Summary of the invention
The purpose of the disclosure is in view of the deficiencies of the prior art, to provide a kind of wireless sensor network topology resource distribution method
And device, using sensor node collection, aggregation node collection and the communication connection between them of bipartite graph modeling wireless sense network
Relationship, and by bipartite graph sampling technique, under conditions of keeping sampling subgraph and original topology structural similarity, according to user
Demand assignment sensor node and its communication link resources, using sensor node collection in bipartite graph modeling wireless sensor network
V1, aggregation node collection V2With the communication path side collection E of sensor node to aggregation node, and use mapping relations ψ:V1→ W=
1,2 ..., and k } modeling sensor node collection V1The categorical attribute of interior joint, V1Interior joint is divided into k class, is respectively labeled as 1,
2 ..., k.For arbitrary node classification w ∈ W, the sensor node quantity for being marked as w of user demand is modeled using L (w),
The present invention is based on the resource allocation methods of bipartite graph sampling, will obtain original bipartite graph topology G=(V1,V2, E) one sampling
Subgraph G '=(V '1,V′2, E '), it is desirable that in the condition for guaranteeing original bipartite graph topology G and sampling subgraph G ' topological structure similitude
Under so as to arbitrary node classification designator w ∈ W, meet | | N ' (w) | | close to L (w), wherein N ' (w)=v | v ∈ V '1∧ψ
(v)=w } it is sampling subset V '1In be classified to label w whole nodes constitute set.
Bipartite graph definition：If the node collection V=V of simple undirected graph G=(V, E)1∪V2, wherein V1∩V2=Φ and right
Two endpoint u and w of any a line e=(u, w) ∈ E can not belong to simultaneously V1Or belong to V simultaneously2, then the figure can indicate
For G=(V1,V2, E), and the figure is referred to as bipartite graph.
To achieve the goals above, the disclosure proposes a kind of wireless sensor network topology resource distribution method, specifically includes
Following steps：
Input：Using G=(V1,V2, E) modeling wireless sensor network bipartite graph topological structure, wherein V1For sensor section
Point set, V2Integrate for aggregation node and E is the communication path side collection of sensor node to aggregation node；Using many-one mapping relations
ψ:V1→ W=1,2 ..., and k } modeling sensor node collection V1The categorical attribute of interior joint, i.e. ψ (v1) it is V1Interior joint v1Classification
Label；It is rightUse L (w) modeling user to classification designator for the quantity required of the sensor node of w；Parameter T is set
(default value 1 is used for class node collection V2The biggish node of moderate) and R (default value 0.096, for adjusting node collection V2In
Spend the error in classification of larger node).
Output：Meet shrinkage in size sampling bipartite graph G '=(V ' of user demand quantity L (w)1,V′2,E′)。
Step 1, by node collection V2Middle whole node presses degree smoothly arrangement from big to small, and extracts preceding nhA maximum section of degree
Point configuration node subsetParameter nhIt is required to meet the following conditions：In bipartite graph G=(V1,V2, E) in, Node subsetsIn
The maximum value for possessing mutually unison number of nodes is T, i.e. nhIt is uniquely determined by input parameter T；
Step 2, calculate node collectionAnd decomposing bipartite graph G is two subgraphsWithWherein, side collection ELHAnd ELLIt is defined as：
Step 3, bipartite graph G interior joint collection V is obtained1Degree comprising all nodes, if d1,d2,…,dsFor these node degrees
In mutual unduplicated whole node degrees, if d1>d2>…>ds>0；
Step 4, calculate node degree d1,d2,…,dsIn node collection V1Distribution ratio f (d1),f(d2),…,f(ds),
Middle f (dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio；
Step 5, bipartite graph G interior joint collection is obtainedMaximal degree d comprising all nodesmax；
Step 6, node collection is obtainedDegree distribution g (d) | d=1,2 ..., dmax, wherein g (d) indicates node collection
Moderate be d number of nodes withRatio；
Step 7, bipartite graph G '=(V ' is initialized1,V′2, E ') and ← G=(V1,V2,E)；
Initialize bipartite graph
Initialize bipartite graph
The distribution of initialization node degree g ' (d) | d=1,2 ..., dmax} ← { g (d) | d=1,2 ..., dmax}；
Step 8, initializing variable t ← 0, η ← 0, and calculate the total E for needing to delete sided=| | E | | RR, wherein parameter
RRCalculation formula be：RR=(| | V1||-∑w∈WL(w))/||V1||；
Step 9, if t<Ed, go to step 10；Otherwise, current state G '=(V '1,V′2, E ') and it is final output,
InAnd E '=ELH′∪ELL′, go to step 18；
Step 10, ifη ← η -1 is then updated, two side collection are calculated：
Wherein, gatherWherein, rightDefinition set N ' (w)=
{v|v∈V′1∧ ψ (v)=w }, indicate sampling subset V '1In be classified to label w whole nodes constitute set, work as section
Point v1∈V′1When, N ' (ψ (v1))=v | v ∈ V '1∧ ψ (v)=ψ (v1), because of ψ (v1) ∈ W, and go to step 14；Otherwise, turn
Step 11；
Step 11, if setIf | | S | |>0, it is any to select in set S
One node v, and obtain the degree d of the nodevIf d=dv, go to step 13；
Step 12, if | | S | |=0, with discrete probability distribution pk/ΣI=1,2 ..., spi(k=1,2 ..., s) it randomly selects
One node degree d ∈ { d1,d2,…,ds, wherein pk(k=1,2 ..., s) it is defined as：Wherein f (d1),f
(d2),…,f(ds) obtained in step 4 by calculating, go to step 13；
Step 13, ifη ← η+1 is then updated, goes to step 17；IfThen calculate
Two side collection:
Go to step 14；If d>1, then calculate two side collection： Go to step 14；
Step 14, ifGo to step 15；IfGo to step 16；
IfThen with probability PLL′=γLL′/(γLH′+γLL′) 15 are gone to step, and with probability 1-PLL′
Go to step 16, wherein γLL′=mLL-(||ELL||-||ELL′| |), γLH′=mLH-(||ELH||-||ELH′| |),mLH=Ed-mLL；
Step 15, set of computations
WhereinIf | | Dh| |=0,
Then update Dh←{argdmaxd∈D{ g ' (d)-g (d) } }, set of computationsA subset：Calculate ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W,
N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, andSet of computations Ea's
A subset：Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' }, from set EbRandomly select to middle equal probability a line e ∈ Eb, and will
Side e is from two bipartite graph GLL′=(V '1,V2 LL′,ELL′) and G '=(V '1,V′2, E ') in delete, i.e., update ELL′←ELL′/{e}
And E ' ← E '/{ e }, update bipartite graph GLL′Interior joint collectionNode degree distribution g ' (d) | d=1,2 ..., dmax, wherein g '
(d) node collection is indicatedModerate is the node ratio of d, goes to step 17；
Step 16, ψ '=arg is calculatedψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W, N ' (ψ (v))
={ v1|v1∈V′1∧ψ(v1)=ψ (v) },
AndSet of computationsA subset：
From set EbRandomly select to middle equal probability a line e ∈ Eb, and by side e from
Two bipartite graphsWith G '=(V '1,V′2, E ') in delete, i.e., update ELH′←ELH′/ { e } and E ' ←
E '/{ e }, goes to step 17；
Step 17, t ← t+1 is updated, and updates three bipartite graph G '=(V '1,V′2,E′)、WithNode collection V '1、WithBy node collection V '1、WithThe node that moderate is 0 is complete
Portion is deleted, and goes to step 9；
Step 18, sampling subgraph G '=(V ' according to output1,V′2, E ') and it is opened up in the original bipartite graph of wireless sensor network
Flutter structure G=(V1,V2, E) in extract sampling subgraph covering sensor node collection V '1, aggregation node collection V '2And communication link
Side collection E ', and by V '1、V′2User is distributed to the topology resource of E ' covering.
Symbol definition：| | S | | indicate the gesture of set S, i.e. set S includes the sum of element.
X ← y indicates the variable x that the function on the right or numerical value y are assigned to the left side.
dvIndicate node v in bipartite graph G '=(V '1,V′2, E ') in degree.
It indicates logic " any ".
It indicates logic " presence ".
∧ indicates logical "and"；A ∧ B indicates that A and B is set up, i.e. A is set up and B is set up.
∈ indicates logic " belonging to ".
It indicates logic " being not belonging to ".
A/B：If A and B is numerical value, A/B indicates A divided by B；If A and B is two set, A/B is indicated from set
The all elements in set B are deleted in A.
G=(V1,V2, E) and indicate the wireless sensor network bipartite graph topological structure inputted.
ψ:V1→ W=1,2 ..., and k } indicate sensor node collection V1Many-one to node-classification label set W maps,
Middle ψ (v1) it is V1Interior joint v1Unique classification label.
L (w) indicates user to the quantity required for the sensor node that classification designator is w.
It is rightDefinition set N ' (w)=v | v ∈ V '1∧ ψ (v)=w }, indicate sampling subset V '1In be classified
The set constituted to whole nodes of label w.As node v1∈V′1When, N ' (ψ (v1))=v | v ∈ V '1∧ ψ (v)=ψ (v1),
Because of ψ (v1)∈W.As node v ∈ V '1When, N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, because of ψ (v) ∈ W and { v
|v∈V′1∧ ψ (v)=w } and { v1|v1∈V′1∧ψ(v1)=w } indicate the same set N ' (w).
T is input parameter, and default value 1 is used for class node collection V2The biggish node of moderate.
Wherein the value of parameter is referred to as default value under default condition, is the default value of system parameter, that is, indicates one
Initial value of the parameter before being modified.
" spending biggish node " refers to the degree and bipartite graph topology G=(V of the node1,V2, E) scale (that is, node is total
Number) proportional.
" spending lesser node " refers to the degree and bipartite graph topology G=(V of the node1,V2, E) scale (that is, node is total
Number) weak correlation.
" degree of node " refers to the sum on the side adjacent with the node.
R is input parameter, default value 0.096, for adjusting node collection V2The error in classification of the larger node of moderate.
Indicate set V2In preceding nhStep 1 is shown in a maximum node configuration node subset of degree, definition.
It indicates from set V2Middle deletion setThe set of all elements residue node.
WithFor bipartite graph topology G=(V1,V2, E) two subgraphs, definition
See step 2.
Expression is contained in set E and two endpoints are belonging respectively to node collection V1
With node collectionWhole sides constitute set.
Expression is contained in set E and two endpoints are belonging respectively to node collection V1
With node collectionWhole sides constitute set.
d1,d2,…,dsIndicate bipartite graph topology G interior joint collection V1Not mutual duplicate degree comprising all nodes, wherein d1
>d2>…>ds>0。
f(d1),f(d2),…,f(ds) indicate bipartite graph topology G interior joint collection V1Degree distribution comprising all nodes, i.e. f
(dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio.
dmaxIndicate bipartite graph topology G interior joint collectionMaximal degree comprising all nodes.
G (d) | d=1,2 ..., dmaxIndicate bipartite graph topology G interior joint collectionDegree distribution, i.e., g (d) is defined as
Node collectionModerate be d number of nodes withRatio.
G '=(V '1,V′2, E ') and indicate bipartite graph topology G=(V1,V2, E) sampling subgraph.
It indicates to decompose topologySampling subgraph.
It indicates to decompose topologySampling subgraph.
G ' (d) | d=1,2 ..., dmaxIndicate bipartite graphInterior joint collectionDegree point
Cloth, i.e. g ' (d) are defined as node collectionModerate be d number of nodes withRatio.
∑w∈WL (w) indicates the summation that L (w) is carried out to arbitrary element w in set W.
Indicate set V '1Middle element v1The a subset of composition, and
Element v1Meet the following conditions：And dv=1.
Indicate side collection ELH′Middle element (v, v2) constitute a subset, and member
Element (v, v2) meet condition：And
(v, u) indicates a line of two nodes v and u of connection.
Indicate side collection ELL′Middle element (v, v2) a subset that constitutes, and element
(v,v2) meet condition：And
RR=(| | V1||-∑w∈WL(w))/||V1| | it indicates to need from bipartite graph topology G=(V1,V2, E) in delete section
The ratio of sum of the points with bipartite graph G comprising node, is equal to the number of edges for needing to delete from bipartite graph G and the bipartite graph
G includes the ratio of the sum on side, because technical solution of the present invention can guarantee the average node degree of shrinkage in size process bipartite graph
Stablize constant.
Ed=| | E | | RRIt indicates from the sum for being input to the side that output needs to delete.
Expression belongs to node collection V '1And node degree is not belonging to set { d1,
d2,…,dsWhole nodes constitute set.
Indicate side collection ELH′Middle all elements (v, v2) constitute
A subset, and element (v, v2) meet condition：v∈V′1AndAnd dv=d.
Indicate side collection ELL′Middle all elements (v, v2) constitute
A subset, and element (v, v2) meet condition：v∈V′1AndAnd dv=d.
Indicate a subset that all elements d is constituted in set D, and member
Plain d meets condition：
It indicates the set that the degree of all node u is constituted, meets condition：Section
Point u belongs to setAnd there is set V '1Interior joint v makes side (v, u) belong to set
argdmaxd∈D{ g ' (d)-g (d) } indicates the value of element d in set D when g ' (d)-g (d) is maximized.
ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) } indicate to incite somebody to action | | N ' (ψ (v)) | |/L (ψ (v)) takes maximum
The value of ψ (v) is assigned to ψ ' when value, and wherein v is the element for belonging to set V.
Indicate side collectionThe a subset that middle all elements (v, u) are constituted meets
Condition：The degree d of node uuBelong to set Dh。
Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' } indicate side collection EaThe a subset that middle all elements (v, u) are constituted meets
Condition：ψ (v)=ψ '.
Expression belongs to set V '1And there is setInterior joint u makes side
(v, u) belongs to side collection EaWhole node v constitute set.
Expression belongs to set V '1And there is setInterior joint u makes
(v, u) belongs to Bian Ji on sideWhole node v constitute set.
The disclosure additionally provides a kind of wireless sensor network topology resource distributor, and described device includes：
First unit, using G=(V1,V2, E) modeling wireless sensor network bipartite graph topological structure, wherein V1For sensing
Device node collection, V2Integrate for aggregation node and E is the communication path side collection of sensor node to aggregation node；It is mapped using many-one
Relationship ψ:V1→ W=1,2 ..., and k } modeling sensor node collection V1The categorical attribute of interior joint, i.e. ψ (v1) it is V1Interior joint v1's
Classification designator；It is rightUse L (w) modeling user to classification designator for the quantity required of the sensor node of w；Parameter T
(default value 1 is used for class node collection V2The biggish node of moderate) and R (default value 0.096, for adjusting node collection V2In
Spend the error in classification of larger node)；
Second unit, by node collection V2Middle whole node presses degree smoothly arrangement from big to small, and extracts preceding nhA degree is maximum
Node configuration node subsetParameter nhIt is required to meet the following conditions：In bipartite graph G=(V1,V2, E) in, Node subsets
In possess mutually unison number of nodes maximum value be T, i.e. nhIt is uniquely determined by input parameter T；
Third unit, calculate node collectionAnd decomposing bipartite graph G is two subgraphs
WithWherein, side collection ELHAnd ELLIt is defined as：
Unit the 4th obtains bipartite graph G interior joint collection V1Degree comprising all nodes, if d1,d2,…,dsFor these nodes
Mutual unduplicated whole node degrees in degree, if d1>d2>…>ds>0；
Unit the 5th, calculate node degree d1,d2,…,dsIn node collection V1Distribution ratio f (d1),f(d2),…,f(ds),
Wherein f (dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio；
Unit the 6th obtains bipartite graph G interior joint collectionMaximal degree d comprising all nodesmax；
Unit the 7th obtains node collectionDegree distribution g (d) | d=1,2 ..., dmax, wherein g (d) indicates node collectionModerate be d number of nodes withRatio；
Unit the 8th initializes bipartite graph G '=(V '1,V′2, E ') and ← G=(V1,V2,E)；
Initialize bipartite graph
Initialize bipartite graph
The distribution of initialization node degree g ' (d) | d=1,2 ..., dmax} ← { g (d) | d=1,2 ..., dmax}；
Unit the 9th, initializing variable t ← 0, η ← 0, and calculate the total E for needing to delete sided=| | E | | RR, wherein
Parameter RRCalculation formula be：RR=(| | V1||-∑w∈WL(w))/||V1||；
Unit the 19th, if t<Ed, turn Unit the 11st；Otherwise, current state G '=(V '1,V′2, E ') and it is final defeated
Out as a result, whereinAnd E '=ELH′∪ELL′, turn Unit the 19th；
Unit the 11st, ifη ← η -1 is then updated, two side collection are calculated：
Wherein, gatherWherein, rightDefinition set
N ' (w)=v | v ∈ V '1∧ ψ (v)=w }, indicate sampling subset V '1In be classified to whole nodes of label w
The set of composition, as node v1∈V′1When, N ' (ψ (v1))=v | v ∈ V '1∧ ψ (v)=ψ (v1), because of ψ (v1) ∈ W, and turn
Unit the 15th；Otherwise, turn Unit the 12nd；
Unit the 12nd, if setIf | | S | |>0, any choice set
A node v in S is closed, and obtains the degree d of the nodevIf d=dv, turn Unit the 14th；
Unit the 13rd, if | | S | |=0, with discrete probability distribution pk/Σi=1,2,…,spi(k=1,2 ..., s) with
Machine extracts a node degree d ∈ { d1,d2,…,ds, wherein pk(k=1,2 ..., s) it is defined as：Wherein
f(d1),f(d2),…,f(ds) obtained in Unit the 5th by calculating, turn Unit the 14th；
Unit the 14th, ifη ← η+1 is then updated, Unit the 18th is turned；If
Then calculate two side collection:
Turn Unit the 15th；If d>1, then calculate two side collection： Turn Unit the 15th；
Unit the 15th, ifTurn Unit the 16th；IfTurn Unit the 17th；IfThen with probability PLL′=γLL′/(γLH′+γLL′) turn Unit the 16th, and with probability 1-PLL′Turn the
Unit 17, wherein γLL′=mLL-(||ELL||-||ELL′| |), γLH′=mLH-(||ELH||-||ELH′| |),mLH=Ed-mLL；
Unit the 16th, set of computations
WhereinIf | | Dh| |=0,
Then update Dh←{argdmaxd∈D{ g ' (d)-g (d) } }, set of computationsA subset：Calculate ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W,
N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, andSet of computations Ea's
A subset：Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' }, from set EbRandomly select to middle equal probability a line e ∈ Eb, and will
Side e is from two bipartite graphsWith G '=(V '1,V′2, E ') in delete, i.e., update ELL′←ELL′/ { e } and
E ' ← E '/{ e } updates bipartite graph GLL′Interior joint collectionNode degree distribution g ' (d) | d=1,2 ..., dmax, wherein g '
(d) node collection is indicatedModerate is the node ratio of d, turns Unit the 18th；
Unit the 17th calculates ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W, N ' (ψ
(v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) },
AndSet of computationsA subset：
From set EbRandomly select to middle equal probability a line e ∈ Eb, and by side e from two bipartite graphs
With G '=(V '1,V′2, E ') in delete, i.e., update ELH′←ELH′/ { e } and E ' ← E '/{ e } turn Unit the 18th；
Unit the 18th updates t ← t+1, and updates three bipartite graph G '=(V '1,V′2,E′)、WithNode collection V '1、WithBy node collection V '1、WithThe node that moderate is 0 is all deleted, and Unit the tenth is turned；
Unit the 19th, sampling subgraph G '=(V ' according to output1,V′2, E ') and at original two points of wireless sensor network
Graph topological structure G=(V1,V2, E) in extract sampling subgraph covering sensor node collection V '1, aggregation node collection V '2And communication
Chain roadside collection E ', and by V '1、V′2User is distributed to the topology resource of E ' covering.
The disclosure has the beneficial effect that：The disclosure provides a kind of wireless sensor network topology resource distribution method and device,
Using sensor node collection, aggregation node collection and the communication connection relationship between them of bipartite graph modeling wireless sense network, and
By bipartite graph sampling technique, under conditions of keeping sampling subgraph and original topology structural similarity, according to user demand point
With sensor node and its communication link resources, the equilibrium assignment of network variety classes resource can be more preferably realized, promote money
Source utilization efficiency solves and is guaranteeing the distribution resource (sensor node comprising topological diagram and its to the communication lines of aggregation node
Diameter side collection) topology with original topology structural similarity under the conditions of wireless sensor network topology resource distribute problem, to reach
Wireless sensor network topology resource equilibrium assignment promotes the economic benefits such as the level of resources utilization, the sensor according to user demand
Node species and quantity are user's essence under conditions of keeping wireless sensor network bipartite graph topological structure attribute to stablize constant
Really distribute the sensor node quantity and corresponding data link resource of its demand.
Specific embodiment
It is carried out below with reference to technical effect of the embodiment and attached drawing to the design of the disclosure, specific structure and generation clear
Chu, complete description, to be completely understood by the purpose, scheme and effect of the disclosure.It should be noted that the case where not conflicting
Under, the features in the embodiments and the embodiments of the present application can be combined with each other.
The classification schematic diagram of sensor node for the topological structure of wireless sensor network and wherein as shown in Figure 1, is tied below
Fig. 1 is closed to illustrate a kind of wireless sensor network topology resource distribution method according to embodiment of the present disclosure.
Sensor node and aggregation node are the chief components of wireless sensor network.Therefore, wireless sensor network
Topological structure can use bipartite graph G=(V1,V2, E) and it is modeled, wherein V1And V2It is two node collection, their object respectively
Reason meaning respectively represents sensor node collection and aggregation node collection, and E is side collection, and physical significance is that sensor node and convergence save
Data link between point.Bipartite graph G=(V1,V2, E) and there are following characteristics：
Bipartite graph definition：If the node collection V=V of simple undirected graph G=(V, E)1∪V2, wherein V1∩V2=Φ and right
Two endpoint u and w of any a line e=(u, w) ∈ E can not belong to simultaneously V1Or belong to V simultaneously2, then the figure can indicate
For G=(V1,V2, E), and the figure is referred to as bipartite graph.
Node collection V1And V2Intersection be empty set, i.e. the uncommon element of two node collection；And every in the side collection E of figure G
Two endpoints on side must be belonging respectively to V1And V2Two different node collection.
In addition to bipartite graph topological structure, wireless sensor network also has other feature：
In aggregation node collection V2It is middle there are two kinds of different type nodes, respectively spend biggish node (its node degree with
The growth of topological scale approximatively linear increase) and the lesser node of degree (its node degree is protected with the growth approximation of topological scale
Hold constant), wherein topological scale refers to the sum of topological diagram interior joint.In addition, in sensor node collection V1Middle all the sensors
Node can be divided into the different types of k, such as it 2 can be humidity that type 1, which can be that temperature sensor node integrates, classifies,
Sensor node collection etc..
Therefore, for sensor node collection V1, a many-one mapping from sensor node to node-classification can be constructed
Relationship ψ:V1→ W={ 1,2 ..., k }, wherein set W includes all classification designators of sensor node.Mapping relations ψ:V1→W
Refer to, to arbitrary node v ∈ V1, element ψ (v) the ∈ W of existence anduniquess is corresponding to it in set W, i.e. ψ (v) is belonging to node v
Classification designator.Symbol ∈ indicates logic " belonging to ".
For a user, it needs from sensor node collection V1Middle choose has certain amount of different types of sensing
Device node.It might as well set, for arbitrary node classification designator w ∈ W, the sensor node quantity that user needs accordingly to classify is L
(w).Then, target of the invention is exactly, be from original bipartite graph topology G=(V1,V2, E) in, obtain a sampling subgraph G '=
(V′1,V′2, E '), whereinAndSo that meeting to arbitrary node classification designator w ∈ W | | N ' (w)
| | close to L (w), wherein N ' (w)={ v1|v1∈V′1∧ψ(v1)=w } indicate sampling subset V '1In be classified to label w's
Whole nodes, and sampling subgraph G ' and original bipartite graph topology G is required to keep similar topological structure attribute.SymbolIndicate collection
" by the comprising " relationship closed, | | N ' (w) | | indicate the element sum that set N ' (w) includes, symbol ∧ expression logical "and", symbol
∈ indicates logic " belonging to ".
The present invention will be using sensor node collection V in bipartite graph modeling wireless sensor network1, aggregation node collection V2And sensing
Device node to aggregation node communication path side collection E, and use mapping relations ψ:V1→ W=1,2 ..., and k } modeling sensor section
Point set V1The categorical attribute of interior joint.Sensor node quantity for user demand different classifications is L (w), wherein w ∈ W, this
The resource allocation methods that invention is sampled based on bipartite graph will obtain original bipartite graph topology G=(V1,V2, E) a sampling son
Scheme G '=(V '1,V′2, E '), it is desirable that in the condition for guaranteeing original bipartite graph topology G and sampling subgraph G ' topological structure similitude
Under, so as to arbitrary node classification designator w ∈ W, meet | | N ' (w) | | close to L (w), wherein N ' (w)={ v1|v1∈V′1∧
ψ(v1)=w } it is sampling subset V '1In be classified to label w whole nodes constitute set.The present invention, which solves, to be guaranteed to divide
With resource (sensor node comprising topological diagram and its to the communication path side collection of aggregation node) topology and original topology structure
Wireless sensor network topology resource under similarity Condition distributes problem, to reach wireless sensor network topology resource equilibrium point
Match, promote the economic benefits such as the level of resources utilization.
Symbol definition：| | S | | indicate the gesture of set S, i.e. set S includes the sum of element.
X ← y indicates the variable x that the function on the right or numerical value y are assigned to the left side.
dvIndicate node v in bipartite graph G '=(V '1,V′2, E ') in degree.
It indicates logic " any ".
It indicates logic " presence ".
∧ indicates logical "and"；A ∧ B indicates that A and B is set up, i.e. A is set up and B is set up.
∈ indicates logic " belonging to ".
It indicates logic " being not belonging to ".
A/B：If A and B is numerical value, A/B indicates A divided by B；If A and B is two set, A/B is indicated from set
The all elements in set B are deleted in A.
G=(V1,V2, E) and indicate the wireless sensor network bipartite graph topological structure inputted.
ψ:V1→ W=1,2 ..., and k } indicate sensor node collection V1Many-one to node-classification label set W maps,
Middle ψ (v1) it is V1Interior joint v1Classification designator.
L (w) indicates user to the quantity required for the sensor node that classification designator is w.
It is rightDefinition set N ' (w)=v | v ∈ V '1∧ ψ (v)=w }, indicate sampling subset V '1In be classified
The set constituted to whole nodes of label w.It note that as node v1∈V′1When, N ' (ψ (v1))=v | v ∈ V '1∧ψ(v)
=ψ (v1), because of ψ (v1)∈W.As node v ∈ V '1When, N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, because of ψ
(v) ∈ W and v | v ∈ V '1∧ ψ (v)=w } and { v1|v1∈V′1∧ψ(v1)=w } indicate the same set N ' (w).
T is input parameter, and default value 1 is used for class node collection V2The biggish node of moderate.
Wherein the value of parameter is referred to as default value under default condition, is the default value of system parameter, that is, indicates one
Initial value of the parameter before being modified.
" spending biggish node " refers to the degree and bipartite graph topology G=(V of the node1,V2, E) scale (that is, node is total
Number) proportional.
" spending lesser node " refers to the degree and bipartite graph topology G=(V of the node1,V2, E) scale (that is, node is total
Number) weak correlation.
" degree of node " refers to the sum on the side adjacent with the node.
R is input parameter, default value 0.096, for adjusting node collection V2The error in classification of the larger node of moderate.
Indicate set V2In preceding nhStep 1 is shown in a maximum node configuration node subset of degree, definition.
It indicates from set V2Middle deletion setThe set of all elements residue node.
WithFor bipartite graph topology G=(V1,V2, E) two subgraphs, definition
See step 2.
Expression is contained in set E and two endpoints are belonging respectively to node collection V1
With node collectionWhole sides constitute set.
Expression is contained in set E and two endpoints are belonging respectively to node collection V1
With node collectionWhole sides constitute set.
d1,d2,…,dsIndicate bipartite graph topology G interior joint collection V1Not mutual duplicate degree comprising all nodes, wherein d1
>d2>…>ds>0。
f(d1),f(d2),…,f(ds) indicate bipartite graph topology G interior joint collection V1Degree distribution comprising all nodes, i.e. f
(dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio.
dmaxIndicate bipartite graph topology G interior joint collectionMaximal degree comprising all nodes.
G (d) | d=1,2 ..., dmaxIndicate bipartite graph topology G interior joint collectionDegree distribution, i.e., g (d) is defined as
Node collectionModerate be d number of nodes withRatio.
G '=(V '1,V′2, E ') and indicate bipartite graph topology G=(V1,V2, E) sampling subgraph.
It indicates to decompose topologySampling subgraph.
It indicates to decompose topologySampling subgraph.
G ' (d) | d=1,2 ..., dmaxIndicate bipartite graphInterior joint collectionDegree distribution,
That is g ' (d) is defined as node collectionModerate be d number of nodes withRatio.
∑w∈WL (w) indicates the summation that L (w) is carried out to arbitrary element w in set W.
Indicate set V '1Middle element v1The a subset of composition, and
Element v1Meet the following conditions：And dv=1.
Indicate side collection ELH′Middle element (v, v2) constitute a subset, and member
Element (v, v2) meet condition：And
(v, u) indicates a line of two nodes v and u of connection.
Indicate side collection ELL′Middle element (v, v2) a subset that constitutes, and element
(v,v2) meet condition：And
RR=(| | V1||-∑w∈WL(w))/||V1| | it indicates to need from bipartite graph topology G=(V1,V2, E) in delete section
The ratio of sum of the points with bipartite graph G comprising node, is equal to the number of edges for needing to delete from bipartite graph G and the bipartite graph
G includes the ratio of the sum on side, because technical solution of the present invention can guarantee the average node degree of shrinkage in size process bipartite graph
Stablize constant.
Ed=| | E | | RRIt indicates from the sum for being input to the side that output needs to delete.
Expression belongs to node collection V '1And node degree is not belonging to set { d1,
d2,…,dsWhole nodes constitute set.
Indicate side collection ELH′Middle all elements (v, v2) constitute
A subset, and element (v, v2) meet condition：v∈V′1AndAnd dv=d.
Indicate side collection ELL′Middle all elements (v, v2) constitute
A subset, and element (v, v2) meet condition：v∈V′1AndAnd dv=d.
Indicate a subset that all elements d is constituted in set D, and member
Plain d meets condition：
It indicates the set that the degree of all node u is constituted, meets condition：Section
Point u belongs to setAnd there is set V '1Interior joint v makes side (v, u) belong to set
argdmaxd∈D{ g ' (d)-g (d) } indicates the value of element d in set D when g ' (d)-g (d) is maximized.
ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) } indicate to incite somebody to action | | N ' (ψ (v)) | |/L (ψ (v)) takes maximum
The value of ψ (v) is assigned to ψ ' when value, and wherein v is the element for belonging to set V.
Indicate side collectionThe a subset that middle all elements (v, u) are constituted meets
Condition：The degree d of node uuBelong to set Dh。
Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' } indicate side collection EaThe a subset that middle all elements (v, u) are constituted meets
Condition：ψ (v)=ψ '.
Expression belongs to set V '1And there is setInterior joint u makes side
(v, u) belongs to side collection EaWhole node v constitute set.
Expression belongs to set V '1And there is setInterior joint u makes side
(v, u) belongs to Bian JiWhole node v constitute set.
The disclosure proposes a kind of wireless sensor network topology resource distribution method, and process is as shown in Fig. 2, specifically include following
Step：
Input：Using G=(V1,V2, E) modeling wireless sensor network bipartite graph topological structure, wherein V1For sensor section
Point set, V2Integrate for aggregation node and E is the communication path side collection of sensor node to aggregation node；Using many-one mapping relations
ψ:V1→ W=1,2 ..., and k } modeling sensor node collection V1The categorical attribute of interior joint, i.e. ψ (v1) it is V1Interior joint v1Classification
Label；It is rightUse L (w) modeling user to classification designator for the quantity required of the sensor node of w；Parameter T is (default
Value is 1, is used for class node collection V2The biggish node of moderate) and R (default value 0.096, for adjusting node collection V2Moderate compared with
The error in classification of big node).
Output：Meet shrinkage in size sampling bipartite graph G '=(V ' of user demand quantity L (w)1,V′2,E′)。
The process and step of method：
Step 1, by node collection V2Middle whole node presses degree smoothly arrangement from big to small, and extracts preceding nhA maximum section of degree
Point configuration node subsetParameter nhIt is required to meet the following conditions：In bipartite graph G=(V1,V2, E) in, Node subsetsIn
The maximum value for possessing mutually unison number of nodes is T.That is, nhIt is uniquely determined by input parameter T.
Step 2, calculate node collectionAnd decomposing bipartite graph G is two subgraphsWithWherein, side collection ELHAnd ELLIt is defined as：
Step 3, bipartite graph G interior joint collection V is obtained1Degree comprising all nodes, if d1,d2,…,dsFor these node degrees
In mutual unduplicated whole node degrees.D might as well be set1>d2>…>ds>0。
Step 4, calculate node degree d1,d2,…,dsIn node collection V1Distribution ratio f (d1),f(d2),…,f(ds),
Middle f (dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio.
Step 5, bipartite graph G interior joint collection is obtainedMaximal degree d comprising all nodesmax。
Step 6, node collection is obtainedDegree distribution g (d) | d=1,2 ..., dmax, wherein g (d) indicates node collection
Moderate be d number of nodes withRatio.
Step 7, bipartite graph G '=(V ' is initialized1,V′2, E ') and ← G=(V1,V2,E)；
Initialize bipartite graph
Initialize bipartite graph
The distribution of initialization node degree g ' (d) | d=1,2 ..., dmax} ← { g (d) | d=1,2 ..., dmax}。
Step 8, initializing variable t ← 0, η ← 0, and calculate the total E for needing to delete sided=| | E | | RR, wherein parameter
RRCalculation formula be：RR=(| | V1||-∑w∈WL(w))/||V1||。
Step 9, if t<Ed, go to step 10；Otherwise, current state G '=(V '1,V′2, E ') and it is final output,
InAnd E '=ELH′∪ELL′, go to step 18.
Step 10, ifη ← η -1 is then updated, two side collection are calculated：
Wherein, gatherWherein N ' (ψ (v1))=v | v ∈
V′1∧ ψ (v)=ψ (v1), and go to step 14；Otherwise, 11 are gone to step.
Step 11, if setIf | | S | |>0, it is any to select in set S
One node v, and obtain the degree d of the nodevIf d=dv, go to step 13.
Step 12, if | | S | |=0, with discrete probability distribution pk/ΣI=1,2 ..., spi(k=1,2 ..., s) it randomly selects
One node degree d ∈ { d1,d2,…,ds, wherein pk(k=1,2 ..., s) it is defined as：
Wherein f (d1),f(d2),…,f(ds) obtained in step 4 by calculating.Go to step 13.
Step 13, ifη ← η+1 is then updated, goes to step 17；
IfThen calculate two side collection:
Turn step
Rapid 14；
If d>1, then calculate two side collection：
Go to step 14.
Step 14, ifGo to step 15；
IfGo to step 16；
IfThen with probability PLL′=γLL′/(γLH′+γLL′) 15 are gone to step, and with probability
1-PLL′Go to step 16.Wherein,
γLL′=mLL-(||ELL||-||ELL′| |), γLH′=mLH-(||ELH||-||ELH′| |),
mLH=Ed-mLL。
Step 15, set of computationsWherein
If | | Dh| |=0, then update Dh←{argdmaxd∈D{g′(d)-g(d)}}。
Set of computationsA subset：
Calculate ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ
(v) ∈ W, N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, and
Set of computations EaA subset：
Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' }, from set EbRandomly select to middle equal probability a line e ∈ Eb, and will
Side e is from two bipartite graph GLL '=(V '1,V2 LL′,ELL′) and G '=(V '1,V′2, E ') in delete, i.e., update ELL′←ELL′/{e}
And E ' ← E '/{ e }.
Update bipartite graph GLL′Interior joint collectionNode degree distribution g ' (d) | d=1,2 ..., dmax, wherein g ' (d) table
Show node collectionModerate is the node ratio of d.Go to step 17.
Step 16, ψ '=arg is calculatedψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W, N ' (ψ (v))
={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, and
Set of computationsA subset：
From set EbRandomly select to middle equal probability a line e ∈ Eb, and by side
E is from two bipartite graphsWith G '=(V '1,V′2, E ') in delete, i.e., update ELH′←ELH′/ { e } and E '
←E′/{e}。
Go to step 17.
Step 17, t ← t+1 is updated, and updates three bipartite graph G '=(V '1,V′2,E′)、WithNode collection V '1、WithBy node collection V '1、WithThe node that moderate is 0 is complete
It deletes in portion.Go to step 9.
Step 18, sampling subgraph G '=(V ' according to output1,V′2, E ') and it is opened up in the original bipartite graph of wireless sensor network
Flutter structure G=(V1,V2, E) in extract sampling subgraph covering sensor node collection V '1, aggregation node collection V '2And communication link
Side collection E ', and by V '1、V′2User is distributed to the topology resource of E ' covering.
The present invention is using the sensor node resource allocation of wireless sensor network as embodiment.According to the sensor of user demand
The sensor node quantity of demand under node species and variety classes can keep sampling by bipartite graph sampling technique
Between figure and original wireless sensor network bipartite graph topology under conditions of structural similarity, opening up needed for it is accurately distributed for user
Flutter resource.The bipartite graph topological structure and respective sensor node-classification schematic diagram of wireless sensor network are as shown in Figure 1.In topology
The similitude of sampling subgraph and original bipartite graph topological structure is kept in resource allocation process, for sensor node to aggregation node
The reasonable distribution of communication link and aggregation node provides technology guarantee, can reach and effectively promotes wireless sensor net topology money
The economic benefit of source utilization efficiency.
In the topological sampling process of bipartite graph, the topological attribute of selection includes：
Node degree is distributed two dimensional attributesIt is defined asWherein d is node degree,
For the ratio for spending number of nodes and topological graph node sum greater than d, f (k) is the ratio that topological diagram moderate is k node.Two dimension belongs to
PropertyIn rectangular coordinate system, be using d as variable and withFor the two-dimensional function curve of functional value.
Cluster coefficients two dimensional attributes C (d) vs.d：
It is defined as C (d)=2Td/ d (d-1), wherein Td=∑I=1,2 ..., tT(vi)/t, d are node degree, and C (d) is node degree
The cluster coefficients of d, v1,v2,…,vtThe node for being d for degree all in topological diagram, t is the node total number that topological diagram moderate is d, T
(vi) it is node viThe sum on side between any two adjacent node.Two dimensional attributes C (d) vs.d is with d in rectangular coordinate system
For variable and with two-dimensional function curve that C (d) is functional value.
Path length distribution two dimensional attributes μ (l) vs.l：Defining μ (l) is that mutual shortest path length is l in topological diagram
Node to sum occupy whole nodes to sum ratio.Two dimensional attributes μ (l) vs.l is with l for change in rectangular coordinate system
Amount and with μ (l) be functional value two-dimensional function curve.
Wherein vs. indicate " relative to ", i.e., expression vs. before function be relative to vs. after variable function.For example,Vs.d is indicatedIt is the function relative to variable d.
Application effect shows to account in the original bipartite graph topology of wireless sensor network when the sensor node sum of user demand
When the ratio of sensor node sum is lower than 10% (the node percentage for needing to delete is 90% or more), the present invention can still have
Effect keeps sampling subgraph and original topology in the similitude of above topology attribute.
The disclosure additionally provides a kind of wireless sensor network topology resource distributor, and described device includes：
First unit, using G=(V1,V2, E) modeling wireless sensor network bipartite graph topological structure, wherein V1For sensing
Device node collection, V2Integrate for aggregation node and E is the communication path side collection of sensor node to aggregation node；It is mapped using many-one
Relationship ψ:V1→ W=1,2 ..., and k } modeling sensor node collection V1The categorical attribute of interior joint, i.e. ψ (v1) it is V1Interior joint v1's
Classification designator；It is rightUse L (w) modeling user to classification designator for the quantity required of the sensor node of w；Parameter T
(default value 1 is used for class node collection V2The biggish node of moderate) and R (default value 0.096, for adjusting node collection V2In
Spend the error in classification of larger node)；
Second unit, by node collection V2Middle whole node presses degree smoothly arrangement from big to small, and extracts preceding nhA degree is maximum
Node configuration node subsetParameter nhIt is required to meet the following conditions：In bipartite graph G=(V1,V2, E) in, Node subsets
In possess mutually unison number of nodes maximum value be T, i.e. nhIt is uniquely determined by input parameter T；
Third unit, calculate node collectionAnd decomposing bipartite graph G is two subgraphs
WithWherein, side collection ELHAnd ELLIt is defined as：
Unit the 4th obtains bipartite graph G interior joint collection V1Degree comprising all nodes, if d1,d2,…,dsFor these nodes
Mutual unduplicated whole node degrees in degree, if d1>d2>…>ds>0；
Unit the 5th, calculate node degree d1,d2,…,dsIn node collection V1Distribution ratio f (d1),f(d2),…,f(ds),
Wherein f (dk) (k=1,2 ..., s) it is defined as set V1Moderate is dkNumber of nodes with | | V1| | ratio；
Unit the 6th obtains bipartite graph G interior joint collectionMaximal degree d comprising all nodesmax；
Unit the 7th obtains node collectionDegree distribution g (d) | d=1,2 ..., dmax, wherein g (d) indicates node collectionModerate be d number of nodes withRatio；
Unit the 8th initializes bipartite graph G '=(V '1,V′2, E ') and ← G=(V1,V2,E)；
Initialize bipartite graph
Initialize bipartite graph
The distribution of initialization node degree g ' (d) | d=1,2 ..., dmax} ← { g (d) | d=1,2 ..., dmax}；
Unit the 9th, initializing variable t ← 0, η ← 0, and calculate the total E for needing to delete sided=| | E | | RR, wherein
Parameter RRCalculation formula be：RR=(| | V1||-∑w∈WL(w))/||V1||；
Unit the tenth, if t<Ed, turn Unit the 11st；Otherwise, current state G '=(V '1,V′2, E ') and it is final output
As a result, whereinAnd E '=ELH′∪ELL′, turn Unit the 19th；
Unit the 11st, ifη ← η -1 is then updated, two side collection are calculated：
Wherein, gatherWherein N ' (ψ (v1))=v | v ∈ V '1∧ψ(v)
=ψ (v1), and turn Unit the 15th；Otherwise, turn Unit the 12nd；
Unit the 12nd, if setIf | | S | |>0, any choice set
A node v in S is closed, and obtains the degree d of the nodevIf d=dv, turn Unit the 14th；
Unit the 13rd, if | | S | |=0, with discrete probability distribution pk/ ΣI=1,2 ..., sPi (k=1,2 ..., s) it is random
Extract a node degree d ∈ { d1,d2,…,ds, wherein pk(k=1,2 ..., s) it is defined as：Wherein f
(d1),f(d2),…,f(ds) obtained in Unit the 5th by calculating, turn Unit the 14th；
Unit the 14th, ifη ← η+1 is then updated, Unit the 18th is turned；If
Then calculate two side collection:
Turn Unit the 15th；If d>1, then calculate two side collection： Turn Unit the 15th；
Unit the 15th, ifTurn Unit the 16th；IfTurn Unit the 17th；IfThen with probability PLL′=γLL′/(γLH′+γLL′) turn Unit the 16th, and with probability 1-PLL′Turn the
Unit 17, wherein γLL′=mLL-(||ELL||-||ELL′| |), γLH′=mLH-(||ELH||-||ELH′| |),mLH=Ed-mLL；
Unit the 16th, set of computations
WhereinIf | | Dh| |=0,
Then update Dh←{argdmaxd∈D{ g ' (d)-g (d) } }, set of computationsA subset：Calculate ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W,
N ' (ψ (v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) }, andSet of computations Ea's
A subset：Eb={ (v, u) ∈ Ea| ψ (v)=ψ ' }, from set EbRandomly select to middle equal probability a line e ∈ Eb, and will
Side e is from two bipartite graphsWith G '=(V '1,V′2, E ') in delete, i.e., update ELL′←ELL′/ { e } and
E ' ← E '/{ e } updates bipartite graph GLL′Interior joint collectionNode degree distribution g ' (d) | d=1,2 ..., dmax, wherein g '
(d) node collection is indicatedModerate is the node ratio of d, turns Unit the 18th；
Unit the 17th calculates ψ '=argψ(v)maxv∈V| | N ' (ψ (v)) | |/L (ψ (v)) }, wherein ψ (v) ∈ W, N ' (ψ
(v))={ v1|v1∈V′1∧ψ(v1)=ψ (v) },
AndSet of computationsA subset：
From set EbRandomly select to middle equal probability a line e ∈ Eb, and by side e from
Two bipartite graphsWith G '=(V '1,V′2, E ') in delete, i.e., update ELH′←ELH′/ { e } and E ' ←
E '/{ e } turns Unit the 18th；
Unit the 18th updates t ← t+1, and updates three bipartite graph G '=(V '1,V′2,E′)、WithNode collection V '1、WithBy node collection V '1、WithThe node that moderate is 0 is all deleted, and Unit the tenth is turned；
Unit the 19th, sampling subgraph G '=(V ' according to output1,V′2, E ') and at original two points of wireless sensor network
Graph topological structure G=(V1,V2, E) in extract sampling subgraph covering sensor node collection V '1, aggregation node collection V '2And communication
Chain roadside collection E ', and by V '1、V′2User is distributed to the topology resource of E ' covering.
A kind of wireless sensor network topology resource distributor can run on desktop PC, notebook, the palm
Upper computer and cloud server etc. calculate in equipment.A kind of wireless sensor network topology resource distributor, can run
Device may include, but be not limited only to, processor, memory.It will be understood by those skilled in the art that the example is only one kind
The example of wireless sensor network topology resource distributor is not constituted to a kind of wireless sensor network topology resource distributor
Restriction, may include component more more or fewer than example, perhaps combine certain components or different components, such as institute
Stating a kind of wireless sensor network topology resource distributor can also include input-output equipment, network access equipment, bus etc..
Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng the processor is a kind of control centre of wireless sensor network topology resource distributor running gear, using each
Kind of interface and connection entirely a kind of wireless sensor network topology resource distributor can running gear various pieces.
The memory can be used for storing the computer program and/or module, and the processor is by operation or executes
Computer program in the memory and/or module are stored, and calls the data being stored in memory, described in realization
A kind of various functions of wireless sensor network topology resource distributor.The memory can mainly include storing program area and deposit
Store up data field, wherein storing program area can application program needed for storage program area, at least one function (for example sound is broadcast
Playing function, image player function etc.) etc.；Storage data area, which can be stored, uses created data (such as audio according to mobile phone
Data, phone directory etc.) etc..In addition, memory may include high-speed random access memory, it can also include non-volatile memories
Device, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure
Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or other volatibility are solid
State memory device.
Although the description of the disclosure is quite detailed and especially several embodiments are described, it is not
Any of these details or embodiment or any specific embodiments are intended to be limited to, but should be considered as is by reference to appended
A possibility that claim provides broad sense in view of the prior art for these claims explanation, to effectively cover the disclosure
Preset range.In addition, the disclosure is described with inventor's foreseeable embodiment above, its purpose is to be provided with
Description, and those equivalent modifications that the disclosure can be still represented to the unsubstantiality change of the disclosure still unforeseen at present.