CN104159251A - Sensor network fault link inference method based on passive end-to-end - Google Patents

Abstract

The invention discloses a fault link inference method based on passive end-to-end. The method comprises the following steps: firstly comparing packet loss rates of all paths with a relevant threshold value to obtain a fault section, detecting whether all link status can explain the fault path, computing the packet loss rate of each path by using end-to-end data, comparing the packet loss rate with the threshold value to simplify a route matrix; secondly, computing a link packet loss probability according to a link packet loss rate inference model, optimizing algorithm through the mode for simplifying the matrix, inferring the packet loss rate of all links in the fault section through the end-to-end data so as to further promote the utilization rate of existing data; and finally presenting a maximum probability inference model, iteratively computing the weight of each link, selecting the link with the optimal weight every time and deleting the path including the link until all faults are explained, and solving the problem by using a heuristic greed method.

Description

Based on the passive link of sensor network fault end to end estimating method

Technical field

The present invention relates to wireless sensor network fault location technology, specifically a kind of based on the passive estimating method of faulty link end to end.

Background technology

Along with the develop rapidly of communication, microelectronics, embedded and sensor technology, people develop the various wireless sensor nodes with perception, calculating and communication capacity.Sensing node can the various physical messages of perception, such as temperature, humidity, illumination and pressure etc.The wireless sensor network being comprised of sensor node just becomes new network application type, it combines sensor technology, embedded computing technique, distributed information processing and the communication technology, by the Various types of data information in real-time perception region, and it is carried out to fusion treatment, finally send to server end, by technology such as data minings, obtain corresponding information.Wireless sensor network can be widely used in the fields such as national defense and military, environmental monitoring, health care, anti-terrorism are combated a natural disaster, building structure inspection.

In actual application, sensor node is deployed in the region of climatic environment around or geographical conditions very severe conventionally, as the monitoring of moon climatic environment, active volcano Real-Time Monitoring, high-risk region Long-distance Control etc.Under the impact of the complicated factors such as change of the physical environment that the service quality of radio sensing network and system operation situation may exhaust at node hardware and software failure, node electric weight, artificial destruction and climate reasons cause, make to occur in network the phenomenons such as node failure, loss of link, thereby have influence on reliability and the practical application effect of whole network.By real time monitoring network state, once there is extremely just initiating failure, locate and repair, thereby guarantee that network application normally moves.Along with wireless sensor network scale increases gradually, structure is day by day complicated, the probability that network breaks down also rises gradually, in order to maintain the availability of network, make its more efficient, safe, stable, operation reliably, cut operating costs to a greater extent, true and reliable data message is provided, at wireless sensor network fault management domain, has launched a series of further investigation both at home and abroad.

Sensor node deployment, after actual application environment, does not often move according to predefined mode, even quits work.For network is normally moved, must set up Network Fault Management System, once exist and may be judged to be the abnormal information that has fault in discovering network, just can also automatically to abnormal information, carry out analyzing and processing by trigger alarm, optimum solution is proposed, until all faults are found.And along with the increase of radio sensing network scale, influencing each other between each sensor node, if middle routing node breaks down, the great deal of nodes of carrying out package forward by this node also can be considered to break down.While therefore breaking down in wireless sensor network, detecting in time extremely, is the key that guarantees network stabilization, reliability service by related data being analyzed to and navigated to fast concrete trouble unit, is also the top priority of network failure management.Therefore, for network, normal operation is vital to radio sensing network fault management.

Faulty link location technology in wireless sensor network, is divided into based on initiatively measurement, based on passive measurement and location and inference technologies based on initiatively adding passive measurement.The energy in active monitoring consuming sensor network, and irregular behavior may mislead fault location.The for example report of node possible errors oneself or neighbours' state information, or middle routing node may be revised the information forwarding in bag.And data are end to end used in passive monitoring, therefore can there are not the problems referred to above.In research work, have scholar to use network tomographic techniques to infer the packet loss of node, the method based on particular model can only be analyzed single fault.Also there is scholar to propose to use correlation diagram to carry out fault detect, correlation diagram has been described the relevant parameter of each node within a period of time in statistical dependence, the in the situation that of the normal operation of sensor network, same node not correlation diagram in the same time has temporal correlation, and the correlation diagram of the different nodes of synchronization has spatial coherence, the fault that abnormal sudden change with the correlation diagram of node on time and space may exist in carrying out Sampling network, the advantage of which is to need less communication overhead, be applicable to the Network Fault Detection of Data Collection class application, shortcoming is the application scenarios that is not suitable for more complicated.Above-mentioned two kinds of methods all need data to carry out polymerization processing, and shortcoming is only to adapt under fixing tree topology, and reasoning also may occur mistake.Network sectional analysis technology is already by very ripe research, and great majority are applied in cable network, and can not directly be used in sensing network.Because the packet that the static topological sum of this Technology Need is associated.In wireless sense network, data are unconnected and topology changes at any time end to end.

Existing research also has a lot in addition, for example, by using one group of parameter relevant to network connectivty, network data flow and node for fault detect, use the fault existing in decision Tree algorithms Sampling network, the method simple and practical, shortcoming is in data-gathering process, to produce larger expense; By revising procotol, reduce fault detect observability expense, observability expense is the fault probability occurring and the summation that detects the product of the required energy consumption of this fault, by modifying to agreement, reduced the expense of energy consumption, but the mode of revising agreement is in most of the cases invalid; By lightweight packet marking strategy, can obtain network topology and a series of network operation state information, and according to above-mentioned information, can obtain dependence graph and the Inference Model of sensor node, then, the symptom observing is input to Inference Model, obtain the posterior probability that different nodes break down in the network under certain node failure condition, and relatively carry out fault detect by various fault posterior probability.

Fault detect, location and inference technologies are proposed widely and are applied in actual scene.But existing technology mostly relies on node and carries out mutual ability, as Sympathy, Memento and dump energy scanning technique.It is the relevant information that each node needs periodic report node self or neighbor node.There is the inferior position of following three aspects in aforesaid way: first, each node need to be monitored self or neighbours, consumes the node energy, shortened the life cycle of network.Secondly, collect the hysteresis quality of information, because the information of node is periodically to send, rather than Real-time Collection.Finally, to need node and link be completely controllable to this mode.

Recently, propose to make full use of the fault that may exist in the passive network of inferred from input data end to end in much research, the method is based on analyzing packet end to end, and sets up inference pattern, finally infers that explanation works as the reason of prior fault.Be different from active probe mode, passive detection does not need extra information loads, has reduced network energy consumption, has fully extended network life cycle.

The present invention uses data end to end to calculate the packet loss of each paths, by with relatively route matrix being simplified of threshold value, object is the path of removing according to data can be judged to be end to end.Secondly, because this inference pattern needs the probability of malfunction of each link, by the mode of simplification matrix, optimized link and inferred that algorithm carries.Finally, the weights of each link of iterative computation, also delete by the link of the optimum weights of each selection the path that comprises this link, until all faults are only interpreted as.The present invention is translated into optimum monitoring sequence problem.And utilize to have saved monitoring number of times optimal design based on node monitoring heuristic greedy algorithm, effectively reduce and initiatively measure number of times, the consuming time and energy consumption of reduction algorithm.

Summary of the invention

For above deficiency of the prior art, the object of the present invention is to provide a kind of minimizing initiatively to measure number of times, reduce the consuming time and energy consumption of algorithm based on the passive link of sensor network fault end to end estimating method, technical scheme of the present invention is as follows: based on the passive link of sensor network fault end to end estimating method, it comprises the following steps:

101, obtain the topological diagram G of sensor network, according to topological diagram G, obtain route matrix R and set of paths P, then collect N wheel sensor network operation data, during wherein k takes turns, k≤N, the data record that the leaf node in topological diagram G sends to sink nodes is from set of paths P, select a not path p for access, judge whether the packet loss of this path p is greater than threshold value T _piif so, this path p is joined to normal route set P _gin, otherwise this path p is joined to failure path collection P _bin, and it is all accessed to travel through the whether all paths of P, if so, from failure path collection P _bone of middle selection is the link A of access not, and whether the state that judges this link responsible, if obtain faulty link and by all paths that comprise this link from failure path collection P _bin delete, if not do not deal with, repeat to be simplified route matrix

102, according to the simplification route matrix obtaining in step 101 be simplified fault zone, according to link packet drop rate Inference Model (Yu Yang, Yongjun Xu.A Loss Inference Algorithm for Wireless Sensor Networks to Improve Data Realiabillity of Digital Ecosystems[J] .IEEE Trans.On INDUSTRIAL ELECTRONICS, 2011,58 (6): 2126-2137), and adopt heuristic greedy faulty link to infer that algorithm calculates the packet loss p that simplifies all links in fault zone _k;

103, calculate failure path collection P _bthe link l of middle weights maximum, and by the link l of this weights maximum from failure path collection P _bmiddle deletion, then adds the link l of this weights maximum in faulty link set to, deletes failure path collection P simultaneously _bin comprise this weights maximum the path of link l, repeating step 103, until failure path collection P _btill sky, complete the searching of faulty link.

Further, step 101 link l _ithe packet loss formula of=(j, k) expression sends n packet from node j, and node k successfully receives the packet loss of m packet.

Further, establish normal link packet drop rate there is the link packet drop rate of fault wherein 0≤α < β≤1, works as path p _iwhile not comprising faulty link, path packet loss Φ _pi≤ α ^h; If path packet loss Φ _pi>=T _pi, be that each link in path is all normal; If path packet loss Φ _pi≤ T _pi, at least there is the packet loss of a link in this path, at least there is a faulty link.

Further, in step 102, in computational short cut fault zone, the computing formula of the packet loss of all links is: wherein A (k) is illustrated in the data that R (k) at least exists a node and arrives under the condition of node k, the probability that received by aggregation node of success, γ (k) represents in leaf node subset, to have at least in descendants's node of node k the data of a node successfully to send to the probability of sink node.

Further, A (k) adopts find_solution algorithm to calculate.

Advantage of the present invention and beneficial effect are as follows:

The present invention uses data end to end to calculate the packet loss of each paths, by with relatively route matrix being simplified of threshold value, object is the path of removing according to data can be judged to be end to end.Secondly, because this inference pattern needs the probability of malfunction of each link, by the mode of simplification matrix, optimized link and inferred that algorithm carries.Finally, the weights of each link of iterative computation, also delete by the link of the optimum weights of each selection the path that comprises this link, until all faults are only interpreted as.The present invention is translated into optimum monitoring sequence problem.And utilize to have saved monitoring number of times optimal design based on node monitoring heuristic greedy algorithm, effectively reduce and initiatively measure number of times, the consuming time and energy consumption of reduction algorithm.

Accompanying drawing explanation

Fig. 1 is that fault of the present invention is inferred process;

Fig. 2 is two network topological diagrams before and after routing table update of the present invention;

Fig. 3 is fault zone of the present invention detection algorithm flow chart;

Fig. 4 is that the present invention simplifies route matrix;

Fig. 5 is that fault zone of the present invention link packet drop rate is inferred algorithm flow chart;

Fig. 6 is find_solution function flow chart of the present invention;

Fig. 7 is that faulty link of the present invention is inferred algorithm flow chart.

Embodiment

The invention will be further elaborated below in conjunction with accompanying drawing, to provide an infinite embodiment.But should be appreciated that, these describe example just, and do not really want to limit the scope of the invention.In addition, in the following description, omitted the description to known configurations and technology, to avoid unnecessarily obscuring concept of the present invention.The present invention designs a kind of based on faulty link estimating method end to end.The method comprises the steps.

The target of faulty link inference problems is basis data analysis end to end, obtains most possibly explaining the faulty link collection of current problem, and this problem is also referred to as np problem.As shown in Figure 1, we have proposed a kind of new solution, first all paths packet loss and respective threshold can be obtained behind fault zone after relatively, detect all Link States and whether can explain failure path, thereby further simplify fault zone; Secondly, according to link packet drop rate Inference Model, need to calculate packet loss of link probability, under the existing research condition that mostly packet loss of all links of hypothesis is identical, yet the packet loss at actual scene link is different, we are by improving algorithm, by the be out of order packet loss of all links in region of inferred from input data end to end, have further promoted the utilance to data with existing; Finally, maximum probability Inference Model is proposed, by using heuristic greedy algorithm to address the above problem.

represent to exist link l _i=(j, k), node j sends the data directly to node k, and node k is the father node of node j.

represent that the n of node j is for ancestor node.

f ⁿ(j)＝f(f ^n-1(j))

d (k): represent the child node of node k, d (k)={ j ∈ N| (j, k) ∈ L}.

R: represent leaf node combination, R={j ∈ N|d (j)=Φ }

t (k): represent to take the subtree collection that node k is root node, T (k)=(V (k), L (k)).

R (k): in descendants's node of node k, be the subset of leaf node, R (k)=R ∩ N (k).

route matrix one of line display is path end to end; Link is shown in list. represent link l _jat path p _iin.After Fixed Time Interval, we report acquisition by routing update

Route matrix comprise network topological diagram in Fig. 2 and upgraded all paths and the link information of front and back, so can well analyze the network under dynamic routing.Wherein the i row that represent route matrix, comprise link l _ipath collection, the j that represents route matrix is capable, i.e. path p _jthe link set comprising.

link l _ithe packet loss of=(j, k), sends n packet from node j, and node k successfully receives m packet, and this link packet drop rate is rate

Φ _pi: path p _ithe packet loss of=(j → s), sends n packet from leaf node j, along path p _itransmission, m the coated sink node of data receives, and path packet loss is Φ _pi=(n-m)/n.

T _pi: path packet loss threshold value.

If normal link packet drop rate there is the link packet drop rate of fault 0≤α < β≤1 wherein, we can obtain:

Inference 1: as path p _iwhile not comprising faulty link, path packet loss Φ _pi≤ α ^h.

For path p _i={ l ₁..., l _h, and link l _knormal, because of path p _iwhether interior link exists fault is separate, therefore

Inference 2: if path packet loss Φ _pi>=T _pi, be that each link in path is all normal.

Inference 3: if path packet loss Φ _pi≤ T _pi, at least there is the packet loss of a link in this path, at least there is a faulty link.

1, fault zone detection algorithm

The flow process of fault zone monitoring problem as shown in Figure 3.

1), according to network topological diagram G, obtain route matrix R;

2) carry out Data Collection n wheel;

3) in k wheel, leaf node sends to the data record of sink node,

4) according to route matrix R and calculate the minimum collection that covers, delete normal route collection p _gin the link that comprises;

5) all links after traversal simplification, the state (responsible or irrelevant) of judgement link, and it is carried out to respective handling, be simplified matrix

Table 1 route and link relation table

be responsible, and if only if at least there is a failure path p _jbe merely able to by link l _iexplain the reason that fault occurs.

be irrelevant, and if only if does not have the paths can be by link l _iexplain the reason that fault occurs.

As seen from table, we can only calculate link subset { l ₃, l ₄, l ₇, l ₉transfer rate just can, the packet loss of thinking other links is normal.By above-mentioned definition, know that the link of this link subset is all responsible again, therefore without the link of inferring that algorithm just can obtain breaking down.

By obtaining a simplification route matrix that does not comprise arbitrary link in normal route and the state of all links of detection residue.If link l _kstate is responsible, and all failure paths that comprise so this link will be explained.According to the data of end-to-end collection compare with path threshold, isolate normal route collection P _g, failure path collection P _b, and then obtain fault zone, simplify route matrix.

2, based on packet loss deduction end to end, improve algorithm

Known paths P ₂, P ₃the path packet loss that is, higher than threshold value, because we only need the packet loss of faulty link, therefore only needs the link l after computational short cut topology ₂, l ₃packet loss, as shown in Figure 4.

According to packet loss Inference Model, infer that algorithm starts to calculate to root node from leaf node.Therefore, difference is simplified with the route matrix in the monitoring of fault zone, the simplification route matrix that we need to simplify route matrix during fault zone is detected the all child nodes of middle root vertex in former route topological R, the subtree that sublink forms.According to the route matrix R in Fig. 4, calculate and simplify topology

$R=\left[\begin{array}{ccccccc}1& 0& 1& 0& 0& 0& 0\\ 1& 0& 0& 1& 0& 0& 0\\ 1& 0& 0& 0& 1& 0& 0\\ 0& 1& 0& 0& 0& 1& 0\\ 0& 1& 0& 0& 0& 0& 1\end{array}\right]\widetilde{\stackrel{R}{\&DoubleRightArrow;}}\left[\begin{array}{ccccccc}0& 0& 1& 0& 0& 0& 0\\ 0& 0& 0& 1& 0& 0& 0\\ 0& 0& 0& 0& 1& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\end{array}\right]=\tilde{R}$

According to the route matrix calculating known, comparing us with former algorithm only needs the number of times calculating seldom just can obtain all link packet drop rates that may break down.

it is the probability that successfully sends and received by node k from node f (k).X=(X _{i, j}) _{i, j ∈ N}, X _{i, j}∈ { 0,1}; X _{i, j}=1 represents that data send from node i, and is received by node j.X _{i, j}=0 represents that data send from node j, but node i is not received these data.

According to existing achievement in research, can obtain following formula:

Taking turns in Data Collection arbitrarily, we can obtain an X, and I take turns data by this and am called X _k=(X _{i, j}) _{i, j ∈ N}∈ { 0,1} ^{| R|}.

γ (k): in R (k), have at least the data of a node successfully to send to the probability of sink node.

γ(k)＝P[V _j∈R(k)X _s，j＝1] (1)

in β (k): R (k), have at least a node data successfully to send to the probability of node k.

β(k)＝P[V _j∈R(k)X _k，j＝1] (2)

$\stackrel{\&OverBar;}{\mathrm{\&beta;}}\left(k\right)={\mathrm{\&Pi;}}_{j\&Element;d\left(k\right)}(\stackrel{\&OverBar;}{\mathrm{\&beta;}}\left(j\right)+\mathrm{\&beta;}\left(j\right)\&times;(1-{\mathrm{\&psi;}}_j))---\left(3\right)$

A (k): at least exist the data of a node to arrive under the condition of node k at R (k), the probability that success is received by sink node.

A(k)＝P[X _s，k＝1|V _j∈R(k)X _k，j＝1] (4)

Based on Bernoulli distributional assumption, $A\left(k\right)={\mathrm{\&Pi;}}_{i~i}{{\mathrm{\&psi;}}_l}_i.---\left(5\right)$

According to above-mentioned three formula of formula, we can obtain:

$(1-\frac{\mathrm{\&gamma;}\left(k\right)}{A\left(k\right)})={\mathrm{\&Pi;}}_{j\&Element;d\left(k\right)}(1-\mathrm{\&gamma;}\left(j\right)/A\left(k\right))---\left(6\right)$

According to (1), (2) and (3), can obtain

γ(k)＝β(k)×A(k) (7)

γ (k) can obtain by the passive X of data end to end. x _kaverage in n wheel DRP data reception process.

${\hat{Y}}_k\left(m\right)={\hat{X}}_k\left(m\right),k\&Element;R---\left(8\right)$

${\hat{Y}}_k\left(m\right)=v_{j\&Element;d\left(k\right)}{\hat{Y}}_j\left(m\right),k\&Element;N\backslash R---\left(9\right)$

According to the definition of γ (k), we obtain:

$\widehat{\mathrm{\&gamma;}}\left(k\right)=n^{-1}{\mathrm{\&Sigma;}}_{m=1}^n{\hat{Y}}_k\left(m\right)---\left(10\right)$

According to the transfer rate of link defining us obtains:

$1-{{\mathrm{\&psi;}}_l}_k=\frac{A\left(k\right)}{A\left(f\left(k\right)\right)},k\&Element;N\left\{s\right\}---\left(11\right)$

According to (7)-(11), obtain contacting of link packet drop rate and X.γ (k) calculates by (8)-(10) obtain. a (k) is used formula (7) to calculate.Finally by formula (11), obtain

Because we are only concerned about the path of breaking down, before inferring, route matrix is simplified to processing.We are only concerned about the packet loss of faulty link, and the packet loss of normal link can not calculate.According to simplifying route matrix, obtain behind faulty link region, we can determining section normal link, so we only calculate the link packet drop rate in faulty link region.Improve algorithm flow as shown in Figure 5.Process infer_subtree calculates to using node k as the transmission rate of the topological tree link of root node, and the transmission rate of so all links can calculate by infer_subtree (s).Parameter a (k), need to carry out before initialization at execution infer_subtree (s).Process infer_subtree travels through route topological tree iterative computation by the degree of depth: for any non-leaf node k, first infer_subtree calculates the parameter of all child node j ∈ d (k) a (j), secondly according to formula, calculate respectively a (k), finally by formula, calculate the packet loss of link k calculate the algorithm find_solution (j) that A (k) is used, the flow process of algorithm as shown in Figure 6.Known according to formula (7), 0 < A (k)≤r (k)≤1, the precision set of link packet drop rate is 0.001.

3, based on passive heuristic greedy faulty link end to end, infer algorithm

Packet loss link inference problems is by data analysis end to end, finds most possible faulty link collection ?

${\arg \max }_{\mathrm{\&chi;}\&SubsetEqual;L}P\left(\mathrm{\&chi;}\right|X)---\left(12\right)$

According to Bayes' theorem, obtain:

P(χ|P _G，P _B)＝P(P _G，P _B|χ)P(χ)/P(P _G，P _B) (13)

P (χ | P _g, P _b) be that χ is the probability of fault link set under data set X condition end to end.P wherein _g, P _bbe respectively normal route collection, failure path collection.

(P _g, P _b) can calculate by X, uncorrelated with χ, so formula can be reduced to:

$\underset{\mathrm{\&chi;}\&SubsetEqual;L}{\arg \max }P\left(P_G\right|\mathrm{\&chi;})P\left(P_B\right|\mathrm{\&chi;})P\left(\mathrm{\&chi;}\right)---\left(14\right)$

P (χ) is the probability that in link set χ, all links exist fault, can calculate by following formula:

$P\left(\mathrm{\&chi;}\right)={\mathrm{\&Pi;}}_{k=1}^{\left|\mathrm{\&chi;}\right|}{p}_k^x{(1-p_k)}^{1-x}---\left(15\right)$

P wherein _kcan infer that algorithm calculates by link packet drop rate.

According to fault zone detection algorithm, the link that all normal routes comprise is all deleted from link set L.So P _gin comprise the link in χ, i.e. whether fault and P of χ _girrelevant and show according to data X end to end: P _gin all links be all normal.

P(P _G|χ)＝1 (16)

Formula (14) is reduced to:

$\underset{\mathrm{\&chi;}\&SubsetEqual;L}{\arg \max P}\left(P_B\right|\mathrm{\&chi;})P\left(\mathrm{\&chi;}\right)---\left(17\right)$

If χ ' is the optimal solution that we will calculate, in χ ', all links are faults so, and explain P completely _b(P (P _b| χ)=1), formula (17) is reduced to:

$\underset{\mathrm{\&chi;}\&SubsetEqual;L}{\arg \max P}\left(\mathrm{\&chi;}\right)=\underset{\mathrm{\&chi;}\&SubsetEqual;L}{\arg \max P}\left(\mathrm{\&chi;}\right){\mathrm{\&Pi;}}_{k=1}^{\left|\mathrm{\&chi;}\right|}{p}_k---\left(18\right)$

4, faulty link collection is inferred algorithm

Formula (16) is a NP-hard problem, and the present invention adopts heuristic greedy algorithm to address the above problem, and algorithm flow as shown in Figure 7.

(1) by fault detection algorithm, obtain fault zone;

(2) the packet loss p of link in the fault zone that faulty link packet loss deduction algorithm is inferred _k.

(3) calculate the weight w eigh (l of all links in link set _k)=p _k* | path (l _k) |;

(4) iteration is selected this link is inserted in χ, and delete all path collection path (l that comprise this link _k);

Failure path collection P _bfor sky, algorithm finishes.

These embodiment are interpreted as only for the present invention is described, is not used in and limits the scope of the invention above.After having read the content of record of the present invention, technical staff can make various changes or modifications the present invention, and these equivalences change and modification falls into the inventive method claim limited range equally.

Claims (5)

1. based on the passive link of a sensor network fault end to end estimating method, it is characterized in that, comprise the following steps:

101, obtain the topological diagram G of sensor network, according to topological diagram G, obtain route matrix R and set of paths P, then collect N wheel sensor network operation data, during wherein k takes turns, k≤N, the data record that the leaf node in topological diagram G sends to sink nodes is from set of paths P, select a not path p for access, judge whether the packet loss of this path p is greater than threshold value T _piif so, this path p is joined to normal route set P _gin, otherwise this path p is joined to failure path collection P _bin, and it is all accessed to travel through the whether all paths of P, if so, from failure path collection P _bone of middle selection is the link A of access not, and whether the state that judges this link responsible, if obtain faulty link and by all paths that comprise this link from failure path collection P _bin delete, if not do not deal with, repeat to be simplified route matrix

102, according to the simplification route matrix obtaining in step 101 be simplified fault zone, according to link packet drop rate Inference Model, and adopt heuristic greedy faulty link to infer that algorithm calculates the packet loss p that simplifies all links in fault zone _k;

103, calculate failure path collection P _bthe link l of middle weights maximum, and by the link l of this weights maximum from failure path collection P _bmiddle deletion, then adds the link l of this weights maximum in faulty link set to, deletes failure path collection P simultaneously _bin comprise this weights maximum the path of link l, repeating step 103, until failure path collection P _btill sky, complete the searching of faulty link.

2. according to claim 1 based on the passive link of sensor network fault end to end estimating method, it is characterized in that step 101 link l _ithe packet loss formula of=(j, k) expression sends n packet from node j, and node k successfully receives the packet loss of m packet.

3. according to claim 1ly based on the passive link of sensor network fault end to end estimating method, it is characterized in that, establish normal link packet drop rate there is the link packet drop rate of fault wherein 0≤α < β≤1, works as path p _iwhile not comprising faulty link, path packet loss Φ _pi≤ α ^h; If path packet loss Φ _pi>=T _pi, be that each link in path is all normal; If path packet loss Φ _pi≤ T _pi, at least there is the packet loss of a link in this path, at least there is a faulty link.

4. according to claim 1ly based on the passive link of sensor network fault end to end estimating method, it is characterized in that, in step 102, in computational short cut fault zone, the computing formula of the packet loss of all links is: wherein A (k) is illustrated in the data that R (k) at least exists a node and arrives under the condition of node k, the probability that received by aggregation node of success, γ (k) represents in leaf node subset, to have at least in descendants's node of node k the data of a node successfully to send to the probability of sink node.

5. according to claim 4ly based on the passive link of sensor network fault end to end estimating method, it is characterized in that, A (k) adopts find_solution algorithm to calculate.