CN107197476A - A kind of method that WiFi and ZigBee node avoid conflict in wireless body area network - Google Patents
A kind of method that WiFi and ZigBee node avoid conflict in wireless body area network Download PDFInfo
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Abstract
The invention discloses the method that WiFi in a kind of wireless body area network and ZigBee node avoid conflict, including ZigBee, WiFi state of transmission channel are modeled by stealthy Markov model；According to the data vestige being obtained ahead of time in network, all parameters, training and the transmission mode for predicting ZigBee and WiFi in the stealthy Markov model of study obtain the channel confliction probability distribution of current state and next state；After ZigBee and WiFi channel confliction is predicted, by the conflict of the virtual WiFi data frames of AP, suppress the WiFi transmission of surrounding, it is ensured that the high ZigBee node of priority is communicated.Make the Monitoring Data of patient more real-time, so hospital and patient can be offered convenience, the present invention can provide efficient, valuable help.
Description
Technical field
Kept away the present invention relates to WiFi in technical field of communication network, more particularly to a kind of wireless body area network and ZigBee node
Exempt from the method for conflict.
Background technology
Wireless body area network (WBAN) is the channel radio for object with bio-sensing node or implantation node of human peripheral etc.
Believe dedicated system.It transmits physiological data, such as electrocardiogram, electroencephalogram, blood pressure etc..The physiological signal recorded is via wireless technology
Send to telegon, be then communicated to medical monitoring center.Due to needing the biography of low rate and long battery life in WBAN
Node is felt, so most widely used wireless technology is exactly ZigBee.But ZigBee-network is faced with the serious dry of WiFi network
Problem is disturbed, because they are all in unwarranted ISM 2.4GHz band operations, it is global unique disclosed frequency range,
Many systems are all in this band operation, and this results in the problem of interfering between different system and increasingly aggravated, and this dry
It is inevitable to disturb, wherein the most serious to ZigBee interference with WiFi.
In unwarranted ISM 2.4GHz frequency ranges, ZigBee node is disturbed by WiFi equipment, because
WiFi transmission power ratio ZigBee is strong 5-20 decibels, and ZigBee signals inadequate can be detected by WiFi equipment, and WiFi signal
Then easily detected by ZigBee equipment, this may result in ZigBee node and actively keeps out of the way conflict.In the doctor of wireless body area network
In field, ZigBee is very crucial, and it is used to transmit substantial amounts of human body physiological data, and we can not be born in WiFi nets
Its handling capacity is reduced under the high interference of network, causes the no longer effective property of sensitive data.It is, therefore, desirable to provide a kind of cost low energy is low
Avoid jamming program apply to medical treatment production in.
In the prior art, solution has focused largely on physical layer, for example, change the type, transmission power, extension of modulation
Frequency spectrum, switching channel etc..The big multipair ZigBee of these methods is infeasible, because the waste of node energy consumption can be caused.Currently without
The solution of ZigBee and WiFi conflicts, which can be largely classified into, in line body area network coexists and avoids, wherein, it is to avoid the side of interference
Method is to scan channel strength by physical layer, finds the idle working channel of comparison, so that WiFi interference is reduced, but with
WiFi equipment and focus are continuously increased, and idle channel is fewer and fewer, and frequently switching channel will also result in ZigBee
Energy resource consumption, it is less feasible；In general, the technology proposed for coexistence problems, substantially there is the following aspects,
The type of such as modulation, transmission power, spread-spectrum is loaded, the size of bag, and geographical distribution for the node that interacts etc. solves to do
Method.Although these research methods reduce interference of the WiFi to ZigBee communication to some extent, there are some researchs not cut
It is actual, such as using the modification of special hardware, control all WiFi access points and system.These requirements are greatly
The consumption of cost and energy is improved, is good at realizing in actual applications.
The content of the invention
In view of the above-mentioned problems, being avoided it is an object of the invention to provide WiFi in a kind of wireless body area network and ZigBee node
The method of conflict, transmits by virtual WiFi data frame conflict suppression WiFi and then meets the higher ZigBee node of priority and lead to
Letter.
The problem of in order to solve in the presence of background technology, the technical scheme is that：
A kind of method that WiFi and ZigBee node avoid conflict in wireless body area network, comprises the following steps：
1) ZigBee, WiFi state of transmission channel are modeled by stealthy Markov model；
2) according to the data vestige being obtained ahead of time in network, all parameters in the stealthy Markov model of study, training and
ZigBee and WiFi transmission mode is predicted, the channel confliction probability distribution of current state and next state is obtained；
3) after ZigBee and WiFi channel confliction is predicted, by the conflict of the virtual WiFi data frames of AP, week is suppressed
The WiFi transmission enclosed, it is ensured that the high ZigBee node of priority is communicated.
The step 1) be specially：According to stealthy Markov model, time of pair state space that can be arranged in discretization
It is modeled on parameter space.
The step 2) specifically include：
2.1st, according to the data vestige being obtained ahead of time in network, the stealthy Markov model of forward-backward algorithm Algorithm Learning is utilized
In all parameters；
2.2nd, by recursive fashion, the parameter system updated in stealthy Markov model restrains it, obtained parameter
System and current sample are the parameters most matched；
2.3rd, using the parameter obtained by step 2.2, the test data of input is predicted using Viterbi algorithm possible
Channel transmission state sequence；
2.4th, according to the stealthy Markov model of training, based on Maximum-likelihood estimation current state and next state
Channel confliction probability distribution.
The step 3) in, AP positions are obtained by using multi-objective genetic algorithm.
Obtaining AP positions using multi-objective genetic algorithm includes：
The first step, the chromosome that positional information is encoded to genetic algorithm, one in space is represented per item chromosome
AP positions, in solution space it is random uniformly choose it is multiple by the position encoded chromosome formed as initial population；
Second step, setting object function：
Function one：The signal intensity of all WiFi equipments in AP positions is collected, its maximum is taken；Function two：Set one
Individual minimum signal strength threshold value, calculates the quantity that all signal intensities in AP positions are more than the WiFi equipment of the threshold value；
3rd step, each individual to initial population, are estimated by object function, are chosen several Paretos and are dominant
Solution be used as population of future generation；
4th step, acquisition crossover operator and mutation operator, generate filial generation；
5th step, using newly-generated filial generation as follow-on former generation population, if met by iteration result several times
It is stable, or more than default iterations, then iteration ends are into the 6th step, otherwise into second step.
The result that 6th step, selection are produced is used as optimal solution.
Compared with prior art, beneficial effects of the present invention are：
The invention provides the method that WiFi in a kind of wireless body area network and ZigBee node avoid conflict, by virtual
The conflict of WiFi data frame suppresses WiFi and transmits and then meet the higher ZigBee node communication of priority, makes user extra
Hardware device, it is not required that change WiFi, ZigBee equipment relevant parameter, only avoid or reduce by changing AP
Interference from WiFi in ZigBee transmission, and then the energy consumption of saving chip, in addition, make the Monitoring Data of patient more real-time,
So hospital and patient can be offered convenience, the present invention can provide efficient, valuable help.
Brief description of the drawings
Fig. 1 is the method flow diagram that WiFi and ZigBee node avoid conflict in this hair wireless body area network；
Fig. 2 is the situation comparison diagram that the present invention predicts the outcome with former data；
Fig. 3 is the figure that predicts the outcome of ZigBee-WiFi conflicts of the present invention.
Embodiment
The present invention is described in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the invention provides the method that WiFi in a kind of wireless body area network and ZigBee node avoid conflict,
Comprise the following steps：
1) ZigBee, WiFi state of transmission channel are modeled by stealthy Markov model；
Specially：According to stealthy Markov model, pair state space that can be arranged is in the time parameter space of discretization
It is modeled.It is many different types of channel conflictions first by the cutting of channel confliction sequence when carrying out channel confliction analysis
(may have overlapping), to each conflict or non conflicting signal carry out feature extraction, so obtain an one-dimensional feature to
Measure sequence o=o1o2o3…oT.It is the random vector for meeting certain probability distribution to think each characteristic vector, it is this it is random to
Amount sequence is changed over time again, therefore can be regarded as a kind of random process.Due to continuous (the i.e. characteristic vector of state space
Discrete), directly it is modeled with this continuous state space sufficiently complex therefore empty based on the discrete state that certain can be arranged
Between collision signal is modeled.There is certain short-term stationarity characteristic in view of collision signal, i.e., in a shorter time
The interior state that can consider conflict is constant, and the whole process of the state of one section of channel is then made up of different conditions,
Therefore a state space that can be arranged is extracted to be modeled in the time parameter space of discretization.
The node of change channel status is divided into three kinds, respectively ZigBee classes, WiFi classes and ZigBee-WiFi by the present invention
Class, wherein ZigBee classes node send ZigBee signals and ZigBee itself collision signals, and WiFi classes node sends WiFi signal
With WiFi itself collision signals, ZigBee-WiFi classes are then that the two sends collision signal in the transmission.Likewise, we are sky
Signal regards that inhomogeneity node is sending spacing wave as, and is classified as three classes, and a class is the empty letter that ZigBee classes node is sent
Number, a class is the spacing wave that WiFi classes node is sent, and last class is the spacing wave that ZigBee-WiFi classes node is sent.So
We can just divide state according to the back to back signal type of spacing wave, and assign these states as hidden state.For example：
Followed by ZigBee signals, we are considered as the situation that this is state one to spacing wave.
2) according to the data vestige being obtained ahead of time in network, all parameters in the stealthy Markov model of study, training and
ZigBee and WiFi transmission mode is predicted, the channel confliction probability distribution of current state and next state is obtained；
Specifically include：
2.1st, according to the data vestige being obtained ahead of time in network, the stealthy Markov model of forward-backward algorithm Algorithm Learning is utilized
In all parameters；
2.2nd, by recursive fashion, the parameter system updated in stealthy Markov model restrains it, obtained parameter
System and current sample are the parameters most matched；
2.3rd, using the parameter obtained by step 2.2, the test data of input is predicted using Viterbi algorithm possible
Channel transmission state sequence；
2.4th, according to the stealthy Markov model of training, based on Maximum-likelihood estimation current state and next state
Channel confliction probability distribution.
3) after ZigBee and WiFi channel confliction is predicted, by AP (wireless access points,
WirelessAccessPoint) the conflict of virtual WiFi data frame, suppresses the WiFi transmission of surrounding, it is ensured that priority is high
ZigBee node is communicated.
In the present invention, traditional AP laying methods are optimized using multi-objective genetic algorithm.Traditional AP is set
Method selects position according to signal intensity, and the distance of stronger explanation AP to the WiFi equipment of signal intensity is nearer, when AP is apart from certain
When individual WiFi equipment is near enough, it relative will become remote apart from other WiFi equipments, so as to cause the signal of other equipment strong
Degree is low, and this method selects that some WiFi equipments can be ignored unavoidably in position, and often distance signal intensity is more for the result drawn
Big equipment compared with covering overlapping area to greatest extent closer to there is no small error.
Therefore, the present invention chooses suitable AP positions using multi-objective genetic algorithm in two-dimensional space, is closed by setting
Suitable object function evaluates the quality of the position, for the disaggregation that is dominant tentatively selected, then is increased by modes such as intersection, variations
Plus the diversity of solution, then carry out successive ignition, it is excellent in take excellent, finally give the optimal solution of AP positions in two-dimensional space.
Obtaining AP positions using multi-objective genetic algorithm includes：
The first step, the chromosome that positional information is encoded to genetic algorithm, one in space is represented per item chromosome
AP positions, in solution space it is random uniformly choose it is multiple by the position encoded chromosome formed as initial population, dye physique
Formula is the coordinate in two-dimensional space；
Second step, setting object function：
Function one：The signal intensity of all WiFi equipments in AP positions is collected, its maximum is taken；Function two：Set one
Individual minimum signal strength threshold value, calculates the quantity that all signal intensities in AP positions are more than the WiFi equipment of the threshold value；
3rd step, each individual to initial population, are estimated by object function, are chosen several Paretos and are dominant
Solution be used as population of future generation；The solution that exemplary 100 Paretos of selection are dominant；
4th step, acquisition crossover operator and mutation operator, generate filial generation；
Crossover operator：
(1) probability intersected is set as 90%；
(2) to every a pair of the solutions selected in previous step, whether calculating intersects；
(3) if intersecting：The ordinate that this pair are solved is exchanged, and produces a pair of new solutions, and this pair of solutions are incorporated to
Progeny population；
Mutation operator：
(1) probability morphed is set as 10%；
(2) scope of variation is set as solution space；
(3) each solved to each selected in previous step, whether calculating morphs；
(4) if morphing：It is random in default scope to obtain a value, as the result of coordinate variation, and will
Solution after variation is incorporated to progeny population.
5th step, using newly-generated filial generation as follow-on former generation population, if met by iteration result several times
It is stable, or more than default iterations, then iteration ends are into the 6th step, otherwise into second step.
The result that 6th step, selection are produced is used as optimal solution.
The present invention proposes to train and predict ZigBee and WiFi transmission mode based on HMM.Understand
After the transmission mode of the two, by the virtual WiFi data frame conflicts of AP so as to suppress before each ZigBee-WiFi conflicts arrive
WiFi is transmitted, it is ensured that the higher ZigBee node of priority is communicated, while not changing the related of WiFi to ZigBee equipment
Parameter.And tested in test platform, it was demonstrated that its validity, the present invention can predict 90% or so ZigBee-
WiFi conflicts and suppressed, it is to avoid or reduce interference of the WiFi signal for ZigBee.
Experimental verification：
It is that first training data draws specific model parameter, then using mould for this method of hidden Markov model
Shape parameter predicts upcoming conflict.In order to prove the validity of proposed method, experimental evaluation has been carried out.Test platform
Set up on the matlab of notebook computer, data set uses WiFi and ZigBee True Data, as shown in Fig. 2 being we
The situation contrast predicted the outcome with former data of method.
In figure, the state change of change and the prediction of true hidden state in observer state chain compared for, it can be seen that when
When observer state length is 100, the accuracy for being predicted result by hidden Markov model accounts for whole state
50% or so, but because each state has the possibility for sending spacing wave, therefore judging ZigBee-WiFi conflict situations and pressing down
There are three kinds of situations when processed：
1. when channel status is the free time, it is mistaken for ZigBee-WiFi and conflicts and then suppress；
2. being busy in channel status, it is mistaken for ZigBee-WiFi and conflicts and then suppress；
3. when channel status is remaining two kinds conflicts, it is mistaken for ZigBee-WiFi and conflicts and then suppress.
Wherein, send in the first scenario and suppress the normal transmission that signal has no effect on other signals, and in remaining feelings
The normal transmission of wifi signals may be influenced whether under the erroneous judgement of condition, causes the time delay of wifi signals to increase.
As shown in figure 3, being predicting the outcome that ZigBee-WiFi conflicts, it can be seen that in the state chain of prediction, state three
Prediction rate have more than 90%, i.e., the situation that (ZigBee-WiFi) spacing wave and ZigBee-WiFi are clashed, therefore can be with
90% or so WiFi transmission is curbed, and then ensures the priority of ZigBee transmission, and then ensures required link throughput
With data packet delivery fraction.Test result indicates that the validity of the invention.
It is obvious to a person skilled in the art that will appreciate that above-mentioned specific embodiment is the preferred side of the present invention
Case, therefore improvement, the variation that those skilled in the art may make to some of present invention part, embodiment is still this
The principle of invention, realization is still the purpose of the present invention, belongs to the scope that the present invention is protected.
Claims (5)
1. a kind of method that WiFi and ZigBee node avoid conflict in wireless body area network, it is characterised in that comprise the following steps：
1) ZigBee, WiFi state of transmission channel are modeled by stealthy Markov model；
2) according to the data vestige being obtained ahead of time in network, all parameters, training and prediction in the stealthy Markov model of study
ZigBee and WiFi transmission mode, obtains the channel confliction probability distribution of current state and next state；
3) after ZigBee and WiFi channel confliction is predicted, by the conflict of the virtual WiFi data frames of AP, surrounding is suppressed
WiFi is transmitted, it is ensured that the high ZigBee node of priority is communicated.
2. the method that WiFi and ZigBee node avoid conflict in wireless body area network according to claim 1, its feature exists
In the step 1) be specially：According to stealthy Markov model, pair state space that can be arranged is empty in the time parameter of discretization
Between on be modeled.
3. the method that WiFi and ZigBee node avoid conflict in wireless body area network according to claim 1, its feature exists
In the step 2) specifically include：
2.1st, according to the data vestige being obtained ahead of time in network, institute in the stealthy Markov model of forward-backward algorithm Algorithm Learning is utilized
There is parameter；
2.2nd, by recursive fashion, the parameter system updated in stealthy Markov model restrains it, obtained parameter system
It is the parameter that most matches with current sample；
2.3rd, using the parameter obtained by step 2.2, possible channel is predicted to the test data of input using Viterbi algorithm
Transmission state sequence；
2.4th, according to the stealthy Markov model of training, the channel based on Maximum-likelihood estimation current state and next state
Collision probability is distributed.
4. the method that WiFi and ZigBee node avoid conflict in wireless body area network according to claim 1, its feature exists
In the step 3) in, AP positions are obtained by using multi-objective genetic algorithm.
5. the method that WiFi and ZigBee node avoid conflict in wireless body area network according to claim 4, its feature exists
In obtaining AP positions using multi-objective genetic algorithm includes：
The first step, the chromosome that positional information is encoded to genetic algorithm, one AP in space are represented per item chromosome
Put, in solution space it is random uniformly choose it is multiple by the position encoded chromosome formed as initial population；
Second step, setting object function：
Function one：The signal intensity of all WiFi equipments in AP positions is collected, its maximum is taken；Function two：Set one most
Small-signal intensity threshold, calculates the quantity that all signal intensities in AP positions are more than the WiFi equipment of the threshold value；
3rd step, each individual to initial population, are estimated by object function, choose the solution that several Paretos are dominant
It is used as population of future generation；
4th step, acquisition crossover operator and mutation operator, generate filial generation；
5th step, using newly-generated filial generation as follow-on former generation population, if meeting stable by iteration result several times,
Or more than default iterations, then iteration ends are into the 6th step, otherwise into second step；
The result that 6th step, selection are produced is used as optimal solution.
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| CN110278590A (en) * | 2019-06-21 | 2019-09-24 | 合肥工业大学智能制造技术研究院 | A kind of intelligent luminaire communications channel selecting method |
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| CN110278590A (en) * | 2019-06-21 | 2019-09-24 | 合肥工业大学智能制造技术研究院 | A kind of intelligent luminaire communications channel selecting method |
| CN110278590B (en) * | 2019-06-21 | 2021-05-25 | 合肥工业大学智能制造技术研究院 | Intelligent lighting lamp communication transmission channel selection method |
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