CN111343690A - Opportunistic network routing method based on fine-grained social relationship and community cooperation - Google Patents
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Abstract
The invention discloses an opportunity network routing method based on fine-grained social relations and community collaboration, and belongs to the technical field of opportunity networks of man-borne mobile devices. The method comprises the following specific steps: the following steps are carried out on each encountering node of the node carrying the message in the moving process: when a node carrying a message encounters a encountering node, firstly judging whether the encountering node is a target node or not, if so, transmitting the message to the encountering node, and otherwise, performing the second step; secondly, judging whether a relation exists among the message carrying node, the encountering node and the target node, and if so, transmitting the message by comparing the node relation; otherwise, adopting inter-community message transmission, and performing message transmission by comparing the prediction probability from the node to the target community; the method designs the message transmission method based on the node relation and the community cooperation, reasonably and fully utilizes the node resources, accelerates the message forwarding and reduces the message redundancy.
Description
Technical Field
The invention particularly relates to an opportunity network routing method based on fine-grained social relations and community collaboration, and belongs to the technical field of opportunity networks of man-borne mobile devices.
Background
With the development of intelligent sensor technology and the popularization of a large number of mobile intelligent devices, an opportunistic network formed by the devices can realize self-organizing communication under complex conditions. Because the intelligent devices are carried by people, the formed opportunistic network has human sociality. Research is carried out on the meeting rule that the intelligent equipment moves along with people, and the mobile behavior of the nodes in the opportunistic network really has the mobile rule based on the social behavior of people. Therefore, how to utilize the social design routing method possessed by the nodes in the opportunistic network becomes a great hot spot of the current opportunistic network research.
In recent years, research on routing methods of opportunistic networks has attracted considerable attention. Some scholars propose message transmission methods based on message replication, which utilize nodes to forward copies of messages, and the process of the method has uncertainty and randomness. For example, the Epidemic method is to flood the message in the whole network; the spread and Wait method performs multi-copy transmission of infectious messages by controlling the number of message copies. A forwarding method based on a greedy mode then appears, and the Prophet method calculates the contact probability between nodes by recording and analyzing contact history, and updates the probability based on the change of node contact. None of the above message transmission methods relate to the sociality of the nodes in the opportunistic network.
After the sociality of nodes in an opportunity network is concerned, many researches on message transmission methods based on the sociality of the nodes appear. The SimBet method utilizes the small world effect in the social network and selects nodes with higher connectivity as agents to assist routing. The BubbleRap method defines and records the activity of the nodes in the whole network and in the community, and the decision of forwarding the message is made based on the sequencing of the activity. Documents identify social relationships of nodes by using contact times and average contact duration of the nodes; the learner also proposes a multi-objective function by combining indexes such as maximum meeting times among nodes, maximum data packet delivery probability, minimum target node distance and the like, and optimizes the function to be used as a basis for forwarding the information of the data packet.
The existing related social opportunistic network routing method has the following defects:
1) the division of communities is established on the basis of the determination of the global topology of the network, and the opportunistic network is intermittent link connection, so that attention should be paid to the connection situation of nodes and surrounding nodes, namely relatively stable and close local connection, and the method is measured by communities.
2) The historical meeting information among the nodes has multidimensional characteristics, the previous work is mostly concerned about single characteristics, and the relationship among the nodes is not subjected to detailed analysis.
3) Many researches select relay nodes according to the centrality indexes of the nodes, so that messages are concentrated to a few nodes, and message redundancy and excessive energy loss are easily caused by unbalanced load.
The social refraction of the transmission association by the nodes in the opportunistic network also has an important influence on transmission prediction and transmission success rate. It is a topic worth studying to accurately mine and reasonably utilize the information as the decision basis of the opportunistic network routing.
Disclosure of Invention
Therefore, the invention provides a routing method in the field of man-carried mobile equipment opportunistic networks, which aims at the defects in the prior art, and particularly provides a method for carrying out multidimensional measurement on node social relations, refining relation priorities and relation classification, and providing a community construction and evolution method.
The specific technical scheme is as follows:
an opportunity network routing method based on fine-grained social relationship and community collaboration is specifically as follows:
the following steps are carried out on each encountering node of the node carrying the message in the moving process:
when a node carrying a message encounters a encountering node, firstly judging whether the encountering node is a target node or not, if so, transmitting the message to the encountering node, and otherwise, performing the second step; secondly, judging whether a relation exists among the message carrying node, the encountering node and the target node, and if so, transmitting the message by comparing the node relation; otherwise, adopting inter-community message transmission, and performing message transmission by comparing the prediction probability from the node to the target community;
the message transmission by comparing the node relationships specifically includes:
when the message is transmitted between the encountering nodes, the nodes carrying the message are compared with the encountering nodes and the relationship types between the nodes and the target nodes, if the encountering nodes and the target nodes belong to the relationship with higher acquaintance degree, the encountering nodes are preferentially selected as forwarding nodes, and the nodes with higher forwarding priority in relation with the target nodes are always selected as the forwarding nodes in the message transmission process until the message is transmitted to the target nodes;
the method specifically comprises the following steps of adopting inter-community message transmission:
when the message is transmitted among communities, the node carrying the message and the encountering node are compared with the cooperative path prediction probability of the target community, and if the probability from the encountering node to the target community is higher, the encountering node is selected as a forwarding node; and in the message transmission process, the node with higher target community prediction probability is always selected as a forwarding node until the message is transmitted to the community where the target node is located, and then a relation transmission strategy is executed.
Further, in the method, the relationship between the nodes is represented by relationship affinity, the relationship affinity refers to the degree of affinity of the relationship between the nodes in the opportunistic network and the meeting nodes, the higher the relationship affinity is, the more stable and the more close the relationship between the two nodes is, and the relationship between the nodes based on the relationship affinity is divided into three types: high acquaintance friendship, general acquaintance familiar stranger relations, unfamiliar acquaintance but indirectly acquainted friends.
Furthermore, in the method, each node records the node with the relationship in the node relationship table, and the query and update of the relationship are performed through the node relationship table every time the nodes meet.
Further, the community establishment and evolution based on the fine-grained social relationship in the method specifically comprises the following steps:
the friend relation node of the node and a familiar stranger relation node are drawn into an initial community of the node; the initial community refers to a set of nodes which are in friend relationship with the node and are familiar with strangers; the node meets other nodes, common friends are compared to judge whether the common friends reach a threshold value, after the proportion of the common friends reaches the threshold value, the communities of the secondary nodes and the communities of the nodes meeting the secondary nodes are combined to generate new communities, and the two nodes form friend relationships with the nodes which do not belong to the original communities respectively.
Further, the comparing, by the node carrying the message and the encountering node in the method, the cooperative path prediction probability to the target community specifically includes:
the probability that a node carrying the message transmits the message to a certain community is expressed by the reachable community probability; the higher the predicted probability, the higher the probability that the message will reach the community; each node maintains a prediction probability table for its reachable communities;
the prediction probability of the node a carrying the message to its reachable community Cb is expressed as:
wherein, X is the set of all nodes encountered by the movement of the node a carrying the message, Ea (Cb) is the number of times that the node a carrying the message encounters the node belonging to the community Cb, Ea (CX) is the number of times that the node a carrying the message encounters all nodes, and the time interval tm updates the once prediction probability; if the node carrying the message meets other nodes, the reachable community prediction probability tables of the two parties are exchanged, so that the reachable community range of each node is expanded, the prediction probability values of the existing reachable communities are compared, and the cooperation path and the probability values are updated if the new path is short or the probability value is high.
Further, the method for predicting the cooperative path from the node carrying the message to the meeting node to the target community further includes:
the node transmits the message to the connectionless community, needs the assistance of a plurality of communities and defines the deliverable attribute of all the prediction probabilities; under the cooperative path, the transitivity of the reachable probability is attenuated along with the number of experienced communities, so that a path with a short distance is preferentially selected under the multi-cooperative path aiming at the same target community.
The invention has the beneficial effects that: compared with the prior art, the opportunistic network routing method based on the fine-grained social relationship and the community cooperation has the following advantages:
1. the relation of the nodes is refined on the basis of the quantification of the multi-dimensional historical encounter information, and the relation priority based on different acquaintance degrees of the nodes are defined.
2. A message transmission method based on node relation and community cooperation is designed, node resources are reasonably and fully utilized, message forwarding is accelerated, and message redundancy is reduced.
Drawings
FIG. 1 is a schematic diagram of community initialization;
FIG. 2a and FIG. 2b are schematic diagrams illustrating dynamic evolution of communities;
FIG. 3 is a diagram illustrating multiple collaboration paths under chance encounters;
FIG. 4 is a flow chart of a message transmission method based on node relationships and community collaboration;
FIG. 5 is a community collaboration diagram;
FIG. 6 is a diagram illustrating a comparison of message delivery success rates under an Infocom2006 dataset;
FIG. 7 is a diagram illustrating a comparison of message delivery success rates under MIT data sets;
FIG. 8 is a diagram illustrating comparison of average latency of messages in Infcom 2006 data set;
FIG. 9 is a graph illustrating average delay of messages in MIT data set;
FIG. 10 is a diagram illustrating a comparison of message redundancy rates under an Infocom2006 data set;
FIG. 11 is a graph showing the comparison of the redundancy rates of messages in MIT data sets.
Detailed Description
The following description of the embodiments of the present invention is provided with reference to the accompanying drawings:
1, the definition of the relationship intimacy degree provided by the method, the nodes in the opportunity network record the historical meeting condition and calculate the relationship intimacy degree.
Define 1 relationship affinity. Refers to the degree to which nodes in the opportunistic network are close to the encountering node. The index comprehensively measures the history information of the encounters between the nodes, including the encounter duration, the encounter frequency and the encounter frequency, and the higher the affinity of the relationship is, the more stable and the more intimate the relationship between the two nodes is.
The encounter duration index MTI reflects the encounter duration condition of the node a and the node u in a certain time period. Is represented as follows:
the encounter time distribution index TED reflects the frequency of the encounters of the node a and the node u in a certain period of time. Is represented as follows:
where n represents the number of encounters of node a and node u within time interval tm. F (ta, u) represents the time duration for which node a meets node u once.
The relationship intimacy degree C is a comprehensive measure of multi-dimensional encounter information:
2 social relationship priority and relationship refinement based on relationship intimacy degree measurement
The social relationship classification method reflects different acquaintance degrees of the nodes, namely high acquaintance, general acquaintance and unfamiliarity, based on the measurement of relationship intimacy, further refines the relationship of the nodes into 3 situations, and the forwarding priorities of the three relationship categories are sequentially reduced.
(1) High-maturity acquaintance relationship
(2) Familiar stranger relationships of general familiarity
(3) Friend relationship of friend not acquainted but acquainted indirectly
Each node records the nodes with the relationship in the node relationship table, and the relationship is inquired and updated through the table each time the nodes meet.
The node a calculates the relationship affinity C (a, k) of the node it meets and finds its average e (xa). And comparing the average value E (Xa) with the relationship intimacy C (a, k) of the meeting node, and if the relationship intimacy C (a, k) of the meeting node is greater than the average value E (Xa), determining that the meeting node is a friendship node of the node a. Otherwise it is a familiar stranger relationship node of node a. The average value e (xa) is expressed as:
c (a, k) represents the relationship intimacy between the node a and the encountering node, and N represents the set of the encountering nodes of the node a.
3 Community establishment and evolution based on fine-grained social relationship
And C, the friend relation node of the node a and the familiar stranger relation node are classified into the initial community of the node a. As shown in fig. 1.
An initial community is defined 2. Refers to a collection of nodes that are friends with the node and familiar strangers.
Given node a, its initial community's set of members Ca can be formally represented as:
Ca＝{u|u∈Na∪Ma} (5)
na is a friendship node set of the node a; ma is a set of stranger relationship nodes familiar to node a.
Each community is a social circle, and the possibility of people in the same social circle to know is high. Have a large number of friends in common between them, accord with the thought that extend own circle of contact through "friends of friends".
And the node a meets the node u, and by comparing whether the common friends reach a threshold value or not, when the proportion of the common friends reaches the threshold value, the community of the node a and the community of the node u are combined to generate a new community. The two nodes respectively form friend relationships with nodes which do not belong to the original community. As shown in fig. 2(a) and (b).
In fig. 2(a), a node a meets a node u, the node a and the node u have common friends, and when the ratio of the common friends of the node a and the node u reaches a set threshold, the community of the node a and the community of the node u are merged. In fig. 2(b), in the new community generated by merging, the nodes a and u respectively generate friend relationships with some nodes which have no relationship originally.
For node a, the set of friendship nodes of its friends, Ca', can be formally represented as:
wherein Ca is a community set before node a is combined; cu is an original community set before node u is merged; sigma is a set community merging threshold. S (a, u) is the ratio of common friends owned by node a and node u.
The community merging decision formula is as follows:
wherein Na is a friend set of the node a; nu is the set of friends for node u.
4 calculation of reach probability in community collaboration
The reachable community probability is the predicted probability of a node reaching a certain community, and the probability of the node transmitting a message to the certain community is represented by the reachable community probability. The higher the predicted probability, the higher the likelihood that the message will reach the community. Each node maintains a prediction probability table of the reachable community, the nodes finish query and update of prediction probability in chance meeting, and information transmission of community cooperation is supported.
The predicted probability of node a to its reachable community Cb is expressed as:
wherein X is the set of all nodes encountered by the movement of the node a, Ea (Cb) is the number of times that the node a encounters a node belonging to the community Cb, Ea (CX) is the number of times that the node a encounters all nodes, and the time interval tm updates the prediction probability once. The updating method comprises the following steps: if the node meets other nodes, the prediction probability table of the other node is checked to find a node reaching the community C5And obtaining a new predicted probability value, comparing the new value with the old value, and replacing the old value with the new value when the new value is higher, which indicates that the new path has higher possibility of reaching the target community. If the node meets other nodes, the reachable community prediction probability tables of the two parties are exchanged, so that the reachable community range of each node is expanded, the prediction probability values of the existing reachable communities are compared, and the cooperation path and the probability value are updated if the new path is short or the probability value is high.
Nodes transmit messages to the disjoint communities with assistance from multiple communities. All prediction probabilities are defined with transitive properties. As shown in fig. 3, although the node a needs to transmit a message to the node g, the node a cannot transmit a message to the node g, and therefore, message transmission between the communities needs to be performed. Node a transmits the message to community Cg, with 3 paths Ca → C2 → Cg, Ca → C3 → Cg, Ca → C1 → C3 → Cg. In the community Ca, the node a transmits messages to the nodes b, C and d, the prediction probability from the node b to the community C2 is Pb (C2), the prediction probability from the node C to the community C3 is Pc (C3), and the prediction probability from the node d to the community C1 is Pd (C1). Node b encounters node h in community C2 with a predicted probability of node h to community Cg of ph (Cg). Node C meets node m in community C3, and the prediction probability from node m to community Cg is pm (Cg). Node d meets node e in community C1, and the predicted probability from node e to community C3 is Pe (C3). Node e encounters node f in community C3 with a predicted probability of Pf (Cg) to community Cg.
Three paths for transmitting the message to the community Cg by the node a are provided, and the prediction probabilities from the node a to the community Cg corresponding to the three paths are respectively as follows:
Pa(Cg)1＝Pb(C2)×Ph(Cg) (9)
Pa(Cg)2＝Pc(C3)×Pm(Cg) (10)
Pa(Cg)3＝Pd(C1)×Pe(C3)×Pf(Cg) (11)
under the cooperative path, the transitivity of the reachable probability is attenuated along with the number of experienced communities, that is, the longer the path distance of the cooperative community (the number of experienced communities is large), the lower the transmission probability thereof, the smaller the chance of successful transmission, and the longer the cooperative path means a high forwarding delay, so that the path with a short distance is preferentially selected under the multi-cooperative path for the same target community. In the example of fig. 5, the cooperative paths pa (cg)1 and pa (cg)2 have equal distances, and then the prediction probabilities of the two paths are compared, and the community path with the higher probability is preferentially selected for the cooperative message transmission, as shown in table 1.
TABLE 1Ca→CgCommunity collaborative path prediction probability
| Route Ca→Cg | Hop count | Pa(Cg) |
| Ca→C2→Cg | 2 | Pb(C2)×Ph(Cg) |
| Ca→C3→Cg | 2 | Pc(C3)×Pm(Cg) |
| Ca→C1→C3→Cg | 3 | Pd(C1)×Pe(C3)×Pf(Cg) |
Opportunistic network message transmission based on node relation and community cooperation
When the message is transmitted between the encountering nodes, the node carrying the message is compared with the encountering nodes with the relation category between the node and the target node, and if the encountering nodes and the target node belong to the relation with higher acquaintance degree, the encountering nodes are preferentially selected as forwarding nodes. And by analogy, in the message transmission process, a node with high forwarding priority in relation to the target node is always selected as a forwarding node until the message is transmitted to the target node.
When the message is transmitted among communities, the node carrying the message and the encountering node are compared with the cooperative path prediction probability of the target community, and if the probability from the encountering node to the target community is higher, the encountering node is selected as a forwarding node. According to the process, the node with higher target community prediction probability is always selected as the forwarding node in the message transmission process until the message is transmitted to the community where the target node is located, and then the relation transmission strategy is executed.
When a node a carrying a message meets a node u, firstly judging whether the node u is a target node or not, if so, transmitting the message to the node u, otherwise, carrying out a second step, and if the node a carrying the message, the meeting node u and the target node have a relationship, transmitting the message by comparing the node relationship; otherwise, inter-community message transmission is adopted, and message transmission is carried out by comparing the prediction probability from the node to the target community. The specific flow is shown in fig. 4.
The actual operation process of the method is as follows:
node u generally refers to the meeting node of node a in the movement, although u may be one or more, and if there are multiple nodes, each node is such a process.
When the node a meets the node u, whether the node u is the target node is judged, if yes, the destination is reached, and the message is directly transmitted to the target node, namely u. If not, judging whether a relationship exists between the node a and the target node or not, and whether a relationship exists between the node u and the target node or not, if so, using a relationship forwarding strategy, and then further judging whether the relationship between the node a and the target node is close or the relationship between the node u and the target node is close, if the relationship between u and the target node is more close, selecting u as a relay forwarding node, transmitting the message to u, otherwise, namely, if the relationship between the node a and the target node is close, not transmitting the message to u, namely, ending the process of selecting the relay node. When the node a meets the node u, whether the node u is the target node is judged, if yes, the destination is reached, and the message is directly transmitted to the target node, namely u. If not, judging whether a relationship exists between the node a and the target node or not, and whether a relationship exists between the node u and the target node or not, if so, using a relationship forwarding strategy, and then further judging whether the relationship between the node a and the target node is close or the relationship between the node u and the target node is close, if the relationship between u and the target node is more close, selecting u as a relay forwarding node, transmitting the message to u, otherwise, namely, if the relationship between the node a and the target node is close, not transmitting the message to u, namely, ending the process of selecting the relay node. The community collaboration diagram is shown in fig. 5.
When the message is transmitted among communities, the node carrying the message and the encountering node are compared to the prediction probability of the target community, and if the prediction probability from the encountering node to the target community is higher, the encountering node is selected as a forwarding node. According to the process, the node with higher target community prediction probability is always selected as the forwarding node in the message transmission process until the message is transmitted to the target node.
In order to verify the feasibility and the effectiveness of the method, a comparison experiment of transmission performance is carried out under the same condition with a Prophet method, a SimBet method and a BubbleRap method under the ONE simulation environment of an opportunity network simulation platform, in order to verify the availability of the method, the experiment is analyzed based on Infocom2006 with high frequency connection rate and an MIT reliability mining real data set with sparse connection, and the individual performance indexes of message delivery success rate, message average delay and message redundancy rate 3 are selected for analysis and comparison, and the results are shown in fig. 6 to fig. 11.
Aiming at the defects in the research of the message transmission method based on the node sociality, the invention provides a dual transmission method based on the relation and community cooperation on the basis of fully considering the multi-dimensional encounter history information and refining the node relation. The method comprises the steps of firstly quantifying the relationship intimacy between nodes according to historical encounter information of contact between the nodes, accurately distinguishing acquaintance degrees of the nodes according to the intimacy, thereby completing the refinement of different node relationships, and simultaneously supporting the initialization and combined evolution of node communities. And when the relationship exists among the nodes, selecting a node relationship transmission strategy according to the priority of the relationship, otherwise, selecting a cooperation path by comparing the probability of reaching the community, and utilizing the community cooperation transmission strategy. On one hand, the method refines the node relation, has higher transmission target, has smaller average message delay and ensures the message delivery success rate. On the other hand, the number of nodes participating in message forwarding is reduced in the message transmission process, and the message redundancy rate is reduced. Experimental simulation shows the effectiveness of the method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. An opportunity network routing method based on fine-grained social relationship and community collaboration is characterized by comprising the following steps:
the following steps are carried out on each encountering node of the node carrying the message in the moving process:
when a node carrying a message encounters a encountering node, firstly judging whether the encountering node is a target node or not, if so, transmitting the message to the encountering node, and otherwise, performing the second step; secondly, judging whether a relation exists among the message carrying node, the encountering node and the target node, and if so, transmitting the message by comparing the node relation; otherwise, adopting inter-community message transmission, and performing message transmission by comparing the prediction probability from the node to the target community;
the message transmission by comparing the node relationships specifically includes:
when the message is transmitted between the encountering nodes, the nodes carrying the message are compared with the encountering nodes and the relationship types between the nodes and the target nodes, if the encountering nodes and the target nodes belong to the relationship with higher acquaintance degree, the encountering nodes are preferentially selected as forwarding nodes, and the nodes with higher forwarding priority in relation with the target nodes are always selected as the forwarding nodes in the message transmission process until the message is transmitted to the target nodes;
the method specifically comprises the following steps of adopting inter-community message transmission:
when the message is transmitted among communities, the node carrying the message and the encountering node are compared with the cooperative path prediction probability of the target community, and if the probability from the encountering node to the target community is higher, the encountering node is selected as a forwarding node; and in the message transmission process, the node with higher target community prediction probability is always selected as a forwarding node until the message is transmitted to the community where the target node is located, and then a relation transmission strategy is executed.
2. The opportunistic network routing method based on fine-grained social relations and community collaboration as claimed in claim 1, wherein the relations among the nodes in the method are represented by relation affinity, the relation affinity refers to the degree of affinity of the relations between the nodes and the meeting nodes in the opportunistic network, the higher the relation affinity, the more stable and the more close the relation between the two nodes is, and the relations among the nodes based on the relation affinity are divided into three types: high acquaintance friendship, general acquaintance familiar stranger relations, unfamiliar acquaintance but indirectly acquainted friends.
3. The opportunistic network routing method based on fine-grained social relations and community collaboration as claimed in claim 2, wherein each node records the node with which the relationship exists in its node relationship table, and the query and update of the relationship are performed through the node relationship table every time the nodes meet.
4. The opportunistic network routing method based on fine-grained social relations and community collaboration as claimed in claim 3, wherein the establishment and evolution of communities based on fine-grained social relations in the method specifically comprises:
the friend relation node of the node and a familiar stranger relation node are drawn into an initial community of the node; the initial community refers to a set of nodes which are in friend relationship with the node and are familiar with strangers; the node meets other nodes, common friends are compared to judge whether the common friends reach a threshold value, after the proportion of the common friends reaches the threshold value, the communities of the secondary nodes and the communities of the nodes meeting the secondary nodes are combined to generate new communities, and the two nodes form friend relationships with the nodes which do not belong to the original communities respectively.
5. The opportunistic network routing method based on fine-grained social relations and community collaboration as claimed in claim 4, wherein the comparing of the nodes carrying messages and the encountering nodes to the cooperative path prediction probability of the target community specifically comprises:
the probability that a node carrying the message transmits the message to a certain community is expressed by the reachable community probability; the higher the predicted probability, the higher the probability that the message will reach the community; each node maintains a prediction probability table for its reachable communities;
the prediction probability of the node a carrying the message to its reachable community Cb is expressed as:
wherein, X is the set of all nodes encountered by the movement of the node a carrying the message, Ea (Cb) is the number of times that the node a carrying the message encounters the node belonging to the community Cb, Ea (CX) is the number of times that the node a carrying the message encounters all nodes, and the time interval tm updates the once prediction probability; if the node carrying the message meets other nodes, the reachable community prediction probability tables of the two parties are exchanged, so that the reachable community range of each node is expanded, the prediction probability values of the existing reachable communities are compared, and the cooperation path and the probability values are updated if the new path is short or the probability value is high.
6. The opportunistic network routing method based on fine-grained social relations and community collaboration as claimed in claim 1, wherein the method for predicting the probability of the collaboration path from the node carrying the message to the target community compared with the meeting node further comprises:
the node transmits the message to the connectionless community, needs the assistance of a plurality of communities and defines the deliverable attribute of all the prediction probabilities; under the cooperative path, the transitivity of the reachable probability is attenuated along with the number of experienced communities, so that a path with a short distance is preferentially selected under the multi-cooperative path aiming at the same target community.
Priority Applications (1)
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