CN109905864A - A kind of cross-layer Resource Allocation Formula towards electric power Internet of Things - Google Patents
A kind of cross-layer Resource Allocation Formula towards electric power Internet of Things Download PDFInfo
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Abstract
The invention mainly relates to the cross-layer Resource Allocation Formulas applied in electric power Internet of Things, are optimized by the data queue for being multiplexed transmission to the base station of other users equipment in Cellular Networks to various equipment for machine type communication, realize the steady in a long-term of queue.By the research to Liapunov optimization and Gai Er-Sha Lipu matching algorithm, the rate control and resource allocation mechanism of a kind of cross-layer are proposed.Original long-term optimization problem is mainly converted to rate control subproblem and resource allocation subproblem in each time slot by algorithm proposed by the invention, two convex functions are decomposited first with Liapunov algorithm, it can use the lower convex optimization tool of algorithm complexity to complete to solve very well, for subchannel select permeability, first according to the difference of different subchannel transmission performances establish machine to and cellular subscriber device two-way preference degree list, recycle the Gai Er-Sha Lipu algorithm of iteration to complete final stable matching.Simulation result shows that in the case where the priori knowledge that no data reach and subchannel counts, the present invention can significantly improve string stability and optimization network performance.
Description
Technical field
The invention belongs to wireless communication fields, and in particular to apply machinery compartment (the Machine in electric power Internet of Things
Machine, M2M) communication cross-layer Resource Allocation Formula reached by being optimized to the data queue that base station reaches
The state of one long-term smooth.Firstly, optimizing the data queue reached at random by Liapunov optimization algorithm,
Then use Gai Er-Sha Lipu matching theory in the network using orthogonal frequency division multiplexing (OFDM) to the data of different priorities
Queue carries out being suitble to subchannel selection, and then improves resource utilization to the maximum extent and minimize network delay.
Background technique:
With the fast development of the communication technology and the extensive access of data collection station, the epoch are undergoing from tradition
Human To Human communication to machine-to-machine communication dramatic shift.And crucial branch of the M2M communication as internet of things networking and operation
One of support technology, because it is with outstanding self-organizing and self-reparing capability, for industrial automation and smart grid implementation extremely
It closes important.For smart grid, the research and construction of electric power Internet of Things are like a raging fire, and existing mobile cellular network
For its it is universal provide good basis, but although the M2M communication technology in electric power Internet of Things have been obtained research extensively and
Using, but there are still some problems and challenge in the urgent need to address, it is summarized as follows:
1) transmit queue stability: in reality scene, due to the arrival of data flow be dynamic and it is uncertain, add
The influence of upper time varying channel, data transmission queue are frequently not Stationary Distribution, this brings very big pressure to the operation of base station.
Channel congestion is avoided in order to solve this problem and reduces packet loss, and effective equipment access control program is very important.
2) the performance optimization of user oriented experience: data and the rapid growth of flow will inevitably lead to wireless frequency spectrum
Inadequate resource, this is also an important restriction factor for limiting user experience quality.Regrettably, current research is usually ignored
The subjective feeling of user and be more focused on and only optimize network performance.Therefore, how to be improved using limited frequency spectrum resource
The important challenge that user experience quality currently faces.
3) long-term system function optimization: in electric power Internet of Things, what the data collection station of various Various Functions generated
Magnanimity real time data pours in existing cellular network, this will make base station can't bear the heavy load, or even the control system being made of these equipment
System collapse, and the optimization of network performance algorithm (such as queueing theory) of current main-stream is typically only capable to realize short-term optimization.However, complicated
Environment always bring many uncertain and randomnesss to data transmission procedure.Accordingly, it is considered to above-mentioned dynamic effects because
Element, design long-term behaviour prioritization scheme is so that data queue is stable, higher resource utilization is a urgent problem to be solved.
Based on issue noted above and challenge, the present invention mainly proposes a kind of cross-layer resource towards electric power Internet of Things
Allocation plan, wherein Liapunov optimization and Gai Er-Sha Lipu algorithm are jointly used in the OFDM cellular network of frequency spectrum share
In M2M communication, maximization network performance and to meet the needs of intensive user.
Summary of the invention:
The present invention simulate first in machine communication pair in multiple normal cellular users and electric power Internet of Things in a cell
The scene coexisted proposes one kind towards electricity so that data transmission queue Stationary Distribution and resource utilization maximum turn to target
The cross-layer Resource Allocation Formula of power Internet of Things.The program considers that cellular subscriber device and M2M pairs of experience requirements, first basis are worked as
Preceding Given information completes the subchannel selection that the latter is multiplexed the former, collected data is sent to base station, in base station side to receipts
To data queue optimize processing, and the arrival of next data and subchannel selection are controlled according to optimization situation,
Quickly solve the problems, such as overloaded base stations and user experience quality decline.Detailed process is as follows:
1) foundation of queuing model
Fig. 1 is the beehive network system model based on M2M uplink, and by a base station, N number of M2M pairs, K honeycomb is used
Family equipment composition.Wherein resource coordination and subchannel distribution in cell are responsible in base station, and different cellular subscriber devices can correspond to
K mutually orthogonal, non-interfering subchannels are generated, M2M transmitter (MT) and receiver (MR) to being made of again respectively, at me
Scene in, only consider that transmitter transfers data to the uplink portion of base station.
In systems, using discrete time model, within the time that total duration is T, it is used as a time slot t within every 1 second,.?
In the communication range of base station, machine communication to and cellular subscriber device quantity remain unchanged, but the considerations of for randomness, they
Position in different time-gap random distribution.In time slot t, it is assumed that have M vehicle and K user equipment, be expressed asWithIt is corresponding to generate K orthogonal sub-channels, it is expressed asAssuming that the M in time slot tnData access rate representation be An(t), corresponding transmission rate is Rn(t),
Current data backlog queue is Qn(t).Data access rate AnIt (t) is Qn(t) input, QnIt (t) is network layer parameter, transmission
Rate RnIt (t) is then output, it is physical layer parameter.Qn(t) as the variation of time is as follows:
Qn(t+1)=[Qn(t)-Rn(t)]++An(t)
Wherein, [x]+It indicates max (x, 0).And when Q (t) meets the following conditions, it is believed that it is strong stability:
In order to realize the stabilization of dynamic queue, it would be desirable to control A respectivelyn(t) and Rn(t), this will be situated between later
It continues.
2) foundation of MOS (Mean Opinion Score) evaluation model
AnIt (t) is the parameter for reflecting network layer performance, it directly affects the Quality of experience of user.In some cases, on
Line link network load is very heavy, so that it cannot meet the quality requirement of all users under bad channel condition, this is just
Need to adjust corresponding data rate according to user experience quality to avoid obstruction, M2M pairs of access speed adjust is also referred to as
Rate control.In order to characterize user experience quality with the method for mathematics, we establish following MOS evaluation model, as follows:
MOS[An(t)]=ηnlog2[An(t)]
Wherein An(t) t moment M is indicatednData access rate, parameter ηn∈ [0,1] is indicated to MnThe priority of setting is joined
Number, ηnBigger expression MnRequirement of the data of generation to time delay is higher, it should it is more to occupy more communication resources.
3) transmission channel models
Beginning in each time slot occurs for uplink communications resources distribution (for example, power optimization and subchannel selection)
When.In cellular subscriber device and M2M in the ofdm system coexisted, bandwidth is divided into K sub-channels, the band of every sub-channels
Width is B.
With MnThe C of shared subchannelkInterference plus noise (Signal to Interference plus Noise
Ratio, SINR) channel SNRs can indicate are as follows:
Wherein, pk(t) C is indicatedkTransmission power in time slot t, gk(t) C is indicatedkTransmission power in time slot t increases
Benefit,Indicate CkThe distance between base station, αCIndicate path loss ginseng of all cellular subscriber devices under current scene
Number.N0Indicate the additive white Gaussian noise size of environment.It is corresponding, pn(t)、gnk(t)、And αMRespectively indicate time slot t
Interior MTnTransmission power, transmission power gain, MTnThe distance between base station and path loss parameter.
Similarly, to the SINR Return Channel signal-to-noise ratio of receiver from base station to M2M are as follows:
Wherein,Indicate MTnWith MRnThe distance between,Indicate CkWith MRnThe distance between.Then exist
In time slot t, MnMT and MR between multipling channel SkThe transmission rate of formation are as follows:
Wherein,Indicate one about whether in time slot t by CkThe S of occupancykDistribute to MnTwo-value decision
Variable.Mean MnMultipling channel Sk。
Every sub-channelsEach time slot t can only be by most a M2M be to reuse, to avoid to CkBetween BS
Existing uplink excessive interference.Therefore, Wo Menyou
4) long-term data access rate constraint and deferred constraint
Since there are many delay-sensitive equipment, it usually needs Delay bound and speed rates rate limit, we are to every
A M2M is constrained application time Mean Speed and deferred constraint.
Specifically, data access rate constraint average in time is as follows:
Wherein, OnIndicate MnMinimum long term data access rate.
Queueing delay is generally defined as data packet and waits time span until that can transmit in the queue.It should be noted that
, compared with queueing delay, transmission delay is lesser in the network with high load, therefore can be ignored.Definition
Average retardation constraint formulations in time are as follows:
Wherein, ρnIndicate average retardation, upper bound Dn。
5) modeling of maximum MOS optimization problem
The optimization of all M2M couples of weighting MOS needs to solve Joint rate control, and power optimization and subchannel selection are asked
Topic, and be related to M2M and the two dimension between subchannel is matched.Therefore in time slot t, if size is the two-dimensional matrix of N × KFor indicating subchannel selection strategy, P={ pnFor indicating power optimization strategy, R={ RnFor indicating number
According to access rate control strategy.Optimization problem is modeled as follows:
C6: queue QnIt (t) is strong stability,
Wherein, constraint C1 and C2 be in order to ensure every sub-channels each time slot at most can by a M2M to reuse,
Vice versa.Specified M2M pairs of the transimission power constraint of C3.C4 is the SINR threshold value constraint of cellular subscriber device and M2M pairs.C5 is
The maximum of base station can bear data queue's rate.C6 is M2M pairs of stable constraint.C7 and C8 ensures to guarantee M2M pairs simultaneously
The rate requirement and time average retardation of every sub-channels.
6) the problem of being based on Liapunov optimization algorithm solution
In order to simulate average retardation and rate constraint, we introduce the concept of virtual queue.Phase is constrained with Mean Speed
Associated virtual queue Y (t) changes over time as follows:
Yn(t+1)=[Yn(t)-An(t)]++On(t)
If virtual power queue Y (t) is that Mean Speed is stable, it meets mean power constraint C7。
Virtual queue Z (t) associated with deferred constraint changes over time as follows:
Zn(t+1)=[Zn(t)-DnRn(t)]++Qn(t)
According to the above analysis, if data queue and two virtual queues (Y, Z) to all M2M to be all it is stable, I
Just think whole network be stable and long-term data access rate constraint and deferred constraint all be meet.
Therefore, we can be according to string stability restrictive condition C1、C2、C3、C4And C5, by the original optimization problem of step 6)
The problem of being converted to the weighting MOS value for maximizing all M2M pairs.Problem after conversion is expressed as follows:
s.t.C1、C2、C3、C4And C5
C6: queue Q (t), Yn(t) and ZnIt (t) is strong stability,
Make Q={ Qn(t) }, Y={ YnAnd Z={ Z (t) }n(t) } overstocking for three queues is respectively indicated, G (t)=[Q is made
(t), Y (t), Z (t)] indicate that M2M pairs of joint queue is overstock, then Lyapunov Equation can be defined such:
Then the Liapunov drift value Δ (G (t)) of instantaneous (from a time slot to next time slot) is defined as:
The average expectation of weighting MOS value is therefrom subtracted, the negative return item of our available following drifts:
Wherein, V is a non-negative adjustable parameter.According to the design principle that Liapunov optimizes, should select appropriate
Rate control and resource allocation decisions keep the upper limit of the negative return item of the drift of each time slot t minimum, it may be assumed that
Wherein, X is a non-negative constant, it meets in all time slot t with lower inequality:
We, which convert original optimization problem, becomes minimizing the upper dividing value of the negative return of drift, similarly in each time slot t,
The formula is similarly subjected to resource allocation constraint C1、C2、C3And C4And rate control constrains C5Influence.Therefore, original Stochastic Networks
Network long-term optimization problem is converted for a series of continuous instantaneous quiescent optimization subproblems, is specifically divided into data access
Rate control subproblem and resource allocation subproblem.
Data access rate control: access rate control strategy means the algorithm based on M2M pairs of requirement and current data
Queue overstocks to adjust access rate relevant to MOS.For example, there are data queue it is overstocked in the case where, M2M is to will be with pole
High probability refusal has higher priority and larger amount of new arrival data, to avoid more serious channel congestion.In addition, right
In some stator channel the case where, it is assumed that non-negative adjustable parameter V is larger, then M2M is to can use looser access rate
Rate control strategy is to allow to receive more data.Therefore, M2M pairs of corresponding MOS value can achieve higher level.
Since the Section 2 for negative return of drifting about only relates to access rate control related parameters An(t), therefore to this most
Smallization can be considered first subproblem, specific as follows:
s.t.C5
Wherein,Because of MOS
[AnIt (t)] is about An(t) a convex function, we can directly optimize above formula using Optimization Problems of Convex Functions tool.
Resource allocation subproblem: since the Section 3 for negative return of drifting about only relates to resource allocation relevant parameter Rn(t), i.e. function
Rate allocation result pn(t) and subchannel selection resultTherefore second subproblem can be considered to the minimum of this, specifically
It is as follows:
s.t.C1, C2, C3And C4
Wherein,Resource allocation problem is one complex
Combinatorial problem, wherein variableIt is discrete, but variable pnIt (t) is continuous.In practical applications, since it is high complicated
Property, it is difficult to it realizes to the exhaustive searches of optimal value, but the method by applying successive overrelaxation, original integer planning problem can be with
It is loosened to convex optimization problem, this method complexity is lower, can easily find the best solution for meeting constraint condition.
In addition, we are related to N number of M2M pairs by solving subchannel select permeability using Gai Er-Sha Lipu matching algorithm
With the two-dimentional matching problem of K sub-channels.We provide defined below first: matchingIt indicates from collection
It closesTo the one-to-one mapping relationship of its own, φ (Mn)=SkIndicate MnWith subchannel SkMatching, at this timeIt otherwise is 0.
As φ (Mn)=Sk, in other words, whenOptimal value can pass through following pass
System is found out:
s.t.C1, C2, C3And C4
This is equally a convex optimization problem, can be gone out by convex optimization tool direct solution optimal
6) the problem of being based on Gai Er-Sha Lipu matching algorithm solution
In order to complete subchannel selection, we firstly the need of establish M2M to and cellular subscriber device two-way preference arrange
Table.We define each M2M to the preference of different subchannels by its transmission rateIndicate, by by each M2M pairs with every sub-channels are of short duration is connected to obtain
Transmission rate corresponding with every sub-channels, rate is higher, and priority is higher.Whether each cellular subscriber device is ready sub- letter
Road is supplied to M2M to then being determined by the size of SINR value, and bigger to interfering caused by user itself, i.e., SINR value is smaller, then gets over
It is unwilling.And then a stable matching φ is finally realized by the matching algorithm of iteration.Its basic procedure is as follows:
Step 1: each MnIt ranked first position into its preference list and not to the S of oneself expression refusalkIt is proposed matching Shen
Please；
Step 2: if corresponding SkStill unselected, then the two successful match compares applicant and original if being selected
Sequence of the matcher in subchannel in, wherein forward one M2M pairs of selection, while refusing another one；
Step 3: steps 1 and 2 are repeated, until each MnOne of subchannel is all selected, and by remaining all subchannel
Refusal.
Detailed description of the invention:
Fig. 1 is the beehive network system model based on M2M uplink.
Fig. 2 is the simulation parameter when present invention is emulated.
Fig. 3 is queue control result proposed by the present invention.
Fig. 4 is resource allocation result proposed by the present invention.
Fig. 5 is system performance proposed by the present invention compared with the result stability of Random matching algorithm.
Specific embodiment
Embodiments of the present invention are divided into two steps, and the first step is to establish model, and second step is the implementation of algorithm.Its
In, the model of foundation as shown in Figure 1, in it and summary of the invention the beehive network system model based on M2M uplink introduction
It is completely corresponding.
1) for system model, with the extensive construction of electric power Internet of Things, existing mobile cellular net that mass data pours in
Network, but the dynamic and unpredictability that are reached due to data flow bring very big pressure to the operation of base station, and urgent need is set
One kind is counted in the case where global information is unknown, optimization steady in a long-term laboriously scheme is carried out to mass data.As shown in Figure 1,
M2M in electric power Internet of Things transmits data to by the channel for being multiplexed suitable cellular subscriber device, using random network
Optimization method controls the data for reaching base station, to realize the steady in a long-term of transmission queue, and greatly promotes network performance
And user experience quality.
2) it in order to solve above-mentioned optimization problem, first has to design a kind of data access rate control based on Liapunov
Scheme, closing lid Er-Sha Lipu matching algorithm in parallel realize the reasonable distribution of the communication resource.The final design scheme is broken down into number
It, can be lower convex using complexity well according to the subproblem of access rate control and two Optimization Problems of Convex Functions of resource allocation
Optimization Toolbox is solved, and is aided with Gai Er-Sha Lipu matching algorithm to realize that subchannel selects.
For the present invention, We conducted a large amount of emulation.Design parameter in emulation as indicated in the chart 2,4 M2M to and 5
A cellular subscriber device is randomly dispersed in the cellular network that radius is R=200m, the control of following data queue rate and power
Two aspect of time delay is illustrated result.
Fig. 3 shows the overstocked variation of different queue and time slot.We can observe that random initial overstocked when providing
When, each queue tends to stablize near corresponding value after only several time slots.Numerical result proves, by utilizing me
Method handle the backlog queue that continually generates, rate control can be well realized.It is noted that overstock
Size and priority are positively correlated, the reason is that the M2M with higher priority is to more data collection amounts and more frequently
Data send to be overstock so as to cause more queues.
Fig. 4 shows the joint Power optimization and subchannel selection scheme of different time-gap.Specifically, the class of virtual queue Z
As stabilization result provided in Fig. 3-(a), wherein the value indicates total power P to be allocatedmax.Fig. 3-(b) shows that power is excellent
Change as a result, wherein four gradients are different because of M2M pairs of different priorities.After distribution, the transmission rate of subchannel such as Fig. 3-(c) institute
Show.The above result shows that can not only keep system using Liapunov optimization and the matched combinational algorithm of Gai Er-Sha Lipu
Stability, and can evade as much as possible time varying channel bring adverse effect.
Fig. 5 overstock from queue and the angle changing rate of power distribution it is proposed that algorithm and Liapunov optimization and with
The total system stability for the algorithm that machine matching combines, which show two box figures to show being dispersed into for one group of data
All.Can as seen from the figure, either queue is overstock or power distribution, the overall distribution ratio of the scheme proposed are believed with loom
The overall distribution of road selection is more concentrated, this is because random fit exists the subchannel of performance difference and height with high priority
A possibility that queue matches.
Although undeclared purpose discloses specific implementation and attached drawing of the invention, of the invention its object is to help to understand
Content is simultaneously implemented accordingly, but it will be appreciated by those skilled in the art that: it is of the invention and the attached claims not departing from
In spirit and scope, various substitutions, changes and modifications are all possible.Therefore, the present invention should not be limited to most preferred embodiment and
Attached drawing disclosure of that, the scope of protection of present invention is subject to the scope defined in the claims.
Claims (3)
1. a kind of cross-layer Resource Allocation Formula towards electric power Internet of Things, it is characterised in that:
1) consider under related data queue and channel statistical information unknown situation using Liapunov optimization method to electric power
The data queue that various equipment for machine type communication in Internet of Things (M2M to) are transferred in honeycomb optimizes, and converts long-term optimization
Problem is a series of rate control subproblems and resource allocation subproblem in time slot t, finally to realize long-term steady shape
State simultaneously optimizes network performance；
2) in optimization process, using Gai Er-Sha Lipu matching algorithm, subchannel selection is completed, i.e. M2M is to by being multiplexed honeycomb
Net Central Plains has the channel of user to complete data transmission.
2. as described in claim 1 step 1) in the case where global information is unknown, utilize Liapunov optimization carry out speed
Rate control, it is characterised in that:
1) queuing model is initially set up, M2M is to MNCorresponding queue is overstock as follows with the situation of change of time slot t:
Qn(t+1)=[Qn(t)-Rn(t)]++An(t)
[x]+It indicates max (x, 0), Rn(t) transmission rate, A are indicatedn(t) data access rate is indicated,
2) MOS evaluation model is set are as follows:
MOS[An(t)]=ηnlog2[An(t)]
Parameter ηn∈ [0.1] is indicated to MnThe priority parameters of setting,
3) in cellular subscriber device and M2M in the ofdm system coexisted, bandwidth is divided into K sub-channels, every sub-channels
Bandwidth be B, transmission channel is modeled as follows:
Indicate one about whether in time slot t by cellular subscriber device CkThe subchannel S of occupancykDistribute to Mn's
Two-value decision variable,It indicates at this time from base station to M2M to the SINR Return Channel signal-to-noise ratio of receiver, and before upper
Uplink transmission signal-to-noise ratio SINR value is represented asCorrespondence is found out by following formula respectively:
pk(t) C is indicatedkTransmission power in time slot t, gk(t) C is indicatedkTransmission power gain in time slot t,Table
Show CkThe distance between base station, αcIndicate path loss parameter of all cellular subscriber devices under current scene, N0Indicate ring
The additive white Gaussian noise size in border, corresponding, pn(t)、gnk(t)、And αMTable respectively
Show MT in time slot tnTransmission power, transmission power gain, MTnThe distance between base station, MTnWith MRnThe distance between, CkWith
MRnThe distance between and path loss parameter,
4) long-term data access rate constraint and deferred constraint are prescribed as follows:
5) finally, maximum weighting MOS optimization problem is established as follows:
s.t.C1:
C2:
C3:
C4:
C5:
C6: queue QnIt (t) is strong stability,
C7:
C8:
For indicating subchannel selection strategy, P={ pnFor indicating power optimization strategy, R={ RnFor indicating
Data access rate control strategy, constraint C1 and C2 is in order to ensure every sub-channels at most can be by one in each time slot
M2M is to reuse, and vice versa, and specified M2M pairs of the transimission power constraint of C3, C4 is the S1NR threshold of cellular subscriber device and M2M pairs
Value constraint, C5 are that the maximum of base station can bear data queue's rate, and C6 is M2M pairs of stable constraint, and C7 and C8 ensure simultaneously
Guarantee the rate requirement and time average retardation of M2M pairs of every sub-channels,
6) to solve above-mentioned optimization problem, introducing is about Mean Speed and the relevant virtual queue of power time delay:
Yn(t+1)=[Yn(t)-An(t)]++On(t)
Zn(t+1)=[Zn(t)-DnRn(t)]++Qn(t)
Then the above problem is converted into:
s.t.C1、C2、C3、C4And C5
C6: queue Q (t), Yn(t) and ZnIt (t) is strong stability,
The joint queue of M2M pairs of three queues is overstock are as follows:
The then Liapunov drift value Δ (G (t)) of instantaneous (from a time slot to next time slot) are as follows:
X is a non-negative constant,
Rate control subproblem is represented as:
s.t. C5
Wherein,It can apply low multiple
The convex optimization tool of miscellaneous degree is solved,
s.t. C1, C2, C3And C4
Wherein,?In the case where determination, it is equally applicable convex
Optimization tool solves.
3. as described in claim 1 step 2) in optimization process, using Gai Er-Sha Lipu matching algorithm, complete subchannel
Selection, i.e. M2M complete data to the channel for having user by being multiplexed Cellular Networks Central Plains and transmit, which is characterized in that firstly the need of building
Vertical M2M to and cellular subscriber device two-way preference list.We define each M2M to the preference of different subchannels by it
Transmission rateIndicate, by by each M2M pairs with every sub-channels are of short duration is connected to
To obtain transmission rate corresponding with every sub-channels, rate is higher, and priority is higher.Whether each cellular subscriber device is ready
Subchannel is supplied to M2M to then being determined by the size of SINR value, bigger to interfering caused by user itself, i.e., SINR value is got over
It is small, then more it is unwilling.And then a stable matching φ is finally realized by the matching algorithm of iteration.Its basic procedure is as follows:
Step 1: each MnIt ranked first position into its preference list and not to the S of oneself expression refusalkIt is proposed matching application；
Step 2: if corresponding SkStill unselected, then the two successful match compares applicant and matches with original if being selected
Sequence of the person in subchannel in, wherein forward one M2M pairs of selection, while refusing another one；
Step 3: steps 1 and 2 are repeated, until each MnOne of subchannel has all been selected, and has been refused by remaining all subchannel.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111162852A (en) * | 2019-12-31 | 2020-05-15 | 华北电力大学 | Ubiquitous power Internet of things access method based on matching learning |
| CN111182509A (en) * | 2020-01-07 | 2020-05-19 | 华北电力大学 | Ubiquitous power Internet of things access method based on context-aware learning |
| CN111552570A (en) * | 2020-04-29 | 2020-08-18 | 章稳建 | Self-adaptive distribution method of data processing resources of Internet of things and cloud computing server |
| CN111800823A (en) * | 2020-06-12 | 2020-10-20 | 云南电网有限责任公司电力科学研究院 | Priority-based power wireless terminal data transmission method and device |
| CN113225672A (en) * | 2021-04-22 | 2021-08-06 | 湖南师范大学 | Base station selection method supporting mobile user |
| CN114375010A (en) * | 2021-06-28 | 2022-04-19 | 山东华科信息技术有限公司 | Power distribution internet of things based on SDN and matching theory |
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