded after the data uploaded by the bottom device is calculated, and the bandwidth occupation of uploading the data to a cloud is effectively reduced.
Step S2, U URLLC device sets are denoted as p= {1,2,3., U }, K FAP sets are denoted as q= {1,2,3., K }, wherein U epsilon P and K epsilon Q, calculating an uploading rate R when the U-th URLLC device selects the K-th FAP for data transmission u，k Thereby calculating the data uploading time delay t of each URLLC device u ；
Wherein when the u-th URLLC device selects the k-th FAP for data transmissionUpload rate R at the time u，k Expressed as:
wherein sigma 2 Is the noise power, B uk 、p uk 、h uk Respectively selecting the bandwidth total amount, the transmission power and the channel gain of the kth FAP for the nth URLLC equipment when the data is uploaded;
the data upload rate expression for each URLLC device is:
R u (x uk )＝x uk R u，k ，
since multiple FAPs are included in the URLLC slice, and each device can select any appropriate FAP as a relay node, x is defined uk E {0,1} is a Boolean variable, x is the value x when the u-th URLLC device selects the k-th FAP for data transmission uk =1, otherwise x uk ＝0；
Data upload delay t for each URLLC device u The expression is:
wherein s is u The size of the data content is uploaded for the URLLC device.
Step S3, M mstc device sets are denoted as t= {1,2,3., M }, N subcarrier sets are denoted as y= {1,2,3., N, where mεT, nεY, calculate the data upload rate R of each mMTC device on any sub-carrier N m，n Thereby calculating the calculated energy consumption Em of each mMTC device in the MEC node through the nth subcarrier and obtaining the total calculated energy consumption sigma m∈M E m ；
Wherein, the data uploading rate R of each mMTC device on any subcarrier n m，n The expression of (2) is:
wherein B is n For the bandwidth of each subcarrier, p mn And h mn Respectively the transmission power and the channel gain of the mth mMTC device when the data is uploaded on any subcarrier n;
the data uploading rate expression of the mth mMTC device is as follows:
R m (x mn )＝x mn R m，n ，
wherein x is defined as mn E {0,1} is a Boolean variable, x mn =1 indicates that the mth mtc device is a flag variable allocated on any nth subcarrier, otherwise x mn ＝0；
Each mMTC device calculates energy consumption E in MEC node through nth subcarrier m The expression is:
E m ＝x mn s m c m αf i 2
s m representing the size of data volume that the mth mMTC device needs to upload, c m Expressed as computational complexity per bit of data, α is an adjustment parameter of the computational power of the MEC node, f i 2 Computing power for the MEC node.
Step S4, in the mMTC slice, a bandwidth release model is established by utilizing the computing capability of the MEC node, and the expression of the bandwidth release model is as follows:
wherein B is nM And for the bandwidth released by each mMTC device after being processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth.
Step S5, after the computation processing of the MEC node, the subcarrier bandwidth born and utilized by each mMTC device is as followsThereby obtaining each mMTC deviceData upload rate R at the time * m，n The expression is:
and S6, under the condition that the total bandwidth of the system is limited, according to service requirements sent to the integrated management terminal by the URLLC slice and the mMTC slice respectively, dynamically adjusting bandwidth resources between the URLLC slice and the mMTC slice according to the requirements, and as shown in FIG. 2, assuming that the total bandwidth pre-allocated to the URLLC slice is B1, the total bandwidth allocated to the mMTC slice is B2, and after calculation processing of MEC nodes serving as relays in the mMTC slice, the total bandwidth released is B3 on the premise of ensuring the lowest uploading rate of the bottom equipment of the mMTC slice, and providing the released bandwidth resources for the URLLC slice to be utilized according to service requests sent by the URLLC slice so as to reduce the data uploading time delay and improve the bandwidth utilization rate in a network.
Step S7, according to QoS requirements of the URLLC slice and the mMTC slice, respectively giving an optimization objective function, wherein the optimization objective of the URLLC slice is to minimize data uploading delay, and the expression of the corresponding optimization objective function is as follows:
the constraint indicates that the delay of URLLC equipment in data uploading through the selected FAP must not exceed the maximum delay which the URLLC equipment can tolerate
On the premise that the optimization target of mMTC slicing is to ensure the real-time uploading of equipment data, the calculation power consumption of the equipment for data processing at MEC nodes is minimized, and the expression of the corresponding optimization objective function is as follows:
constraint (b 1) indicates that the data processing energy consumption of MEC node must not exceed the maximum energy consumption tolerated by the systemConstraint (b 2) indicates that the transmission rate of the uploaded data of each mMTC device after being processed by MEC node data must not be lower than the lowest tolerable transmission rate +.>
The method comprises the steps of converting a resource allocation problem formed between a URLLC slice and an mMTC slice into a layered game problem, modeling interaction between the URLLC slice and the mMTC slice as a Stackelberg game, taking the URLLC slice as a follower, taking the mMTC slice as a leader, determining the quantity of released bandwidths acquired from the mMTC slice as the follower to minimize the data uploading delay of the URLLC slice, making a decision by the mMTC slice according to the data uploading delay request sent by the URLLC slice, determining the computing capacity of the uploaded data in MEC nodes and optimizing the computing power consumption on the premise that the data uploading rate of each device in the mMTC slice is higher than the lowest rate limit of the mMTC slice, and obtaining the optimal bandwidth allocation solution by researching the game between the URLLC slice and the mMTC slice.
Claims (4)
1. A dynamic allocation method of industrial Internet of things resources based on network slicing is characterized by comprising the following steps: the method comprises the following steps:
step S1, building a three-layer bidirectional communication network architecture: the three-layer bidirectional communication network architecture comprises a comprehensive management terminal, a cloud center, and URLLC slices and mMTC slices which are mutually independent and isolated and are established by different QoS requirements, wherein each URLLC slice comprises U URLLC devices and K FAPs; the mMTC slice comprises M mMTC devices, N subcarriers and one MEC node;
step S2, U URLLC device sets are denoted as p= {1,2,3., U }, K FAP sets are denoted as q= {1,2,3., K }, wherein U epsilon P and K epsilon Q, calculating an uploading rate R when the U-th URLLC device selects the K-th FAP for data transmission u，k Thereby calculating the data uploading time delay t of each URLLC device u ；
Step S3, M mstc device sets are denoted as t= {1,2,3., M }, N subcarrier sets are denoted as y= {1,2,3., N, where mεT, nεY, calculate the data upload rate R of each mMTC device on any sub-carrier N m，n Thereby calculating the calculated energy consumption E of each mMTC device in the MEC node through the nth subcarrier m And get the total calculated energy consumption sigma m∈ M E m ；
Step S4, in the mMTC slice, a bandwidth release model is established by utilizing the computing capability of the MEC node, and the expression of the bandwidth release model is as follows:
wherein B is nM For the bandwidth released by each mMTC device after being processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth;
step S5, after the computation processing of the MEC node, the subcarrier bandwidth born and utilized by each mMTC device is as followsThereby obtaining the data uploading of each mMTC device at the momentRate R * m，n ；
Step S6, under the condition that the total bandwidth of the system is limited, according to the service requirements sent by the URLLC slice and the mMTC slice to the integrated management terminal respectively, dynamically adjusting the bandwidth resources between the URLLC slice and the mMTC slice according to the requirements;
step S7, according to QoS requirements of the URLLC slice and the mMTC slice, respectively giving out an optimized objective function, converting a resource allocation problem formed between the URLLC slice and the mMTC slice into a layered game problem, and obtaining a game equilibrium solution by researching the game;
wherein the optimization objective of the URLLC slice is to minimize the data uploading delay, and the expression of the corresponding optimization objective function is:
wherein x is defined as uk E {0,1} is a Boolean variable, x is the value x when the u-th URLLC device selects the k-th FAP for data transmission uk =1, otherwise x uk ＝0；s u Uploading a size of the data content for the URLLC device;
the constraint indicates that the delay of URLLC equipment in data uploading through the selected FAP must not exceed the maximum delay which the URLLC equipment can tolerate
On the premise that the optimization target of mMTC slicing is to ensure the real-time uploading of equipment data, the calculation power consumption of the equipment for data processing at MEC nodes is minimized, and the expression of the corresponding optimization objective function is as follows:
wherein x is defined as mn E {0,1} is a Boolean variable, x mn =1 indicates that the mth mtc device is a flag variable allocated on any nth subcarrier, otherwise x mn ＝0；p mn And h mn Respectively the transmission power and the channel gain of the mth mMTC device when the data is uploaded on any subcarrier n; sigma (sigma) 2 Is the noise power;
constraint (b 1) indicates that the data processing energy consumption of MEC node must not exceed the maximum energy consumption tolerated by the systemConstraint (b 2) indicates that the transmission rate of the uploaded data of each mMTC device after being processed by MEC node data must not be lower than the lowest tolerable transmission rate +.>
Modeling the interaction between the URLLC slice and the mctc slice as a stack game, taking the URLLC slice as a follower, taking the mctc slice as a leader, determining the amount of released bandwidth acquired from the mctc slice as the follower to minimize the data uploading delay of the mctc slice, and determining the computing power of the uploaded data in the MEC node while optimizing the computing power consumption on the premise that the data uploading rate of each device in the mctc slice is higher than the minimum rate limit of the mctc slice according to the data uploading delay request sent by the URLLC slice, and obtaining the optimal bandwidth allocation solution by researching the game between the URLLC slice and the mctc slice under the game balancing solution, so that the Qos requirements of the URLLC slice and the mctc slice are balanced.
2. The method for dynamically allocating resources of the industrial internet of things based on network slicing according to claim 1, wherein the method is characterized by comprising the following steps: in the step S2, when the u-th URLLC device selects the k-th FAP for data transmission, the upload rate R u，k Expressed as:
wherein sigma 2 Is the noise power, B uk 、p uk 、h uk Respectively selecting the bandwidth total amount, the transmission power and the channel gain of the kth FAP for the nth URLLC equipment when the data is uploaded;
the data upload rate expression for each URLLC device is:
R u (x uk )＝x uk R u，k ，
wherein x is defined as uk E {0,1} is a Boolean variable, x is the value x when the u-th URLLC device selects the k-th FAP for data transmission uk =1, otherwise x uk ＝0；
Data upload delay t for each URLLC device u The expression is:
wherein s is u The size of the data content is uploaded for the URLLC device.
3. The method for dynamically allocating resources of the industrial internet of things based on the network slice according to claim 2, wherein the method is characterized by comprising the following steps of: the data uploading rate R of each mctc device on any subcarrier n in step S3 m，n The expression of (2) is:
wherein B is n For the bandwidth of each subcarrier, p mn And h mn Respectively the transmission power and the channel gain of the mth mMTC device when the data is uploaded on any subcarrier n;
the data uploading rate expression of the mth mMTC device is as follows:
R m (x mn )＝x mn R m，n ，
wherein x is defined as mn E {0,1} is a Boolean variable, x mn =1 indicates that the mth mtc device is a flag variable allocated on any nth subcarrier, otherwise x mn ＝0；
Each mMTC device calculates energy consumption E in MEC node through nth subcarrier m The expression is:
s m representing the size of data volume that the mth mMTC device needs to upload, c m Expressed as the computational complexity per bit of data, alpha is an adjustment parameter of the computational power of the MEC node,computing power for the MEC node.
4. The method for dynamically allocating resources of the industrial internet of things based on network slicing according to claim 1, wherein the method is characterized by comprising the following steps: the data uploading rate R of each mctc device after calculation processing of the MEC node in step S5 * m，n The expression is:
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