CN108901058A - Internet of things node access channel optimization selection method - Google Patents
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- 238000010187 selection method Methods 0.000 title claims abstract description 13
- 230000002452 interceptive effect Effects 0.000 claims abstract description 8
- 238000003780 insertion Methods 0.000 claims description 18
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- 238000005516 engineering process Methods 0.000 description 2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W48/00—Access restriction; Network selection; Access point selection
- H04W48/18—Selecting a network or a communication service
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W36/00—Hand-off or reselection arrangements
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- H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W36/00—Hand-off or reselection arrangements
- H04W36/14—Reselecting a network or an air interface
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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Abstract
The invention provides an Internet of things node access channel optimization selection method, which comprises the following steps: step 1, dividing different network requirements of an internet of things terminal into background services, streaming services, interactive services and session services according to the requirements of different internet applications on network performance in a real network scene; and 2, performing network access based on a Markov model on the basis of distinguishing the service types. The invention can meet the network requirements of different services of the terminal of the Internet of things, and can also improve the utilization rate of network resources.
Description
Technical field
The invention belongs to internet of things field, and in particular to a kind of Internet of things node access CHANNEL OPTIMIZATION selection method.
Background technique
Not with the high speed development of mobile communication, the evolution increasingly of wireless access technology and terminal service quality requirement
Disconnected soaring, a variety of wireless technologys simultaneously deposit the diversified network service of offer as main trend.Multiple network is complementary to one another, mutually
Promote fusion, meets internet-of-things terminal personalization, diversified QoS demand jointly.When terminal device is located at such heterogeneous network
The selection access of network is just faced with when in network.Access which kind of network could while meeting the network demand of terminal itself guarantor
The resource of card network is utilized effectively.
Present existing network insertion selection scheme also has very much.Network selection is carried out by network received signal intensity
, this scheme only simply selects access decision-making foundation using network received signal intensity as network.There are also be based on game
The network selecting method of opinion, network insertion selection is modeled as a betting model by it, and wherein the participant of game is heterogeneous network
Terminal user group under network covering, and total group is respectively adopted and evolves with nitrification enhancement and reaches the dynamic equalization of game, this
Sample, which is avoided that, leads to network insertion selection performance decline because of network congestion.There are also multiple attributes to influence network insertion selection
As judgement index, network insertion selection is carried out using the method based on multiple attribute decision making (MADM).In recent years occur again based on fuzzy mind
Method for network access through network.It is set respectively in connection with the input that particle swarm algorithm and efficiency utility function are fuzzy neural network
Initial value is adaptively made decisions by trained neural network to reach the load balancing of heterogeneous network.
The technological deficiency of existing network access scheme includes：
1) it is not well positioned to meet the network demand of different service types terminal；
2) it cannot be guaranteed that the long-term resource utilization for guaranteeing network.
Summary of the invention
The object of the present invention is to provide a kind of Internet of things node access CHANNEL OPTIMIZATION selection method, solve full IP isomery without
Under the line network architecture, network select permeability in the network integration can guarantee that for a long time Internet resources are utilized effectively, and energy
Meet the network demand of different terminals.
The present invention provides a kind of Internet of things node to access CHANNEL OPTIMIZATION selection method, including：
Step 1, the demand according to Internet applications different in real network scenarios to network performance, by internet-of-things terminal
Heterogeneous networks demand divides into background class, stream class, interactive class and session service；
Step 2, on the basis of differentiated service type, network insertion is carried out based on Markov model.
Further, step 1 includes：
The weight of network attribute parameter is determined based on analytic hierarchy process (AHP), it is specific to wrap to distinguish the network demand of different business
It includes：
According to analytic hierarchy process (AHP) target and the bit error rate of selection, network load, time delay, delay variation, available bandwidth attribute,
Construct hierarchy Model；
It is constructed respectively for the heterogeneous networks performance requirement of a variety of different business according to 9 grades of scaling laws of analytic hierarchy process (AHP)
Two discrimination matrix compared two-by-two, the terminal of different service types are needed to select different discrimination matrix according to its business, be obtained
To different network attribute weighted values；
Based on obtained different network attribute weighted values, the weight factor of different service types is calculated.
Further, step 2 specifically includes：
1) terminal monitoring module monitors change to terminal traffic, and mobile management module, mobile management module is notified to receive
The request of detection network environment information is initiated after changing information to terminal traffic to network access module；
2) information being collected into is back to mobility by wireless network interface collection network relevant information by network access module
Management module；
3) mobility module starts network selection functional, is selected using the Access Algorithm based on Markov model optimal
Network is accessed, mobile management module sends network insertion request to Network Management System；
4) Network Management System determines whether respond request according to current network state, if network request is received, terminal
Equipment accesses new network.
Further, the Access Algorithm based on Markov model selectes optimal access network and includes：
(1) determination of single revenue function：
Different terminals can obtain single income after carrying out network selection switching, and expression formula is as follows：
In formula, r (s, a, s ') indicates terminal under conditions of from network state s, is transferred to next net after taking movement a
The income at once obtained after network state；P (s, a, s ') is indicated when time probability of transfer；R can just be born, if r is regular representation
It can obtain preferable performance after network insertion, it is on the contrary then indicate that the network of access is unable to meet demand；
Multiple state parameters of wireless network are added, revenue function r (s, a, s ') is constructed according to the weighting of its weight, it can
?：
R (s, a, s ')=ωDrD(s,a,s′)+ωPrP(s,a,s′)+ωBrB(s,a,s′)+ωErE(s,a,s′)+ωLrL
(s,a,s′)；
In formula,Y ∈ Y={ D, P, B, E, L } represents each network parameter, and y ∈ Y={ D, P, B, E, L } is indicated
The income at once of network parameter y, ωiIndicate weight corresponding to revenue function；
(2) iteration of multiple revenue function：
Enabling objective function of decision-making is O, then O is expressed as：
In formula, r=r (s, a, s ') represents income of the terminal at the decision moment of different service types；γ indicates reaction
The discount factor for selecting short-term yield or long-term gain indicates as γ=1 Tactic selection to be long-term gain, decision objective
It is converted into：
Above formula after decision objective conversion represents the average yield obtained at each decision moment, and π is defined as terminal and is taken
The strategy of switching action then has π:S → A, i.e. π (s) expression can be taken at network state s determines that movement and probability are 1；
Enable Vπ(s a) is state action function, indicates the movement a length obtained for taking tactful π to provide at state s
Phase expected revenus, expected income function Vπ(s a) is expressed as:
In formula, Eπ[] indicates the expectation on tactful π and state transition probability P distribution；
(3) determination of network insertion strategy：
For arbitrary network state s and instantly the movement a, expected income function V under policy takenπ(s a) is indicated
For:
In formula, R (s, the income at once after a) indicating network state transfer；
Optimal network selection strategy is found at each decision moment, so that the terminal of different service types is in current network shape
Under state s and movement a, network is carried out according to π strategy and selects to obtain maximum expected revenus, i.e., for any s ∈ S, a ∈ A and plan
Slightly π meets V*(s,a)≥Vπ(s,a)。
Compared with prior art the beneficial effects of the invention are as follows：
It can satisfy the network demand of internet-of-things terminal different business, while network resource utilization can also be promoted.
Detailed description of the invention
Fig. 1 is the hierarchy Model that the present invention constructs；
Fig. 2 is the method for network access flow chart the present invention is based on Markov model.
Specific embodiment
The present invention is described in detail for each embodiment shown in reference to the accompanying drawing, but it should be stated that, these
Embodiment is not limitation of the present invention, those of ordinary skill in the art according to these embodiments made by function, method,
Or equivalent transformation or substitution in structure, all belong to the scope of protection of the present invention within.
A kind of Internet of things node access CHANNEL OPTIMIZATION selection method is present embodiments provided, including：
Step 1, the demand according to Internet applications different in real network scenarios to network performance, by internet-of-things terminal
Heterogeneous networks demand divides into background class, stream class, interactive class and session service；
Step 2, on the basis of differentiated service type, network insertion is carried out based on Markov model.
CHANNEL OPTIMIZATION selection method is accessed by the Internet of things node, can satisfy the network of internet-of-things terminal different business
Demand, while network resource utilization can also be promoted.
Invention is further described in detail below.
1, the heterogeneous networks demand of internet-of-things terminal is distinguished.
4 kinds of basic service types are obtained according to practical application situation analysis first：
It, can be according to their demand differences to network performance point according to the different Internet applications in real network scenarios
For several classes.It under normal conditions, can will be four seed types according to its delineation of activities：Background class traffic, stream class service, interactive class industry
Business, session service.Their characteristic feature is analyzed as follows.
1) background class traffic：It includes Email, MS, MMS etc. that it, which represents application,.
Background class traffic generally using the transmission mode done one's best, has network delay and delay variation higher
Tolerance.In order to guarantee the integrality and consistency of the application contents such as Email, such business need network has lower
The bit error rate.
2) stream class service：It includes Streaming Media, electronic certificate, FTP downloading etc. that it, which represents application,.
The data transmission of stream class service has one-way, does not need too many interactive operation.There is no interactive operation meaning
The time limit be not especially important, therefore such business is of less demanding to network delay.In order to guarantee the usage experience of user, this
Class business cannot stop, it is necessary to assure stability and duration, therefore delay variation and packet loss become the pass for influencing this business
Bond parameter.In addition to this, also there is bigger demand to bandwidth for streaming media service, so bit rate is for such business
Also relatively important.
3) interaction service：It includes mobile search, e-commerce, internet browsing etc. that it, which represents application,.
Many applications of interaction service are all client and server mode (C/S models), and user is being operated
When can experience network-feedback.Therefore propagation delay time just becomes the critical network parameter of such business.Delay variation
It will not influence user experience, therefore this kind of business is very low to time delay jitter-sensitive degree.Business as this interactive class is answered
With also compare concern is some the accuracy that network must assure that data, so packet loss and the bit error rate are to such business
It is more important.
4) session service：It includes instant messaging, video conference etc. that it, which represents application,.
Session service is mainly used for interpersonal communication, and the sense organ of the mankind is very clever for such business
It is quick, therefore the business belongs to real-time service.If session service propagation delay time is too big, this will affect the use body of user
Test, and if video class business delay variation it is excessive will lead to picture deformation.Therefore propagation delay time and delay variation are that it connects
The key factor to be considered when entering network.In terms of packet loss angle, because of of short duration fuzzy pictures or speech pause common people
It is not felt by, so such business is not high to packet loss requirement.
It is very heavy to the service quality for guaranteeing different application to can be seen that network attribute parameter by above-mentioned qualitative analysis
It wants.Therefore reasonable access network is needed just to can guarantee outstanding user experience.
Then 4 kinds of different business network attribute weights can be obtained respectively by analytic hierarchy process (AHP).
By above-mentioned analysis, in heterogeneous network system, demand of the different kinds of business to network performance has biggish
Difference.In order to distinguish the network demand of different business, the present invention determines network attribute parameter using Hierarchy Analysis Method
Weight, detailed step can decompose as follows：
1) hierarchy Model is established：According to technical method target and five attributes chosen, (bit error rate, network are negative
Load, time delay, delay variation, available bandwidth), the hierarchy Model of building is as shown in Figure 1.
2) discrimination matrix is constructed：For the heterogeneous networks performance requirement of a variety of different business, according to 9 grades of analytic hierarchy process (AHP)
Scaling law, can construct two discrimination matrix compared two-by-two respectively, and the terminals of different service types can be according to oneself industry
Business needs to select different discrimination matrix, to obtain different network attribute weighted values.The form of discrimination matrix such as formula (1) institute
Show.
Enable Z=(zij)5*5, wherein zijDifferent service types are represented to the sensitivity of two kinds of parameters, are illustrated simultaneously
Its influence degree to network performance.Available multiple and different discrimination matrix.
3) status attribute weight is calculated by judgment matrix：Enable ωiShow respectively the power of each attribute of wireless network
Weight values, according to the weight factor of the expression available different service types of formula 2.
2, based on the method for network access of Markov model.The basic procedure of access is as shown in Figure 2.
1) terminal traffic changes, and terminal monitoring module gives this change notification to mobile management module.Mobile management
Module just initiates the request of detection network environment information after receiving these information to network access module.
2) information being collected into then is returned to shifting by wireless network interface collection network relevant information by network access module
Mobility management module.
3) mobility module starts network selection functional.Optimal access is selected using based on the improved network selection algorithm of MDP
Network.Mobile management module sends network insertion request to Network Management System.
4) Network Management System determines whether respond request according to current network state.Once network request is received, eventually
End equipment just has access to new network.
Network insertion algorithm based on Markov model.
1) determination of single revenue function.
Any one t moment, network state s receiving movement a, which is transferred to next network state, can obtain single income r
(s a) indicates the internetworking of this decision moment selection switching after different terminals take switching action if the r value obtained the big
Can be more preferable, while the network service quality that terminal traffic obtains is also better.Different terminals meeting after carrying out network selection switching
Single income is obtained, expression formula is such as shown in (3).
R (s, a, s ') indicate terminal under conditions of from network state s, take movement a after be transferred to it is next network-like
The income at once obtained after state, P (s, a, s ') are indicated when time probability of transfer.R can just be born, because different conditions transfer may
Generate different incomes.If r is can obtain preferable performance after regular representation network insertion, network that is on the contrary then indicating access is not
It is able to satisfy demand.
Now be added wireless network multiple state parameters, then according to the weighting of its weight construct revenue function r (s, a,
S '), it can obtain:
R (s, a, s ')=ωDrD(s,a,s′)+ωPrP(s,a,s′)+ωBrB(s,a,s′)+ωErE(s,a,s′)+ωLrL
(s,a,s′) (4)
WhereinY ∈ Y={ D, P, B, E, L } represents each network parameter, and y ∈ Y={ D, P, B, E, L } indicates net
The income at once of network parameter y, ωiIndicate weight corresponding to revenue function.Each network parameter is represented in building income letter
Role degree in number finally will affect the strategy of network selection.
2) iteration of multiple revenue function
Decision-making time scale is lengthened, it can be seen that over time, it will obtain a discrete income sequence
Column.Long-term maximum value in order to obtain, it is only necessary to acquire the expectation of this return series.Therefore objective function of decision-making is enabled
For O.Then O is represented by：
Wherein r=r (s, a, s ') represents income of the terminal at the decision moment of different service types.γ then indicates to react
The discount factor for selecting short-term yield or long-term gain indicates as γ=1 Tactic selection to be long-term gain, decision objective
It is converted into：
Formula (6) represents the average yield obtained at each decision moment.If π, which is defined as terminal, takes a switching action
It is tactful then have π:S → A, i.e. π (s) expression can be taken at network state s determines that movement and probability are 1.Enable Vπ(s a) is state
Function of movement indicates the movement a long-term expected revenus obtained for taking tactful π to provide at state s, it can predict to grow
The case where phase income.It can in the long run reflect the quality of current selection movement, expected income function Vπ(s can a) be indicated
For:
Wherein Eπ[] indicates the expectation on tactful π and state transition probability P distribution.
3) determination of network insertion strategy.
Each network switching can all obtain a switching income, and an income letter can be obtained by the accumulation of a period of time
Number Sequence, can be in the hope of the average yield in a period of time finally by mathematic expectaion.Average yield number represent network
The quality of switching.
For arbitrary network state s and instantly the movement a, expected income function V under policy takenπ(s, a) can table
It is shown as:
Wherein R (s, the income at once after a) can be understood as network state transfer.
Optimal network selection strategy is found at each decision moment, so that the terminal of different service types is in current network shape
Under state s and movement a, network is carried out according to π strategy and selects available maximum expected revenus, i.e., for any s ∈ S, a ∈ A
Meet V with tactful π*(s,a)≥Vπ(s,a).Terminal can select a suitable network insertion in this case.
Internet of things node provided by the invention accesses CHANNEL OPTIMIZATION selection method, can satisfy internet-of-things terminal different business
Network demand, while network resource utilization can also be promoted.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.
Claims (4)
1. a kind of Internet of things node accesses CHANNEL OPTIMIZATION selection method, which is characterized in that including：
Step 1, the demand according to Internet applications different in real network scenarios to network performance, by the difference of internet-of-things terminal
Background class, stream class, interactive class and session service are divided into network demand；
Step 2, on the basis of differentiated service type, network insertion is carried out based on Markov model.
2. a kind of Internet of things node according to claim 1 accesses CHANNEL OPTIMIZATION selection method, which is characterized in that the step
Rapid 1 includes：
The weight of network attribute parameter is determined based on analytic hierarchy process (AHP), to distinguish the network demand of different business, is specifically included：
According to analytic hierarchy process (AHP) target and the bit error rate of selection, network load, time delay, delay variation, available bandwidth attribute, building
Hierarchy Model；
For the heterogeneous networks performance requirement of a variety of different business, according to 9 grades of scaling laws of analytic hierarchy process (AHP), two are constructed respectively
The discrimination matrix compared two-by-two, the terminal of different service types are needed to select different discrimination matrix according to its business, be obtained not
Same network attribute weighted value；
Based on obtained different network attribute weighted values, the weight factor of different service types is calculated.
3. a kind of according to claim 2, Internet of things node access CHANNEL OPTIMIZATION selection method, which is characterized in that described
Step 2 specifically includes：
1) terminal monitoring module monitors change to terminal traffic, and mobile management module, mobile management module is notified to receive end
End business is initiated to detect the request of network environment information to network access module after changing information；
2) information being collected into is back to mobile management by wireless network interface collection network relevant information by network access module
Module；
3) mobility module starts network selection functional, and optimal access is selected using the Access Algorithm based on Markov model
Network, mobile management module send network insertion request to Network Management System；
4) Network Management System determines whether respond request according to current network state, if network request is received, terminal device
Access new network.
4. a kind of Internet of things node according to claim 3 accesses CHANNEL OPTIMIZATION selection method, which is characterized in that the base
Selecting optimal access network in the Access Algorithm of Markov model includes：
(1) determination of single revenue function：
Different terminals can obtain single income after carrying out network selection switching, and expression formula is as follows：
In formula, r (s, a, s ') indicate terminal under conditions of from network state s, take movement a after be transferred to it is next network-like
The income at once obtained after state；P (s, a, s ') is indicated when time probability of transfer；R can just be born, if r is regular representation network
It can obtain preferable performance after access, it is on the contrary then indicate that the network of access is unable to meet demand；
Multiple state parameters of wireless network are added, revenue function r (s, a, s ') is constructed according to the weighting of its weight, can be obtained：
R (s, a, s ')=ωDrD(s,a,s′)+ωPrP(s,a,s′)+ωBrB(s,a,s′)+ωErE(s,a,s′)+ωLrL(s,a,
s′)；
In formula,Y ∈ Y={ D, P, B, E, L } represents each network parameter, and y ∈ Y={ D, P, B, E, L } indicates network
The income at once of parameter y, ωiIndicate weight corresponding to revenue function；
(2) iteration of multiple revenue function：
Enabling objective function of decision-making is O, then O is expressed as：
In formula, r=r (s, a, s ') represents income of the terminal at the decision moment of different service types；γ indicates selecting response
The discount factor of short-term yield or long-term gain indicates to be long-term gain Tactic selection as γ=1, decision objective conversion
For：
Above formula after decision objective conversion represents the average yield obtained at each decision moment, and π is defined as terminal and is taken once
The strategy of switching action then has π:S → A, i.e. π (s) expression can be taken at network state s determines that movement and probability are 1；
Enable Vπ(s a) is state action function, indicates the movement a long-term phase obtained for taking tactful π to provide at state s
Hope income, expected income function Vπ(s a) is expressed as:
In formula, Eπ[] indicates the expectation on tactful π and state transition probability P distribution；
(3) determination of network insertion strategy：
For arbitrary network state s and instantly the movement a, expected income function V under policy takenπ(s a) is expressed as:
In formula, R (s, the income at once after a) indicating network state transfer；
Optimal network selection strategy is found at each decision moment, so that the terminal of different service types is in current network state s
Under movement a, network is carried out according to π strategy and selects to obtain maximum expected revenus, i.e., for any s ∈ S, a ∈ A and strategy π
Meet V*(s,a)≥Vπ(s,a)。
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| CN110460880A (en) * | 2019-08-09 | 2019-11-15 | 东北大学 | Wireless industrial streaming media self-adapting transmission method based on population and neural network |
| CN110677861A (en) * | 2019-09-26 | 2020-01-10 | 兰州交通大学 | Network selection method facing 5G heterogeneous network |
| CN112020032A (en) * | 2020-07-22 | 2020-12-01 | 广州杰赛科技股份有限公司 | Internet of vehicles network optimized access method |
| CN113573284A (en) * | 2021-06-21 | 2021-10-29 | 吉林大学 | Random access backoff method for large-scale machine type communication based on machine learning |
| CN113630886A (en) * | 2021-08-27 | 2021-11-09 | 黑龙江八一农垦大学 | Spectrum allocation method based on particle swarm algorithm in heterogeneous Internet of things |
| CN115190496A (en) * | 2021-04-02 | 2022-10-14 | 华为技术有限公司 | Intelligent network selection method and related device |
| CN115442315A (en) * | 2022-07-25 | 2022-12-06 | 互赢科技(东莞)有限公司 | Multi-source heterogeneous network access method based on deep learning |
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| CN115190496A (en) * | 2021-04-02 | 2022-10-14 | 华为技术有限公司 | Intelligent network selection method and related device |
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| CN113630886A (en) * | 2021-08-27 | 2021-11-09 | 黑龙江八一农垦大学 | Spectrum allocation method based on particle swarm algorithm in heterogeneous Internet of things |
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