CN107018080B - Delay tolerant network topology routing method considering node energy - Google Patents

Delay tolerant network topology routing method considering node energy Download PDF

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CN107018080B
CN107018080B CN201710174046.9A CN201710174046A CN107018080B CN 107018080 B CN107018080 B CN 107018080B CN 201710174046 A CN201710174046 A CN 201710174046A CN 107018080 B CN107018080 B CN 107018080B
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energy
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CN107018080A (en
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贾建鑫
韩德志
刘广钟
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Shanghai Maritime University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/126—Shortest path evaluation minimising geographical or physical path length
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/02—Topology update or discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/121—Shortest path evaluation by minimising delays

Abstract

The invention discloses a delay tolerant network topology routing method considering node energy, abstracting node correlation dimension according to the self characteristics and the environment of nodes in a network and defining a node dimension weight factor matrix, solving the dimension number count [ i ] of any node i except the node i and the node i in the previous s level for each node j in the matrix, if the count [ i ] is more than a threshold value theta, adding the node i into the 'relative static local network topology' of the node j, and expanding and perfecting the network topology of the node through node meeting exchange routing information in a set time period T; in the network topology of the nodes, a revenue matrix is established according to the energy of two nodes associated with each connection, a Nash equilibrium solution is solved, a simplified binary network topology of the nodes is solved according to the Nash equilibrium solution, then the shortest path and the optimal forwarding node set of the nodes for sending data packets are solved according to the binary network topology, the data packets are sent, and the network topology of the nodes is dynamically updated according to the node change.

Description

Delay tolerant network topology routing method considering node energy
Technical Field
The invention particularly relates to a delay tolerant network topology routing method considering node energy.
Background
Currently, an effective routing algorithm in a Delay Tolerant Network (DTN) becomes a focus of attention in academic and industrial fields. The traditional routing protocol suite cannot be adapted to delay tolerant networks due to some of its features. The main reason is that the traditional network has a stable network topology, and the routing protocol is designed based on the stable network topology. However, there is no stable network topology in the delay tolerant network, and the network topology is in a constantly changing state due to the mobility and intermittent connection of the nodes, and the energy of each node is also limited, so that a new method for researching a routing method suitable for the delay tolerant network is needed. As early as 2004 Sushant Jain et al pointed out that the routing problem is a core problem in delay tolerant network research and clearly elucidated the main problem of routing in DTN, which is essentially how to make fast and efficient communication between nodes in a dynamically changing topology.
Existing routing algorithms can be classified into single copy routing algorithms, multiple copy routing algorithms, probabilistic routing algorithms, social routing algorithms, and the like. The single copy routing algorithm does not copy the message in the message transmission process, and only a single copy of the message exists in the network, so that the message transmission hit rate is low, and the transmission delay is high. The multi-copy routing algorithm duplicates messages during message delivery, and thus there are multiple copies in the network, resulting in higher routing costs. The probabilistic routing algorithm predicts the future route according to the historical information of the nodes, and the historical information only considers the number of times of meeting between the nodes in the past, so that the message transmission is low in hit rate. The social routing algorithm utilizes the sociality of the nodes for routing, and only the sociality is considered, so that the message transmission is low in hit rate. How to perform effective routing in a delay tolerant network, especially such a binary topology routing method based on node energy consideration, is rarely reported in literature.
Disclosure of Invention
The invention aims to provide a delay tolerant network topology routing method considering node energy, which improves the hit rate of data transmission and reduces data transmission delay and routing energy consumption by effective routing selection.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a delay tolerant network topology routing method considering node energy is characterized by comprising the following steps:
s1, abstracting the dimensionality related to the nodes according to the characteristics of the nodes in the delay tolerant network and the environment;
s2, defining a k multiplied by n dimension weight factor matrix to represent the importance of each dimension to a node, wherein k rows of the matrix represent that k nodes exist, and n columns represent that each node has n dimensions;
s3, performing grade division on each dimension according to different measuring standards and performing grade classification on different dimensions of the node according to the self characteristics of the node;
s4, counting the number of dimensions of S, except for a node j, of the nodes i and the nodes j, in the matrix, and putting the dimensions into a count [ i ], simultaneously adding corresponding dimension identifiers into a dimension identifier set A [ i ], judging whether the count [ i ] is larger than a threshold value theta, and if so, adding the node i into a relatively static local network topology of the node j;
s5, judging whether the relative static local network topology of all nodes in the dimension weight factor matrix is solved, if so, performing a step S6, otherwise, updating the node j and entering the step S4;
s6, comprehensively defining the network topology connection weight of the node;
s7, in a set time period T, the network topology of each node is expanded and perfected through the node meeting exchange routing information;
s8, establishing a revenue matrix and solving a Nash equilibrium solution according to the energy of two nodes associated with each connection in the network topology, and simplifying the network topology;
s9, according to Nash equilibrium, the flag bit of each connection in the static network topology of each node is marked as binary 1 or 0, 1 represents the connection energy sending data packet, and 0 is opposite;
s10, according to the simplified network topology, the shortest path and the optimal forwarding node set of the sending data packet are obtained;
and S11, sending the data packet, updating the binary network topology and sending a new data packet.
In step S1, each mobile node is identified by an n-dimensional vector, each value of the n-dimensional vector is an integer, i.e. the level of the node in each dimension, and the n-dimensional vector for any node a is expressed as:
HVa＝[Ha1Ha2…Hap…Han](1)。
the importance of each dimension to the node in step S2 varies with time, wherein the dimension weight factor matrix is expressed as:
Figure GDA0002375614200000031
each value in the dimensional weight factor matrix in equation (2) is calculated as:
Figure GDA0002375614200000032
h in formula (3)p minRepresents the minimum rank value in the p-th dimension, Hp maxRepresents the maximum rank value in the p-th dimension, and a is any one of k nodes.
In the step 3, each dimension needs to be classified into s grades in the grade classification, wherein r is the highest grade, and the grade r-s +1 to the grade r are the first s grades with the highest grade.
The connection weight calculation in step S6 includes single connection weight calculation and multiple connection weight calculation, as follows:
when the relations between the dimensions are independent, the calculation formula of the single connection weight is as follows:
Figure GDA0002375614200000041
equation (4) is a formula for calculating the weight of a single connection between node a and node f, where they are ranked in the top s-th dimension in β dimensions;
wherein the following values in equation (4) are the inverse of the weighting factors:
Figure GDA0002375614200000042
Figure GDA0002375614200000043
when the dimensions are mutually influenced, the calculation formula of the single connection weight is as follows:
Figure GDA0002375614200000051
wherein in equation (7):
Figure GDA0002375614200000052
wherein in equation (7):
Figure GDA0002375614200000053
wherein L isast,Lfst,Lasp,LfspRank values of the nodes a and f in the moving speed dimension and the staying time dimension respectively, that is, when there is a dimension of the number of times of encounters between the nodes in β dimensions, and the moving speed dimension and the staying time dimension are not at the previous s level, the constraint condition is:
Lat∈Dt∩Lat≥r-s+1Lft∈Dt∩Lft≥r-s+1
Last＜r-s+1,Lfst＜r-s+1,Lasp＜r-s+1,Lfsp＜r-s+1
the formula for calculating the multi-connection weight is as follows:
Waw＝Waea×Weaw+Waka×Wkaw+Wawa×Wwaw(10)
in the formula (10), taking the example of solving the multi-connection weight between any two nodes a and W, where the node ea, the node ka, and the node wa are three common friend nodes of the node a and the node W, and WaeaIs a single connection weight, W, between node a and node eaeawIs a single connection weight, W, between node ea and node WakaIs a single connection weight, W, between node a and node kakawIs a single connection weight, W, between node ka and node WawaIs a single connection weight, W, between node a and node wawawAnd (4) solving a multi-connection weight between the node a and the node w according to probability multiplication and addition principles for the single connection weight between the node wa and the node w.
The expanding and perfecting the network topology of each node through the node encounter exchange routing information in the step S7 specifically includes: when the nodes meet, the local topology information of the nodes are mutually sent to the opposite side, and the nodes merge different parts into the network topology of the nodes according to the received topology information of the opposite side.
The revenue matrix in step S8 is: a matrix established for solving Nash equilibrium in the game theory;
the node energy comprises the ability of the node willing to receive the data packet, the ability of the node willing to transmit the data packet and the ability of the node to transmit the data packet;
connect to each of the topology
Figure GDA0002375614200000062
Establishing a revenue matrix according to two nodes i and j associated with connection and solving a Nash equilibrium solution;
the revenue matrix is as follows:
Figure GDA0002375614200000061
wherein (U)i,Uj) Middle UiTotal revenue, U, for node i to forward and send packetsjTotal revenue for node j to forward and send packets, (V)i,Vj) Middle ViTotal revenue, V, for forwarding and sending packets for node ijTotal revenue from sending a packet for node j without forwarding the packet from the previous node, (W)i,Wj) Middle WiSame Ui，WjIndicating the total benefit of node j neither forwarding nor transmitting packets, (X)i,Xj) In (C) XiIndicating that node i sends its own packet but does not forward the total revenue from the previous node packet, XjSame Uj，(Yi,Yj) Y in (1)iIs the same as Xi，YjSame Vj，(Zi,Zj) Middle ZiIs the same as Xi，ZjSame as Wj，(Oi,Oj) Middle OiIndicates the total benefit of node i sending a packet but not forwarding a packet sent from a previous node, OjIs the same as Xj，(Pi,Pj) Middle PiIs in the same Oi，PjSame Vj，(Qi,Qj) Middle QiIs in the same Oi，QjSame as Wj。
The step S8 is simplified to the following steps:
if any one of two nodes related to the connection has insufficient energy or a data packet buffer queue is full, the connection flag bit is 0;
if the above does not occur, then a determination is made that a game theory revenue matrix is established based on the respective energy parameters of the two nodes associated with the connection and a nash equilibrium solution between the two nodes is solved therefrom, if the nash equilibrium solution is equal to (U)i,Uj) Wherein U isiThe representative node i has energy to forward the data packet sent by the predecessor node and has energy to send the data packet by itself, UjIs explained as UiThen the flag bit of the connection is equal to 1, otherwise it is equal to 0.
If the Nash equilibrium solution of the revenue matrix of equation (11) is equal to (U)i,Uj) If so, the flag bit of the connection is 1, otherwise, the flag bit is 0; nash equilibrium solution (U)i,Uj) That is, both node i and node j participate in the transmission and forwarding of the data packet.
Compared with the prior art, the invention has the following advantages:
1. the hit rate of data transmission is improved. The nodes are dynamically selected to form the topology of the nodes through some similarities among the nodes in multiple dimensions, and then the definition of the weight values among the nodes is more accurate, so that the hit rate of data transmission is improved.
2. The average transmission delay of data is reduced. The final binary topology of each node is constructed based on the similarity of the nodes and other nodes in multiple dimensions, so that the topological graph is relatively stable, and the optimal data transmission set obtained by applying the shortest path algorithm based on the binary topology is accurate, so that the average transmission delay of data is reduced.
3. And the routing energy consumption is reduced. The binary topology of each node is obtained according to Nash equilibrium solution, so unnecessary energy consumption is avoided when data packets are sent and forwarded between the nodes, and total routing energy consumption is saved.
Drawings
FIG. 1 is a flow chart of a delay tolerant network topology routing method that takes node energy into account in accordance with the present invention;
FIG. 2 is a relatively static local topology of node a;
FIG. 3 is a topology formed by node a after a time T;
FIG. 4 is a simplified first binary topology formed by node a;
FIG. 5 is a simplified second binary topology formed by node a;
FIG. 6 is a simplified diagram of a third binary topology formed by node a;
FIG. 7 is a simplified fourth binary topology formed by node a;
fig. 8 is a fifth binary topology formed by the simplified node a.
Detailed Description
The present invention will now be further described by way of the following detailed description of a preferred embodiment thereof, taken in conjunction with the accompanying drawings.
As shown in fig. 1, a routing method of a delay tolerant network topology considering node energy includes:
step 1, abstracting dimensionality related to nodes according to the characteristics of the nodes in a delay tolerant network and the environment of the nodes;
the Delay Tolerant Network (DTN) is a new type of Network, and has different characteristics from the traditional Network, the nodes in the Network have limited resources, the nodes in the Network have discontinuous connection due to continuous movement between the nodes, and the Network topology is dynamically changed. In the DTN, k nodes are provided, wherein k is more than or equal to 1;
the dimension related to the nodes represents some characteristics represented by dimensions, for example, in a DTN formed by nodes carried by human beings, the characteristics include the geographic positions of the nodes, the moving speed, the residence time in a certain area, the interests and hobbies, the social level, the past times of encounters of the nodes, whether the nodes belong to the same family, whether the nodes belong to the same age group, whether the learned professions are the same, and whether the economic backgrounds are the same. In the DTN, the number of nodes is n, wherein n is more than or equal to 2;
step 2, defining a k multiplied by n dimensionality weight factor matrix to represent the importance of each dimensionality to a node, wherein k rows of the matrix represent k nodes, and n columns represent n dimensionalities of each node;
the dimension weight factor refers to the ratio of the importance factor of a certain dimension to the importance factors of all dimensions of the node;
step 3, grade division is carried out on each dimension according to different measuring standards, and grade classification is carried out on different dimensions of the node according to the self characteristics of the node;
the measurement standard is determined by the attribute of the dimension, for example, the moving speed dimension of the node is classified according to the size of the speed value, and the geographical position dimension of the node is classified according to the distance between the geographical position dimension and the reference object.
The step of carrying out level classification on the nodes in the dimensionality according to the self characteristics of the nodes means that each dimensionality of each node is classified into a determined level within a period of time;
step 4, counting the number of dimensions of the node j, except the node i, and the node j, arranged in the front s in the matrix, and putting the dimension into a count [ i ], simultaneously adding a corresponding dimension identifier into a dimension identifier set A [ i ], judging whether the count [ i ] is larger than a threshold value theta, and if so, adding the node i into a 'relative static local network topology' of the node j;
the dimension identification set A [ i ] is a dimension identification set, namely a dimension sequence number set, which stores all dimension identification sets with nodes i and j arranged in the previous s levels;
the threshold value theta refers to the lowest dimension degree of the previous s grades required by the 'relative static local network topology' added into the node;
the relatively static local network topology refers to the network topology of the nodes in a time period, and because the network topology of the nodes in the DTN is dynamically changed, the network topology of the nodes in a time period which is relatively static can only be taken for research;
step 5, judging whether the 'relative static local network topology' of all nodes in the dimension weight factor matrix is solved or not? If yes, performing step 6, otherwise, updating the node j and entering step 4;
step 6, comprehensively defining the network topology connection weight of the nodes, namely defining the level ranking value of the nodes in relevant dimensions, dimension weight factors and the connection among the dimensions;
step 7, in a set time period T, the network topology of each node is expanded and perfected by the node meeting exchange routing information;
the expansion and perfection of the network topology through the routing information exchange during the encounter of the nodes means that local topological graph information of the nodes is mutually sent to the opposite side when the nodes meet, and the nodes merge different parts into the network topology of the nodes according to the received topological information of the opposite side;
step 8, establishing a revenue matrix and solving a Nash equilibrium solution for each connection in the network according to the energy of the two nodes associated with the connection, and simplifying the network topology;
the income matrix is a matrix established for solving Nash equilibrium in the game theory;
the node energy refers to the capability of the node to forward the data packet, and comprises the factors that the node is willing to receive the data packet, is willing to forward the data packet, has the capability of forwarding the data packet and the like;
step 9, according to Nash equilibrium, the flag bit of each connection in the static network topology of each node is marked as binary 1 or 0, wherein 1 represents that the connection is an energy transmission data packet, and 0 is just opposite;
step 10, solving the shortest path and the optimal forwarding node set of the sending data packet according to the simplified binary topology;
step 11, sending a data packet, updating a binary network topology, and sending a new data packet;
the updating of the binary network topology is caused by dynamic changes of the network topology due to changes of node energy when a data packet is sent.
The step of updating the initial energy of the relevant nodes with the connection zone bits of 1 in the topology means that the nodes consume certain energy after the initial energy is sent, so that the residual energy value needs to be updated;
the zero clearing of the connection zone bit of 1 in the topology means that the connection zone bit of exhausted energy is cleared by 0, and the next data packet of the transmission queue pointed by the transmission pointer is prepared for the transmission of the next data packet;
in step 1, for each mobile node, it is identified by an n-dimensional vector, each value of the n-dimensional vector is an integer, i.e. the level of the node in each dimension, for example, the n-dimensional vector of any node a is represented as:
HVa＝[Ha1Ha2…Hap…Han](1)
in step 2, the importance of each dimension to the node is changed along with time, wherein the dimension weight factor matrix is expressed as:
Figure GDA0002375614200000101
each value in the dimensional weight factor matrix in equation (2) is calculated as:
Figure GDA0002375614200000102
h in formula (3)p minRepresents the minimum rank value in the p-th dimension, Hp maxRepresenting the maximum rank value in the p-th dimension. a is any one of k nodes.
In step 3, the grade division needs to divide each dimension into s grades, wherein r is the highest grade, and the grade r-s +1 to the grade r are the first s grades with the highest grade;
in step 4, a dimension identification set a [ i ] related to the node i is counted and a dimension count array element count [ i ] is calculated, taking any node a as an example, the correlation algorithm 1 is as follows:
algorithm 1: dimension statistics, algorithm parameters are null
The first step is as follows: let i be the node number variable and make its initial value 1, let j be the dimension number
The variable has its initial value 1.
The second step is that: starting from i ═ 1 (i.e., the node with index 1), the operation is as started in the third step.
The third step: starting with j equal to 1 (i.e., the dimension numbered 1), the operation starts as in the fourth step.
The fourth step: if the ranking value of the node a in the dimension j is greater than or equal to r-s +1 and the ranking value of the node i in the dimension j is also greater than or equal to r-s +1, the dimension count array element (count [ i ]) is added with 1, and the sequence number of the dimension j is added to the dimension identification set A [ i ]. And (5) adding 1 to the dimension serial number variable j, and turning to the fifth step. And if the ranking level of one node in the dimension j is smaller than r-s +1 in the node a and the node i, the dimension counting array element count [ i ] is unchanged, the dimension identification set A [ i ] is also unchanged, the dimension sequence number variable j is added with 1, and the fifth step is carried out.
The fifth step: if j is less than or equal to n (i.e. n dimensions in total), then go on to the fourth step, if j is greater than n (i.e. node a and node i count up in n dimensions), then go to the sixth step.
And a sixth step: and adding 1 to the node serial number i (namely counting the node a and the next node), and if i is less than or equal to k-1 (namely k-1 nodes are available besides the node a), performing the work expressed from the third step to the fifth step. And if i is larger than k-1 (namely the node a and other k-1 nodes are counted completely), the seventh step is carried out.
The seventh step: and (6) ending.
The node a executing the algorithm currently determines whether the node i is added into the local topology of the node according to the comparison between the count [ i ] and the threshold value theta, and the related algorithm 2 is as follows:
and 2, algorithm: the local topology construction of the node a is carried out, and the algorithm is shaped as count [ i ]
The first step is as follows: let the threshold value be θ (constant), and let i be the node sequence number variable and make its initial value be 1.
The second step is that: starting from i ═ 1 (i.e., the node with index 1), the operation is as started in the third step.
The third step: if the count [ i ] is larger than or equal to theta, adding the point i into the topology Tpa of the node a, adding 1 to the i, and turning to the fourth step. If the count [ i ] is smaller than theta, ignoring the current i node, adding 1 to i, and turning to the fourth step.
The fourth step: if i is less than k-1 (i.e., there are k-1 nodes in addition to a), then the work is done as stated from the second step to the third step. If i is larger than k-1 (i.e. the count [ i ] of the node a and other k-1 nodes is counted up), then go to the fifth step.
The fifth step: and (6) ending.
In step 6, calculating the weight of each connection in the topological graph into a single connection condition and a multi-connection condition;
the single connection is the connection with only one path between two points;
the multi-connection refers to the connection of a plurality of paths between two points;
the relation between the dimensions comprises mutual independence and mutual influence;
when the relations between the dimensions are independent, the calculation formula of the single connection weight is as follows:
Figure GDA0002375614200000121
equation (4) is a formula for calculating the weight of a single connection between node a and node f, where they are ranked in the top s-th dimension in β dimensions;
wherein the following values in equation (4) are the inverse of the weighting factors:
Figure GDA0002375614200000131
Figure GDA0002375614200000132
when the dimensions are mutually influenced, the calculation formula of the single connection weight is as follows:
Figure GDA0002375614200000133
wherein in equation (7):
Figure GDA0002375614200000134
wherein in equation (7):
Figure GDA0002375614200000141
wherein L isast,Lfst,Lasp,LfspRank values of the nodes a and f in the moving speed dimension and the staying time dimension respectively, that is, when there is a dimension of the number of times of encounters between the nodes in β dimensions, and the moving speed dimension and the staying time dimension are not at the previous s level, the constraint condition is:
Lat∈Dt∩Lat≥r-s+1 Lft∈Dt∩Lft≥r-s+1
Last＜r-s+1,Lfst＜r-s+1,Lasp＜r-s+1,Lfsp＜r-s+1
the formula for calculating the multi-connection weight is as follows:
Waw＝Waea×Weaw+Waka×Wkaw+Wawa×Wwaw(10)
in the formula (10), for example, a multi-connection weight between any two nodes a and w is obtained, where the node ea, the node ka, and the node wa are three common friend nodes of the node a and the node w. WaeaIs a single connection weight, W, between node a and node eaeawIs a single connection weight, W, between node ea and node WakaIs a single connection weight, W, between node a and node kakawIs a single connection weight, W, between node ka and node WawaIs a single connection weight, W, between node a and node wawawFor a single connection weight between node wa and node w, according to the principles of probability multiplication and additionAnd solving the multi-connection weight between the node a and the node w.
In step 7, after each node receives the topology maps of other nodes, the topology maps are merged into the topology map of the node according to the principle of finding the difference and the identity, taking the node w encountered by the node a as an example, the correlation algorithm 3 is as follows:
algorithm 3: expanding the local topology of the node a, wherein the algorithm is formed as the topology Tpa of the node a and the topology Tpw of the node w
The first step is as follows: and the node a sends the topology Tpa to the node w, and the node a receives the topology Tpw sent by the node w.
The second step is that: in Tpa common
Figure GDA0002375614200000142
Strip connection (
Figure GDA0002375614200000143
Constant), a total of δ connections (δ is constant) in Tpw, and a count variable of the connections in the topology graph Tpa of the node a is set as
Figure GDA0002375614200000144
Let its initial value be 1, and let the count variable of the connection in the topology Tpw of the node w be § 1.
The third step: to Tpa from
Figure GDA0002375614200000151
The initiated connection does the work initiated in the fourth step.
The fourth step: the work started at the fifth step is performed for the connection in Tpw started from § 1.
The fifth step: if connected to
Figure GDA0002375614200000152
With connection § having a common node, connecting
Figure GDA0002375614200000153
To join, § plus 1 (i.e. the next connection in Tpw), if § less than or equal to δ (i.e. δ connections in Tpw have not been cycled through), the work beginning at the fifth step continues. If it is notAnd e, rotating to the sixth step when the speed is larger than delta.
And a sixth step:
Figure GDA0002375614200000154
plus 1 (i.e., the next connection in Tpa), if
Figure GDA0002375614200000155
Is less than or equal to
Figure GDA0002375614200000156
The work stated from the fourth step to the fifth step is done. If it is not
Figure GDA0002375614200000157
Is greater than
Figure GDA0002375614200000158
The seventh step is executed.
The seventh step: and (6) ending.
In step 8, the topology is simplified according to the destination address identified by the data packet pointed by the transmission pointer in the current node transmission data queue, and each connection in the topology is simplified
Figure GDA00023756142000001510
Establishing a revenue matrix according to two nodes i and j associated with connection and solving a Nash equilibrium solution;
the revenue matrix is as follows:
Figure GDA0002375614200000159
wherein (U)i,Uj) Middle UiTotal revenue, U, for node i to forward and send packetsjTotal revenue for node j to forward and send packets, (V)i,Vj) Middle ViTotal revenue, V, for forwarding and sending packets for node ijTotal revenue from sending a packet for node j without forwarding the packet from the previous node, (W)i,Wj) Middle WiSame Ui，WjRepresents node jTotal revenue of not forwarding and not sending packets, (X)i,Xj) In (C) XiIndicating that node i sends its own packet but does not forward the total revenue from the previous node packet, XjSame Uj，(Yi,Yj) Y in (1)iIs the same as Xi，YjSame Vj，(Zi,Zj) Middle ZiIs the same as Xi，ZjSame as Wj，(Oi,Oj) Middle OiIndicates the total benefit of node i sending a packet but not forwarding a packet sent from a previous node, OjIs the same as Xj，(Pi,Pj) Middle PiIs in the same Oi，PjSame Vj，(Qi,Qj) Middle QiIs in the same Oi，QjSame as Wj；
Taking the topology Tpa of the node a as an example, the node i and the node j are connected with the topology Tpa
Figure GDA0002375614200000161
Two related nodes, assuming that node a sends a packet to node f at this time, simplify the network topology: if any one of two nodes related to the connection has insufficient energy or a data packet buffer queue is full, the connection flag bit is 0;
if the above does not occur, then a determination is made that a game theory revenue matrix is established based on the respective energy parameters of the two nodes associated with the connection and a nash equilibrium solution between the two nodes is solved therefrom, if the nash equilibrium solution is equal to (U)i,Uj) Wherein U isiThe representative node i has energy to forward the data packet sent by the predecessor node and has energy to send the data packet by itself, UjIs explained as UiThen the flag bit of the connection is equal to 1, otherwise equal to 0; the topology reduction algorithm 4 is as follows:
and algorithm 4: simplifying the topology after the node a is expanded, wherein the algorithm is in the shape of Tpa
The first step is as follows: in Tpa common
Figure GDA0002375614200000162
Strip connection (
Figure GDA0002375614200000163
Constant), let the connection count variable in Tpa be
Figure GDA00023756142000001613
And its initial value is 1.
The second step is that: to Tpa from
Figure GDA0002375614200000164
And (5) making each connection from the beginning, and performing the work started in the third step.
The third step: if it is connected with
Figure GDA0002375614200000165
When one (or two) of the two nodes i and j is/are in short energy or the queue of the received data packet is full, the connection is established
Figure GDA0002375614200000166
Is equal to 0 and the flag bit of (c) is equal to 0,
Figure GDA0002375614200000167
add 1 (continue the judgment of the next connection), if
Figure GDA0002375614200000168
Is less than
Figure GDA0002375614200000169
The operation described from the third step to the fifth step is continued. If it is not
Figure GDA00023756142000001610
Is greater than
Figure GDA00023756142000001611
Namely, it is
Figure GDA00023756142000001612
The strip connection is processed), the sixth step is executed.
The fourth step: establishing a game theory income matrix shown in the formula (11) based on the related energy parameters of the node i and the node j, and solving a Nash equilibrium solution if the Nash equilibrium solution is equal to (U)i,Uj) (i.e., nodes i and j both have energy forwarding predecessor node packets and send packets themselves), then the connection is made
Figure GDA0002375614200000171
Flag bit 1, otherwise connect
Figure GDA0002375614200000172
Is equal to 0. And turning to the fifth step.
The fifth step:
Figure GDA0002375614200000173
add 1 (continue the judgment of the next connection), if
Figure GDA0002375614200000174
Is less than
Figure GDA0002375614200000175
The operation described from the third step to the fifth step is continued. If it is not
Figure GDA0002375614200000176
Is greater than
Figure GDA0002375614200000177
(i.e. the
Figure GDA0002375614200000178
The strip connection is processed), the sixth step is executed.
And a sixth step: and (6) ending.
If the Nash equilibrium solution of the revenue matrix of equation (11) is equal to (U)i,Uj) The flag bit of the connection is 1, otherwise, the flag bit is 0; nash equilibrium solution (U)i,Uj) That is, both the node i and the node j participate in the sending and forwarding of the data packet, and then the shortest path for sending the data packet and the optimal forwarding node set are solved according to the simplified binary topologyCo-setops；
Taking the example that the node a sends a data packet to the node f, after the topology simplification of the algorithm 4, the shortest path and the optimal forwarding node set are solvedopsThe algorithm 5 of (1) is:
and algorithm 5: finding the shortest path and the optimal forwarding set, and taking the algorithm as the topology Tpa of the node a
The first step is as follows: if all the connections with the flag bits of 1 on the path from the node a to the node f form a tree structure or all the connections with the flag bits of 1 on the path from the node a to the node f form a linear structure, the optimal forwarding node setopsThe set is formed by all nodes related to the connection with the flag bit being 1 on the path from the node a to the node f. Otherwise, the second step is carried out.
The second step is that: and (4) running a shortest path algorithm by taking the destination node f as the source node to sequentially select the nodes to enter the shortest path, and adding the nodes which appear in the algorithm before the node a into the optimal node forwarding set.
The third step: and (6) ending.
Updating residual energy of related nodes in the binary network topology, clearing 0 from 1 to the connection zone bits with exhausted energy, and sending a new data packet, wherein the process comprises an algorithm 6, an algorithm 7 and an algorithm 8;
the algorithm 6 for updating the node residual energy in the optimal forwarding node set is as follows:
and 6, algorithm: updating the residual energy of the nodes in the optimal forwarding node set, and forming the algorithm parameters into the optimal forwarding node setops
The first step is as follows: is provided with
Figure GDA0002375614200000181
Set for optimal forwarding nodeopsThe node number in (1) counts the variable and makes its initial value 1. Node forwarding setopsChinese character of' Zhong
Figure GDA0002375614200000182
A node (
Figure GDA0002375614200000183
Is a constant). Is provided with
Figure GDA0002375614200000184
Is a node
Figure GDA0002375614200000185
The initial energy of the energy of,
Figure GDA0002375614200000186
the energy consumed to transmit a data packet,
Figure GDA0002375614200000187
the energy consumed to forward a packet of data,
Figure GDA0002375614200000188
in order to gain the benefit of transmitting a data packet,
Figure GDA0002375614200000189
the benefit of forwarding a packet.
The second step is that: set of slave best forwarding nodesopsIn
Figure GDA00023756142000001810
The node (i.e., the first node) starts and the work started in the third step is done.
The third step:
Figure GDA00023756142000001811
plus 1 (i.e. the best set of forwarding nodes set)opsThe next node in) if
Figure GDA00023756142000001812
Is less than or equal to
Figure GDA00023756142000001813
And continuing to do the work started in the third step, and otherwise, turning to the fourth step.
The fourth step: and (6) ending.
Taking the topology Tpa of the node a as an example, the connection clear algorithm 7 with the flag bit 1 is:
and algorithm 7: connection flag bit zero clearing, algorithm form parameter as optimum forwarding node setops
The first step is as follows: node forwarding setopsChinese character of' Zhong
Figure GDA00023756142000001814
A node (
Figure GDA00023756142000001815
Is constant), is provided
Figure GDA00023756142000001816
Set for optimal forwarding nodeopsThe node in (1) counts the variable and has its initial value 1.
The second step is that: for slave setopsIn
Figure GDA00023756142000001817
The (i.e., first) starting node, does the work from the third step.
The third step: after the observation passes algorithm 6
Figure GDA00023756142000001818
Whether it is equal to 0 (i.e., energy depletion), and if it is equal to 0, then
And node
Figure GDA0002375614200000191
And (5) clearing 0 the related connected zone bits, and turning to the fourth step. If not, go to the fourth step.
The fourth step:
Figure GDA0002375614200000192
plus 1, if
Figure GDA0002375614200000193
Is less than or equal to
Figure GDA0002375614200000194
Then go to the third step, otherwise go to the fifth step.
The fifth step: and (6) ending.
Taking the node a as an example, to sum up, the delay tolerant network topology routing method algorithm 8 considering the node energy is:
algorithm 8: a delay tolerant network topology routing method considering node energy is characterized in that an algorithm is formed as a topology Tpa of a node a, and a sending queue Qa
The first step is as follows: transmit packet queue QaFor a total of p packets. Let y be the transmit queue QaAnd has its initial value 1 (i.e., the first packet in the transmit queue).
The second step is that: to QaEach data packet P inyAnd performing the work started in the third step.
The third step: if P isyTTL equals 0 (i.e. packet P)yEnd of life cycle), packet P is discardedyOtherwise, the work from the fourth step is done.
The fourth step: firstly, the algorithm 4 is operated to simplify the topology Tpa, and then the algorithm 5 is operated to obtain the optimal forwarding node setops. If the node that encountered node a belongs to setopsAnd before the meeting node, the data packet P is not receivedyThen the data packet P is sentySending to the meeting node, otherwise reserving PyWaiting for a better chance. Running algorithm 6 to set of optimal forwarding nodesopsAnd (5) updating the energy of the middle node, and running an algorithm 7 to clear 0 the connection flag bit related to the node with exhausted energy. And y is added with 1, and if y is less than or equal to p (namely, data packets still exist in the sending queue), the work expressed from the third step to the fourth step is carried out. If y is larger than p, go to the fifth step.
The fifth step: and (6) ending.
The method is illustrated below by way of an example;
the concept of physical and mathematical commonly known as spatial dimensions comes from the broad relativistic theory. For two ants at two diagonal ends on a piece of paper, the distance between them is relatively far because the ants can only perceive two dimensions, but if the paper is folded in half, the distance between them is very close along the third dimension folded at the two end pairs. So for distance, it is first discussed in one dimension, since distances far in one dimension may be very close in another dimension. The method makes reference to the concept of dimensions, which are not strictly defined in the broad relativity. When a node is moved by a human being, characteristics related to the human being are inevitably embodied because the human being is in different dimensions, and the node is also in different dimensions, and the different dimensions include the geographical position of the node, the moving speed of the node, the staying time in a certain area, the number of times of meeting with other nodes in the past (meeting with other nodes and successfully performing data transmission), social hierarchy positioning (such as a city manager, a teacher, a student and the like), common hobbies (such as reading books, playing basketball and the like), common economic backgrounds, whether the nodes are in the same age group, whether the nodes are a family, whether the nodes learn the same specialty and the like. In summary, all conceivable dimensions can be defined. Some dimensions are related to each other, and the dimension of the number of past encounters for a node is related to the dimension of interest and hobbies, the dimension of stay time of the node at a certain place and the dimension of moving speed of the node. As interest will often be reflected in the geographical location where the node is present. For example, two persons who like sports may meet each other frequently in a gym, but how long the two persons meet depends on their stay in the gym and their moving speed. Some dimensions are independent of each other, such as a social hierarchy dimension and a node moving speed dimension. Over time, the influence of the dimension on the node is different, and a weight coefficient of the dimension is defined to measure. For example, even if the family is separated from the family by thousands of mountains in water, people in the same family prefer to communicate with more family people far away from the family, the communication cannot be performed with nearby strange neighbors, so the distance becomes very close in the dimension of whether the family is the family or not, and the dimension of the geographic position seems to become ineffective.
For example, for a mobile node a, a 5-dimensional vector is used to identify the mobile node a, and the 5 dimensions are respectively the moving speed, the staying time of a certain place, the social hierarchy, whether the mobile node is a parent or not, the social hierarchy and the age group. Each value of the 5-dimensional vector is an integer, that is, the level of the vector in each dimension, and the 5 dimensions are identified by the level values, which is favorable for unifying the metrics, where the threshold value θ is 3 and s is 6.
And for the dimension of the stay time of the node at a certain place, carrying out grade division according to the stay time of the node at the corresponding place.
For the dimension of the moving speed of the node, the moving speed of the node is inversely proportional to the grade, and the faster the moving speed of the node is, the lower the grade of the node is.
For the dimension of relatives or not, the grade division can be performed according to whether the relatives are direct relatives or not, whether the relatives are collateral relatives or not, whether the relatives are not relatives but are relatively familiar, strangers and the like.
For the dimension of social hierarchy, if in a school, the division of the levels can be made according to the length of the school, the master, the teacher, the student, etc.
For the dimension of age groups, ranking can be done according to whether they are peers, whether they are 5 years old or 10 years old, etc.
Assume that node a establishes a relatively static topology as shown in fig. 2 below.
The letters of the nodes in fig. 2 represent the IDs of the nodes. The numerical value on the connection in the graph represents the weight of the connection and is obtained according to a weight calculation formula. All nodes and node a in the graph are ranked in the top 6 levels in 3 dimensions greater than the threshold, and for node a and node ea in the graph, they are ranked in the top 6 levels in 3 dimensions greater than the threshold. Ideally, if node a and node ea rank at the top 6 in each of 5 dimensions, then count [ ea ] is 5, where the total number of dimensions n is always equal to or greater than the threshold value θ.
If the node a encounters other nodes after the time T, the topology formed by the node a is as shown in fig. 3 below.
The ellipses in fig. 3 indicate that the topology is followed by connections, which are weighted, and node b in the graph causes a temporary topology split because there is temporarily no intervening node in common with other nodes. However, as a node meets other nodes, there always exists a certain meeting node with a relatively high probability, and the nodes in the topological graph connect the split parts.
Finally, since the top 6 highest ranking levels are selected in each dimension when constructing the local topology of the nodes, and the reciprocal of each connection is taken when calculating the weight of each connection, the corresponding number becomes the minimum 6. Assuming that a current sending pointer in a node a points to a data packet sent to a node sp, each node sends energy consumption of one own data packet according to the current residual energy of each node in fig. 3, each node forwards the energy consumption of the data packet sent to the node sp, and the topology is simplified by establishing a rights and interests matrix for each connection to solve a nash equilibrium solution, and five binary topologies shown in fig. 4-8 can be generated after simplification;
after simplification, all the connections with flag bits 1 form a linear structure, as shown in fig. 4.
As shown in FIG. 5, the blue and red values represent the connected flag bits, the black value is the connection weight, the connection with flag bit 1 in FIG. 4 forms a linear structure, and the shortest path from node a to node sp and the optimal forwarding node set can be obtained without running the shortest path algorithmops＝{a,k,o,s,sp}；
After simplification, all the connections with flag bit 1 form a tree structure, as shown in fig. 5.
As shown in FIG. 5, the connection with the flag bit of 1 forms a tree structure, and the shortest path from node a to node sp and the optimal forwarding node set can be obtained without running the shortest path algorithmops＝{a,k,o,s,sp}；
After simplification, all the connections with flag bit 1 form a pattern structure, as shown in fig. 6.
As shown in FIG. 6, the connection with flag bit 1 forms a graph structure, and the shortest path from node a to node sp and the optimal path can be obtained by running the shortest path algorithm with node sp as the source nodeSet of forwarding nodesops；
After simplification, all the connected flags in the figure are 1, as shown in fig. 7.
If all the connected flags are 1 in fig. 7, the shortest path algorithm is run by using the node sp as the source node, and the shortest path from the node a to the node sp and the optimal forwarding node set can be obtainedops；
After simplification, all the connected flags in the figure are 0, as shown in fig. 8 below.
For example, the flag bit of all connections in fig. 8 is 0, which is a special case, in future work, other strategies will be considered for forwarding packets, for example, excitation strategies for nodes to find the shortest path and the optimal forwarding node setops；
In summary, the delay tolerant network topology routing method considering node energy of the present invention improves the hit rate of data transmission and reduces data transmission delay and routing energy consumption by performing effective routing selection.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (8)

1. A method for delay tolerant network topology routing that considers node energy, the method comprising:
step S1, abstracting the dimensionality related to the nodes according to the characteristics of the nodes in the delay tolerant network and the environment;
step S2, defining a k multiplied by n dimension weight factor matrix to represent the importance of each dimension to the node, wherein k rows of the matrix represent that k nodes exist, and n columns represent that each node has n dimensions;
step S3, grade division is carried out on each dimension according to different measuring standards and grade classification is carried out on different dimensions of the node according to the self characteristics of the node;
step S4, counting the number of dimensions of S, except for a node j, of which the node i and the node j are arranged in front of the node j in the matrix, and putting the dimensions into a count [ i ], simultaneously adding corresponding dimension identifiers into a dimension identifier set A [ i ], judging whether the count [ i ] is larger than a threshold value theta, and if so, adding the node i into a relative static local network topology of the node j;
step S5, judging whether the relative static local network topology of all nodes in the dimension weight factor matrix is solved, if so, performing step S6, otherwise, updating the node j and entering step S4;
step S6, comprehensively defining the network topology connection weight of the node;
step S7, in the set time period T, the network topology of each node is enlarged and perfected through the node meeting exchange routing information;
step S8, establishing a revenue matrix and solving Nash equilibrium solution according to the energy of two nodes associated with each connection in the network topology, and simplifying the network topology;
step S9, according to Nash equilibrium, the zone bit of each connection in the static network topology of each node is marked as binary 1 or 0, 1 represents the connection energy sending data packet, and 0 is just opposite;
step S10, according to the simplified network topology, the shortest path and the optimal forwarding node set of the sending data packet are obtained;
step S11, sending data packet, updating binary network topology, and sending new data packet;
the connection weight calculation in step S6 includes single connection weight calculation and multiple connection weight calculation, as follows:
when the relations between the dimensions are independent, the calculation formula of the single connection weight is as follows:
Figure FDA0002375614190000021
equation (4) is a formula for calculating the weight of a single connection between node a and node f, where they are ranked in the top s-th dimension in β dimensions;
wherein the following values in equation (4) are the inverse of the weighting factors:
Figure FDA0002375614190000022
Figure FDA0002375614190000023
when the dimensions are mutually influenced, the calculation formula of the single connection weight is as follows:
Figure FDA0002375614190000031
Figure FDA0002375614190000032
Figure FDA0002375614190000033
wherein in equation (7):
Figure FDA0002375614190000034
wherein in equation (7):
Figure FDA0002375614190000035
wherein L isast,Lfst,Lasp,LfspRank ranking values of the node a and the node f in a moving speed dimension and a staying time dimension respectively, namely when the β dimensions have the dimension of the number of times of meeting between the nodes, and the moving speed dimension and the staying time dimension are not at the previous s level, the constraint conditions are as follows:
Lat∈Dt∩Lat≥r-s+1Lft∈Dt∩Lft≥r-s+1
Last＜r-s+1,Lfst＜r-s+1,Lasp＜r-s+1,Lfsp＜r-s+1
the formula for calculating the multi-connection weight is as follows:
Waw＝Waea×Weaw+Waka×Wkaw+Wawa×Wwaw(10)
in the formula (10), taking the example of solving the multi-connection weight between any two nodes a and W, where the node ea, the node ka, and the node wa are three common friend nodes of the node a and the node W, and WaeaIs a single connection weight, W, between node a and node eaeawIs a single connection weight, W, between node ea and node WakaIs a single connection weight, W, between node a and node kakawIs a single connection weight, W, between node ka and node WawaIs a single connection weight, W, between node a and node wawawAnd (4) solving a multi-connection weight between the node a and the node w according to probability multiplication and addition principles for the single connection weight between the node wa and the node w.
2. The method for routing delay tolerant network topology considering node energy as claimed in claim 1, wherein in said step S1, each mobile node is identified by an n-dimensional vector, each value of the n-dimensional vector is an integer, i.e. the level of the node in each dimension, and the n-dimensional vector for any node a is expressed as:
HVa＝[Ha1Ha2… Hap… Han](1)。
3. the method for delay tolerant network topology routing considering node energy according to claim 1, wherein the importance of each dimension to a node in step S2 is varied with time, wherein the dimension weight factor matrix is expressed as:
Figure FDA0002375614190000041
each value in the dimensional weight factor matrix in equation (2) is calculated as:
Figure FDA0002375614190000042
h in formula (3)p minRepresents the minimum rank value in the p-th dimension, Hp maxRepresents the maximum rank value in the p-th dimension, and a is any one of k nodes.
4. The method for routing in a delay tolerant network topology considering node energy as claimed in claim 1, wherein said step 3 of ranking requires that each dimension be classified into s ranks, r being the highest rank, and from r-s +1 to the first s ranks, where r is the highest.
5. The method for routing delay tolerant network topology considering node energy according to claim 1, wherein the step S7 of expanding and perfecting the network topology of each node by exchanging routing information through node encounters is specifically: when the nodes meet, the local topology information of the nodes are mutually sent to the opposite side, and the nodes merge different parts into the network topology of the nodes according to the received topology information of the opposite side.
6. The method for routing a delay tolerant network topology considering node energy according to claim 1, wherein the revenue matrix in step S8 is: a matrix established for solving Nash equilibrium in the game theory;
the node energy comprises the ability of the node willing to receive the data packet, the ability of the node willing to transmit the data packet and the ability of the node to transmit the data packet;
connect to each of the topology
Figure FDA0002375614190000052
Establishing a revenue matrix according to two nodes i and j associated with connection and solving a Nash equilibrium solution;
the revenue matrix is as follows:
Figure FDA0002375614190000051
wherein (U)i,Uj) Middle UiTotal revenue, U, for node i to forward and send packetsjTotal revenue for node j to forward and send packets, (V)i,Vj) Middle ViTotal revenue, V, for forwarding and sending packets for node ijTotal revenue from sending a packet for node j without forwarding the packet from the previous node, (W)i,Wj) Middle WiSame Ui，WjIndicating the total benefit of node j neither forwarding nor transmitting packets, (X)i,Xj) In (C) XiIndicating that node i sends its own packet but does not forward the total revenue from the previous node packet, XjSame Uj，(Yi,Yj) Y in (1)iIs the same as Xi，YjSame Vj，(Zi,Zj) Middle ZiIs the same as Xi，ZjSame as Wj，(Oi,Oj) Middle OiIndicates the total benefit of node i sending a packet but not forwarding a packet sent from a previous node, OjIs the same as Xj，(Pi,Pj) Middle PiIs in the same Oi，PjSame Vj，(Qi,Qj) Middle QiIs in the same Oi，QjSame as Wj。
7. The method for routing delay tolerant network topology considering node energy according to claim 6, wherein said step S8 simplifies the network topology to:
if any one of two nodes related to the connection has insufficient energy or a data packet buffer queue is full, the connection flag bit is 0;
if this does not occur, then a determination is made to establish based on the respective energy parameters of the two nodes associated with the connectionGame theory profit matrix is calculated to obtain Nash equilibrium solution between two nodes if Nash equilibrium solution is equal to (U)i,Uj) Wherein U isiThe representative node i has energy to forward the data packet sent by the predecessor node and has energy to send the data packet by itself, UjIs explained as UiThen the flag bit of the connection is equal to 1, otherwise it is equal to 0.
8. The method for routing delay tolerant network topologies considering node energy as recited in claim 6, wherein the Nash equilibrium solution of the revenue matrix of equation (11) is equal to (U)i,Uj) If so, the flag bit of the connection is 1, otherwise, the flag bit is 0; nash equilibrium solution (U)i,Uj) That is, both node i and node j participate in the transmission and forwarding of the data packet.
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