CN109874159A - Moving machine based on comentropy can network node social relationships measurement, cluster foundation and update and method for routing - Google Patents
Moving machine based on comentropy can network node social relationships measurement, cluster foundation and update and method for routing Download PDFInfo
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Abstract
The invention discloses a kind of, and the moving machine based on comentropy understands network node social relationships measure, select the feature for influencing social relationships between node as social relationships evaluation index, influence of the different evaluation index to social relationships intensity between node is measured using comentropy, and using the influence as weight, by being weighted social relationships intensity summation obtains node to each evaluation index value.The invention also discloses a kind of clusters to establish and update and method for routing, and the method for routing based on cluster, each node selection forms a local cluster with the node for oneself having close social relationships, makes full use of the close social relationships between cluster member to carry out the exchange of information and the forwarding of message.The present invention can social relationships between accurate node metric, social relationships, which carry out routing, between calculated node can effectively improve network performance.
Description
Technical field
The moving machine that the present invention relates to a kind of based on comentropy can network node social relationships measurement, cluster establish with update and
Method for routing.
Background technique
Mobile opportunistic network is a kind of novel mobile ad-hoc network, human-centred, is formed using various kinds of sensors
Sensing network, real-time perception people's production and living.It moves opportunistic network in recent years to be widely applied, and in data collection
With increasingly important role has been played in data sharing.Mobile opportunistic network is passed using the data of " storage-carrying-forwarding "
Defeated strategy, node store the data being ready for sending first, and it is mobile laggard up to encountering suitable relay node then to carry data
The transmission of row data.In mobile opportunistic network, the transmission process of message is that dynamic carries out, i.e., any node all may be used in network
It can be used as relay node, help message forwarding.Different from network topology environment metastable in conventional ad-hoc network, even
The presence that not can guarantee communication path end to end off and on chain link road, this allows for first establishing in traditional routing algorithms
The operating mode transmitted after routing can not effectively be run in mobile opportunistic network.Therefore, the routing of conventional ad-hoc network is calculated
Method is no longer desirable for mobile opportunistic network, and routing algorithm becomes a research hotspot of mobile opportunistic network.
The transmission of message is dependent on the movement between node in mobile opportunistic network.In mobile opportunistic network, node society
Relationship has a major impact routing performance.Node in network is made of the portable movable equipment of people, therefore it is contacted
Behavior can show certain social.Carrying out Design of Routing Algorithm using node social relationships can effectively help message to turn
It sends out, while avoiding the generation of redundancy message in message transmitting procedure, be conducive to the raising of network performance.Have some researchs
Work [1] show the social relationships between node will affect node meet event and meet the duration situations such as, these research
Work using node social property design data forwarding mechanism and achieve good effect.
The interval time that Most scholars are contacted twice using frequency of exposure, the node of node at present, duration of contact
Etc. indexs carry out the social relationships between node metric.When two node contacts are frequent, then it is assumed that the two nodes have close society
Relationship.
The probability that some scholars go prediction node to meet by historical data, and realize the optimization to routing algorithm；It is some
Scholar goes the power of social relationships between supposition node by the frequency that node meets, and forms node cluster, construction with social relationships
Routing mechanism based on cluster.Document [2] devises a kind of forwarding algorithm based on friends.Think that there is higher connect in text
Touching number, the node of longer time of contact are friends, and design routing algorithm with this.Document [3] has studied user social contact
Figure routing algorithm, it is believed that friends is a kind of social relationships of high stability, message can be helped to forward.
In Bubble[4]In, the social status of node is by Global center degree and local centrad two indices evaluation.It is setting
When counting forwarding mechanism, consider node in the status of this community and locating community in global status.When data are not transferred to
It before community locating for destination node, is forwarded according to the social status position sequence of node, i.e. message is to possessing higher social status
Node forwarding.When transmitting destination node in the community locating for destination node, the node for selecting community Zhong She to hold a high place in society turns
Hair, reduces network routing cost with this.SimBet value of the document [5] by centrad and similarity common metrics node, SimBet
Being worth high node has higher social status, and message is prioritized when forwarding.
In document [6], propose social relationships chance routing algorithm SROR, use node social relationships and overview as
The key index of optimal forward node is selected in routing procedure, optimizes routing performance.Dynamic society's relationship road in document [7]
By algorithm, the social relationships in network are assessed by the Physics society relationship and its collision probability of node, then dynamic updates
Social relationships matrix between calculate node.According to social relationships matrix, the source node of message selects suitable member node to make
For relay node, it is delivered to destination node, avoids a possibility that selfish node abandons message in network to greatest extent.
In document [8], it was found that one for moving the context-aware frame for routing and forwarding in opportunistic network.The frame is logical
, and the context-aware routing of various styles can be carried.Scholar proposes a kind of specific agreement HiBOp, utilizes
Frame is forwarded over to push by information such as contextual information study, user behavior performance and the social relationships between them
Journey.
Document [9] is auxiliary information using the geographical location information of node and social relationships, proposes that a kind of society that is based on closes
The more copy routing algorithms of the opportunistic network low latency of system.Selection has the node and and destination node of substantial connection with destination node
The close node in geographical location helps message forwarding as relay node.Pass through mobile node in analysis network in document [10]
Real time data, summarize and propose to influence the factor of social relationships between these mobile nodes.Then from multiple angles to these
The complexity and dynamic of factor are inferred and are assessed, from the society calculated between each sensor node in these factors
It can relation value.Finally, combing the network topological information of neighbours according to the value of social relationships, optimal next-hop is selected to relay
Node carries out information router.
In the routing mechanism based on social relationships, critical issue is the social relationships between discovery and accurate node metric,
This problem, which depends on, meets the analyses of data to node history.The social relationships between excavating node however, existing research works
There are still the places of not thorough consideration in problem.Many features between node between social relationships and node are related, such as node
Between the number that meets, meet the duration (from establish a communication connection to disconnect communication between elapsed time, actually
Call duration time), the case where message, the operation rail between common good friend's quantity, common interest, node are forwarded between node
Mark registration etc., these features all can be used as the evaluation index of social relationships between node.Existing research work often considers
One or several indexs.Meanwhile influence of the different characteristic for social relationships between node may be different, and in opportunistic network
In, moving and meeting with uncertainty due to node causes the uncertainty that (social relationships) are contacted between node,
Influence of the various different characteristics for social relationships between node be also it is uncertain, there is presently no a kind of methods can be accurate
Assess influence of the various different characteristics for social relationships between node.Therefore, existing method is in social relationships measurement accuracy
It still needs to improve energetically.
In view of the technical problems existing in the prior art, it is necessary to design one kind it can be found that simultaneously between accurate node metric
Social relationships method, and the method for routing based on the social relationships.
Summary of the invention
Technical problem solved by the invention is in view of the deficiencies of the prior art, to provide a kind of movement based on comentropy
Opportunistic network node social relationships measurement and method for routing, can social relationships between accurate node metric, using being calculated
Node between social relationships carry out routing can effectively improve network performance.
Technical solution provided by the present invention are as follows:
A kind of moving machine meeting network node social relationships measure based on comentropy, selection influence society between node
Feature (number that meets, meet the case where message is forwarded between duration, node, common good friend between such as node of relationship
Quantity, common interest, running track registration between node etc.) it is used as social relationships evaluation index, utilize comentropy
It measures influence of the different evaluation index to social relationships intensity between node, and using the influence as weight, is asked by weighting
And obtain social relationships intensity between node.Specifically include following 5 steps:
Step 1: statistics initial data
Being located in mobile opportunistic network has N number of node, the evaluation index of M node social relationships.Then according to historical information,
Different evaluation between available node refers to target value.The evaluation indice of definition node social relationships is combined into A={ ai|i
=1,2 ..., M }, the collection of evaluation opportunistic network interior joint is combined into V={ vj| j=1,2 ..., N }.Each node is according to oneself
With the historical information of meeting of other nodes, obtains the different evaluation between other nodes and refer to target value, as shown in table 1, in table 1
Value sijIndicate present node and node vjBetween evaluation index aiValue.
1 node v of tablexIn evaluation index value between other nodes
Step 2: initial data is handled
For each social relationships evaluation index, the unit that uses may be different, such as the number and average of meeting
Encounter Time, if directly calculated these information, obtained result difference may be very big.So being counted
It needs to pre-process these data before calculating, guarantees that each evaluation index has identical value range.Specific processing side
Method is as shown in formula 1.
Wherein, mini{sijIndicate index a in table 1iThe minimum value of corresponding row element, i.e. min { sij| j=1,2 ...,
N }, maxi{sijIndicate index a in table 1iThe maximum value of corresponding row element, i.e. max { sij| j=1,2 ..., N }, s 'ijIt indicates
To sijValue after being normalized, s 'ij∈ [0,1].After data prediction, present node and node can be calculated
vjBetween evaluation index aiValue in evaluation index aiAccounting P in the sum of corresponding all valuesij:
Step 3: calculating the entropy H of each evaluation indexi:
It, can be according to P according to the definition of the information theory medium entropy of ShannonijObtain the entropy H of each evaluation indexi:
Wherein, Hi>=0, ln are natural logrithm；If setting Pij=0, then Pij*lnPij=0.For some index ai
For, if s 'i1=s 'i2=...=s 'iN, then Hi=hlnN is maximum.If enablingSo Hi∈ [0,1].If certain
A social relationships evaluation index ai, each element value difference opposite sex is bigger, then HiSmaller, then the information content that the index is contained is got over
Greatly, just there is bigger influence for the measurement of social relationships between node.
Step 4: calculate the weight of each evaluation index:
After having obtained the entropy of each evaluation index, then the weight of corresponding each evaluation index can be calculated.To evaluation
Index ai, weight QiFor formula (4).
Because of HiIt is smaller, 1-HiIt is bigger, QiAlso bigger, the influence to social relationships value is bigger.
Step 5: social relationships intensity between calculate node:
To present node and node vj, each evaluation index value and its weight can be obtained by formula 1-4, then present node with
Node vjSocial relationships intensity RjAs shown in formula 5.
RjIndicate present node and node vjSocial relationships intensity, RjValue it is bigger, the social relationships of the two are closer.
Method for routing based on social relationships
By the way that based on comentropy, between the valid metric of social relationships node, the invention also provides a kind of moving machines can net
Routing in network based on social relationships (is calculated to distinguish over other routing algorithms based on social relationships and with routing based on cluster
Method, will this invention simply if referred to as ICR, i.e. Information entropybased Clustering Routing), including cluster
It establishes and update and the routing based on cluster；
(1) foundation and update of cluster
In ICR method, each node has a history information table, record the node that meets in nearest T time with
And the historical information of corresponding social relationships evaluation index.According to history information table, node can be by above-mentioned based on comentropy
Calculation method calculates the social relationships intensity with other nodes.After the completion of the measurement of social relationships, each node is selected and oneself
Oneself has the node of close social relationships to enter in local cluster.Local cluster can be dynamically updated to control its size and guarantee cluster
In node be to possess close social relationships with this node always.When cluster updates, one deletion chained list of node maintenance, wherein
Store the node for waiting and seeing whether to delete from local cluster.For node vk, Nk、CkAnd DkIt is its historical information respectively
Table, local cluster and deletion chained list.
Initially, the correlation table of node is all empty.When two nodes meet, they update mutual history information table.It hands over
After changing the information of meeting stored in history information table each other, each evaluation index between two nodes of node updates it is initial
Value.Then, social relationships intensity to each other is calculated according to formula 1-5.
As node vkWith node vjIt meets, if RjIt (t) is node vkT moment be calculated according to above-mentioned formula and vj's
Social relationships intensity, parameter ω are the social relationships intensity thresholds as local cluster member.According to Rj(t) do not sympathize with ω
Condition, the foundation and update of cluster are as follows:
Situation 1: work as Rj(t) it is greater than ω and node vjIt is not node vkLocal cluster member when, node vjIt will be added to node
vkLocal cluster CkIn；Otherwise, if node vjIt is CkMember, then by CkMiddle storage with node vkSocial relationships intensity
RjIt is updated to set Rj(t), and judge node vjWhether chained list D is being deletedkIn, if the node v ifjIt will be by from DkMiddle rejecting, avoids
vjIt is deleted from local cluster；
Situation 2: work as Rj(t) it is equal to or less than ω and node vjIt is not node vkLocal cluster member when, node vkWill not into
Any operation of row；Otherwise, if node vjIt is CkMember and delete chained list DkIn, node vjIt will be by from Ck、DkMiddle rejecting；
However, if node vjChained list D is not being deletedkIn, vjIt will be added into and delete chained list Dk, determine whether again depending on future condition from
It is deleted in local cluster；
The local cluster of node can be dynamically updated, if relevant information changes, the above process can be performed.Cluster is more
New process is as shown in Figure 1.
If current social relationships strength Calculation Result Rj(t) > ω, then Rj(t) it can be updated and be stored in local cluster.
Set the frequency n of social relationships intensity self damping and each pad value θ in T time section；Before update next time, local cluster
The social relationships intensity of middle storage can be reduced certain value (self damping θ) at regular intervals (T/n), facilitate to delete in this way
Fall node out-of-date in local cluster, to control the size of cluster, guarantees close social relationships between node and its cluster member.Such as
Upper described, time T is that node meets the information collection duration, so if indicating a cluster member in this phase without information
Between be it is active, then node will see whether to delete cluster member from its local cluster.If the society stored in local cluster
It can relationship strength RjWithout update and R in T time sectionjSelf damping is to being equal to or less than ω, then vjNode v will be added intok
Deletion chained list in, waiting see whether to delete from local cluster.
(2) based on the Design of Routing Algorithm of cluster
As described above, node selection forms a local cluster with the node for oneself there are close social relationships, cluster is made full use of
Close social relationships between member can help to improve message forwarding success rate, reduce routing cost.Herein, when two
Node meets, only destination node in the local cluster for the node that meets when just will do it message transmission.Routing procedure includes two
In the stage, one is information exchange, the other is message forwards.
A. information exchange
When two nodes meet, " hello " message can be sent to each other first, obtain mutual node identifier and
Information of meeting in history information table.After information of meeting reaches, node is according to mutual information of meeting, statistics and calculating two
The value of each evaluation index between node updates history information table, calculates the social relationships intensity between two nodes, then build
The local cluster of vertical and update.
B. message forwards
If source node vkEncounter node vj；If node vjIt is the destination node of message, node vkForward messages to node vj,
This message is deleted from transmit queue simultaneously；Otherwise, node vkMessage forwarding process have following two situation:
1) message forwards in cluster
If the destination node of message is in node vkLocal cluster in, and node vjIt is vkLocal cluster member, then node vk
To node vjForward a message copy；Otherwise, node vkNot to vjForward message.
2) message forwards between cluster
If the destination node of message is not in node vkLocal cluster in, node vkIt can be to node vjSend a request packet
Whether (" Request " packet), judge destination node in node vjLocal cluster in；As node vjAfter receiving request packet, according to asking
The information of packet is asked to check local cluster, then to node vkA response bag (" Response " packet) is replied, notifies node vkIts
It whether there is destination node in ground cluster；If node vkIt receives response bag and destination node is node vjLocal cluster member, node
vkA message copy is forwarded to give node vj；Otherwise, node vkNot to node vjForward message.
Message can be in node vkThe length of storage time depends on node v in cachingkThe size of caching.In this period
It is interior, if node vkThe forwarding target for meeting above-mentioned rule is encountered, then can arrange to send message.Otherwise, message will be according to caching
Management algorithm is from node vkCaching be deleted.If the destination node of message is not local cluster member, there are message can not
A possibility that forwarding.At this point, biggish caching will be helpful to message forwarding.
The utility model has the advantages that
In Shannon information theory, information is probabilistic description to thing movement state or existing way, and certain
Probabilistic size of a information is defined as self-information, that is, the information content of the information[11], comentropy is then some letter
Cease all possible average uncertainty.In opportunistic network, moving and meeting with uncertainty due to node is caused
The uncertainty of (social relationships) is contacted between node.Social relationships between node are more intimate, then uncertain lower,
Information content is smaller.For this purpose, invention considers all relevant node social relationships evaluation indexes, in order to which more accurate obtains not
With the weighing factor of evaluation index, the weight of each evaluation index is calculated using comentropy, and social between calculate node accordingly
Then relationship strength establishes cluster according to node social relationships intensity, design the routing policy based on cluster.It is closed with other societies
Be routing mechanism carried out experiment compare, the experimental results showed that, compared with other algorithms, algorithm proposed in this paper have it is higher
Message transmission success rate, and also have good performance in terms of transmission delay and average number of hops, demonstrate the present invention and mentioned
The validity and accuracy of social relationships measurement based on comentropy.
Detailed description of the invention
Fig. 1 cluster renewal process schematic diagram of the embodiment of the present invention；
Fig. 2 is that experiment parameter influences routing performance；Wherein Fig. 2 (a) is influence of the ω to data forwarding success rate, Fig. 2
It (b) is influence of the ω to transmission delay, Fig. 2 (c) is influence of the ω to routing Overhead Ratio, and Fig. 2 (d) is n to routing cost ratio
The influence of rate.
Fig. 3 is that data of each algorithm under four data sets successfully forwarded rate comparison diagram；Wherein Fig. 3 (a) is that each algorithm exists
Data under Infocom5 data set successfully forwarded rate comparison diagram, and Fig. 3 (b) is number of each algorithm under Infocom6 data set
According to successfully forwarded rate comparison diagram, Fig. 3 (c) is that data of each algorithm under Cambridge data set successfully forwarded rate comparison diagram,
Fig. 3 (d) is that data of each algorithm under Intel data set successfully forwarded rate comparison diagram；
Fig. 4 is transmission delay comparison diagram of each algorithm under four data sets；Wherein Fig. 4 (a) is that each algorithm exists
Transmission delay comparison diagram under Infocom5 data set, Fig. 4 (b) are transmission delay of each algorithm under Infocom6 data set
Comparison diagram, Fig. 4 (c) are transmission delay comparison diagram of each algorithm under Cambridge data set, and Fig. 4 (d) is that each algorithm exists
Transmission delay comparison diagram under Intel data set；
Fig. 5 is routing cost ratio comparison diagram of each algorithm under four data sets；Wherein Fig. 5 (a) is that each algorithm exists
Routing cost ratio comparison diagram under Infocom5 data set, Fig. 5 (b) are routing of each algorithm under Infocom6 data set
Overhead Ratio comparison diagram, Fig. 5 (c) are routing cost ratio comparison diagram of each algorithm under Cambridge data set, Fig. 5 (d)
The routing cost ratio comparison diagram for being each algorithm under Intel data set；
Specific embodiment
The present invention is described in more detail below in conjunction with the drawings and specific embodiments.
The present invention comprehensively considers the different evaluation index that social relationships between node are influenced in opportunistic network, proposes that evaluation refers to
Comentropy calculation method, and the social relationships intensity using evaluation index comentropy between weight calculation node are marked, proposes one
Cluster of the kind based on social relationships is established and update method, and the method for routing based on cluster.In the method, each node has
One history information table, records the node to meet within nearest a period of time and corresponding various social relationships evaluations refer to
Target historical information.According to history information table, node can calculate strong with the social relationships of other nodes according to comentropy
Degree.Then, each node selection forms a local cluster with the node for oneself having close social relationships, makes full use of cluster member
Between close social relationships carry out the exchange of information and the forwarding of message.
The present embodiment illustrates node social relationships Strength co-mputation process with the data instance in actual opportunistic network.
(1) initial data is counted
In the example shown, it is contemplated that the case where 9 nodes and 5 evaluation indexes (as shown in table 2).In general, chance
Nodes can save the information of node encountered in moving process, including Encounter Time, the number that meets, forwarding message time
Number etc., and can be with the continuous update of meeting between the movement and node of node.When the relationship for needing to calculate two nodes
When, these information can be counted from the history information table of node.Assuming that present node is v9, need to count node v9And its
Social relationships intensity between his node, then according to opportunistic network interior joint v9Historical information can count the number of table 3
According to.
2 social relationships evaluation index of table
| a1 | Meet number |
| a2 | The average approach duration |
| a3 | Forward message number |
| a4 | Common friend number |
| a5 | Common interest number |
3 v of table9Evaluation index value between node and other nodes
(2) initial data is handled
It is as shown in table 4 that normalization data is obtained after being handled according to data of the formula 1 to table 3.
New table after 4 normalized of table
(3) the specific gravity P of each standard is calculatedij
According to formula 2, accounting of each element value in corresponding evaluation index can be calculated, the results are shown in Table 5.
Accounting of the 5 each element value of table in corresponding evaluation index
(4) the entropy Hk of each evaluation index is calculated
According to the data in formula 3 and table 5, the entropy of each evaluation index can be calculated, specific value is such as 6 institute of table
Show.Here entropy represents the size for the average information that each evaluation index is contributed, according to the discussion in Shannon information theory
It is found that entropy herein is smaller, information content provided by corresponding evaluation index is bigger.
The entropy of each evaluation index of table 6
(5) weight of each index is calculated
It is as shown in table 7 that each index weights can be obtained by formula 4.
The weight of each evaluation index of table 7
| H | 1-H | Q | |
| a1 | 0.453364 | 0.546636 | 0.238872 |
| a2 | 0.454025 | 0.545975 | 0.238583 |
| a3 | 0.427482 | 0.572518 | 0.250182 |
| a4 | 0.759979 | 0.240021 | 0.104886 |
| a5 | 0.616746 | 0.383254 | 0.167477 |
| SUM | 2.711596 | 2.288404 | 1 |
(6) social relationships intensity is calculated
There is between 4 interior joint of table the corresponding weight of each index in the social relationships value of each index and table 7, by public affairs
Formula 5 can calculate egress v9With the social relationships intensity of other nodes, the value is bigger, corresponding node and node v9Between society
Relationship is closer.Specific value is as shown in table 8.
Each node of table 8 and v9Social relationships intensity value
| v1 | v2 | v3 | v4 | v5 | v6 | v7 | v8 | |
| a1 | 2/3 | 1/3 | 2/5 | 4/5 | 1 | 0 | 0 | 2/15 |
| a2 | 1/2 | 2/3 | 1/3 | 1/6 | 2/5 | 0 | 0 | 1 |
| a3 | 11/14 | 6/7 | 1/2 | 9/14 | 1 | 0 | 0 | 1/7 |
| a4 | 1/16 | 3/16 | 0 | 1/8 | 0 | 7/16 | 1/16 | 1 |
| a5 | 1/7 | 2/7 | 1/7 | 1/7 | 1/14 | 0 | 2/7 | 1 |
| Rt | 0.505592 | 0.520638 | 0.324093 | 0.428729 | 0.59645 | 0.045888 | 0.054406 | 0.578536 |
It can see by table 8, node v5With v9Social relationships intensity values it is maximum, illustrate that the relationship of the two nodes is most tight
It is close.From the point of view of initial data, compared with other nodes, v5With v9There is maximum number and the most message hop counts of meeting.
Emulation experiment
This part has carried out emulation experiment to the ICR algorithm of proposition using opportunistic network emulation simulator (ONE), and and its
Its three moving machines meetings algorithm network routing PRoPHETv2, DRAFT and BUBBLE are compared under identical simulated environment
Experiment.In an experiment, real data set Infocom5, Infocom6, Cambridge and Intel is used to make node motion drive
Dynamic, these data sets can be downloaded from CRAWADA [12], and data set last time renewal time is in August, 2016, details
As shown in table 9.In this experiment, nodal cache size is 5M, message size 1K, when the quantity and TTL of the node of four data
Between as shown in table 10.
9 four experimental data set information of table
The simulation parameter of four data sets in 10 ONE of table
Generally, use following three indexs as the measurement foundation of algorithm performance.
Data successfully forwarded rate (PDR): the message count and source node that successfully forwarded destination node within a certain period of time need
The ratio between message sum of transmission.The index features the ability that routing algorithm correctly transmits a message to destination node, is most heavy
The metric wanted.
Transmission delay (TD): message is transferred to destination node the time it takes from source node.
Routing cost: it as shown in Equation 6, is usually evaluated with overhead rate, i.e., the message count all relayed in network is subtracted into
Function forwards message count, then again with the ratio between the message count that successfully forwarded destination node.
Experiment parameter analysis
Tri- parameters of ω, μ and n are shared in ICR routing algorithm.Parameter ω is the social relationships intensity threshold of cluster member,
For each node, the size of local cluster can increase with the reduction of ω.The society of two nodes of parameter μ and measurement closes
Be intensity the number that meets it is related.Under prescribed conditions, the value of μ is bigger, and the social relationships strength calculations of two nodes are got over
It is small.When local cluster constructs, ω and μ have similar influence.On the other hand, when the value of ω is fixed, thus it is possible to vary the value of μ
So that local cluster is added in node.It is apparent that the change of social relationships intensity threshold and the change of local cluster threshold value is added between node
There is same affect for the building of local cluster, therefore influence of the parameter ω to experimental result is only analyzed in this part.Parameter n is
The number of social relationships intensity self damping in T time section is used to carry out self damping to social relationships intensity, rejected the time
Point.Given simulation time is three days, experimental result such as Fig. 2 institute of ICR algorithm each Parameters variation under Infocom5 data set
Show.
As shown in Fig. 2, the smaller data forwarding success rate of the value of ω is higher, propagation delay time is smaller, and routing cost is higher.From reason
By upper analysis, the increase of local cluster means that cluster member becomes more, considerably increases the probability that message is forwarded to destination node, from
And data forwarding success rate is improved, and reduces propagation delay time.But it causes to have more in network since cluster interior joint becomes larger
More data copies, increases routing cost.In view of data forwarding success rate, transmission delay and routing cost these three refer to
Balance between mark, it is proposed that parameter ω is set as 0.4, comparison is respectively provided with ω=0.4 below.
Judging from the experimental results, parameter n has little effect data forwarding success rate and transmission delay.The present invention recognizes
For n is related with the speed that out-of-date node is rejected from local cluster, but successfully forwarded the key relays node of message with help
It is not related.As considering herein, for routing cost, the value of n is smaller to cause more how useless node to rest on local
In cluster, more message copies are forwarded in a network, and therefore, routing cost increases, as shown in Fig. 2 (d).
Comparative test
Four kinds of algorithms are carried out emulating under identical environment simultaneously contrast properties by this part.
In ICR mechanism, parameter is set as ω=0.4, and n=500, μ are all node average approaches in each data set
1.2 times of number.In DRAFT algorithm, parameter is set as τ=7, δ=0.9, t=3600s.
(1) data successfully forwarded rate
In emulation experiment, ICR, PRoPHETv2, DRAFT and BUBBLE algorithm are transported under four data sets respectively
Row, runing time is the duration of data set, and as shown in table 9, experimental result is as shown in Figure 3.
Fig. 3 illustrates the data forwarding success rate experimental result with four algorithms of time change.When simulation time is less than
When one, the result of ICR algorithm is close to other three algorithms.This is because the formation of cluster is needed by a timing in network
Between, in short time range, cluster interior joint is less, can not carry out efficient message forwarding.As simulation time increases, ICR is calculated
It has been more than other three routing algorithms that the data of method, which successfully forwarded rate,.Experimental result such as table of the ICR algorithm in contrast to three algorithms
Shown in 11.
Raising (%) of the table 11 compared to other three algorithm ICR in data forwarding success rate
(2) transmission delay
The transmission delay of four routing algorithms as shown in Figure 4.Compared to other three routing algorithms, ICR algorithm is being transmitted
Have in delay and decline to a certain degree, as shown in table 12.Because ICR algorithm is a kind of algorithm based on cluster forwarding, have between cluster member
Close social relationships, effectively message can be helped to forward, reduce transmission delay.
Reduction (%) of the table 12 compared to other three algorithm ICR on transmission delay
(3) routing cost
The comparative situation of route messages is as shown in Figure 5.Relative to other three routing algorithms, ICR algorithm is in routing cost
On have and decline to a certain degree, as shown in table 13.Due to there is close social relationships between cluster member in ICR algorithm, node only to
Destination node is located at the relay node forwarding message copy with cluster, effectively reduces unnecessary hop count.Therefore, not
Influence data reduce message number in network under the premise of successfully forwarded rate, reduce routing cost.
Reduction (%) of the table 13 compared to other three algorithm ICR on routing cost
The experimental results showed that compared with other algorithms, algorithm message transmission success rate with higher proposed in this paper, and
And also there is good performance in terms of transmission delay and average number of hops.
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Claims (6)
1. a kind of moving machine meeting network node social relationships measure based on comentropy, which is characterized in that selection influences section
The feature of social relationships is as social relationships evaluation index between point, measured using comentropy different evaluation index to node it
Between the influence of social relationships intensity obtained by being weighted summation to each evaluation index value and using the influence as weight
Social relationships intensity between node.
2. a kind of moving machine meeting network node social relationships measure based on comentropy, which is characterized in that define moving machine
Node set in meeting network is V={ vj| j=1,2 ..., N }, the evaluation indice of node social relationships is combined into A={ ai|i=
1,2 ..., M }, wherein N is the node number in mobile opportunistic network, and M is the evaluation index number of node social relationships；For
Each node calculates its social relationships intensity with other nodes according to following steps respectively:
Step 1: statistics initial data
According to the historical information of meeting of oneself and other nodes, obtains the different evaluation between other nodes and refer to target value, use sij
Indicate present node and node vjBetween evaluation index aiValue, i=1,2 ..., M, j=1,2 ..., N；
Step 2: the corresponding initial data of each index is respectively processed；
To evaluation index ai, first to sij, j=1,2 ..., N be normalized, formula are as follows:
Wherein, mini{sij}=min { sij| j=1,2 ..., N }, maxi{sij}=max { sij| j=1,2 ..., N }, s 'ijTable
Show to sijValue after being normalized；
Then, s ' is calculatedijIn evaluation index aiAccounting P in the sum of corresponding all valuesij:
Step 3: calculate separately the entropy of each evaluation index:
To evaluation index ai, entropy HiCalculation formula are as follows:
Wherein, h is coefficient, Hi>=0, ln are natural logrithm, and if setting Pij=0, then Pij*lnPij=0；
Step 4: calculate separately the weight of each evaluation index:
To evaluation index ai, weight QiCalculation formula are as follows:
Step 5: social relationships intensity between calculate node:
Present node and node vjSocial relationships intensity RjCalculation formula are as follows:
RjValue it is bigger, the social relationships of the two are closer.
3. the moving machine meeting network node social relationships measure according to claim 2 based on comentropy, feature
It is, in the step 3, enablesSo that Hi∈ [0,1].
4. a kind of cluster based on social relationships is established and update method, which comprises the following steps:
1) each node vkRespectively establish a history information table Nk, local cluster CkAnd delete chained list Dk；Wherein history information table NkWith
In record node vkIn the node to meet in nearest T time and the value of its each evaluation index between each node；Local cluster
CkFor storing and node vkSocial relationships intensity be greater than threshold value ω node and corresponding social relationships intensity value, delete
Chained list DkSeeing whether for storage waiting will be from local cluster CkThe node of middle deletion；Initial NkAnd DkIt is an empty table,
CkFor empty set；
2) as node vkWith node vjIt meets, node vkIts history information table is first updated, further according to described in claims 1 to 3
Method calculates and vjSocial relationships intensity Rj；If RjIt (t) is node vkIn the R that current time is calculatediValue, node vkAccording to
Rj(t) with the different situations of ω, cluster update is carried out:
Situation 1: work as Rj(t) when being greater than ω, node vkJudge node vjIt whether is CkMember, if it is not, then by node vjIt is added
To CkIn；Otherwise first by CkMiddle storage with node vkSocial relationships intensity RjIt is updated to set Rj(t), then judge node vjWhether
Deleting chained list DkIn, if then by node vjFrom DkMiddle rejecting；
Situation 2: work as Rj(t) when being equal to or less than ω, node vkJudge node vjIt whether is CkMember, if it is not, then without
Any operation；Otherwise judge node vjWhether chained list D is being deletedkIn, if if by node vjFrom CkAnd DkMiddle rejecting, if not if
Node vkBy node vjIt is added and deletes chained list DkIn；
3) local cluster CkThe social relationships intensity R of middle storagejCertain self damping is just carried out at regular intervals, if in local cluster
The social relationships intensity R of storagejWithout update and R in T time sectionjSelf damping is to equal to or less than ω, node vkBy node vj
It is added to and deletes chained list DkIn.
5. the cluster according to claim 4 based on social relationships is established and update method, which is characterized in that the ω=
0.4。
6. a kind of method for routing based on cluster, which is characterized in that routing procedure includes two stages, and one is information exchange, separately
One is message forwarding；
A. information exchange
When two nodes meet, the information of meeting in oneself node identifier and history information table can be sent to each other first；
After information of meeting reaches, node counts and calculates the value of each evaluation index between two nodes according to mutual information of meeting,
History information table is updated, and uses method foundation as claimed in claim 4/update local cluster；
B. message forwards
If source node vkEncounter node vj；If node vjIt is the destination node of message, node vkForward messages to node vj, simultaneously
This message is deleted from transmit queue；Otherwise, node vkMessage forwarding process have following two situation:
1) message forwards in cluster
If the destination node of message is in node vkLocal cluster in, and node vjIt is vkLocal cluster member, then node vkXiang Jie
Point vjForward a message copy；Otherwise, node vkNot to vjForward message；
2) message forwards between cluster
If the destination node of message is not in node vkLocal cluster in, node vkIt can be to node vjA request packet is sent, is judged
Whether destination node is in node vjLocal cluster in；As node vjAfter receiving request packet, checked according to the information of request packet local
Cluster, then to node vkA response bag is replied, notifies node vkIt whether there is destination node in its local cluster；If node vk
It receives response bag and destination node is node vjLocal cluster member, node vkA message copy is forwarded to give node vj；Otherwise,
Node vkNot to node vjForward message.
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