CN110536309B - Mobile social network routing method based on node activity and energy factors - Google Patents
Mobile social network routing method based on node activity and energy factors Download PDFInfo
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Abstract
The invention relates to a mobile social network routing method based on node liveness and energy factors, which comprises three parts: a. establishing a mobile model; b. constructing an activity indicating parameter of the node; c. forwarding a routing strategy based on the information of the activity and the energy; regarding each person holding the wireless communication equipment as a node, establishing an activity index of the node by utilizing meeting information among the nodes, setting an activity threshold value according to the activity condition, making a routing strategy, considering the relation between typical information transmission performance indexes and energy consumption, and seeking excellent information transmission performance and energy consumption ratio. The problem of information transmission in the mobile social network is solved. By setting a suitable activity threshold using the method proposed by the present invention, an optimal trade-off between information transfer performance and energy consumption can be obtained.
Description
Technical Field
The invention provides a routing method based on node activity and energy factors for Mobile Social Networks (MSNs for short), mainly used for solving the problem of information transmission in the Mobile Social Networks, and belongs to the technical field of computer wireless communication.
Background
With the development of wireless communication technology, mobile social networks have received much attention. The mobile social network belongs to one of Delay Tolerant Networks (DTNs). In the DTNs, mobile subscribers establish a distributed self-organizing dynamic network, there is no fixed network connection between mobile subscribers, and the information transfer mode is store-carry-forward. In addition to possessing the characteristics of traditional DTNs, mobile social networks take advantage of the social characteristics of mobile nodes to improve information transfer performance.
Many protocols for MSNs have been proposed, including Epidemic, LABEL, BUBBLE, ProPHET, SimBet, and others. The main idea of Epidemic protocol is a flooding mechanism of copy-on-encounter, in which nodes indicate information stored in buffers of the nodes through summary vectors, when two mobile nodes meet, the vector information of each other is checked to obtain information situations carried by each mobile node but not carried by the other mobile node, and the information is transmitted to the other mobile node. The protocol has high information transmission success rate due to the fact that information of all encountered node buffer areas is copied, but huge information average delay exists, and the requirements on the bandwidth/the buffer area/the power of the nodes are large. The LABEL protocol assigns the same LABEL to the nodes belonging to the same organization by using the social characteristics of the nodes, and considers that the nodes having the same LABEL with the destination node are more suitable to be used as the relay node, thereby improving the relay performance. The BUBBLE protocol provides a routing scheme combining betweenness centrality and a node target community, the same labels of nodes in the same community are specified, two concepts of local centrality and global centrality are defined, the liveness of the nodes in the community and under the global community is reflected respectively, and the node with higher centrality is considered to have higher interaction possibility with other nodes and is more suitable to be used as a relay node. The ProPHET protocol is based on a single copy mechanism, obtains and updates the probability of information transmission by utilizing the historical meeting condition, and takes the probability of information transmission as the basis for whether the information is transmitted or not. The signaling probability when two nodes meet for the first time is set as a certain initial value, and when the two nodes meet more times, the signaling probability value is updated. If two nodes do not meet each other within a certain period of time, the signaling probability value is reduced according to the action of the time factor. The updated information passing probability of the directly encountered node also leads to the signaling probability of other nodes being updated indirectly. The SimBet protocol provides concepts of self-centrality and social centrality based on a six-degree space theory to reflect the ability of the node to contact other nodes and serve as a transfer node, and the node with high centrality has higher possibility of receiving information than the node with low centrality.
These protocols are focused on a few typical information transfer performance indicators, such as information transfer success rate and information average delay, considering energy consumption. Battery capacity and energy consumption are very important indicators for mobile devices. The tradeoff between power consumption and traditional information transfer metrics is a significant challenge, and more attention is being focused on this.
Disclosure of Invention
The technical problem is as follows: the invention aims to provide a mobile social network routing method based on node liveness and energy factors, and solves the problem of information transmission in a mobile social network. By setting a suitable activity threshold using the method proposed by the present invention, an optimal trade-off between information transfer performance and energy consumption can be obtained.
The technical scheme is as follows: the invention relates to a mobile social network routing method based on node liveness and energy factors, which comprises three parts: a. establishing a mobile model; b. constructing an activity indicating parameter of the node; c. forwarding a routing strategy based on the information of the activity and the energy; regarding each person holding the wireless communication equipment as a node, establishing an activity index of the node by utilizing meeting information among the nodes, setting an activity threshold value according to the activity condition, making a routing strategy, considering the relation between typical information transmission performance indexes and energy consumption, and seeking excellent information transmission performance and energy consumption ratio.
Wherein the content of the first and second substances,
the activity degree comprises a friendship degree, a popularity degree grade and a relationship degree.
The performance indexes comprise average energy consumption, information transmission hop count, information transmission success rate and information average delay.
Establishing a mobile model:
a1. setting the simulation area as a larger square area, setting a plurality of small square areas with the areas smaller than the simulation area as communities in the area, regarding each person holding the wireless communication equipment as a node, and enabling a plurality of nodes to move in the simulation area; defining three communities of a certain node with relatively high probability as three favorite communities of the node; the nodes go to other communities with relatively low probability, the total probability of each node going to the three favorite communities is 0.9, the three probability values of each node are randomly generated at the initial stage of mobile model establishment and are not changed after generation, and the total probability of the nodes going to the other communities is 0.1; the node is located in one of three favorite communities at the initial stage of establishment of the mobile model, and a target community is selected according to probability; the movement direction of the node is a linear direction from the initial community to the target community, the movement speed is randomly selected between the specified maximum speed and the specified minimum speed, once the node reaches the target community, the node stays in the community for a period of time, and after the time is up, the previous actions are repeated again;
a2. in both cases, nodes are deemed to meet: the first is that when the distance between two nodes is less than a certain distance, the other node is considered to be detected because the nodes use wireless communication equipment; the other is that when the detection moment, two nodes are located in the same community, and due to the existence of the residence time and the convenience of communication of the same community, the two nodes can meet.
B, constructing an activity indicating parameter of the node:
b1. friendship degree
Friendship degree between nodes is related to encounter frequency and encounter interval, encounter frequency means encounter frequency between two nodes, encounter interval means time between two encounters, in a period of time 0 to T, if two nodes encounter m times, the time interval between the kth and the kth +1 is TkM-1, the interval t between two encounterskArranged from small to large, a set { t'1,t'2,...,t'm-1The difference between the front and back values in the set is Δ tj＝t'j+1-t'jJ 1,2.. m-2, the friendship degree Fri of the node x and the node y(x,y)Is shown as
b2. Degree of temperament of human body
The popularity of a node is influenced by the nodes it encounters, in particular by the number of nodes it encounters and the degree of friendship of the node to other nodes, information being more easily transmitted to a destination by nodes of relatively high popularity than by nodes of relatively low popularity; popularity PopxIs represented by the following formula, where q is the number of nodes encountered by node x, Fri(x,y)Is the friendship degree of node x and node y;
b3. grade of popularity
According to the value of the human strengthArranging from small to large, selecting 2 thresholds, and dividing the popularity into 3 popularity grades {1,2,3 }; node personal popularity value PopxJudging that the size belongs to a certain grade; if set to { Pth,1,Pth,2As the popularity threshold value, the popularity grade Rank of the node xxWhich of the 3 levels {1,2,3} belongs can be determined by a formula;
b4. degree of relationship
The relationship degree describes the relationship between the node and the node with the relatively high popularity level, the higher the relationship degree of the node is, the higher the probability of encountering the node with the high popularity is, and the higher the possibility that the two nodes establish the connection through the node with the high popularity is; assuming that the popularity level is set to be three levels, the following formula is used for calculating the degree of relationship; wherein, relationshipxIndicates the degree of relationship, Fri(x,m),lRepresenting the degree of friendship between the node x and the node with the popularity grade l, 1 ≦ l ≦ 3, m ≦ i, j, k, 1 ≦ i ≦ N1,1≤j≤N2,1≤k≤N3Wherein N isnN is more than or equal to 1 and less than or equal to 3, w is the number of nodes with popularity level l encountered by the node xlIs a weight factor of each person's gas level, generally according to w in subsequent simulations1＜w2＜w3The size of the set value;
the information forwarding routing strategy based on the activity and the energy comprises the following steps:
c1. after the information is generated, energy consumption exists in the whole process from route detection, route establishment and route maintenance to information transmission, and the energy required for successfully transmitting one piece of information to a destination is the sum of the energy required by each hop; average Energy consumption required to successfully deliver a messageiIs obtained by the following formula, wherein e0Is each hop stationRequired energy, hossiIs the total number of hops required from the source node to the destination node of the information generation,
Energyi＝e0×hopsi
c2. if node x thinks to transmit a message to node z, if x first encounters z, x will directly pass the message to z; if x encounters node y before z, x needs to decide whether to pass information to y, x will pass information to y in three cases:
c21.y is stronger than x and z by a threshold value alpha
Fri(y,z)-Fri(x,z)＞α
c22. If the degree of friendship between y and z and the degree of friendship between x and z are both 0, i.e. neither x nor y has encountered z, but the degree of relation of y is stronger than the degree of relation of x by a threshold value beta, i.e. y is greater than the degree of relation of x
Fri(y,z)＝Fri(x,z)＝0andRelationy-Relationx＞β
c23. If the degree of friendship between y and z and the degree of friendship between x and z are both 0, i.e. neither x nor y meets z, but the popularity level of y is higher than that of x by a threshold value gamma, i.e. y is a little higher than that of x
Fri(y,z)＝Fri(x,z)＝0andRanky-Rankx＞γ
c3. According to the average hop number required by the successful transmission of the information at the end of the simulation, the formula Energyi＝e0×hopsiCalculating to obtain energy consumption; the information transmission success rate of each time point is taken as a horizontal axis, the average energy consumption required on the time point is taken as a vertical axis, a relation graph of the energy consumption and the information transmission success rate can be drawn, and then the relation of the energy consumption and the information transmission success rate is obtained. Taking the average delay of information at each time point as the horizontal axis and the average energy consumption required at the time point as the vertical axis, a graph of the relationship between the energy consumption and the average delay of information can be drawn, and the relationship between the energy consumption and the average delay of information can be obtained.
If the friendship degree threshold value, the relation degree threshold value and the popularity level threshold value (alpha/beta/gamma) are changed, different routing schemes are obtained, and different hop counts are involved, so that the protocol performance parameters, energy, information transmission success rate and information average delay are different.
Compared with the prior art, the method provided by the invention has the following advantages:
(1) the method utilizes the meeting information among the nodes to establish the activity degree (friendship degree/popularity degree grade/relation degree) index of the nodes, sets an activity degree threshold value according to the activity degree condition, develops a routing strategy and improves the information sending efficiency; (2) in the routing strategy, the method not only focuses on typical information transmission performance indexes such as information transmission success rate and information average delay, but also considers the problem of energy consumption and seeks a better relation between transmission performance and energy consumption.
Drawings
FIG. 1 is a schematic diagram of a mobile social network routing method based on node liveness and energy factors, wherein a node N1 goes to a community,
figure 2 is a general flow chart of a mobile social network routing method based on node liveness and energy factors,
FIG. 3 is a flow chart of information transfer of a mobile social network routing method based on node liveness and energy factors,
figure 4 is a graph of energy consumption versus information transfer success rate for different activity thresholds,
FIG. 5 is a graph of energy consumption versus information average delay for different activity thresholds.
Detailed Description
There are two main stages in the implementation. The first stage mainly completes the establishment of a mobile model and the acquisition of an activity index. The second stage is mainly based on the routing condition to transmit information.
In the first stage 0-T, the nodes move or are static according to the mode described by a1, node meeting data is generated, and node activity indexes are generated.
1) And constructing a mobile model, generating the favorite community and the going-to probability of each node, and generating mobile information such as a target community/direction/speed/stay condition and the like. The simulation area is set to be a square area of 1000 multiplied by 1000 square metersA plurality of square areas with the area of 100 multiplied by 100 are set in the area as communities, each person holding the wireless communication equipment is regarded as a node, each node goes to three communities with higher probability, and goes to other communities with lower probability. As shown in FIG. 1, node N1 goes to three favorite community PCs1/PC2/PC3The total probability of the three favorite communities is 0.9, the probability values P1/P2/P3 of the three favorite communities are randomly generated at the initial stage of establishment of the mobile model and are not changed any more after generation, and the node N1 is sent to other communities PC4/PC5…PCNThe total probability of (2) is 0.1. The node is located in one of the three favorite communities at the initial stage of mobile model establishment, and a target community is selected according to probability. The moving direction of the node is the linear direction from the starting point in the initial community to the destination point in the destination community, and the moving speed is randomly selected from the specified maximum speed to the specified minimum speed.
2) Dividing the total time T into time points with fixed intervals, recording the meeting condition of each time point node, and determining the moving condition of each time point node.
21) The positions of all nodes are checked at each point in time. If at the time point of inspection, when the distance of two nodes is less than certain distance, because the node uses wireless communication equipment, think that two nodes meet, perhaps two nodes are located same community, because the existence of dwell time and the convenience of same community interchange also think that two nodes can meet. And at the time point of the check, checking whether the two nodes which are judged to meet at the moment meet at any time, if so, recording the time from the meeting time to the respective encountered node time list, and if not, adding the information that the two nodes meet into the respective encountered node list except for recording the time from the meeting time to the respective encountered node time list.
22) Each time point determines the mobility of all nodes. And judging whether the node reaches a target community, if not, moving the node according to the original speed/direction, and if so, checking a time label of the node staying in the community. If the stay time of the node in the community is not up to the specified length, the node stays in the community continuously, and if the stay time is up to the specified length, the node generates a next target community according to the probability of going to other communities generated in the process of initially establishing the model, and goes to the next target community in the linear motion direction and the newly generated speed.
3) Various metrics indicative of node liveness are calculated. And when the first period T is up, each node saves the number of other nodes encountered/time of each encounter/number of encounters, the friendship degree between the nodes can be calculated according to a formula in b1, and the popularity degree can be calculated according to a formula in b2. And selecting two proper threshold values according to the popularity value, and obtaining popularity grade through the formula in b3. Setting the popularity grade weight factor as w1＝1,w2＝2,w3The degree of relationship of each node can be obtained from the formula in b4 as 3.
And in the second stage, within 0-T time, generating information, setting an activity threshold value, and transmitting the information according to a routing strategy.
1) The total time T of the second stage is divided into time points of fixed intervals, and at each time point within the second stage 0-T/4, each node generates one piece of information. The lengths of the information generated by all the nodes are the same, and the life cycle of the information is set to be infinite.
2) At each time point, the positions of the nodes are checked, if the distance between the two nodes is smaller than the specified meeting distance or the two nodes are in the same community, namely meeting is carried out, whether the opposite side is the target node of the node carrying the information is checked, if yes, the information is directly transmitted to the opposite side, and if not, whether the node of the opposite side has higher activity degree than the node of the opposite side needs to be judged. And the node judges according to the three routing conditions described in the c2, if the activity of the node of the opposite side is higher, the information carried by the node is transmitted to the opposite side, otherwise, the node retains the information. As shown in FIG. 3, node x has a message to be transmitted to node z, if x directly encounters z, x transmits the message to z, if x encounters node y before z, the friendship degree between y and z and the friendship degree between x and z need to be determined to determine whether x transmits the message to y. If the degree of friendship between y and z is higher than the threshold degree of friendship between x and z, x transmits the message to y, otherwise x retains the message. If the degree of friendship between y and z and the degree of friendship between x and z are both 0, it is necessary to determine whether the degree of relationship between y is higher than that between x or whether the degree of popularity between y is higher than that between x. And if the degree of relationship of y is higher than the degree of relationship threshold beta of x or the popularity of y is higher than the popularity threshold gamma of x, transmitting the message to y by x, otherwise, retaining the message by x.
3) At each time point, counting data such as the number of messages successfully transmitted to a destination node/the number of messages successfully transmitted through the relay node/the number of messages in the relay node, counting data such as the number of nodes required to pass each message successfully transmitted/delay time passed by each message successfully transmitted, and calculating the current information transmission success rate/information average delay.
4) And when the second stage T is reached, calculating the energy consumption of the current time by using the energy consumption formula in c1 according to the hop count required by the successful transmission of the information. The information transmission success rate of each time point is taken as a horizontal axis, the average energy consumption required on the time point is taken as a vertical axis, a relation graph of the energy consumption and the information transmission success rate can be drawn, and then the relation of the energy consumption and the information transmission success rate is obtained. Taking the average delay of information at each time point as the horizontal axis and the average energy consumption required at the time point as the vertical axis, a graph of the relationship between the energy consumption and the average delay of information can be drawn, and the relationship between the energy consumption and the average delay of information can be obtained. Comparing the relation between the energy consumption and the threshold value of the signaling rate/average delay/activity, adjusting the threshold value, and executing the second stage again to obtain a new signaling performance to energy consumption ratio.
In implementation, the two scenes of case a (200 nodes in the community and 10 favorite communities) and case B (100 nodes in the community and 20 favorite communities) are respectively performed. Under each scene, setting the activity threshold (alpha/beta/gamma) as 0/0/0,3/0.3/1,6/0.6/1 and 8/0.8/1 respectively.
Similar conclusions are obtained in different scenarios, and fig. 4 and fig. 5 are a graph of power consumption versus information transmission success rate and a graph of power consumption versus information average delay at different activity thresholds for case a, respectively. As can be seen from fig. 4, for information whose life cycle is infinitely long, the change in the activity threshold has little effect on the information transmission success rate, but when the information transmission success rate is 1, the scene with the high activity threshold consumes less average energy. In fig. 5, as the liveness threshold increases, the average number of hops to successfully deliver information to the destination decreases, the required energy consumption decreases, but the average latency of the information increases. If lower energy consumption is desired, the activity threshold value needs to be set to a higher value; if the average delay of less information is needed to be obtained, the activity threshold value needs to be set to a lower value; if a better information transfer performance to energy consumption ratio is desired, the activity threshold value is set to a suitable intermediate value.
Claims (2)
1. A mobile social network routing method based on node liveness and energy factors is characterized by comprising three parts: a. establishing a mobile model; b. constructing an activity indicating parameter of the node; c. forwarding a routing strategy based on the information of the activity and the energy; regarding each person of the handheld wireless communication equipment as a node, establishing an activity index of the node by utilizing meeting information among the nodes, setting an activity threshold value according to the activity condition, making a routing strategy, considering the relation between typical information transmission performance indexes and energy consumption, and seeking an excellent information transmission performance and energy consumption ratio;
wherein the content of the first and second substances,
the activity degree comprises a friendship degree, a popularity grade and/or a relationship degree;
the performance index comprises average energy consumption, information transmission hop count, information transmission success rate and/or information average delay;
establishing a mobile model:
a1. setting the simulation area as a larger square area, setting a plurality of small square areas with the areas smaller than the simulation area as communities in the area, regarding each person holding the wireless communication equipment as a node, and enabling a plurality of nodes to move in the simulation area; defining three communities of a certain node with relatively high probability as three favorite communities of the node; the nodes go to other communities with relatively low probability, the total probability of each node going to the three favorite communities is 0.9, the three probability values of each node are randomly generated at the initial stage of mobile model establishment and are not changed after generation, and the total probability of the nodes going to the other communities is 0.1; the node is located in one of three favorite communities at the initial stage of establishment of the mobile model, and a target community is selected according to probability; the movement direction of the node is a linear direction from the initial community to the target community, the movement speed is randomly selected between the specified maximum speed and the specified minimum speed, once the node reaches the target community, the node stays in the community for a period of time, and after the time is up, the previous actions are repeated again;
a2. in both cases, nodes are deemed to meet: the first is that when the distance between two nodes is less than a certain distance, the other node is considered to be detected because the nodes use wireless communication equipment; the other is that when the detection moment is carried out, two nodes are located in the same community, and due to the existence of the residence time and the convenience of communication in the same community, the two nodes can meet each other;
b, constructing an activity indicating parameter of the node:
b1. friendship degree
Friendship degree between nodes is related to encounter frequency and encounter interval, encounter frequency means encounter frequency between two nodes, encounter interval means time between two encounters, in a period of time 0 to T, if two nodes encounter m times, the time interval between the kth and the kth +1 is TkM-1, the interval t between two encounterskArranged from small to large, a set { t'1,t'2,...,t'm-1The difference between the front and back values in the set is Δ tj＝t'j+1-t'jJ 1,2.. m-2, the friendship degree Fri of the node x and the node y(x,y)Is shown as
b2. Degree of temperament of human body
The popularity of a node is influenced by the nodes it encounters, in particular by the number of nodes it encounters and the degree of friendship of the node to other nodes, information being more easily transmitted to a destination by nodes of relatively high popularity than by nodes of relatively low popularity; popularity PopxAs represented by the following formula,where q is the number of nodes encountered by node x, Fri(x,y)Is the friendship degree of node x and node y;
b3. grade of popularity
Arranging the popularity degrees from small to large, selecting 2 threshold values, and dividing the popularity degrees into 3 popularity degree grades {1,2,3 }; node personal popularity value PopxJudging that the size belongs to a certain grade; if set to { Pth,1,Pth,2As the popularity threshold value, the popularity grade Rank of the node xxWhich of the 3 levels {1,2,3} belongs can be determined by a formula;
b4. degree of relationship
The relationship degree describes the relationship between the node and the node with the relatively high popularity level, the higher the relationship degree of the node is, the higher the probability of encountering the node with the high popularity is, and the higher the possibility that the two nodes establish the connection through the node with the high popularity is; assuming that the popularity level is set to be three levels, the following formula is used for calculating the degree of relationship; wherein, relationshipxIndicates the degree of relationship, Fri(x,m),lRepresenting the degree of friendship between the node x and the node with the popularity grade l, 1 ≦ l ≦ 3, m ≦ i, j, k, 1 ≦ i ≦ N1,1≤j≤N2,1≤k≤N3Wherein N isnN is more than or equal to 1 and less than or equal to 3, w is the number of nodes with popularity level l encountered by the node xlIs a weight factor of each person's gas level, generally according to w in subsequent simulations1＜w2＜w3The size of the set value;
the information forwarding routing strategy based on the activity and the energy comprises the following steps:
c1. after the information is generated, energy consumption exists in the whole process from route detection, route establishment and route maintenance to information transmission, and the energy required for successfully transmitting one piece of information to a destination is the sum of the energy required by each hop; average Energy consumption required to successfully deliver a messageiIs obtained by the following formula, wherein e0Is the energy required per hop, hossiIs the total number of hops, Energy, required from the source node to the destination node of the information generationi＝e0×hopsi
c2. If node x thinks to transmit a message to node z, if x first encounters z, x will directly pass the message to z; if x encounters node y before z, x needs to decide whether to pass information to y, x will pass information to y in three cases:
c21.y is stronger than x and z by a threshold value alpha
Fri(y,z)-Fri(x,z)＞α
c22. If the degree of friendship between y and z and the degree of friendship between x and z are both 0, i.e. neither x nor y has encountered z, but the degree of relation of y is stronger than the degree of relation of x by a threshold value beta, i.e. y is greater than the degree of relation of x
Fri(y,z)＝Fri(x,z)＝0and Relationy-Relationx＞β
c23. If the degree of friendship between y and z and the degree of friendship between x and z are both 0, i.e. neither x nor y meets z, but the popularity level of y is higher than that of x by a threshold value gamma, i.e. y is a little higher than that of x
Fri(y,z)＝Fri(x,z)＝0and Ranky-Rankx＞γ
c3. According to the average hop number required by the successful transmission of the information at the end of the simulation, the formula Energyi＝e0×hopsiCalculating to obtain energy consumption; the information transmission success rate of each time point is taken as a horizontal axis, the average energy consumption required on the time point is taken as a vertical axis, a relation graph of the energy consumption and the information transmission success rate can be drawn, and the energy consumption and the information transmission success rate are further obtainedInformation transfer success rate relationship; taking the average delay of information at each time point as the horizontal axis and the average energy consumption required at the time point as the vertical axis, a graph of the relationship between the energy consumption and the average delay of information can be drawn, and the relationship between the energy consumption and the average delay of information can be obtained.
2. The method of claim 1, wherein the performance metrics include average energy consumption, hop count for message transmission, success rate for message transmission, and average delay for message transmission.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011144538A1 (en) * | 2010-05-17 | 2011-11-24 | Associazione Create-Net | Method and system for network virtualization |
| CN102546393A (en) * | 2011-12-12 | 2012-07-04 | 华中科技大学 | Social network route optimizing method based on integral liveness |
| CN103561426A (en) * | 2013-11-04 | 2014-02-05 | 南京邮电大学 | Probability route improving method in delay-tolerance mobile sensor network based on node activeness |
| CN103647714A (en) * | 2013-12-05 | 2014-03-19 | 北京理工大学 | Social energy-based mobile social delay-tolerant network routing method |
| CN105847149A (en) * | 2016-03-18 | 2016-08-10 | 北京理工大学 | Wireless delay-tolerant network routing method based on multi-layer network |
-
2019
- 2019-08-13 CN CN201910743076.6A patent/CN110536309B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011144538A1 (en) * | 2010-05-17 | 2011-11-24 | Associazione Create-Net | Method and system for network virtualization |
| CN102546393A (en) * | 2011-12-12 | 2012-07-04 | 华中科技大学 | Social network route optimizing method based on integral liveness |
| CN103561426A (en) * | 2013-11-04 | 2014-02-05 | 南京邮电大学 | Probability route improving method in delay-tolerance mobile sensor network based on node activeness |
| CN103647714A (en) * | 2013-12-05 | 2014-03-19 | 北京理工大学 | Social energy-based mobile social delay-tolerant network routing method |
| CN105847149A (en) * | 2016-03-18 | 2016-08-10 | 北京理工大学 | Wireless delay-tolerant network routing method based on multi-layer network |
Non-Patent Citations (1)
| Title |
|---|
| 基于节点关键度的DTN路由算法;李建铎等;《计算机仿真》;20180415(第04期);全文 * |
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