CN106714220A - WSN (Wireless Sensor Network) anomaly detection method based on MEA-BP neural network - Google Patents

WSN (Wireless Sensor Network) anomaly detection method based on MEA-BP neural network Download PDF

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CN106714220A
CN106714220A CN201710008709.XA CN201710008709A CN106714220A CN 106714220 A CN106714220 A CN 106714220A CN 201710008709 A CN201710008709 A CN 201710008709A CN 106714220 A CN106714220 A CN 106714220A
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sensor node
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node
cluster
mea
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CN106714220B (en
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李光辉
顾晓勇
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Jiangnan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses a WSN (Wireless Sensor Network) anomaly detection method based on an MEA-BP neural network. The method comprises the following steps: initializing various distributed sensor nodes, and starting to acquire data by various sensor nodes; using a K-means algorithm to perform space clustering on the various sensor nodes to obtain a plurality of cluster structures; using a mind evolutionary algorithm to perform parameter optimization on a BP neural network, optimizing the weight and threshold of the BP neural network through a convergence and dissimilation operation, obtaining optimal weight and threshold, inputting the optimal weight and threshold, and establishing an MEA-BP neural network model; and adopting a distributed algorithm to execute anomaly detection on the sensor nodes in each group of clusters independently, after anomaly detection is finished, transferring a detection result to cluster head nodes of the group of clusters for further verification by the sensor nodes. The WSN anomaly detection method based on the MEA-BP neural network provided by the invention improves the algorithm performance of the BP neural network, accelerates the learning rate of the BP neural network, effectively improves the accuracy of the abnormal data detection and reduces the false positive rate.

Description

One kind is based on MEA-BP neutral net WSN method for detecting abnormality
Technical field
The invention belongs to wireless sensor network (WSN) data reliability detection technique field, a kind of base is specifically related to In MEA-BP neutral net WSN method for detecting abnormality.
Background technology
Used as a kind of wireless self-organization network, wireless sensor network has low energy consumption, section to wireless sensor network (WSN) Put and distinguish flexibly, even without manual maintenance, the features such as being worked long hours in adverse circumstances.By by sensor network Node is dispersed in Target monitoring area, and the monitoring for carrying out collection and the particular event of environmental data is presently the most universal One of using.Due to wireless sensor node resource-constrained, and easily by the interference and destruction of extraneous factor, or external rings The influence of border accident, the data that node is collected probably produce obvious deviation with environmental characteristic under normal circumstances, This kind of data are referred to as abnormal data.Therefore, it is that wireless sensor network is different in recent years to design a kind of effective method for detecting abnormality The emphasis of often detection research.
The no theoretical foundation of selection of traditional BP neural network many parameters when study is trained so that actual nerve net Network application has a limitation, and Shortcomings part is main that pace of learning is slow, fault-tolerant ability is poor, can converge on local minimum Deng.As a example by environmental monitoring wireless sensor network node, the temperature data that wireless sensor network node is collected without By the amplitude, the frequency that are fluctuation, or the statistical nature such as average, intermediate value, variance all can be with other in section of same sampling time Data have obvious difference, discounting for the otherness between different types of data, can undoubtedly influence the performance of detection algorithm, Want more accurately to judge data exception, except the temporal correlation of data itself is also contemplated that spatial coherence.In addition, being directed to The abnormality detection problem of wireless sensor network environment data, BP neural network algorithm presence is easily trapped into locally optimal solution, instruction Practice that the time is long, the low problem of efficiency.
The content of the invention
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention to provide a kind of based on MEA-BP nerve nets Network WSN method for detecting abnormality, exists for BP neural network algorithm and is easily trapped into that locally optimal solution, training time be long, efficiency is low The problems such as, BP neural network is improved using mind evolutionary, BP neural network algorithm performance is improve, accelerate BP The learning rate of neutral net, effectively increases the accuracy rate of anomaly data detection, reduces rate of false alarm.
Technical scheme：To achieve the above object, it is of the invention based on MEA-BP neutral net WSN method for detecting abnormality, institute The method of stating is comprised the following steps：
Each distribution sensor node initializing, each sensor node are started gathered data by S1；
If sensor node number is n, each sensor node is Xtj(j=1,2 ..., n), sensor node XtjSlip Window is Wj, the sliding window size of each sensor node is m, then sensor node XtjIn its sliding window WjOn measurement Data sequence isSensor node XtjIn tpMoment collection data beThe data include h Property measurement value, then
S2 carries out space division to each sensor node and obtains some component clusters using K-means algorithms；
If q+1 sensor node constitutes a component cluster, 1 leader cluster node Xt is included per component clustercWith q distribution section Point (Xt1,Xt2,…,Xtq)；
S3 carries out parameter optimization using mind evolutionary to BP neural network, by convergent operation dissimilation to BP nerve nets The weights and threshold value of network are optimized, and obtain best initial weights and threshold value, are input into best initial weights and threshold value, set up MEA-BP nerve nets Network model；
S4 uses distributed algorithm, to sensor node (Xt in every component cluster1,Xt2,…,Xtq) independently execute exception Detection, abnormality detection finishes rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the cluster head section of the component cluster Point XtcFurther checking.
Further, the step S2 is comprised the following steps：
S21 any K sensor node object of selection first from the distribution sensor node object of Target monitoring area is made It is K cluster centre；
S22 calculates sensor node object with K respectively then for the sensor node object in addition to cluster centre Similarity between cluster centre, obtains and the sensor node immediate cluster centre of object similarity；
S23 distributes to sensor node object poly- with the sensor node immediate cluster centre of object similarity Class, will obtain K cluster after the completion of all the sensors node distribution；
S24 recalculates this K cluster centre of cluster, obtains new cluster centre；
S25 recalculates the similarity of each sensor node and new cluster centre, returns to step S22；
S26 terminates this operation when the cluster centre for recalculating is restrained.
Further, the step S3 is comprised the following steps：Produce training data；Determine BP neural network topological structure； Parameter setting is carried out by mind evolutionary；Randomly generate initial population, winning sub- population and interim sub- population；To sub- population Carry out operation similartaxis；Operation dissimilation is carried out to sub- population；Judge whether to meet termination condition, if it is satisfied, then optimal of output Body, obtains best initial weights and threshold value, otherwise re-starts convergent operation dissimilation.
Further, the step S4 is comprised the following steps：
S41 is by temporal correlation to sensor node (Xt1,Xt2,…,Xtq) abnormality detection is independently executed, using each Current time passes through sensor node XtjSliding window WjDataTo train neutral net, complete The forecast of subsequent time data, chooses training dataRear v sample data, by formula (1) count Calculate the model residual error S of MEA-BP neutral nets：
Wherein, Er(r=1,2 ..., v) be choose sampled data values, F be choose sample data averages,
S42 calculates the confidential interval at sensor node current time, and confidential interval is Wherein SprePrediction for MEA-BP neutral nets to subsequent time data Value, tα/2,v-1For the t of free degree v-1 is distributed, suitable α values are chosen in t distribution tables, obtain t values；
S43 works as subsequent time data SnewWhen in into sensor node sliding window, subsequent time data S is judgednewIt is In the no fiducial interval range for falling into current time, if so, then judging data SnewIt is normal data；Otherwise judge the data SnewIt is abnormal data；
S44 abnormality detections finish rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the component cluster Leader cluster node XtcIn；
S45 leader cluster nodes XtcVoting mechanism verificating sensor node abnormal data is detected and introduced by spatial coherence Producing cause, reason includes that event anomalies, node failure are abnormal and judge by accident.
Further, in the step S42, α=0.05 is chosen.
Further, spatial coherence is carried out in the step S45 detect and introduce voting mechanism to comprise the following steps：
S451 is by the abnormal data of sensor node and other node datas with the sensor node in same sub-clustering It is compared；If q+1 sensor node constitutes a component cluster, 1 leader cluster node Xt is included per component clustercWith q distribution Node (Xt1,Xt2,…,Xtq)；
S452 presets error amount θ, if the abnormal data of sensor node is STIf, with the sensor node same The data of other nodes in sub-clustering are Si(i=1,2 ..., q-1), if | ST-Si|<θ, then initial value is that 0 counting NN plus 1, The final NN values of statistics；
If S453Then judge that the detection node abnormal data is due to event anomalies；IfThen judging should Detection node abnormal data is due to node failure or erroneous judgement；IfThe reference node in the sub-clustering is then chosen, if reference The data of node are SCCIf, | ST-SCC|≤θ, then judge the sensor node abnormal data be due to event anomalies, if | ST-SCC |>θ, then judge that the sensor node abnormal data is due to node failure or erroneous judgement；It is in the sub-clustering wherein with reference to node The nearest node of cluster centre node Euclidean distance；
During S454 is for the step S453, when judge to obtain the sensor node abnormal data be due to node failure or During erroneous judgement, determine whether that the sensor node abnormal data is, due to node failure or erroneous judgement, to specifically include following steps： Temporal correlation detection is carried out by sensor node, when abnormal data is produced in sensor node continuous time, is then sentenced The disconnected sensor node abnormal data is due to node failure；When sensor node only has the moment for abnormal data, other when Carve and produce data to be normal data, then judge that the sensor node abnormal data is due to erroneous judgement.
Beneficial effect：The present invention compared with the prior art, this have the advantage that：
Traditional BP neural network algorithm has being easily trapped into that locally optimal solution, training time be long, efficiency is low, it is difficult to Meet detection demand, utilization mind evolutionary proposed by the present invention is improved to improve BP neural network to BP neural network Algorithm performance, when multidimensional data is processed using between the event correlation between sensor network circuit-switched data stream and different nodes Spatial coherence, so as to effectively increase the accuracy rate of anomaly data detection；
Compared with conventional BP neural network, context of methods by optimizing weights and threshold value after accelerate BP neural network Learning rate, improve abnormality detection rate, reduce False Rate.
Brief description of the drawings
Fig. 1 is t distribution table schematic diagrames.
Fig. 2 is k-means clustering algorithm flow charts.
Fig. 3 is MEA Optimized BP Neural Network weight threshold flow charts.
Fig. 4 is MEA-BP neutral net anomaly data detection flow charts.
Specific embodiment
The present invention is further described below in conjunction with the accompanying drawings.
The present invention propose it is a kind of based on MEA-BP neutral net WSN method for detecting abnormality, introduce the inventive method it Before, some definition are introduced first：
1st, sensor network model, in distributed sensor networks, if sensor node number is n, each sensor section Point is Xtj(j=1,2 ..., n).
2nd, time series data, is a series of sequence datas produced in chronological order by sensor node, its feature It is fast, a large amount of and continuous arrival of change.So before detection model is set up, first having to introduce sliding window mechanism, utilize Sliding window observes the situation of change of data in a nearest time period, and rejecting outliers are carried out inside sliding window.
3rd, sliding window model, sliding window model is for observing the time series number in nearest sampling time section According to method is the sliding window that a regular length is taken to sensing data, by the new data reduction for adding and just leaving for the treatment of Time complexity；In distributed sensor networks, if sensor node number is n, each sensor node is Xtj(j=1, 2 ..., n), sensor node XtjSliding window be Wj, the sliding window size of each sensor node is m, then sensor section Point XtjIn its sliding window WjOn measurement data sequence beSensor node XtjIn tpMoment adopts The data of collection areThe data include h property measurement value, then
4th, the ratio between verification and measurement ratio refers to the abnormal data sample number that detects of algorithm with the abnormal data total sample number of reality.
5th, rate of false alarm, refer to by algorithm be mistaken for abnormal normal data sample number and total normal data sample number it Than.
The present invention is divided space nodes based on the temporal correlation of sensor node using K-means algorithms Cluster, the similar sensor node of data is divided into same cluster, then proposes to be examined extremely based on MEA-BP neutral nets WSN Survey method, the method is broadly divided into parameter optimization, three steps of the detection of abnormal data and the judgement of data exception, its main spy Point has：(1) parameter optimization is carried out to BP neural network using mind evolutionary, is operated to BP nerve nets by convergent alienation etc. Weights, threshold value of network etc. are optimized；(2) data flow mainly collected to sensor node in the detection-phase of abnormal data In exceptional data point that may be present be identified, the step use distributed algorithm, independently executed in each sensor node, Then result is delivered to leader cluster node and further verified by each sensor node；(3) propose that each is worked as during anomaly data detection The preceding moment trains neutral net to complete the forecast of subsequent time by the history data set of sensor node sliding window, leads to The model residual error of neutral net is crossed, it is determined that the confidential interval for judging subsequent time data exception, when subsequent time data fall Enter in confidential interval, then data Ei is judged to normally, conversely, the data are further by spatial coherence in leader cluster node Ground checking, specific the method is comprised the following steps：
By the initialization of each branch's sensor node, each sensor node starts gathered data, if sensor node number is N, each sensor node is Xtj(j=1,2 ..., n), sensor node XtjSliding window be Wj, the cunning of each sensor node Dynamic window size is m, then sensor node XtjIn its sliding window WjOn measurement data sequence beSensor node XtjIn tpMoment collection data beThe data include h property measurement value, Then
Space sub-clustering is carried out to each sensor node using K-means algorithms and obtains some groups of clusters, by the similar biography of data Sensor node is divided into same cluster, improves cluster interior nodes space similarity, and this is completed before being operated in node failure detection, complete Cheng Houzai carries out temporal correlation detection and spatial coherence detection to node gathered data, is utilized when spatial coherence is detected The result that leading space is divided, sensor node first carries out temporal correlation detection, works as the time to the data that current time gathers When correlation detection shows problematic, then suspicious data is informed leader cluster node by the sensor node, and leader cluster node is to the data Carry out spatial coherence detection；Reference picture 2, basic thought is：It is any first from sensor node object to select K object work It is cluster centre, for remaining sensor node, then according to their similarity (Euclideans with the cluster centre for being selected out Distance), sensor node of system distribution is given the cluster of its most like cluster centre respectively, each new cluster is then calculated again Cluster centre (averages of all objects in the cluster), constantly repeat this process until cluster centre start convergence untill；
K-means algorithm steps are as follows：
If sensor node number is p, the coordinate of sensor node is { x(1),x(2),…,x(p), each x(i)(i=1, 2,…,p)∈R；
Step 1, randomly selects K sensor node as cluster centre, and the K coordinate of cluster centre is u(j)(j=1, 2 ..., k) ∈ R, K cluster of K cluster centre correspondence；
Step 2, repeats following process until cluster centre is restrained
{
For each sensor node sample i, its cluster that should belong to is calculated, formula is as follows：
c(i)：=argminj||x(i)-u(j)||2Formula (2)
For each cluster j, the cluster centre of the cluster is recalculated, formula is as follows：
U in formula (2)(j)One in K cluster centre is represented, (j=1,2 ..., k), make by continuous adjusting parameter j Obtain the overhead functions c of each point(i)Reach minimum value, c(i)Representative sensor node sample i is with distance in K cluster centre most Each point, is divided into the cluster of cluster centre nearest apart from its by near cluster；
Denominator represents the sum of sample in each cluster in formula (3), and molecule is sensor node sample i in each cluster Corresponding coordinate and.
Some groups of clusters are finally given, if q+1 sensor node constitutes a component cluster, 1 cluster head is included per component cluster Nodes X tcWith q distribution node (Xt1,Xt2,…,Xtq)；
Parameter optimization is carried out to BP neural network using mind evolutionary, by convergent operation dissimilation to BP neural network Weights and threshold value optimize, obtain best initial weights and threshold value, be input into best initial weights and threshold value, set up MEA-BP neutral nets Model, the flow of Optimized BP Neural Network is as shown in figure 3, substantially step is as follows：
Step 1：Mind-evolution initial population is produced, and N group numbers is randomly generated as initial population in solution space, in every group of number An individual (i.e. neural network structure) is represented comprising n element, each individual matrix is 1*n, and group matrix is N*n, Colony includes individuality；
Step 2：According to BP neural network topological structure, solution space is mapped to space encoder, each space encoder correspondence One solution of problem, i.e. an individual, code length are equal to the element number in each individuality, and code length n is
N=tL+wL+L+w formula (4)
In formula (4), t is neutral net input number of nodes, and w is output node number, and L is node in hidden layer, is selected herein Take t=19, L=20, w=1；
Step 3：The number of generation number iter, winning sub-group M and interim sub-group T is fallen in definition；Evolutionary process it is each All groups of individuals in generation turn into a colony, and a colony is divided into M winning sub-group and T interim sub-group, often Individual winning sub-group and interim sub-group contain SG individuality, and SG is：
SG=N/ (M+T) formula (5)
Generally choose iter=10, M=T=5, N=200, SG=20；
Step 4：The determination of scoring function, neutral net is made up of input layer, hidden layer, output layer, training sample Input layer is AK=(a1 k,a2 k,…,at k), (K=1,2 ..., P, P are number of training), input matrix is t*p, expectation network Output layer is YK=(Y1 k,Y2 k,…,Yw k), output matrix is w*p, each sample input one output of correspondence, one in sample Have p inputoutput pair；Each node input of hidden layer is Z in the middle of networkK=(z1,z2,…,zL), middle each node of hidden layer It is output as BK=(b1,b2,…,bL)；Each node input of network is QK=(q1,q2,…,qw), each node of network output layer is output as GK=(g1,g2,…,gw).Define input layer and hidden layer weights Wij(i=1,2 ..., t, j=1,2 ..., L), hidden layer with it is defeated Go out a layer weights Vju(j=1,2 ..., L, u=1,2 ..., w), hidden layer threshold value be { θj(j=1,2 ..., L) }, output layer threshold value {βu(u=1,2 ..., w) }, matrix is w*w.According to coding rule, WijFor it is single individuality in the 1st to (L*t) individual element, Matrix is L*t；Vju(L*t+1) is individual to (L*t+w*L) individual element in single individuality, and matrix is w*L；θjFor in single individuality (L*t+w*L+1) is individual to (L*t+w*L+L) individual element, and matrix is w*L；βu(L*t+w*L+1) is individual in single individuality To last element.Each node input Z of calculating network hidden layerj, then with { ZjCalculate implicit by S types activation primitive Each node output { b of layerj, S type function expression formulas are：
bj=f (Zj), j=1,2 ... L formula (7)
∑ Wa is referred to as the activation value of this model in formula (7), is the input summation of model；In formula (7) f () for model swash Function living.
Then output { the b according to hidden layerj, weights VjuAnd threshold value { βuCalculate each node input Q of output layeru, Ran Houyong {QuOutput { the G of each node of output layer is calculated by S type functionsu}：
Gu=f (Qu) (u=1,2 ..., w) formula (9)
Formula (8) (9) is ibid；
Select the scoring function as each individual and colony reciprocal of the mean square error of training sampleykThe desired output of k-th training sample is represented, matrix is w*p, GkActual output valve is represented, I.e. by neural metwork training value out, matrix is also w*p, and p is training sample number；
Step 5：Training weights and threshold value, for each individuality to be uniformly distributed generation n group random numbers between (- 1,1), Individual volume matrix is 1*n, as initial weight threshold colony, according to network calculations rule, is calculated per each and every one according to scoring function Body score, the individuality of highest scoring is referred to as winner, and the q best individuality of score is chosen in punching as winner, chooses q= 10；
Step 6：Sub-group operation similartaxis, in sub-group, individuality as victor the process that competes be called it is convergent, one Sub-group it is convergent during, if not producing new victor, as subgroup body maturation, convergent process terminates, respectively with every Centered on one winner, Normal Distribution produces individuality, forms M winning sub-group and T interim sub-group, every height Colony is individual comprising SG, and the normal distribution can be expressed as N (u, ∑), and u is the center vector of normal distribution in formula, and ∑ is this The covariance matrix of normal distribution, the center of normal distribution is exactly the coordinate of victor, i.e. the weights and threshold value of victor；
Step 7：Sub-group operation dissimilation.Dissimilation is the mistake that each sub-group turns into victor and competes in whole solution space Journey.By global advertisement plate, it have recorded each sub-group scoring function value and maturity, be carried out between each sub-group complete Office's competition, if an interim sub-group score is higher than certain ripe winning sub-group score, interim sub-group replaces winning Sub-group, the individuality in former winning sub-group is abandoned；If a score for the interim sub-group of maturation is excellent less than any one Win the score of sub-group, then the interim sub-group is abandoned, and individuality therein is released.The number of the interim sub-group being abandoned Tr is designated as, the number of the winning sub-group being abandoned is designated as Mr, under the guidance of global advertisement plate, is regenerated in solution space Mr+Tr interim sub-group；
Step 8：Parsing optimum individual.Repeat above-mentioned 6,7 step, when iteration stopping condition is met, mind evolutionary Terminate optimization process.Now, according to coding rule, the optimum individual to searching out is parsed, so as to obtain corresponding BP god Through the best initial weights and threshold value of network, best initial weights and threshold value are input into, set up MEA-BP neural network models；
Reference picture 4, using distributed algorithm, to sensor node (Xt in every component cluster1,Xt2,…,Xtq) independently hold Row abnormality detection, abnormality detection finishes rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the component cluster Leader cluster node XtcFurther checking, comprises the following steps：
S41 is by temporal correlation to sensor node (Xt1,Xt2,…,Xtq) abnormality detection is independently executed, using each Current time passes through sensor node XtjSliding window WjDataTo train neutral net, complete The forecast of subsequent time data, chooses training dataRear v sample data, by formula (1) count Calculate the model residual error S of MEA-BP neutral nets：
Wherein, Er(r=1,2 ..., v) be choose sampled data values, F be choose sample data averages,
S42 calculates the confidential interval at sensor node current time, and confidential interval is Wherein SprePrediction for MEA-BP neutral nets to subsequent time data Value, tα/2,v-1For the t of free degree v-1 is distributed, suitable α values are chosen in t distribution tables, obtain t values；T distribution tables such as Fig. 1 institutes Show, normal distribution also known as Gaussian Profile, if it is that μ, variance are the Gaussian Profile of σ 2 that stochastic variable obeys a mathematic expectaion, be designated as N (μ, σ 2), our usually said standardized normal distributions are μ=0, the normal distribution of σ=1.From average value be μ, variance be σ 2 Standardized normal distribution totality in extract capacity for v sample, sample obey average value be μ, variance for σ 2/v normal distribution, Population variance σ 2 is always unknown, so as to can only be replaced with s2.If v is very big, then, s2 is exactly one of σ 2 and preferably estimates Metering, is still an approximate standardized normal distribution；If v is smaller, s2 and σ's 2 differs greatly, therefore, now sample divides Cloth is no longer just a standardized normal distribution, but obeys t distributions, and t distributions are the curves become according to the free degree, in coordinate Axle Y-axis both sides are symmetrical, and average is that 0, t distributions are applied to when population standard deviation is unknown, then uses sample standard deviation generation For population standard deviation.The free degree in t distributions refers to the number that can freely change in any variable.Sample t-test now Only estimate a parameter：Population mean, sample size v constitutes v kinds for estimating the information of population mean and its variability, disappears Consume one degree of freedom to estimate average, remaining v-1 free degree is used to estimate variability, therefore, sample t-test uses the free degree For the t of v-1 is distributed；When that need not estimate parameter, the free degree is v；Generally choose α=0.05.
S43 works as subsequent time data SnewWhen in into sensor node sliding window, subsequent time data S is judgednewIt is In the no fiducial interval range for falling into current time, if so, then judging data SnewIt is normal data；Otherwise judge the data SnewIt is abnormal data；
S44 abnormality detections finish rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the component cluster Leader cluster node XtcIn；
S45 leader cluster nodes XtcVoting mechanism verificating sensor node abnormal data is detected and introduced by spatial coherence Producing cause, reason includes that event anomalies, node failure are abnormal and judge by accident, specifically includes following steps：
S451 is by the abnormal data of sensor node and other node datas with the sensor node in same sub-clustering It is compared；If q+1 sensor node constitutes a component cluster, 1 leader cluster node Xt is included per component clustercWith q distribution Node (Xt1,Xt2,…,Xtq)；
S452 presets error amount θ, if the abnormal data of sensor node is STIf, with the sensor node same The data of other nodes in sub-clustering are Si(i=1,2 ..., q-1), if | ST-Si|<θ, then initial value is that 0 counting NN plus 1, The final NN values of statistics；
If S453Then judge that the detection node abnormal data is due to event anomalies；IfThen judging should Detection node abnormal data is due to node failure or erroneous judgement；IfThe reference node in the sub-clustering is then chosen, if reference The data of node are SCCIf, | ST-SCC|≤θ, then judge the sensor node abnormal data be due to event anomalies, if | ST-SCC |>θ, then judge that the sensor node abnormal data is due to node failure or erroneous judgement；It is in the sub-clustering wherein with reference to node The nearest node of cluster centre node Euclidean distance；With reference to node：Space is carried out to each sensor node using K-means algorithms Sub-clustering obtains some groups of clusters, and the similar sensor node of data is divided into same cluster, according to final sub-clustering result, choosing It is with reference to node to take the node nearest apart from the cluster barycenter Euclidean distance；
During S454 is for the step S453, when judge to obtain the sensor node abnormal data be due to node failure or During erroneous judgement, determine whether that the sensor node abnormal data is, due to node failure or erroneous judgement, to specifically include following steps： Temporal correlation detection is carried out by sensor node, when abnormal data is produced in sensor node continuous time, is then sentenced The disconnected sensor node abnormal data is due to node failure；When sensor node only has the moment for abnormal data, other when Carve and produce data to be normal data, then judge that the sensor node abnormal data is due to erroneous judgement；
For the abnormal data in detection node, each node can be formed and belonged to the section with some cycles gathered data The data flow of point, in order to ensure the correctness of the regional nodes gathered data sample, each node is all needed before data are uploaded The exceptional value that utilize the value of neural network prediction to replace in sliding window.
Data sample from Intel's Berkeley laboratory sensor network data, the data sampling frequency be every Sampling in 31 seconds is once.Choose sensor node 1-5 100 groups of temperature of node, humidity as training data, 20 groups of temperature, humidity As prediction data.
S1 training=[20.6156,20.6254,20.6450,20.6352,20.6450,20.6156,20.6058, 20.576420.4882,20.4588,20.4392,20.4196,20.3804,20.3510,20.3020,20.2726, 20.2530,20.1942,20.184420.1354,20.0864,20.0668,20.0374,20.0178,19.9982, 19.9786,19.9688,19.8414,19.8022,19.8022,19.7826,19.8022,19.8218,19.8316, 19.8610,19.9002,19.9296,19.9786,19.9884,20.0080,20.0374,20.0472,20.1060, 20.1060,20.1256,20.1354,20.1550,20.2236,20.2432,20.2432,20.3020,20.3216, 20.3608,20.4588,20.4980,20.5176,20.5568,20.5666,20.5960,20.6254,21.4192, 21.4094,21.3996,21.3604,21.3212,21.3016,21.2624,21.2330,21.2232,21.2036, 21.1252,21.1252,21.1056,21.1056,21.0860,21.0860,21.0860,21.0762,21.0664, 21.0664,21.0468,21.0076,20.9880,20.9586,20.9684,20.9096,20.8998,20.8998, 20.8802,20.8704,20.8704,20.8704,20.8704,20.8802,20.8704,20.8802,20.8802, 20.8802,20.8704,20.8704]
[37.5737,37.6079,37.6422,37.6422,37.7107,37.7107,37.7792,37.8477, 38.0529,38.1213,38.189738.1897,38.3263,38.3946,38.4629,38.5311,38.5311, 38.7357,38.8039,38.8720,39.0082,39.0763,39.1443,39.1783,39.2123,39.2803, 39.3143,39.6200,39.755739.7896,39.755739.5521,39.5521,39.5521,39.4162, 39.4502,39.3143,39.2803,39.2123,39.178339.1443,39.110339.0082,39.0082, 38.9401,39.0082,38.9401,38.8379,38.8720,38.8039,38.7357,38.769838.7357, 38.6675,38.4970,38.3946,38.3946,38.3263,38.2580,38.2580,38.2239, 38.155537.9845,38.1897,38.1555,38.0529.37.9845,37.9161,37.8134, 37.813437.8819,37.8819,37.9161,37.9503,37.9503,37.9161,37.9503,37.9161, 37.8477,37.7792,37.7450,37.7450,37.7107,37.7107,37.7107,37.7107,37.6765, 37.6765,37.6422,37.8134,37.7792,37.7107,37.7107,37.6765,37.6765,37.7107, 37.7450,37.7792,37.9161,38.0529]
S1 predictions=[20.7724,20.7332,20.7528,20.7430,20.7332,20.7332,20.7234, 20.7136,20.7234,22.1530,20.7038,20.6940,20.6940,20.7038,20.6940,22.1530, 20.6744,20.6450,20.6352,20.6254][39.6200,39.9929,40.3652,40.9055,41.1414, 41.2761,41.3771,41.4780,41.7805,41.8812,41.9818,41.9483,42.2500,42.3840, 42.3170,52.3170,42.2500,42.2835,42.0824,41.9818]
S2 training=[20.6226,20.6224,20.6350,20.6352,20.6350,20.6256,20.6358, 20.5764,20.4782,20.4388,20.4492,20.4296,20.3604,20.3310,20.3320,20.2326, 20.2330,20.1042,20.1644,20.1354,20.0464,20.0468,20.0274,20.0178,19.9482, 19.9486,19.9588,19.8614,19.8322,19.8222,19.7926,19.8122,19.8518,19.8516, 19.8510,19.9502,19.9296,19.9386,19.9884,20.0080,20.0374,20.0472,20.1060, 20.1060,20.1236,20.1454,20.1350,20.2236,20.2332,20.2432,20.3020,20.3216, 20.3608,20.4588,20.4380,20.5376,20.5568,20.5666,20.5960,20.6354,20.6450, 20.7136,20.7038,20.6744,20.7528,20.8412,20.8506,20.8600,20.8796,20.8802, 20.8402,20.8704,20.8902,20.8704,20.8704,20.8704,20.8802,20.9096,20.9586, 20.9782,20.9380,21.0376,21.0076,21.0174,21.0076,21.0272,21.0468,21.0566, 20.9978,21.0370,21.0468,21.0366,21.0360,21.0368,21.0368,21.0634,21.0732, 21.0938,21.0360,21.1356]
[37.5337,37.6179,37.6222,37.6322,37.7207,37.7307,37.7492,37.8277, 38.0129,38.1113,38.149738.1597,38.3163,38.3546,38.4629,38.5311,38.5311, 38.7357,38.8039,38.8720,39.0082,39.0763,39.1443,39.1783,39.2123,39.2803, 39.3153,39.6200,39.7557,39.7896,39.7357,39.5521,39.5521,39.5521,39.4152, 39.4502,39.3143,39.2803,39.2123,39.1783,39.1443,39.1103,39.0032,39.0082, 38.9431,39.0042,38.8401,38.8479,38.8520,38.8039,38.7357,38.7698,38.7357, 38.6635,38.4970,38.3946,38.3946,38.3263,38.2380,38.2380,38.2339,38.1355, 37.9345,38.1897,38.1555,38.0529,37.9845,37.9161,37.8134,37.8134,37.8819, 37.8819,37.9161,37.9503,37.9503,37.9161,37.9503,37.9161,37.8477,37.7792, 37.7450,37.7450,37.7107,37.7107,37.7107,37.7107,37.6765,37.6765,37.6522, 37.8034,37.7592,37.6907,37.6807,37.6565,37.6865,37.7207,37.7550,37.7732, 37.9261,38.0629]
S2 predictions=(20.5670,20.5376,20.4788,20.4494,20.4984,20.4788,20.4592, 20.4494,20.4788,20.4690,20.4788,20.4592,20.4592,20.4494,20.4396,20.4396, 22.4396,20.4396,20.4396,20.3710)(39.4300,40.7863,40.4722,40.8955,41.3214, 41.4361,41.5671,41.6880,41.8205,41.9036,42.0142,42.0100,42.3200,42.4140, 42.3270,42.3370,52.3500,42.3825,42.1824,41.8918)
S3 training=[20.6156,20.6254,20.6450,20.6352,20.6450,20.6156,20.6058, 20.576420.4882,20.4588,20.4392,20.4196,20.3804,20.3510,20.3020,20.2726, 20.2530,20.1942,20.1844,20.1354,20.0864,20.0668,20.0374,20.0178,19.9982, 19.9786,19.9688,19.8414,19.8022,19.8022,19.7826,19.8022,19.8218,19.8316, 19.8610,19.9002,19.9296,19.9786,19.9884,20.0080,20.0374,20.0472,20.1060, 20.1060,20.1256,20.1354,20.1550,20.2236,20.2432,20.2432,20.3020,20.3216, 20.3608,20.4588,20.4980,20.5176,20.5568,20.5666,20.5960,20.6254,20.6450, 20.7136,20.7038,20.6744,20.7528,20.8312,20.8606,20.8900,20.9096,20.8802, 20.8802,20.8704,20.8802,20.8704,20.8704,20.8704,20.8802,20.9096,20.9586, 20.9782,20.9880,21.0076,21.0076,21.0174,21.0076,21.0272,21.0468,21.0566, 20.9978,21.0370,21.0468,21.0566,21.0860,21.0468,21.0468,21.0664,21.0762, 21.0958,21.0860,21.1056]
[37.5737,37.6079,37.6422,37.6422,37.7107,37.7107,37.7792,37.8477, 38.0529,38.1213,38.189738.1897,38.3263,38.3946,38.4629,38.5311,38.5311, 38.7357,38.8039,38.8720,39.0082,39.0763,39.1443,39.1783,39.2123,39.2803, 39.3143,39.6200,39.7557,39.7896,39.7557,39.5521,39.5521,39.5521,39.4162, 39.4502,39.3143,39.2803,39.2123,39.1783,39.1443,39.1103,39.0082,39.0082, 38.9401,39.0082,38.9401,38.8379,38.8720,38.8039,38.7357,38.7698,38.7357, 38.6675,38.4970,38.3946,38.3946,38.3263,38.2580,38.2580,38.2239,38.1555, 37.9845,38.1897,38.1555,38.0529,37.9845,37.9161,37.8134,37.8134,37.8819, 37.8819,37.9161,37.9503,37.9503,37.9161,37.9503,37.9161,37.8477,37.7792, 37.7450,37.7450,37.7107,37.7107,37.7107,37.7107,37.6765,37.6765,37.6422, 37.8134,37.7792,37.7107,37.7107,37.6765,37.6765,37.7107,37.7450,37.7792, 37.9161,38.0529]
S3 predictions=(20.4396,20.4102,20.4102,20.4004,20.3710,20.3612,20.3612, 20.3612,20.3612,20.3514,20.3710,20.3808,20.3416,20.3220,20.3220,20.3318, 20.3318,22.3220,20.3514,20.3416)(39.4200,40.7963,40.4422,40.8755,41.3614, 41.4161,41.5871,41.6780,41.8405,41.9236,42.0242,42.0200,42.3300,42.4240, 42.3170,42.3270,42.3400,52.3525,42.1624,41.8718)
S4 training=[20.4154,20.4254,20.4450,20.4352,20.4450,20.4154,20.4057, 20.574420.4772,20.4577,20.4392,20.4194,20.3704,20.3510,20.3020,20.2724, 20.2530,20.1942,20.1744,20.1354,20.0744,20.0447,20.0374,20.0277,19.9972, 19.9774,19.9477,19.7414,19.7022,19.7022,19.7724,19.7022,19.7217,19.7314, 19.7410,19.9002,19.9294,19.9774,19.9774,20.0070,20.0374,20.0472,20.1040, 20.1040,20.1254,20.1354,20.1350,20.2234,20.2432,20.2432,20.3020,20.3214, 20.3407,20.4577,20.4970,20.5174,20.5547,20.5444,20.5940,20.4254,20.4450, 20.7134,20.7037,20.4744,20.7527,20.7312,20.7404,20.7900,20.9094,20.7702, 20.7702,20.7704,20.7702,20.7704,20.7704,20.7704,20.7702,20.9094,20.9574, 20.9772,20.9770,21.0074,21.0074,21.0174,21.0074,21.0272,21.0447,21.0544, 20.9977,21.0370,21.0447,21.0544,21.0740,21.0447,21.0447,21.0444,21.0742, 21.0957,21.0840,21.1054]
[37.5737,37.4079,37.4422,37.4422,37.7107,37.7107,37.7792,37.8477, 38.0529,38.1213,38.189738.1897,38.3243,38.3944,38.4429,38.5311,38.5311, 38.7357,38.8039,38.8720,39.0082,39.0743,39.1443,39.1783,39.2123,39.2803, 39.3143,39.4200,39.755739.7894,39.755739.5521,39.5521,39.5521,39.4142, 39.4502,39.3143,39.2803,39.2123,39.178339.1443,39.110339.0082,39.0082, 38.9401,39.0082,38.9401,38.8379,38.8720,38.8039,38.7357,38.749838.7357, 38.4475,38.4970,38.3944,38.3944,38.3243,38.2580,38.2580,38.2239, 38.155537.9845,38.1897,38.1555,38.0529.37.9845,37.9141,37.8134, 37.813437.8819,37.8819,37.9141,37.9503,37.9503,37.9141,37.9503,37.9141, 37.8477,37.7792,37.7450,37.7450,37.7107,37.7107,37.7107,37.7107,37.4745, 37.4745,37.4422,37.8134,37.7792,37.7107,37.7107,37.4745,37.4745,37.7107, 37.7450,37.7792,37.9141,38.0529]
S4 predictions=(20.4394,20.4102,20.4788,20.4494,20.4984,20.4788,20.3412, 20.3412,20.3412,20.3514,20.3710,20.3808,20.3414,20.2240,20.2240,20.2338, 20.2240,20.3318,22.3514,20.3414)(39.4500,40.7343,40.4222,40.8555,41.4214, 41.5341,41.4471,41.4980,41.8805,41.9334,42.0142,42.0120,42.3100,42.4240, 42.3070,42.3470,42.3800,42.3925,52.2024,41.9118)
S5 training=[20.4154,20.4254,20.4450,20.4352,20.4450,20.4154,20.4058, 20.574420.4882,20.4588,20.4392,20.4194,20.3804,20.3510,20.3020,20.2724, 20.2530,20.1942,20.184420.1354,20.0844,20.0448,20.0374,20.0178,19.9982, 19.9784,19.9488,19.8414,19.8022,19.8022,19.7824,19.8022,19.8218,19.8314, 19.8410,19.9002,19.9294,19.9784,19.9884,20.0080,20.0374,20.0472,20.1040, 20.1040,20.1254,20.1354,20.1550,20.2234,20.2432,20.2432,20.3020,20.3214, 20.3408,20.4588,20.4980,20.5174,20.5548,20.5444,20.5940,20.4254,20.4450, 20.7134,20.7038,20.4744,20.7528,20.8312,20.8404,20.8900,20.9094,20.8802, 20.8802,20.8704,20.8802,20.8704,20.8704,20.8704,20.8802,20.9094,20.9584, 20.9782,20.9880,21.0074,21.0074,21.0174,21.0074,21.0272,21.0448,21.0544, 20.9978,21.0370,21.0448,21.0544,21.0840,21.0448,21.0448,21.0444,21.0742, 21.0958,21.0840,21.1054]
[37.5737,37.4079,37.4422,37.4422,37.7107,37.7107,37.7792,37.8477, 38.0529,38.1213,38.189738.1897,38.3243,38.3944,38.4429,38.5311,38.5311, 38.7357,38.8039,38.8720,39.0082,39.0743,39.1443,39.1783,39.2123,39.2803, 39.3143,39.4200,39.755739.7894,39.755739.5521,39.5521,39.5521,39.4142, 39.4502,39.3143,39.2803,39.2123,39.178339.1443,39.110339.0082,39.0082, 38.9401,39.0082,38.9401,38.8379,38.8720,38.8039,38.7357,38.749838.7357, 38.4475,38.4970,38.3944,38.3944,38.3243,38.2580,38.2580,38.2239, 38.155537.9845,38.1897,38.1555,38.0529.37.9845,37.9141,37.8134, 37.813437.8819,37.8819,37.9141,37.9503,37.9503,37.9141,37.9503,37.9141, 37.8477,37.7792,37.7450,37.7450,37.7107,37.7107,37.7107,37.7107,37.4745, 37.4745,37.4422,37.8134,37.7792,37.7107,37.7107,37.4745,37.4745,37.7107, 37.7450,37.7792,37.9141,38.0529]
S5 predictions=(20.4204,205002,20.4200,20.4200,20.3906,20.3612,20.3024, 20.2828,20.3122,20.2828,20.2828,20.2632,20.2436,20.2240,20.2240,20.2338, 20.2240,20.1848,20.1848,22.1848)(39.4050,40.7663,40.4532,40.8865,41.3314, 41.4261,41.5871,41.7080,41.7905,41.9236,42.0242,42.0180,42.3150,42.4040, 42.2970,42.3270,42.3700,42.3535,42.1924,51.9218)
Now the temperature with sensor node collection is as sample.As shown in figure 1, being saved to space by K-means algorithms first Point is divided, it is assumed that then 1-5 nodes carry out parameter optimization using the data of node as shown in Figure 2 in same cluster, Set up MEA-BP neural network models.Determine confidential interval using neural network model residual error, this posterior nodal point gathers new data, It is predicted by sliding window, if the data of collection judge that the data are exceptional value not in confidential interval, while transmission Verified to cluster head.Otherwise, the data of collection are normal.
The initial weight and threshold value of neutral net are random, and optimum individual is obtained by optimized algorithm, are advised according to coding Optimum individual is then parsed, the element in individuality is divided into four parts, input layer and hidden layer weights W, hidden layer and output layer Weights V, hidden layer threshold value θ, output layer threshold value beta.
W=[0.5773, -0.0366, -0.5442,0.6235,1.1156,0.3768,0.3825,0.3136, 0.1267,-0.7622,-0.5179,1.0760,-0.7651,-0.6149,0.3441,0.9941,0.0842,0.9022,- 0.2135,0.0192；
1.0865,0.2526,0.6696,-0.5513,0.2104,0.2332,0.5453,-0.1723,1.3676, 0.3422,-0.3744,0.8603,0.0330,0.3656,-0.2953,0.0479,-0.3960,-0.6000,0.2272,- 0.7731；
-0.5749,0.5627,0.5373,1.0035,-0.9583,1.1374,0.9935,0.5132,0.5842, 0.4948,-0.5388,0.9441,0.3067,-0.5114,0.3832,-0.0877,-0.6106,-0.8577,-0.4521, 0.5040；
-0.1253,-0.9239,0.5082,0.5222,0.0151,0.0033,-0.2452,-0.3727,-0.7296, 1.0824,1.0505,-0.6283,-0.5399,0.4255,0.3935,-0.5921,-0.6411,-0.8547,0.5181, 0.7837；
-0.4296,-0.6532,0.1644,0.4362,1.2707,0.4877,-0.6715,0.9923,1.2893, 0.8293,,0.9933,-0.6541,-0.1126,0.5300,-0.7672,0.0751,0.2299,-0.2186,0.8131,- 0.9794；
-0.9365,-0.2176,1.0960,0.0105,1.3308,-0.3746,-1.0590,-0.5488,-0.8550, 0.2305,0.4455,-0.2741,-0.0727,-0.8329,0.6602,0.6955,1.2929,-0.2030,0.6805,- 0.1515；
0.9026,0.4729,0.4579,0.0247,-0.3627,-0.1774,0.3376,0.2647,-0.2081,- 0.9794,0.8114,0.0375,-0.1698,0.1191,-0.1169,0.3339,0.4083,-0.3918,-0.3013, 0.8716；
-0.1600,0.5712,-0.4983,-0.5316,-0.4528,-0.1343,0.6454,-0.6407, 0.9763,-0.3584,-0.5402,0.1329,0.7943,0.3628,-0.3161,0.5328,0.5403,0.7219,- 0.3242,0.4581；
-0.0458,-0.7775,0.1541,-0.0951,-0.5324,0.1833,-0.6400,-0.1086,- 0.8386,-0.1227,-1.1306,0.3747,-1.2653,-0.4936,-0.1682,0.7071,-0.3657,1.0828, 0.5700,1.1866；
-0.5898,-0.7801,0.5664,0.0111,0.3069,0.1700,0.3791,-0.3305,0.3633, 0.3725,0.0423,-0.0695,0.2999,0.9656,0.6382,0.1977,-0.4587,0.2812,-0.5573,- 0.3800；
0.9481,-0.0495,0.6783,-0.5981,-0.4970,0.8797,-0.9796,-0.3040,0.9687,- 1.1954,0.3528,-0.1131,-0.1752,-0.8480,0.0283,-0.1791,1.1680,0.1180,1.0543,- 0.7890；
0.9876,-0.5693,0.2064,1.0028,-0.7505,-0.0915,0.8364,0.0396,1.3324, 0.6910,0.4675,0.2987,-0.7527,-0.5169,-0.8266,1.0537,0.9445,-0.1559,-0.2290, 0.7849；
0.7757,0.0837,0.6734,-1.0255,0.2378,0.4449,0.5047,0.0004,0.4182, 0.1178,0.5781,-0.2788,0.5544,-0.6854,0.2246,-0.9034,-0.5780,-1.0345,-1.0602,- 0.5880；
-0.9348,-0.4230,-0.7827,-0.9307,-0.1429,-0.1718,0.2480,0.8521,- 0.9502,0.8372,0.5072,-0.5041,0.0417,-0.0333,0.1855,-0.1131,0.7978,1.2911, 0.3152,0.5734；
-0.4313,0.3686,-0.4494,-0.2113,-0.8895,-0.6692,0.8727,0.5028,- 1.2660,-0.1608,-1.1177,0.9249,-0.7744,-0.8300,-0.6689,-0.6472,0.5649,- 0.0527,-0.2023,0.7030；
0.8426,0.8520,0.6722,-0.9511-0.7137,-0.6196,0.1978,0.5044,-0.6855,- 0.3957,-1.1713,0.8359,0.2987,-0.3332,-0.7046,-0.0307,0.7013,-1.0424,-0.2173,- 0.8635；
0.4429,-0.5530,0.2594,-0.0055,-0.2293,-0.1312,0.1046,-1.1803,0.6552, 1.1167,0.2293,0.8949,0.2872,0.0639,-0.7069,-0.6443,-0.5359,-0.7943,0.0749, 0.2322；
0.1580,0.3637,-0.3663,0.3326,0.5618,-0.0908,0.6683,-0.1572,0.0053,- 0.6843,-1.1245,0.6485-0.4312,-0.7010,-0.1113,0.7301,-0.0746,0.0622,0.1535,- 0.0938；
-1.1087,-0.0046,-0.9751,-0.0265,1.1469,-0.7158,0.6888,-0.5601,0.5212, 0.6409,-0.5567,1.1806,-0.1444,0.5397,-0.5531,0.5221,-0.5053,-1.2739,-0.4797, 0.3794]
V=[- 0.8852, -0.4987, -1.0526,0.3578, -0.0436, -0.6403,1.1844, -1.2437, - 0.9961,-0.1289,-0.8779,-0.0577,-0.5220,0.4834,0.3050,0.5123,0.4276,-0.0404, 0.9207,0.0199]
θ=[- 0.3454, -0.0724, -0.9978, -1.0797,1.0350,0.2819,0.8036,0.2719, 0.2332,-0.6689,0.9885,-0.7912,0.1800,-0.6851,-0.7595,0.2428,-0.1237,-0.2353,- 0.0445,0.4698]
β=[- 1.2285]
Then confidential interval is calculated, 1 one predicted values at moment of node are randomly selected, it is 20.8692 to be worth, now choose instruction Practice rear 40 data of data by the computation model residual error of formula 1, the model residual values S=0.1776 for obtaining obtains t values by Fig. 1 It is 2.02108, interval bound now is calculated as [20.5058,21.2326] by formula 2.
Assuming that node S1 has trained MEA-BP models, new data are added by sliding window, as it appears from the above, working as When sliding window predicts the 1st data, now temperature prediction value be 20.8692, fiducial interval range for [20.5058, 21.2326], this moment sensing data is 20.7724, it can be seen that 20.7724 this data are obvious within interval range, Then think that the time data is normal；When sliding window predicts the 10th data, now temperature prediction value is 20.7062, Fiducial interval range is [20.3428,21.0696], and this moment sensing data is 22.1530, and the data are in interval range Outside, being then passed to leader cluster node carries out spatiality checking, finds the data at the moment in same cluster by voting mechanism Interior temperature is abnormal, the then temperature data exception that node 1 is gathered, and node breaks down or reports by mistake, if finding in same cluster Middle data are similar, then it is assumed that event occur.Humidity temperature data for other nodes can similarly be recognized accurately exception.
The inventive method is divided with K-means algorithms based on the temporal correlation of sensor node to node, Then MEA-BP neural network models are set up on each node, and following each distribution node of each moment is detected according to the model Whether the data of interior arrival are abnormal, the result after detection is then delivered to leader cluster node and is verified.At present, abnormality detection exists The characteristics of being all a problem for further investigation, wireless sensor network uniqueness in every field and strict constraints are caused The research of the problem is more challenging.Method proposed by the present invention is multiple primarily directed to resource-constrained wireless sensor network Miscellaneous abnormality detection, greatly reduces the communication consumption between node, and can accurately detect abnormal data, suitable with more environment Should be able to power.
The above is only the preferred embodiment of the present invention, it should be pointed out that：Come for those skilled in the art Say, under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be regarded as Protection scope of the present invention.

Claims (6)

  1. It is 1. a kind of to be based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The described method comprises the following steps：
    Each distribution sensor node initializing, each sensor node are started gathered data by S1；
    If sensor node number is n, each sensor node is Xtj(j=1,2 ..., n), sensor node XtjSliding window It is Wj, the sliding window size of each sensor node is m, then sensor node XtjIn its sliding window WjOn measurement data Sequence isSensor node XtjIn tpMoment collection data beThe data include that h attribute is surveyed Value, then
    S2 carries out space sub-clustering to each sensor node and obtains some groups of clusters using K-means algorithms；
    If q+1 sensor node constitutes one group of cluster, every group of cluster includes 1 leader cluster node XtcWith q distribution node (Xt1, Xt2,…,Xtq)；
    S3 carries out parameter optimization using mind evolutionary to BP neural network, by convergent operation dissimilation to BP neural network Weights and threshold value are optimized, and obtain best initial weights and threshold value, are input into best initial weights and threshold value, set up MEA-BP neutral net moulds Type；
    S4 uses distributed algorithm, to sensor node (Xt in every component cluster1,Xt2,…,Xtq) abnormality detection is independently executed, Abnormality detection finishes rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the leader cluster node Xt of the component clusterc Further checking.
  2. 2. according to claim 1 based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The step Rapid S2 is comprised the following steps：
    S21 arbitrarily selects K sensor node object as K cluster centre first from distribution sensor node object；
    S22 calculates sensor node object with K cluster respectively then for the sensor node object in addition to cluster centre Similarity between center, obtains and the sensor node immediate cluster centre of object similarity；
    S23 is distributed to the cluster with the sensor node immediate cluster centre of object similarity by sensor node object, K cluster will be obtained after the completion of all the sensors node distribution；
    S24 recalculates this K cluster centre of cluster, obtains new cluster centre；
    S25 recalculates the similarity of each sensor node and new cluster centre, returns to step S22；
    S26 terminates this operation when the cluster centre for recalculating is restrained.
  3. 3. according to claim 1 based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The step Rapid S3 is comprised the following steps：Produce training data；Determine BP neural network topological structure；Line parameter is entered by mind evolutionary Set；Randomly generate initial population, winning sub- population and interim sub- population；Operation similartaxis are carried out to sub- population；Sub- population is entered Row operation dissimilation；Judge whether to meet termination condition, if it is satisfied, then output optimum individual, obtains best initial weights and threshold value, it is no Then re-start convergent operation dissimilation.
  4. 4. according to claim 1 based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The step Rapid S4 is comprised the following steps：
    S41 is by temporal correlation to sensor node (Xt1,Xt2,…,Xtq) abnormality detection is independently executed, it is current using each Moment passes through sensor node XtjSliding window WjDataTo train neutral net, complete next The forecast of time data, chooses training dataRear v sample data, by formula (1) calculate The model residual error S of MEA-BP neutral nets：
    Wherein, Er(r=1,2 ..., v) be choose sampled data values, F be choose sample data averages,
    S42 calculates the confidential interval at sensor node current time, and confidential interval is Wherein SprePrediction for MEA-BP neutral nets to subsequent time data Value, tα/2,v-1For the t of free degree v-1 is distributed, suitable α values are chosen in t distribution tables, obtain t values；
    S43 works as subsequent time data SnewWhen in into sensor node sliding window, subsequent time data S is judgednewWhether fall Enter in the fiducial interval range at current time, if so, then judging data SnewIt is normal data；Otherwise judge data SnewFor Abnormal data；
    S44 abnormality detections finish rear sensor node (Xt1,Xt2,…,Xtq) testing result is delivered to the cluster head of the component cluster Nodes X tcIn；
    S45 leader cluster nodes XtcVoting mechanism verificating sensor node abnormal data is detected and introduced by spatial coherence produces original Cause, reason includes that event anomalies, node failure are abnormal and judge by accident.
  5. 5. according to claim 4 based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The step In rapid S42, α=0.05 is chosen.
  6. 6. according to claim 4 based on MEA-BP neutral net WSN method for detecting abnormality, it is characterised in that：The step Spatial coherence is carried out in rapid S45 and detects and introduce voting mechanism to comprise the following steps：
    S451 carries out the abnormal data of sensor node and other node datas with the sensor node in same sub-clustering Compare；If q+1 sensor node constitutes a component cluster, 1 leader cluster node Xt is included per component clustercWith q distribution node (Xt1,Xt2,…,Xtq)；
    S452 presets error amount θ, if the abnormal data of sensor node is STIf, with the sensor node in same sub-clustering In other nodes data be Si(i=1,2 ..., q-1), if | ST-Si|<θ, then initial value is that 0 counting NN plus 1, is counted Final NN values；
    If S453Then judge that the detection node abnormal data is due to event anomalies；IfThen judge the detection Node abnormal data is due to node failure or erroneous judgement；IfThe reference node in the sub-clustering is then chosen, if with reference to node Data be SCCIf, | ST-SCC|≤θ, then judge the sensor node abnormal data be due to event anomalies, if | ST-SCC|>θ, Then judge that the sensor node abnormal data is due to node failure or erroneous judgement；It is to be clustered in the sub-clustering wherein with reference to node The nearest node of Centroid Euclidean distance；
    It is due to node failure or erroneous judgement when judgement obtains the sensor node abnormal data during S454 is for the step S453 When, determine whether that the sensor node abnormal data is, due to node failure or erroneous judgement, to specifically include following steps：Pass through Temporal correlation detection is carried out to sensor node, when abnormal data is produced in sensor node continuous time, then judging should Sensor node abnormal data is due to node failure；When sensor node only has the moment for abnormal data, other moment produce Raw data are normal data, then judge that the sensor node abnormal data is due to erroneous judgement.
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