CN110445566A - A kind of resource allocation methods towards industrial internet of things data reliable transmission - Google Patents
A kind of resource allocation methods towards industrial internet of things data reliable transmission Download PDFInfo
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- CN110445566A CN110445566A CN201910725323.XA CN201910725323A CN110445566A CN 110445566 A CN110445566 A CN 110445566A CN 201910725323 A CN201910725323 A CN 201910725323A CN 110445566 A CN110445566 A CN 110445566A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
- H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
- H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range
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Abstract
The present invention provides a kind of resource allocation methods towards industrial internet of things data reliable transmission, it is related to the industrial Internet of Things communications field, the present invention has studied to be associated with including relaying, the association of sink node, the resource allocation methods of channel distribution and power control, the industrial Internet of Things Optimized model of fusion full duplex relaying is obtained, its model includes relay selection and channel distribution vector and power allocation vector, relay selection and method for channel allocation based on genetic algorithm are calculated separately using two stage optimization method, with the power distribution method based on genetic algorithm, the characteristic of full duplex relaying is maximally utilized with it, improve the reliability of industrial data transmission of internet of things.
Description
Technical field
The present invention relates to the industrial Internet of Things communications fields, and in particular to a kind of towards industrial internet of things data reliable transmission
Resource allocation methods.
Background technique
Industrial Internet of Things refers to by that will have the intelligent terminal of sensing capability, ubiquitous mobile computing mode, general
Mobile network communication mode be applied to industrial links, improve manufacture efficiency, hold product quality, reduce at
This, reduces pollution, so that the intelligent production of industry is realized, to effectively improve industrial production level.However, due to
Traditional technology of Internet of things is there is no reliability and Delay Factor required for industrial communication is considered, when using technology of Internet of things
When, how for industrial equipment data transmission high reliability communication support is provided, become industrial Internet of Things and move towards practical application face
The difficulties faced.
Compared with the single-hop communication in traditional Internet of Things, based on the collaboration communication of relaying by the way that relay node deployment exists
It sends and receives between node, the transmission power of sending node can be greatly reduced, reduce the interference to links other in network, just
Cause the growing interest of people.More it is essential that by disposing full-duplex communication device, relay node on relay node
It can be improved on the basis of not reducing network delay in the reception and transmission of the synchronization of same channel completion signal
The performance of network.
It should be noted that can be brought when introducing multiple full duplex relay nodes in traditional industry scenes of internet of things
The feature different from traditional Internet of Things single-hop communication, is mainly manifested in:
1, the relay node that traditional Internet of things node (sensor) needs to select to be associated；
2, relay node needs the aggregation node (sink) for selecting to be associated；
3, sensor node needs to select the optimal communication channel and power of communications with relay node；
4, relay node is it needs to be determined that optimal communication power with sink node.
Summary of the invention
The present invention provides a kind of resource allocation methods towards industrial internet of things data reliable transmission, for containing full duplex
This new network traffic model of the industrial Internet of Things of relaying has studied including relaying association, the association of sink node, channel distribution
And the resource allocation methods of power control, the characteristic of full duplex relaying is maximally utilized with it, is improved industrial internet of things data and is passed
Defeated reliability.
A kind of resource allocation methods towards industrial internet of things data reliable transmission, the specific steps are as follows:
Step 1: any sensor node s in industrial Internet of Things being set and uses power of communications on each channelAppoint
The relay node r that anticipates uses maximum communication powerAnd group is combined into initial power allocation vector p0；
Step 2: being based on power allocation vector p0, relay selection and channel distribution are optimized using genetic algorithm, and
Relaying and channel distribution vector v after being optimized；
Step 3: based on step 2 obtain relaying and channel distribution vector v, using genetic algorithm to power distribution carry out into
One-step optimization, and the power allocation vector p after being optimized；
Step 4: relaying of each sensor node based on optimization and channel distribution vector v and power allocation vector p in network,
Acquire optimal data transmission credibility As(v,p)。
It include S sensor node, D sink node and R full duplex relay node in the industry Internet of Things, M is a
Available channel；IfFor the set of sensor node,For the set of sink node,For the set of relay node,For all sets of sub-channels；Binary variableIt indicates
Whether s-th of sensor node and d-th of sink node, which pass through m-th of subchannel, carries out direct communication: ifIndicate m
Sub-channelsIt is allocated to s-th of sensor nodeFor with d-th of sink nodeIt carries out straight
Letter is connected,It is then opposite；Similarly collaboration communication is carried out in order to describe sensor node by relay node and sink node
Channeling and trunking distribution in mode, binary variableIndicate s-th of sensor node and d-th sink node whether
Collaboration communication is carried out by m-th of subchannel with the help of r-th of relay node: ifIt indicates r-thWith m-th of subchannelIt is allocated to s-th of sensor nodeFor with d-th of sink
NodeCollaboration communication is carried out,It is then opposite；
IfFor the transmission power that s-th of sensor node is communicated by m-th of subchannel,
IfFor the transmission power that r-th of relay node is communicated by m-th of subchannel, whereinWithRespectively indicate the maximum value and r-th of relay of the total transmission power of s-th of sensor node
The maximum value of the transmission power of node, therefore optimization problem can constrain power with formula (1) and formula (2).
Other than power constraints, for each sensor node, each of which subchannel at most can only be by one
Link occupies, therefore obtains constraint equation (3) and formula (4):
For each relay node, it is at most only a sensor node job, therefore obtain constraint condition
Formula (5)；
In summary condition, the constraint that the resource allocation optimization problem of fusion duplex relaying proposed by the present invention needs to pay close attention to
Condition is formula (1) to formula (5), merges the reliability optimization model of duplex relaying are as follows:
S.t. (1)~(5)
In formulaFor relay selection and channel distribution vector,
For power allocation vector, As(v, p) is the data transmission credibility mapping function of s-th of sensor, is responsible for resource allocation knot
Fruit (v, p) is mapped as specific probable reliability.
The specific steps of step 2 are as follows:
Step 2.1: counter being initialized, sets the number of iterations counter t=1, population count device g=1 simultaneously
Set population G={ }；
Step 2.2: based on method of randomization to g-th of individual G in populationgMember in the C1 matrix and C2 matrix of=C1 ∪ C2
The element value of element is initialized；
Wherein C1 matrix combines the relay selection and channel distribution for illustrating all sensor nodes in network with C2 matrix
Scheme；C1 matrix indicates that the communication mode of sensor node and sink node, i.e. direct communication or collaboration communication, C2 matrix indicate
The connection relationship of relay node and sink node in collaboration communication link, respectively indicates are as follows:
In Matrix C 1, cefIndicate communication mode of the sensor node e on subchannel f, wherein
In Matrix C 2, cklIndicate whether relay node k is communicated with sink node l, wherein
, can be with the distribution of complete representation relay node and subchannel by Matrix C 1 and C2, wherein relay node passes through
Which subchannel is communicated with sink node, can be calculated by C1 and C2 convolution (9), (10).
Step 2.3: utilizing the population G in the step 2.1 and G in the step 2.2gCalculating finds out G=G ∪ { Gg,
And counter g=g+1 is set；
Step 2.4: if judgement g≤R, wherein R is quantity individual in population, return step 2.2；Otherwise jump procedure
2.5；
Step 2.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each in population G
The adaptive value of individual；
Step 2.6: counter being initialized, g=1 is made, while interim population G'={ } is set；
Step 2.7: two individual a and b being selected from population G using wheel disc bet method, and based on crossover algorithm generation
Body a' and b'；
Step 2.7 specifically includes:
Step 2.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability
Pc, then continued to execute from step 2.7.2, otherwise jump procedure 2.7.5；
Gene is R in step 2.7.2: individual a Matrix C 1αGene position and the Matrix C 1 of individual b in correspond to gene position
Gene exchange；
Gene is R in step 2.7.3: individual b Matrix C 1αGene position and the Matrix C 1 of individual a in correspond to gene position
Gene exchange；
Optional a line is exchanged with the corresponding row in the Matrix C 2 of individual b in step 2.7.4: individual a Matrix C 2.
Step 2.7.5: two new individuals a ' and b ' are obtained.
Step 2.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 2.9: if judgement g≤R, wherein R is quantity individual in population, return step 2.7；Otherwise jump procedure
2.10；
Step 2.10: to individual g' each in interim population G', g' being updated using mutation algorithm.
Step 2.10 specifically includes:
Step 2.10.1: for individual g', the random number in (0,1) range is generated, if it is less than mutation probability Pm, then from
Step 2.10.2 is continued to execute, otherwise jump procedure 2.10.3.
Step 2.10.2: a gene position is randomly choosed in the Matrix C 1 of individual g', if protogene position "-", at random
Variation is " Rα" or " Sβ".If protogene position is not "-", random variation is " Rα" or " Sβ" or "-", wherein α, β not with former base
Because identical.
Step 2.10.3: randomly choosing a line in the Matrix C 2 of individual g', the gene location 0, In for being 1 by the row gene
Other gene positions randomly choose a position and set 1.
Step 2.11: according to the power assignment value p of individual each in interim population G' and Matrix C 1 and C2, calculating population G'
In each individual adaptive value；
Step 2.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and in interim population G'
The low adaptive value individual of high adaptive value individual replacement population G, and the number of iterations counter t=t+1 is set；
Step 2.13: if judgement t≤T, wherein T is total the number of iterations, return step 2.6, otherwise jump procedure 3.
The specific steps of step 3 are as follows:
Step 3.1: counter being initialized, sets the number of iterations counter t=1, population count device g=1 simultaneously
Set population Q={ }；
Step 3.2: based on g-th of individual Q in power initialization of population method initial populationgThe P1 matrix of=P1 ∪ P2 with
The element value of element in P2 matrix；
Wherein P1 matrix combines the power choosing with relay node for illustrating all sensor nodes in network with P2 matrix
Select scheme.P1 matrix indicates the distribution power of sensor node, and P2 matrix indicates the distribution power of relay node, respectively indicates
For,
In matrix P1, pijIndicate power of communications of the sensor node i on subchannel j, whereinAnd
In matrix P2, piIndicate the power of communications of relay node i, whereinAnd
In specific initialization procedure, in order to guarantee that the element value of initialization meets constraint condition (1) and constraint condition
(2), step 3.2 specifically includes:
Step 3.2.1: to each element p in P1ij, its initialization value is arranged first is
Step 3.2.2: element value reparation is executed to P1, it is made to meet constraint condition (1).
The specific element value restorative procedure are as follows:
To each row element value { p in P1ij| j=1 ... M }, judgementIf so, then
Step 3.2.3: initialization is to each element in P2
Step 3.3: making Q=Q ∪ { Gg, and counter g=g+1 is set；
Step 3.4: judging if g≤R (R as quantity individual in population), return step 2.2；Otherwise jump procedure
Step2.5；
Step 3.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each in population G
The adaptive value of individual；
Step 3.6: setting counter g=1, and G'={ }；
Step 3.7: two individual a and b being selected from population G using wheel disc bet method, and based on crossover algorithm generation
Body a' and b'；
Step 3.7 specifically includes:
Step 3.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability
Pc, then continued to execute from step 3.7.2, otherwise jump to step 3.7.3.
Gene is in step 3.7.2: individual a matrix P1Gene is in individual b matrix P1It is new then to intersect generation
The correspondence gene difference of matrix P1 in individual c and dWithIt is updated to respectively
Wherein β is
Wherein u=rand (0,1) is the random number between 0 to 1.
Gene is in step 3.7.3: individual a matrix P2Gene is in individual b matrix P2It is new then to intersect generation
The correspondence gene difference of matrix P2 in individual c and dWithIt is updated to respectively
Step 3.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 3.9: if judgement g≤R, wherein R is quantity individual in population, return step step 3.7；Otherwise it jumps
Step step 3.10；
Step 3.10: to individual g' each in interim population G', g' being updated using mutation algorithm.
Step 3.10 specifically includes:
Step 3.10.1: for individual a, the random number in (0,1) range is generated, if it is less than mutation probability pu, then from
Step 2 continues to execute, and otherwise terminates.
Step 3.10.2: it is if corresponding to gene in the matrix P1 of individual aThen the gene updates as follows
WhereinFor exponential function, it is expressed as
Wherein random number of the u between (0,1), t current the number of iterations, T are total the number of iterations.
Step 3.10.3: it is if corresponding to gene in the matrix P2 of individual aThen the gene updates as follows
Step 3.11: according to the power assignment value p of individual each in interim population G' and Matrix C 1 and C2, calculating interim kind
The adaptive value of each individual in group G'；
Step 3.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and in interim population G'
The low adaptive value individual of high adaptive value individual replacement population G, and the number of iterations counter t=t+1 is set；
Step 3.13: if judgement t≤T, wherein T is total the number of iterations, otherwise return step 2.6 terminates.
Beneficial effects of the present invention: for this new network traffic model of the industrial Internet of Things containing full duplex relaying,
The resource allocation methods including relaying association, the association of sink node, channel distribution and power control are had studied, benefit is maximized with it
With the characteristic of full duplex relaying, the reliability of industrial data transmission of internet of things is improved, the energy consumption of sensor node is saved,
Extend the working time of network.
Detailed description of the invention
Fig. 1 is the industrial Internet of Things schematic diagram based on full duplex relaying；
Fig. 2 is situation of change of the reliability with different sensor numbers；
In figure, the curve of the number of the change curve (b) " 9 " of (a) real reliability；
Fig. 3 is the optimal system reliability under different number subchannel, different number UE；
In figure, (a) achieved reliability compares the number of (b) " 9 ".
Specific embodiment
It is right in the following with reference to the drawings and specific embodiments in order to be more clear the purpose of the present invention, technical solution and advantage
The present invention is described in further details.Described herein specific examples are only used to explain the present invention, is not used to limit this
Invention.
A kind of resource allocation methods towards industrial internet of things data reliable transmission, include the following steps:
The reliability optimization industry Internet of Things pessimistic concurrency control of fusion full duplex relaying include relay selection and channel distribution vector and
Power allocation vector, initialization power allocation vector p realize relay selection and method for channel allocation based on constant power, obtain
Take current relaying and channel distribution vector v；Further to the relaying of acquisition and channel distribution vector v, power optimization point is realized
Method of completing the square；The two stage optimization method the following steps are included:
Step 1: any sensor node s in industrial Internet of Things being set and uses power of communications on each channelAppoint
The relay node r that anticipates uses maximum communication powerAnd group is combined into initial power allocation vector p0；
Step 2: being based on power allocation vector p, relay selection and channel distribution are optimized using genetic algorithm, and obtains
Relaying and channel distribution vector v after must optimizing；
Step 3: relaying and channel distribution vector v based on acquisition carry out power distribution using genetic algorithm further
Optimization, and the power allocation vector p after being optimized；
Step 4: relaying of each sensor node based on optimization and channel distribution vector v and power allocation vector p in network,
Acquire optimal data transmission credibility As(v,p)。
As shown in Figure 1, including S sensor node, D sink node and R full duplex in the industry Internet of Things
Relay node, M available channel；IfFor the set of sensor node,For sink node
Set,For the set of relay node,For all sets of sub-channels；Binary variableIndicate whether s-th of sensor node and d-th of sink node pass through m-th of subchannel and carry out direct communication: if
Indicate m-th of subchannelIt is allocated to s-th of sensor nodeFor with d-th of sink nodeDirect communication is carried out,It is then opposite；Similarly saved to describe sensor node by relay node and sink
Point carries out the channeling and trunking distribution in collaboration communication mode, binary variableIndicate s-th of sensor node and d-th
Whether sink node passes through m-th of subchannel progress collaboration communication with the help of r-th of relay node: ifIt indicates
R-thWith m-th of subchannelIt is allocated to s-th of sensor nodeFor with d
A sink nodeCollaboration communication is carried out,It is then opposite；
IfFor the transmission power that s-th of sensor node is communicated by m-th of subchannel,
IfFor the transmission power that r-th of relay node is communicated by m-th of subchannel, whereinWithRespectively indicate the maximum value and r-th of relay of the total transmission power of s-th of sensor node
The maximum value of the transmission power of node, therefore the power constraint problem of optimization problem can be expressed as formula (1) and formula (2).
Other than power constraints, for each sensor node, each of which subchannel at most can only be by one
Link occupies, therefore obtains constraint equation (3) and formula (4):
For each relay, it is at most only a sensor job, therefore obtain constraint equation (5)；
In summary condition, the constraint that the resource allocation optimization problem of fusion duplex relaying proposed by the present invention needs to pay close attention to
Condition is formula (1) to formula (5), Optimized model are as follows:
S.t. (1)~(5)
In formulaFor relay selection and channel distribution vector,
For power allocation vector, As(v, p) is the data transmission credibility mapping function of s-th of sensor, is responsible for resource allocation knot
Fruit (v, p) is mapped as specific probable reliability.
The specific steps of the step 2 are as follows:
Step 2.1: counter being initialized, sets the number of iterations counter t=1, population count device g=1 simultaneously
Set population G={ }；
Step 2.2: based on method of randomization to g-th of individual G in populationgMember in the C1 matrix and C2 matrix of=C1 ∪ C2
The element value of element is initialized；
Wherein C1 matrix combines the relay selection and channel distribution for illustrating all sensor nodes in network with C2 matrix
Scheme.C1 matrix indicates that the communication mode of sensor node and sink node, i.e. direct communication or collaboration communication, C2 matrix indicate
The connection relationship of relay node and sink node in collaboration communication link, is expressed as,
In Matrix C 1, cefIndicate communication mode of the sensor node e on subchannel f, wherein
In Matrix C 2, cklIndicate whether relay node k is communicated with sink node l, wherein
, can be with the distribution of complete representation relay node and subchannel by Matrix C 1 and C2, wherein relay node passes through
Which subchannel is communicated with sink node, can be calculated by C1 and C2 convolution (9), (10).
Step 2.3: utilizing the population G in the step 2.1 and G in the step 2.2gCalculating finds out G=G ∪ { Gg,
And counter g=g+1 is set；
Step 2.4: if judgement g≤R, wherein R is quantity individual in population, return step 2.2；Otherwise jump procedure
2.5；
Step 2.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each in population G
The adaptive value of individual；
Step 2.6: counter being initialized, g=1 is made, while interim population G'={ } is set；
Step 2.7: two individual a and b being selected from population G using wheel disc bet method, and based on crossover algorithm generation
Body a' and b'；
The wheel disc bet method refers to the adaptation value function for calculating each body, based on all in each individual fitness function and population
The ratio of the sum of ideal adaptation value function determines the area that each individual occupies in wheel disc；It is individual based on roulette method choice,
That is the high function of adaptive value, accounting is big in wheel disc, and select probability is also big；
Step 2.7 specifically includes:
Step 2.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability
Pc, then continued to execute from step 2.7.2, otherwise jump procedure 2.7.5；
Gene is R in step 2.7.2: individual a Matrix C 1αGene position and the Matrix C 1 of individual b in correspond to gene position
Gene exchange；
Gene is R in step 2.7.3: individual b Matrix C 1αGene position and the Matrix C 1 of individual a in correspond to gene position
Gene exchange；
Optional a line is exchanged with the corresponding row in the Matrix C 2 of individual b in step 2.7.4: individual a Matrix C 2.
Step 2.7.5: two new individuals a ' and b ' are obtained.
Step 2.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 2.9: if judgement g≤R, wherein R is quantity individual in population, return step 2.7；Otherwise jump procedure
2.10；
Step 2.10: to individual g' each in interim population G', g' being updated using mutation algorithm.
Step 2.10 specifically includes:
Step 2.10.1: for individual g', the random number in (0,1) range is generated, if it is less than mutation probability Pm, then from
Step 2.10.2 is continued to execute, otherwise jump procedure 2.10.3.
Step 2.10.2: a gene position is randomly choosed in the Matrix C 1 of individual g ', if protogene position "-", at random
Variation is " Rα" or " Sβ".If protogene position is not "-", random variation is " Rα" or " Sβ" or "-" (α, β not with protogene phase
Together).
A line is randomly choosed in step 2.10.3: individual g ' Matrix C 2, the gene location 0 for being 1 by the row gene, at it
His gene position randomly chooses a position and sets 1.
Step 2.11: according to the power assignment value p of individual each in interim population G' and Matrix C 1 and C2, calculating interim kind
The adaptive value of each individual in group G'；
Step 2.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and in interim population G'
The low adaptive value individual of high adaptive value individual replacement population G, and the number of iterations counter t=t+1 is set；
Step 2.13: if judgement t≤T, wherein T is total the number of iterations, return step 2.6, otherwise jump procedure 3.
The specific steps of the step 3 are as follows:
Step 3.1: counter being initialized, sets the number of iterations counter t=1, population count device g=1 simultaneously
Set population Q={ }；
Step 3.2: based on g-th of individual Q in power initialization of population method initial populationgThe P1 matrix of=P1 ∪ P2 with
The element value of element in P2 matrix；
Wherein P1 matrix combines the power choosing with relay node for illustrating all sensor nodes in network with P2 matrix
Select scheme.P1 matrix indicates the distribution power of sensor node, and P2 matrix indicates the distribution power of relay node, respectively indicates
For,
In matrix P1, pijIndicate power of communications of the sensor node i on subchannel j, whereinAnd
In matrix P2, piIndicate the power of communications of relay node i, whereinAnd
In specific initialization procedure, in order to guarantee that the element value of initialization meets constraint condition (1) and constraint condition
(2), step 3.2 specifically includes:
Step 3.2.1: to each element p in P1ij, its initialization value is arranged first is
Step 3.2.2: element value reparation is executed to P1, it is made to meet constraint condition (1).
The specific element value restorative procedure are as follows:
To each row element value { p in P1ij| j=1 ... M }, judgementIf so, then
Step 3.2.3: initialization is to each element in P2
Step 3.3:Q=Q ∪ { Gg, and counter g=g+1 is set；
Step 3.4: if judgement g≤R, wherein R is quantity individual in population, return step 2.2；Otherwise jump procedure
2.5；
Step 3.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each in population G
The adaptive value of individual；
Step 3.6: setting counter g=1, and G'={ }；
Step 3.7: two individual a and b being selected from population G using wheel disc bet method, and based on crossover algorithm generation
Body a' and b'；
Step 3.7 specifically includes:
Step 3.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability
Pc, then continued to execute from step 3.7.2, otherwise jump to step 3.7.3.
Gene is in step 3.7.2: individual a matrix P1Gene is in individual b matrix P1It is new then to intersect generation
The correspondence gene difference of matrix P1 in individual c and dWithIt is updated to respectively
Wherein β is
Wherein u=rand (0,1) is the random number between 0 to 1.
Gene is in step 3.7.3: individual a matrix P2Gene is in individual b matrix P2It is new then to intersect generation
The correspondence gene difference of matrix P2 in individual c and dWithIt is updated to respectively
Step 3.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 3.9: if judgement g≤R, wherein R is quantity individual in population, return step step 3.7；Otherwise it jumps
Step step 3.10；
Step 3.10: to individual g' each in G', g' being updated using mutation algorithm.
Step 3.10 specifically includes:
Step 3.10.1: for individual a, the random number in (0,1) range is generated, if it is less than mutation probability pu, then from
Step 2 continues to execute, and otherwise terminates.
Step 3.10.2: it is if corresponding to gene in the matrix P1 of individual aThen the gene updates as follows
WhereinFor exponential function, it is expressed as
Wherein random number of the u between (0,1), t current the number of iterations, T are total the number of iterations.
Step 3.10.3: it is if corresponding to gene in the matrix P2 of individual aThen the gene updates as follows
Step 3.11: according to the power assignment value p of individual each in population G' and Matrix C 1 and C2, calculating interim population G'
In each individual adaptive value；
Step 3.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and in interim population G'
The low adaptive value individual of high adaptive value individual replacement population G, and the number of iterations counter t=t+1 is set.
Step 3.13: if judgement t≤T, wherein T is total the number of iterations, otherwise return step 2.6 terminates.
Sensor node number S=6~12, relay node number R=6 are set in the present embodiment, subchannel number is
M=8, sensor node maximum powerReliability is usually measured with 9 number, wherein 19 expression transmission
Reliability is 0.9, corresponding, and 69 reliabilities indicate that probability value is 0.999999；As shown in Fig. 2, with sensor node
The promotion of quantity, system reliability gradually decrease, this is because sensor node is more, the interference of generation can be more, thus
Reduce whole network system reliability.
Sensor node number S=6~12, relay node number R=6 are set, subchannel number is M=2~14,
Sensor node maximum powerAs shown in figure 3, system reliability is promoted with the increase of number of sub-channels, but
It is after subchannel increases to certain amount, system reliability is promoted limited.It can further be seen that when sensor number of nodes increases
Added-time needs more subchannels if wanting to reach identical reliability.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that；It still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；Thus these are modified or replaceed, defined by the claims in the present invention that it does not separate the essence of the corresponding technical solution
Range.
Claims (4)
1. a kind of resource allocation methods towards industrial internet of things data reliable transmission, it is characterised in that: the following steps are included:
Step 1: any sensor node s in industrial Internet of Things being set and uses power of communications on each channelArbitrarily
Relay node r uses maximum communication powerAnd group is combined into initial power allocation vector p0；
Step 2: being based on power allocation vector p0, relay selection and channel distribution are optimized using genetic algorithm, and obtains excellent
Relaying and channel distribution vector v after change；
Step 3: the relaying and channel distribution vector v obtained based on step 2 carries out power distribution using genetic algorithm further
Optimization, and the power allocation vector p after being optimized；
Step 4: relaying of each sensor node based on optimization and channel distribution vector v and power allocation vector p in network are acquired
Optimal data transmission credibility As(v,p)。
2. a kind of resource allocation methods towards industrial internet of things data reliable transmission according to claim 1, feature
It is:
It include S sensor node, D sink node and R full duplex relay node in the industry Internet of Things, M available
Channel；IfFor the set of sensor node,For the set of sink node,For the set of relay node,For all sets of sub-channels；Binary variableIt indicates
Whether s-th of sensor node and d-th of sink node, which pass through m-th of subchannel, carries out direct communication: ifIndicate m
Sub-channelsIt is allocated to s-th of sensor nodeFor with d-th of sink nodeIt carries out
Direct communication,It is then opposite；Similarly cooperate to describe sensor node by relay node and sink node logical
Channeling and trunking distribution in letter mode, binary variableIndicate whether are s-th of sensor node and d-th sink node
Collaboration communication is carried out by m-th of subchannel with the help of r-th of relay node: ifIt indicates r-thWith m-th of subchannelIt is allocated to s-th of sensor nodeFor with d-th of sink
NodeCollaboration communication is carried out,It is then opposite；
IfFor the transmission power that s-th of sensor node is communicated by m-th of subchannel, ifFor the transmission power that r-th of relay node is communicated by m-th of subchannel, whereinWithRespectively indicate the maximum value and r-th of relay of the total transmission power of s-th of sensor node
The maximum value of the transmission power of node, therefore optimization problem can constrain power with formula (1) and formula (2)；
Other than power constraints, for each sensor node, each of which subchannel at most can only be by a chain
Road occupies, therefore obtains constraint equation (3) and formula (4):
For each relay node, it is at most only a sensor node job, therefore obtain constraint equation
(5)；
In summary condition, the constraint condition that the resource allocation optimization problem of fusion duplex relaying proposed by the present invention needs to pay close attention to
For formula (1) to formula (5), the reliability optimization model of duplex relaying is merged are as follows:
S.t. (1)~(5)
In formulaFor relay selection and channel distribution vector,For function
Rate allocation vector, As(v, p) be s-th of sensor data transmission credibility mapping function, be responsible for by resource allocation result (v,
P) it is mapped as specific probable reliability.
3. a kind of resource allocation methods towards industrial internet of things data reliable transmission according to claim 1, feature
It is: the specific steps of the step 2 are as follows:
Step 2.1: counter being initialized, make the number of iterations counter t=1, population count device g=1, while kind is set
Group G={ }；
Step 2.2: based on method of randomization to g-th of individual G in populationgElement in the C1 matrix and C2 matrix of=C1 ∪ C2
Element value is initialized；
Wherein C1 matrix combines the relay selection and channel assignment scheme for illustrating all sensor nodes in network with C2 matrix；
C1 matrix indicates that the communication mode of sensor node and sink node, i.e. direct communication or collaboration communication, C2 matrix indicate cooperation
The connection relationship of relay node and sink node in communication link, respectively indicates are as follows:
In Matrix C 1, cefIndicate communication mode of the sensor node e on subchannel f, wherein
In Matrix C 2, cklIndicate whether relay node k is communicated with sink node l, wherein
, can be with the distribution of complete representation relay node and subchannel by Matrix C 1 and C2, wherein which item relay node passes through
Subchannel is communicated with sink node, can be calculated by C1 and C2 convolution (9), (10)；
Step 2.3: utilizing the population G in the step 2.1 and G in the step 2.2gCalculating finds out G=G ∪ { Gg, and set
Set counter g=g+1；
Step 2.4: if judgement g≤R, wherein R is quantity individual in population, return step 2.2；Otherwise jump procedure 2.5；
Step 2.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each individual in population G
Adaptive value；
Step 2.6: counter being initialized, g=1 is made, while interim population G'={ } is set；
Step 2.7: two individual a and b being selected from population G using wheel disc bet method, and individual a' is generated based on crossover algorithm
And b'；
Step 2.7 specifically includes:
Step 2.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability Pc, then from
Step 2.7.2 is continued to execute, otherwise jump procedure 2.7.5；
Gene is R in step 2.7.2: individual a Matrix C 1αGene position and the Matrix C 1 of individual b in correspond to the gene of gene position
It exchanges；
Gene is R in step 2.7.3: individual b Matrix C 1αGene position and the Matrix C 1 of individual a in correspond to the gene of gene position
It exchanges；
Optional a line is exchanged with the corresponding row in the Matrix C 2 of individual b in step 2.7.4: individual a Matrix C 2；
Step 2.7.5: two new individuals a ' and b ' are obtained；
Step 2.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 2.9: if judgement g≤R, wherein R is quantity individual in population, return step 2.7；Otherwise jump procedure
2.10；
Step 2.10: to individual g' each in interim population G', g' being updated using mutation algorithm；
Step 2.10 specifically includes:
Step 2.10.1: for individual g', the random number in (0,1) range is generated, if it is less than mutation probability Pm, then from step
2.10.2 it continues to execute, otherwise jump procedure 2.10.3；
Step 2.10.2: a gene position is randomly choosed in the Matrix C 1 of individual g', if protogene position "-", random variation
For " Rα" or " Sβ"；If protogene position is not "-", random variation is " Rα" or " Sβ" or "-", wherein α, β not with protogene phase
Together；
Step 2.10.3: randomly choosing a line in the Matrix C 2 of individual g', the gene location 0 for being 1 by the row gene, at other
Gene position randomly chooses a position and sets 1；
Step 2.11: according to the power assignment value p of individual each in interim population G' and Matrix C 1 and C2, calculating every in population G'
The adaptive value of individual；
Step 2.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and the Gao Shi in interim population G'
The low adaptive value individual of individual replacement population G should be worth, and the number of iterations counter t=t+1 is set；
Step 2.13: if judgement t≤T, wherein T is total the number of iterations, return step 2.6, otherwise jump procedure 3.
4. a kind of resource allocation methods towards industrial internet of things data reliable transmission according to claim 2, feature
It is: the specific steps of the step 3 are as follows:
Step 3.1: counter being initialized, make the number of iterations counter t=1, population count device g=1, while kind is set
Group Q={ }；
Step 3.2: based on g-th of individual Q in power initialization of population method initial populationgThe P1 matrix and P2 square of=P1 ∪ P2
The element value of element in battle array；
Wherein P1 matrix combines the power selection side with relay node for illustrating all sensor nodes in network with P2 matrix
Case；P1 matrix indicates the distribution power of sensor node, and P2 matrix indicates the distribution power of relay node, is expressed as,
In matrix P1, pijIndicate power of communications of the sensor node i on subchannel j, whereinAnd
In matrix P2, piIndicate the power of communications of relay node i, whereinAnd
In specific initialization procedure, in order to guarantee that the element value of initialization meets constraint condition (1) and constraint condition (2),
Step 3.2 specifically includes:
Step 3.2.1: to each element p in P1ij, its initialization value is arranged first is
Step 3.2.2: element value reparation is executed to P1, it is made to meet constraint condition (1)；
The specific element value restorative procedure are as follows:
To each row element value { p in P1ij| j=1 ... M }, judgementIf so, then
Step 3.2.3: initialization is to each element in P2
Step 3.3: making Q=Q ∪ { Gg, and counter g=g+1 is set；
Step 3.4: judging if g≤R (R as quantity individual in population), return step 2.2；Otherwise jump procedure
Step2.5；
Step 3.5: according to the power assignment value p of individual each in population G and Matrix C 1 and C2, calculating each individual in population G
Adaptive value；
Step 3.6: setting counter g=1, and G'={ }；
Step 3.7: two individual a and b being selected from population G using wheel disc bet method, and individual a' is generated based on crossover algorithm
And b'；
Step 3.7 specifically includes:
Step 3.7.1: for individual a and individual b, the random number in (0,1) range is generated, if it is less than crossover probability Pc, then from
Step 3.7.2 is continued to execute, and otherwise jumps to step 3.7.3；
Gene is in step 3.7.2: individual a matrix P1Gene is in individual b matrix P1Then intersect generation new individual
The correspondence gene difference of matrix P1 in c and dWithIt is updated to respectively
Wherein β is
Wherein u=rand (0,1) is the random number between 0 to 1；
Gene is in step 3.7.3: individual a matrix P2Gene is in individual b matrix P2Then intersect and generates new individual c
With the correspondence gene difference of matrix P2 in dWithIt is updated to respectively
Step 3.8: making G'=G' ∪ { a'} ∪ { b'}, and counter g=g+2 is set；
Step 3.9: judging if g≤R (R as quantity individual in population), return step step 3.7；Otherwise jump procedure walks
Rapid 3.10；
Step 3.10: to individual g' each in interim population G', g' being updated using mutation algorithm；
Step 3.10 specifically includes:
Step 3.10.1: for individual a, the random number in (0,1) range is generated, if it is less than mutation probability pu, then from step 2
It continues to execute, otherwise terminates；
Step 3.10.2: it is if corresponding to gene in the matrix P1 of individual aThen the gene updates as follows
Whereinδ (t, x) is exponential function, is expressed as
Wherein random number of the u between (0,1), t current the number of iterations, T are total the number of iterations；
Step 3.10.3: it is if corresponding to gene in the matrix P2 of individual aThen the gene updates as follows
Step 3.11: according to the power assignment value p of individual each in interim population G' and Matrix C 1 and C2, calculating interim population G'
In each individual adaptive value；
Step 3.12: being respectively compared the adaptive value of interim population G' individual corresponding with population G, and the Gao Shi in interim population G'
The low adaptive value individual of individual replacement population G should be worth, and the number of iterations counter t=t+1 is set；
Step 3.13: if judgement t≤T, wherein T is total the number of iterations, otherwise return step 2.6 terminates.
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