CN111162852A - Ubiquitous power Internet of things access method based on matching learning - Google Patents

Ubiquitous power Internet of things access method based on matching learning Download PDF

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CN111162852A
CN111162852A CN201911406929.3A CN201911406929A CN111162852A CN 111162852 A CN111162852 A CN 111162852A CN 201911406929 A CN201911406929 A CN 201911406929A CN 111162852 A CN111162852 A CN 111162852A
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channel
model
time slot
access method
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CN111162852B (en
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周振宇
潘超
廖海君
刘念
汪中原
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • 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 ubiquitous power Internet of things access method based on matching learning, which comprises the following steps of: s1, constructing a system model; s2, refining the model to obtain a task/data transmission model, an energy consumption model, a time delay model and a service reliability model; s3, maximizing long-term throughput and determining an optimization problem; s4, converting the optimization problem based on the virtual queue theory and the lyapunov optimization theory; and S5, realizing the optimal channel selection through learning and matching, and further realizing the maximization of throughput. The invention realizes the optimization on channel selection by learning and matching, thereby realizing the maximization of throughput; based on the MAB theory, the lyapunov optimization theory and the matching theory, energy perception and service reliability perception are combined with machine learning, and therefore the maximum and the optimum of energy utilization rate and service reliability are achieved.

Description

Ubiquitous power Internet of things access method based on matching learning
Technical Field
The invention relates to an electric power Internet of things, in particular to a ubiquitous electric power Internet of things access method based on matching learning.
Background
The fourth industrial revolution aims at realizing interconnection response, intellectualization and self-optimization of manufacturing processes and systems through seamless integration of advanced manufacturing technologies and industrial internet of things. In this new model, billions of Machine Type Devices (MTDs) will be deployed for continuously performing various tasks such as monitoring, billing, and protection, etc. However, the contradiction between resource-limited MTD and computationally intensive tasks has become a bottleneck in providing reliable services.
Shifting the compute-intensive tasks from resource-limited MTDs to powerful edge servers provides a promising solution to accommodate the rapidly increasing computing demands. In conventional cloud computing, a remote cloud server is usually located far away from the MTD, and data transmission over a long distance causes many problems, including connection instability, network congestion, and intolerable delay. In contrast, edge computing, which shifts computing power from a remote cloud to an edge server in a wireless access network, is a promising paradigm for reducing latency, alleviating congestion, and extending battery life.
However, although edge computing provides a promising way to take advantage of the rich computing resources of edge servers, its performance may be severely impacted due to limited spectrum resources, limited capacity batteries, and context-imperceptible issues. First, in order to deliver large numbers of tasks from MTDs to edge servers in real-time, channel selection must be dynamically optimized according to time-varying context parameters such as Channel State Information (CSI), Energy State Information (ESI), server load, and service reliability requirements. Traditional centralized optimization methods rely on a common assumption that there is a central node, such as a base station, that has complete information of all context parameters. This assumption is difficult to implement in practice, considering the high cost of the signalling overhead for collecting the entire network information. Therefore, a distributed optimization approach, where each MTD individually optimizes its local information-based channel selection strategy, is preferable. However, when the number of MTDs far exceeds the number of available channels, and multiple MTDs contend for the same channel, selection conflicts frequently occur, thereby coupling channel selection strategies between different MTDs. Second, due to the limited battery capacity, the MTD will cease to be used when the battery energy is depleted. Thus, short-term channel selection strategies also couple long-term energy budgets. Last but not least, industrial applications often need to guarantee a certain service reliability. How to meet stringent reliability requirements with limited resources and information presents another area of difficulty.
The matching theory provides a flexible, low-complexity and efficient tool for solving the combination problems of channel selection, task selection, server selection and the like. However, it requires full knowledge of Global State Information (GSI) to construct a preference list, which specifies the basic matching criteria. There have been several attempts to optimize research by channel selection based on matching and game theory. However, they rely on uncertain context parameters, which can suffer severe performance loss if the actual probability distribution of the uncertain factors is not consistent with the assumed statistical model.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a ubiquitous power Internet of things access method based on matching learning, which can realize the optimal channel selection through learning and matching and further realize the maximization of throughput.
The purpose of the invention is realized by the following technical scheme: a ubiquitous power Internet of things access method based on matching learning comprises the following steps:
s1, constructing a system model;
s2, refining the model to obtain a task/data transmission model, an energy consumption model, a time delay model and a service reliability model;
s3, maximizing long-term throughput and determining an optimization problem;
s4, converting the optimization problem based on the virtual queue theory and the lyapunov optimization theory;
and S5, realizing the optimal channel selection through learning and matching, and further realizing the maximization of throughput.
Preferably, the ubiquitous power internet of things access method further comprises the step of ubiquitous power internet of things access: after the channel selection is optimized in step S5, the ubiquitous power internet of things is accessed through the channel, and data transmission is completed.
Further, the system model constructed in step S1 includes:
setting a base station service cell which comprises a base station and an edge server which are distributed in a similar way, wherein the base station provides connection service for K MTDs in the cell, and the edge server provides calculation service; wherein the K MTDs are defined as: m ═ M1,…,mk,…,mK}；
A total of J orthogonal subchannels defined as: c ═ C1,…,cj,…,cJ}；
The model adopts a time slot model, namely, the whole optimization time is divided into T small time intervals with the length of tau, and all time slots are defined as follows: t {1, …, T, …, T };
in this model, the information of the channel remains unchanged in each time slot; the information of the channel changes between different time slots;
in each time slot, each MTD independently determines the channel selection strategy; specifically, each MTD may face J +1 choices, i.e., selecting one of J subchannels or staying dormant.
Further, the process of refining and obtaining the task data/transmission model in step S2 includes:
each device m generates new data in each time slot, which is temporarily stored in a memory local to device m, thus forming a data queue at each device m:
Qk(t+1)＝max{Qk(t)-Uk(t)，0}+γk-Ak(t)+Yk(t)
wherein Qk(t) is the current time slot device mkAmount of data stored on memory, Uk(t) amount of data offloaded to the edge server for the current timeslot, Ak(t) and γkAre respectively a device mkThe rate at which data is generated and the average size of the generated data, the product of the two terms representing the amount of newly generated data, Yk(t) represents the amount of data that needs to be retransmitted due to the presence of errors;
each time slot device m will unload certain data to the edge server, the unloading rate of the data is determined by the channel selected by the current time slot of the device m, and the signal-to-noise ratio and the transmission rate of the information are obtained according to the selected channel j:
the signal-to-noise ratio is:
Figure BDA0002348901770000031
the information transmission rate is:
Figure BDA0002348901770000032
wherein delta2Is the noise power, PTXIs the transmission power, Hk，j，tIndicates that at the t-th time slot, device mkAnd a sub-channel c between the base stationsjChannel gain of, BjRepresenting the transmission bandwidth, thereby obtaining a device mkThroughput of (2):
zk，j，t＝min{Qk(t)，τRk，j，t}.
after the throughput is known, the current time slot device m is obtainedkAmount of data transferred to edge server:
Figure BDA0002348901770000033
wherein xk，j，tRecording the channel selection condition of each device in each time slot when xk，j，tWhen 1, it stands for device mkWithin time slot t selectA channel j;
acquiring the transmission error rate according to the signal-to-noise ratio:
Figure BDA0002348901770000041
obtaining the data volume needing to be retransmitted according to the obtained error rate:
Figure BDA0002348901770000042
further, the process of refining the energy consumption model in step S2 includes:
device mkThe energy consumed for selecting the channel j to transmit data in the tth time slot is as follows:
Figure BDA0002348901770000043
wherein P isTXWhich is representative of the power of the transmission,
Figure BDA0002348901770000044
represents the total amount of data to be transmitted divided by the data transmission rate, i.e., the time it takes to transmit all the data; τ represents the length of a time slot, i.e. data transmission can be performed only in one time slot; therefore, it is not only easy to use
Figure BDA0002348901770000045
Representing the actual transmission time; pTXAnd
Figure BDA0002348901770000046
the multiplication represents the energy consumed by transmission; if the J +1 th channel is selected, it represents the device mkThe sleep state is maintained at the current time slot, i.e. no data is transmitted, so the energy consumption is 0;
obtaining a device m based on a restriction of battery capacitykLong-term energy constraint:
Figure BDA0002348901770000047
wherein Ek,maxRepresentative apparatus mkThe capacity of the battery is constrained.
The process of refining and obtaining the time delay model in step S2 includes:
dividing the time delay into two parts on the whole, wherein the first part is transmission time delay, namely time consumed in data transmission; the second part is to calculate the time delay, i.e. the time consumed in data processing, as follows:
Figure BDA0002348901770000051
the concrete model of the transmission delay is as follows:
Figure BDA0002348901770000052
when the equipment mkWhen the mobile terminal is in a dormant state, data transmission is not carried out, so that the transmission delay is infinite;
the specific model for calculating the time delay is as follows:
Figure BDA0002348901770000053
wherein λ isk,tPresentation apparatus mkThe calculation intensity of the transmitted data is in the unit of each bit of the CPU period, and the calculated equipment m is obtained according to the calculation intensitykThe transmitted data collectively require zk,j,tλk,tCPU cycle ξk,tRepresentative apparatus mkAvailable computing resources; when the equipment mkIn the sleep state, there is no data transmission, and from the viewpoint of service reliability, there is no data transmission that meets the requirement of the service, so the calculation delay when the device is in sleep is defined as infinity here.
The process of refining and obtaining the service reliability model in step S2 includes:
assuming a delay requirement of d, if this indicates a task offload failure, then this is availableDevice m in T time slotskNumber of successful task unloads:
Figure BDA0002348901770000054
wherein II { x } represents: if x is true, then II { x } ═ 1, whereas if x is false, then II (x) ═ 0; (ii) a
Based on the successful uninstallation times, the service reliability is defined as:
Figure BDA0002348901770000055
wherein, ηkFor the proposed service reliability requirements.
Further, the optimization constructed in the step S3 is entitled P1:
P1
Figure BDA0002348901770000061
s.t. C1：
Figure BDA0002348901770000062
C2：
Figure BDA0002348901770000063
C3：
Figure BDA0002348901770000064
C4：
Figure BDA0002348901770000065
wherein the optimized variable is the long-term throughput of all devices; constraint C1And C2Represents a constraint on channel selection, i.e. each device can only select one channel at the same time, and one channel can only be selected by one device at the same time; constraint C2And C3For energy constraints and service reliability constraints。
Further, the step S4 includes:
firstly, based on the theory of virtual queue, the above C is2And C3Two constraints are converted into a virtual queue:
Figure BDA0002348901770000066
Figure BDA0002348901770000067
these two queues represent the energy constraint and the service reliability constraint, respectively;
then, based on the lyapunov optimization theory and in combination with the established virtual queue, converting the long-term energy efficiency optimization problem into a problem of maximizing energy efficiency and service reliability and minimizing energy consumption in each time slot to obtain an optimization problem P2:
P2：
Figure BDA0002348901770000068
s.t. C1～C2.
i.e. C in the optimization problem P12And C3The constraints are transformed into optimization objectives, and perception of energy consumption and service reliability is achieved.
Further, the step S5 includes:
the first step is as follows: for any equipment mkBelong to M ═ M1,…,mk,…,mKIs associated with channel cjBelong to the group C ═ C1,…,cj,…,cJTemporarily matching, and observing the throughput, energy consumption and time delay performance of the system;
the second step is that: first of all, device mkEstimating its preference degree for the jth sub-channel according to the following formula and constructing its preference list
Figure BDA0002348901770000071
Figure BDA0002348901770000072
Figure BDA0002348901770000073
Figure BDA0002348901770000074
Wherein
Figure BDA0002348901770000075
Indicating the optimization variable theta until the current time slot tk,j,tAverage value of (d);
Figure BDA0002348901770000076
presentation apparatus mkSelecting channel c as of time slot t-1jThe number of times of (c); rhok,jPresentation apparatus mkSelecting channel cjThe cost to be paid;
then the device mkTransmitting the preference list to an edge server, then matching all MTDs with channels based on a pricing matching theory, and gradually selecting each MTD to own channel;
the third step: solving an optimization problem P2 based on the second selected channel;
step four, making t equal to t +1, calculating and updating according to steps S1-S5 to obtain new
Figure BDA0002348901770000077
Uk(t)，Yk(t)，Qk(t+1),Nk(t +1) and Fk(t +1), performing channel optimization again; by analogy, the value of t is added with 1 every time, and continuous learning is carried out, so that the optimal channel selection is realized, and the maximum throughput is realized.
The invention has the beneficial effects that: the invention applies machine learning to channel selection, so that the optimal decision of channel selection can be completed without global information, and the cost is lower; the optimization on channel selection is realized through learning and matching, and further the maximization of throughput is realized; based on the MAB theory, the lyapunov optimization theory and the matching theory, energy perception and service reliability perception are combined with machine learning, and therefore the maximum and the optimum of energy utilization rate and service reliability are achieved.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of a system model constructed according to the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a ubiquitous power internet of things access method based on matching learning includes the following steps:
s1, constructing a system model:
as shown in fig. 2: a base station and an edge server are contained in a base station service cell and are distributed closely. Wherein, the base station provides connection service for K MTDs in the cell, and the edge server provides calculation service. Wherein the K MTDs are defined as: m ═ M1,…,mk,…,mK}；
A total of J orthogonal subchannels defined as: c ═ C1,…,cj,…,cJ}
Here, a slot model is used, i.e. the whole optimization time is divided into T small time intervals with length τ, and all slots are defined as: t ═ 1, …, T, …, T }
In this model, the information of the channel remains unchanged in each time slot; and the information of the channel changes between different time slots.
In each time slot, each MTD independently determines the channel selection strategy; specifically, each MTD is faced with J +1 choices, i.e., selecting one of J subchannels or staying dormant (idle, not transmitting data)
S2, refining the model to obtain a task/data transmission model, an energy consumption model, a time delay model and a service reliability model;
A. task/data transfer model
Each device m generates new data in each time slot, which is temporarily stored in a memory local to device m, thus forming a data queue at each device m:
Qk(t+1)＝max{Qk(t)-Uk(t)，0}+γkAk(t)+Yk(t)
wherein Qk(t) is the current time slot device mkAmount of data stored on memory, Uk(t) amount of data offloaded to the edge server for the current timeslot, Ak(t) and γkAre respectively a device mkThe rate at which data is generated and the average size of the generated data, the product of the two terms representing the amount of newly generated data, Yk(t) represents the amount of data that needs to be retransmitted due to the presence of errors.
Each time slot device m will unload certain data to the edge server, the unloading rate (transmission rate) of the data is determined by the channel selected by the current time slot of the device m, and the signal-to-noise ratio and the transmission rate of the information are obtained according to the selected channel j:
the signal-to-noise ratio is:
Figure BDA0002348901770000091
the information transmission rate is:
Figure BDA0002348901770000092
wherein delta2Is the noise power, PTXIs the transmission power, Hk，j，tIndicates that at the t-th time slot, device mkAnd a sub-channel c between the base stationsjChannel gain of, BjRepresenting the transmission bandwidth, thereby obtaining a device mkThroughput of (2):
zk，j，t＝min{Qk(t)，τRk，j，t}.
after the throughput is known, the current time slot device m can be obtainedkAmount of data transferred to edge server:
Figure BDA0002348901770000093
wherein xk，j，tRecording the channel selection condition of each device in each time slot when xk，j，tWhen 1, it stands for device mkChannel j is selected within time slot t.
Meanwhile, the bit error rate of transmission can be obtained according to the signal-to-noise ratio:
Figure BDA0002348901770000101
the BPSK modulation method is taken as an example, and corresponding formulas are different for different modulation methods.
And obtaining the data volume needing to be retransmitted according to the obtained error rate:
Figure BDA0002348901770000102
B. energy consumption model
Device mkThe energy consumed for selecting the channel j to transmit data in the tth time slot is as follows:
Figure BDA0002348901770000103
wherein P isTXWhich is representative of the power of the transmission,
Figure BDA0002348901770000104
represents the total amount of data to be transmitted divided by the data transmission rate, i.e., the time it takes to transmit all the data; τ represents the length of a time slot, i.e. data transmission can be performed only in one time slot; therefore, it is not only easy to use
Figure BDA0002348901770000105
Representing the actual transmission time; pTXAnd
Figure BDA0002348901770000106
the multiplication represents the energy consumed by transmission; if the J +1 th channel is selected, it represents the device mkThe sleep state is maintained at the current time slot, i.e. no data is transmitted, so the energy consumption is 0;
on the former basis, we can get a device m due to the limited capacity of the batterykLong-term energy constraint:
Figure BDA0002348901770000107
wherein Ek,maxRepresentative apparatus mkCapacity constraint of battery
C. Time delay model
The time delay is divided into two parts as a whole, wherein the first part is transmission time delay, namely time consumed in data transmission; the second part is the computation delay, i.e. the time consumed in data processing. The method comprises the following specific steps:
Figure BDA0002348901770000111
the concrete model of the transmission delay is as follows:
Figure BDA0002348901770000112
when the equipment mkWhen the mobile terminal is in the dormant state, data transmission is not performed, so that the transmission delay is infinite.
The specific model for calculating the time delay is as follows:
Figure BDA0002348901770000113
wherein λ isk,tPresentation apparatus mkThe calculation strength of the transmitted data is in units of CPU cycles per bit, soCan obtain the calculated device mkThe transmitted data collectively require zk,j,tλk,tCPU cycle ξk,tRepresentative apparatus mkAvailable computing resources; when the equipment mkIn the sleep state, there is no data transmission, and from the viewpoint of service reliability, there is no data transmission that meets the requirement of the service, so the calculation delay when the device is in sleep is defined as infinity here.
D. Service reliability model
Assuming a delay requirement of d, if this indicates a task offload failure, then device m is available in T slotskNumber of successful task unloads:
Figure BDA0002348901770000114
wherein II { x } represents: if x is true, then II { x } ═ 1, whereas if x is false, then II (x) ═ 0;
based on the successful uninstalling times, the service reliability is defined as:
Figure BDA0002348901770000115
wherein, ηkFor the proposed service reliability requirements.
S3, maximizing long-term throughput, and determining an optimization problem P1:
P1：
Figure BDA0002348901770000121
s.t. C1：
Figure BDA0002348901770000122
C2：
Figure BDA0002348901770000123
C3：
Figure BDA0002348901770000124
C4：
Figure BDA0002348901770000125
wherein the optimized variable is the long-term throughput of all devices; constraint C1And C2 represents the constraint on channel selection, i.e. each device can only select one channel at a time, and one channel can only be selected by one device at a time; constraint C2And C3The previously mentioned energy constraint and service reliability constraint.
S4, converting the optimization problem based on the theory of the virtual queue and the lyapunov optimization theory:
first, based on the theory of virtual queues, we will refer to C above2And C3Two constraints are converted into a virtual queue:
Figure BDA0002348901770000126
Figure BDA0002348901770000127
these two queues represent the energy constraints and service reliability constraints previously proposed, respectively.
Then, based on the lyapunov optimization theory, in combination with the virtual queues established previously, we transform the long-term energy efficiency optimization problem into a problem of maximizing energy efficiency and service reliability and minimizing energy consumption at each time slot (i.e., transform one long-term optimization problem into a plurality of short-term optimization problems, that is, implement optimization at each time slot to implement optimization at a long time). We therefore get the following problem P2 to be solved:
P2：
Figure BDA0002348901770000128
s.t. C1～C2.
it can be seen that through the theory of virtual queue correlation, we solve problem C in P12And C3The constraints translate into our optimization goals, thus enabling awareness of energy consumption and service reliability.
S5, realizing the optimal channel selection through learning and matching, and further realizing the maximization of throughput:
the step S5 includes:
the first step is as follows: for any equipment mkBelong to M ═ M1,…,mk,…,mKIs associated with channel cjBelong to the group C ═ C1,…,cj,…,cJTemporarily matching, and observing the throughput, energy consumption and time delay performance of the system;
the second step is that: first of all, device mkEstimating its preference degree for the jth sub-channel according to the following formula and constructing its preference list
Figure BDA0002348901770000131
Figure BDA0002348901770000132
Figure BDA0002348901770000133
Figure BDA0002348901770000134
Wherein
Figure BDA0002348901770000135
Indicating the optimization variable theta until the current time slot tk,j,tAverage value of (d);
Figure BDA0002348901770000136
presentation apparatus mkSelecting channel c as of time slot t-1jThe number of times of (c); rhok,jPresentation apparatus mkSelecting channel cjThe cost to be paid;
then the device mkTransmitting the preference list to an edge server, then matching all MTDs with channels based on a pricing matching theory, and gradually selecting each MTD to own channel;
the third step: solving an optimization problem P2 based on the second selected channel;
step four, making t equal to t +1, calculating and updating according to steps S1-S5 to obtain new
Figure BDA0002348901770000137
Uk(t)，Yk(t)，Qk(t+1),Nk(t +1) and Fk(t +1), performing channel optimization again;
by analogy, the value of t is added with 1 every time, and continuous learning is carried out, so that the optimal channel selection is realized, and the maximum throughput is realized.
The ubiquitous power Internet of things access method further comprises the step of ubiquitous power Internet of things access: after the channel selection is optimized in step S5, the ubiquitous power internet of things is accessed through the channel, and data transmission is completed.
In conclusion, the learning algorithm is used for channel selection, service reliability sensing, energy sensing and context sensing are integrated, each device can dynamically select a channel for data transmission, and the optimal overall network throughput can be achieved without global information; the MAB problem, the lyapunov optimization theory and the matching theory are combined with machine learning, so that the effects of energy perception and service reliability perception are achieved, and the network energy utilization rate and the service reliability are improved.
The foregoing is a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments, and is capable of other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A ubiquitous power Internet of things access method based on matching learning is characterized in that: the method comprises the following steps:
s1, constructing a system model;
s2, refining the model to obtain a task/data transmission model, an energy consumption model, a time delay model and a service reliability model;
s3, maximizing long-term throughput and determining an optimization problem;
s4, converting the optimization problem based on the virtual queue theory and the lyapunov optimization theory;
and S5, realizing the optimal channel selection through learning and matching, and further realizing the maximization of throughput.
2. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the method further comprises the following steps of ubiquitous power Internet of things access: after the channel selection is optimized in step S5, the ubiquitous power internet of things is accessed through the channel, and data transmission is completed.
3. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the system model constructed in step S1 includes:
setting a base station service cell which comprises a base station and an edge server which are distributed in a similar way, wherein the base station provides connection service for K MTDs in the cell, and the edge server provides calculation service; wherein the K MTDs are defined as: m ═ M1，…，mk，…，mK}；
A total of J orthogonal subchannels defined as: c ═ C1，…，cj，…，cJ}；
The model adopts a time slot model, namely, the whole optimization time is divided into T small time intervals with the length of tau, and all time slots are defined as follows: t {1, …, T, …, T };
in this model, the information of the channel remains unchanged in each time slot; the information of the channel changes between different time slots;
in each time slot, each MTD independently determines the channel selection strategy; specifically, each MTD may face J +1 choices, i.e., selecting one of J subchannels or staying dormant.
4. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the process of refining and obtaining the task data/transmission model in the step S2 includes:
each device m generates new data in each time slot, which is temporarily stored in a memory local to device m, thus forming a data queue at each device m:
Qk(t+1)＝max{Qk(t)-Uk(t)，0}+γkAk(t)+Yk(t)
wherein Qk(t) is the current time slot device mkAmount of data stored on memory, Uk(t) amount of data offloaded to the edge server for the current timeslot, Ak(t) and γkAre respectively a device mkThe rate at which data is generated and the average size of the generated data, the product of the two terms representing the amount of newly generated data, Yk(t) represents the amount of data that needs to be retransmitted due to the presence of errors;
each time slot device m will unload certain data to the edge server, the unloading rate of the data is determined by the channel selected by the current time slot of the device m, and the signal-to-noise ratio and the transmission rate of the information are obtained according to the selected channel j:
the signal-to-noise ratio is:
Figure FDA0002348901760000021
the information transmission rate is:
Figure FDA0002348901760000022
wherein delta2Is the noise power, PTXIs the transmission power, Hk，j，tIndicates that at the t-th time slot, device mkAnd a sub-channel c between the base stationsjChannel gain of, BjRepresenting the transmission bandwidth, thereby obtaining a device mkThroughput of (2):
zk，j，t＝min{Qk(t)，τRk，j，t}.
after the throughput is known, the current time slot device m is obtainedkAmount of data transferred to edge server:
Figure FDA0002348901760000023
wherein xk，j，tRecording the channel selection condition of each device in each time slot when xk，j，tWhen 1, it stands for device mkChannel j is selected within time slot t;
acquiring the transmission error rate according to the signal-to-noise ratio:
Figure FDA0002348901760000024
obtaining the data volume needing to be retransmitted according to the obtained error rate:
Figure FDA0002348901760000025
5. the ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the process of refining and obtaining the energy consumption model in the step S2 includes:
device mkThe energy consumed for selecting the channel j to transmit data in the tth time slot is as follows:
Figure FDA0002348901760000031
wherein P isTXWhich is representative of the power of the transmission,
Figure FDA0002348901760000032
represents the total amount of data to be transmitted divided by the data transmission rate, i.e., the time it takes to transmit all the data; τ represents the length of a time slot, i.e. data transmission can be performed only in one time slot; therefore, it is not only easy to use
Figure FDA0002348901760000033
Representing the actual transmission time; pTXAnd
Figure FDA0002348901760000034
the multiplication represents the energy consumed by transmission; if the J +1 th channel is selected, it represents the device mkThe sleep state is maintained at the current time slot, i.e. no data is transmitted, so the energy consumption is 0;
obtaining a device m based on a restriction of battery capacitykLong-term energy constraint:
Figure FDA0002348901760000035
wherein Ek，maxRepresentative apparatus mkThe capacity of the battery is constrained.
6. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the process of refining and obtaining the time delay model in step S2 includes:
dividing the time delay into two parts on the whole, wherein the first part is transmission time delay, namely time consumed in data transmission; the second part is to calculate the time delay, i.e. the time consumed in data processing, as follows:
Figure FDA0002348901760000036
the concrete model of the transmission delay is as follows:
Figure FDA0002348901760000037
when the equipment mkWhen the mobile terminal is in a dormant state, data transmission is not carried out, so that the transmission delay is infinite;
the specific model for calculating the time delay is as follows:
Figure FDA0002348901760000038
wherein λ isk，tPresentation apparatus mkThe calculation intensity of the transmitted data is in the unit of each bit of the CPU period, and the calculated equipment m is obtained according to the calculation intensitykThe transmitted data collectively require zk，j，tλk，tCPU cycle ξk，tRepresentative apparatus mkAvailable computing resources; when the equipment mkIn the sleep state, there is no data transmission, and from the viewpoint of service reliability, there is no data transmission that meets the requirement of the service, so the calculation delay when the device is in sleep is defined as infinity here.
7. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the process of refining and obtaining the service reliability model in step S2 includes:
assume a delay requirement of dk，tIf, if
Figure FDA0002348901760000041
Indicating a task offload failure, device m may be obtained in T slotskNumber of successful task unloads:
Figure FDA0002348901760000042
wherein II { x } represents: if x is true, then II { x } ═ 1, whereas if x is false, then II (x) ═ 0;
based on the successful uninstallation times, the service reliability is defined as:
Figure FDA0002348901760000043
wherein, ηkFor the proposed service reliability requirement, i.e. the proportion of successful device data offload during the whole time T should be greater than ηk。
8. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the optimization constructed in step S3 is entitled P1:
P1：
Figure FDA0002348901760000044
s.t.C1：
Figure FDA0002348901760000045
C2：
Figure FDA0002348901760000046
C3：
Figure FDA0002348901760000047
C4：
Figure FDA0002348901760000048
wherein the optimized variable is the long-term throughput of all devices; constraint C1And C2Represents a constraint on channel selection, i.e. each device can only select one channel at the same time, and one channel can only be selected by one device at the same time; aboutBundle Condition C2And C3Energy constraints and service reliability constraints, i.e. the energy consumed cannot exceed the limits of the energy constraints, and the reliability of the service is to meet the requirements set forth.
9. The ubiquitous power internet of things access method based on matching learning of claim 8, wherein: the step S4 includes:
firstly, based on the theory of virtual queue, the above C is2And C3Two constraints translate into two virtual queues:
Figure FDA0002348901760000051
Figure FDA0002348901760000052
these two queues represent the energy constraint and the service reliability constraint, respectively;
then, based on the lyapunov optimization theory and in combination with the established virtual queue, converting the long-term energy efficiency optimization problem into a problem of maximizing energy efficiency and service reliability and minimizing energy consumption in each time slot to obtain an optimization problem P2:
P2：
Figure FDA0002348901760000053
s.t.C1～C2.
wherein theta isk，j，tTo optimize the variables, it is a weighted sum of the latter three terms; vk，αk，βkThe weights of the three terms are respectively used for adjusting the latter three terms to be the same order of magnitude; n is a radical ofk(t)，Fk(t) virtual queues representing energy constraints and service reliability constraints, respectively; p2 will optimize C in problem P12And C3Two constraints are transformed into an optimization objective, achieving awareness of energy consumption and service reliability.
10. The ubiquitous power internet of things access method based on matching learning of claim 1, wherein the ubiquitous power internet of things access method comprises the following steps: the step S5 includes:
the first step is as follows: for any equipment mkBelong to M ═ M1，…，mk，…，mKIs associated with channel cjBelong to the group C ═ C1，…，cj，…，cJTemporarily matching, and observing the throughput, energy consumption and time delay performance of the system;
the second step is that: first of all, device mkEstimating its preference degree for the jth sub-channel according to the following formula and constructing its preference list
Figure FDA0002348901760000054
Figure FDA0002348901760000061
Figure FDA0002348901760000062
Figure FDA0002348901760000063
Wherein
Figure FDA0002348901760000064
Indicating the optimization variable theta until the current time slot tk，j，tAverage value of (d);
Figure FDA0002348901760000065
presentation apparatus mkSelecting channel c as of time slot t-1jThe number of times of (c); rhok，jPresentation apparatus mkSelecting channel cjThe cost to be paid;
then the device mkThe preference list is transmitted to the edge server, which in turn transmits the preference list to the edge serverAll MTDs are matched with channels based on a pricing matching theory, and each MTD gradually selects the channel of the MTD;
the third step: solving an optimization problem P2 based on the second selected channel;
step four, making t equal to t +1, calculating and updating according to steps S1-S5 to obtain new
Figure FDA0002348901760000066
Uk(t)，Yk(t)，Qk(t+1)，Nk(t +1) and Fk(t +1), performing channel optimization again; by analogy, the value of t is added with 1 every time, and continuous learning is carried out, so that the optimal channel selection is realized, and the maximum throughput is realized.
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