CN114070773A - Space network routing strategy based on shortest path length - Google Patents

Space network routing strategy based on shortest path length Download PDF

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CN114070773A
CN114070773A CN202111193019.9A CN202111193019A CN114070773A CN 114070773 A CN114070773 A CN 114070773A CN 202111193019 A CN202111193019 A CN 202111193019A CN 114070773 A CN114070773 A CN 114070773A
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CN114070773B (en
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夏永祥
林泓
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Hangzhou Dianzi 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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
    • H04L45/125—Shortest path evaluation based on throughput or bandwidth
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a space network routing strategy based on shortest path length, which comprises the following steps: step S1) space network initialization; step S2) configuring a flow model; step S3) calculating the shortest path length between the node pairs; step S4) load transfer. The invention provides a space network routing strategy based on shortest path length, which improves network throughput and further improves network transmission performance by considering the space positions of nodes in a space network and the length attributes of connecting edges among the nodes and considering that the performance of congestion phenomena of networks with different structures is different.

Description

Space network routing strategy based on shortest path length
Technical Field
The invention relates to the technical field of network routing strategies, in particular to a space network routing strategy based on shortest path length.
Background
Many complex networks represented by communication networks, power networks, and traffic networks have a basic function of transmitting loads. In these networks, the throughput of the network is an important index for measuring the transmission performance of the network, and how to improve the throughput of the network is one of the research hotspots. Researches show that the network transmission capability mainly depends on the topology and routing mode of the network, and for the two influencing factors, two optimization modes of a hard strategy and a soft strategy are generally available, namely, the optimization of the topology and the optimization of the routing mode of the network. In actual life, the structure of many networks is fixed, and high cost is required to be borne for modifying the structure, so that the improvement of the network transmission performance is mainly realized by optimizing a routing mode. The routing strategy of the shortest path in the traditional sense is a routing mode which is widely applied at present, namely, the load is transmitted from the node A to the node B through the least number of edges, but the mode easily causes the congestion phenomenon at some nodes with larger values. Therefore, many researchers put forward a more efficient routing strategy, for example, Yan et al put forward a "the effective path" routing strategy, and reduce the possibility of congestion by bypassing nodes with a large degree of divergence, thereby greatly improving the transmission performance of the network; huang et al propose a routing strategy with memory information, and nodes record the transmission source of the load, thereby avoiding backtracking, greatly reducing the opportunity of the load to transmit back and forth between two different nodes, and effectively improving the network throughput.
However, the routing strategies proposed in the prior art are based on the topology of a complex network, and few studies consider the spatial location of nodes. In fact, because the connection between the nodes in the spatial network is constrained by the actual location, the spatial network has a certain difference from a general complex network in terms of topology, robustness and the like. Moreover, Zhao et al indicate that networks with different structures have different congestion performances, which also results in poor applicability of the existing routing strategy in a spatial network.
Meanwhile, the traditional routing strategy of the shortest path is to transmit load by means of the least hop count. In the context of a spatial network, the edges between different nodes may be given a length attribute, and in this case, the shortest path may be understood as the shortest path length. In the actual life, the aviation network, the traffic network, the wireless sensor network, the unmanned aerial vehicle network and the like are limited by the space position, so that the method has strong theoretical value and practical significance for the research of the networks.
Disclosure of Invention
The invention mainly aims to solve the problems that the space position of nodes is rarely considered in the existing routing strategy and the applicability of the space network is not strong, and provides a space network routing strategy based on the shortest path length based on the space network and considering the space position of the nodes and the length attribute of connecting edges between the nodes so as to improve the network throughput and further improve the network transmission performance, and has certain practical significance on the transmission of loads in the actual network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a space network routing strategy based on shortest path length comprises the following steps: step S1) space network initialization; step S2) configuring a flow model; step S3) calculating the shortest path length between the node pairs; step S4) load transfer. The invention is based on the space network, consider the space position of the node and length attribute of connecting the edge between the nodes, have proposed a space network routing strategy based on shortest path length, including the following steps, step S1) the space network is initialized; step S2) configuring a flow model; step S3) calculating the shortest path length between the node pairs; step S4) load transfer. Specifically, in step S1, considering that the networks with different structures indicated by Zhao et al have different congestion performances, the spatial network is divided into a homogeneous spatial network and a heterogeneous spatial network based on the idea of a complex network, wherein the homogeneous spatial network is initialized to generate a random geometry model network, and the heterogeneous spatial network is initialized to generate a LAEE model network. In step S2, at each time step, the network generates R units of load, the source node and the target node of which are determined randomly, the load is transmitted from the source node to the target node according to the routing strategy, and at each time step, the load can be transmitted only one step forward, i.e. from one node to its neighboring nodes, and when the load reaches its target node, the load is automatically deleted from the network. In step S3, since the length of the connecting edge connecting the node i and the node j is:
Figure BDA0003301986250000021
wherein (x)i,yi) And (x)j,yj) Two-dimensional coordinates of nodes i and j respectively;
in the spatial network, for an arbitrary node pair (s, t), the length of a path between the node s and the node t is defined as the sum of the lengths of all connected sides passed by the path, and the path is expressed as:
P(s→t)＝(s≡x(0),x(1)…,x(n)≡t)
the path length is:
Figure BDA0003301986250000022
therefore, the shortest path length between any pair of nodes (s, t) in the present invention is:
Figure BDA0003301986250000023
in step S4, for a node pair (S, t) consisting of any source node and any destination node, the load is transmitted along the direction of the shortest path length min L (S → t) between the two nodes. The invention provides a space network routing strategy based on shortest path length, which improves network throughput and network transmission capability, is suitable for space networks and has certain practical significance for load transmission in actual networks by considering the space positions of nodes in the space networks and considering that the networks with different structures have different congestion phenomena.
Preferably, the spatial network in step S1 includes a heterogeneous spatial network and a homogeneous spatial network, and the heterogeneous spatial network is initialized to generate a LAEE model network; and initializing the homogeneous space network to generate a random geometric graph model network. Considering that networks with different structures indicated by Zhao et al have different congestion performances, the spatial network is divided into a homogeneous spatial network and a heterogeneous spatial network based on the idea of a complex network, wherein the homogeneous spatial network is initialized to generate a random geometric graph model network, and the heterogeneous spatial network is initialized to generate a LAEE model network.
Preferably, the specific process of generating the LAEE model network by initializing the heterogeneous spatial network includes the following steps: step a1) randomly distributing N nodes in a1 × 1 area S; step A2) forming a communication area by taking each node as a circle center and r as a radius, wherein all nodes in the communication area are potential neighbors of the node at the circle center, and the node closest to the node at the circle center among the potential neighbors is defined as a sink node; step a3) performs network topology evolution starting from the sink node. The LAEE model network belongs to a space network with heterogeneous characteristics, and the topological evolution of the LAEE model network is mainly divided into two stages: the first stage, distributing N nodes randomly in a1 x 1 area S, wherein the transmission and load processing capacity of each node is the same, all the nodes are isolated at the moment, a communication area is formed by taking each node as the center of a circle and r as the radius, all the nodes in the communication area are potential neighbors of the node, and the node closest to the center of the circle is defined as a sink node; and in the second stage, topology evolution of the network is carried out from the aggregation node.
Preferably, the specific process of network topology evolution in step a3 includes the following steps: step B1) having the sink node communicate with a within r0The potential neighbors are connected to form an initial network T0(ii) a Step B2) at time step i, defining a network TiThe node with the most isolated potential neighbors is v, and a node n is selected from the potential neighborsiJoining a network TiPerforming the following steps; step B3) selecting a network TiA nodes in the network are connected with II on the basis of the principle of preferential connectioniThe connection probability of a nodes and n nodesiConnecting; step B4) repeats steps B2 and B3 until all nodes within the area S are joined to the network T. Wherein, when a nodes are selected in the step B3, the a nodes are all required to be at the node niIf the number of selectable nodes is less than a, all the nodes are connected.
Preferably, the connection probability IIiThe calculation formula of (2) is as follows:
Figure BDA0003301986250000031
wherein, local-area refers to node niAs the center of a circle, r is the radius communication range, kmaxIs a default node maximum value, q is that k has been reachedmaxThe number of nodes of (c), f (E)i) Is a function.
Preferably, the specific process of generating the random geometric graph model network by initializing the homogeneous space network includes the following steps: step C1) randomly distributing N nodes in the 1 × 1 area S; step C2) randomly selecting a node, and forming a communication area by taking the node as the center of a circle and u as the radius; step C3) defining a connection probability p, and connecting the node with other nodes in the connection area according to the connection probability p; step C4) repeats step C2 and step C3 until all nodes are traversed to.
Preferably, the specific process of step S2 is: at each time step i, the network T generates R unit loads, and the loads are transmitted to the target node by the source node according to the routing strategy; at each time step i, the load can only be transmitted one step forward, from one node to a neighboring node, and when the load reaches the target node, the load is automatically removed from the network. At each time step i, the network T generates R units of load, the source node and the destination node of which are both determined randomly, the load is transmitted from the source node to the destination node according to the routing policy, and at each time step i, the load can be transmitted only one step forward, i.e. from one node to its neighbor nodes, and when the load reaches its destination node, the load is automatically removed from the network.
Preferably, for any pair of nodes (s, t), the length of a path between node s and node t is defined as the sum of the lengths of all the connected sides passed by the path, and the path is expressed as:
P(s→t)＝(s≡x(0),x(1)…,x(n)≡t)
the path length is:
Figure BDA0003301986250000041
preferably, the shortest path length between any pair of nodes (s, t) is calculated by the following formula:
Figure BDA0003301986250000042
because in the two-dimensional plane, the length of the connecting edge connecting the node i and the node j is as follows:
Figure BDA0003301986250000043
wherein (x)i,yi) And (x)j,yj) Two-dimensional coordinates of nodes i and j respectively;
in the spatial network, for an arbitrary node pair (s, t), the length of a path between the node s and the node t is defined as the sum of the lengths of all connected sides passed by the path, and the path is expressed as:
P(s→t)＝(s≡x(0),x(1)…,x(n)≡t)
the path length is:
Figure BDA0003301986250000044
therefore, the shortest path length between any pair of nodes (s, t) in the present invention is:
Figure BDA0003301986250000045
preferably, the length of the connecting edge connecting the node i and the node j is:
Figure BDA0003301986250000046
wherein (x)i,yi) And (x)j,yj) Two-dimensional coordinates of nodes i and j, respectively.
Therefore, the invention has the advantages that:
(1) considering the spatial position of nodes in a spatial network and the length attribute of connecting edges among the nodes, considering that the performance of congestion phenomena of networks with different structures is different, the applicability of the invention in the spatial network is strong;
(2) the network throughput is improved, and the network transmission performance is improved;
(3) in actual life, such as an aviation network, a traffic network, a wireless sensor network, an unmanned aerial vehicle network and the like are limited by space positions, so that the method has strong theoretical value and practical significance.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic structural diagram of generating a LAEE model network in an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a network for generating a random geometry model in an embodiment of the present invention.
FIG. 4 is a simulation result diagram corresponding to the LAEE model network under different node scales when the average degree k is greater than or equal to 4 in the embodiment of the invention.
FIG. 5 is a simulation result diagram corresponding to the stochastic geometry graph model network under different node scales and with the average degree k being greater than or equal to 4 in the embodiment of the invention.
Fig. 6 is a simulation result diagram corresponding to the LAEE model network under different average degrees with the node number N being 1000 in the embodiment of the present invention.
Fig. 7 is a simulation result diagram corresponding to the random geometric graph model network with different average degrees, where the node number N is 1000 in the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
As shown in fig. 1, the present invention provides a routing policy of a spatial network based on shortest path length, considering the length attribute of the connecting edge between nodes, based on the spatial network, and includes the following steps, step S1), initializing the spatial network; step S2), initializing the time step to be 0, and configuring a flow model; step S3) calculating the shortest path length between the node pairs; step S4) load transfer. The specific process is as follows:
in step S1, considering that networks with different structures indicated by Zhao et al have different congestion performance, the spatial network is divided into a homogeneous spatial network and a heterogeneous spatial network based on the idea of a complex network, the homogeneous spatial network is initialized to generate a random geometric graph model network, and the heterogeneous spatial network is initialized to generate a LAEE model network.
As shown in fig. 2, the generation of the LAEE model network by the heterogeneous space network initialization includes two stages, in the first stage, N nodes are randomly distributed in a1 × 1 area S, the transmission and load processing capabilities of each node are the same, all the nodes are isolated at this time, each node is used as a circle center, r is used as a radius to form a connected area, all the nodes in the connected area are used as potential neighbors of the node, and the node closest to the circle center is defined as a sink node; and in the second stage, topology evolution of the network is carried out from the aggregation node, and the topology evolution comprises the following steps: step B1) having the sink node communicate with a within r0The potential neighbors are connected to form an initial network T0(ii) a Step B2) at time step i, defining a network TiThe node with the most isolated potential neighbors is v, and a node n is selected from the potential neighborsiJoining a network TiPerforming the following steps; step B3) selecting a network TiA nodes in the network are connected with II on the basis of the principle of preferential connectioniThe connection probability of a nodes and n nodesiConnecting; step B4) repeats steps B2 and B3 until all nodes within the area S are joined to the network T. When a nodes are selected in the step B3, the a nodes are all required to be at the node niIf the number of selectable nodes is less than a, all the nodes are connected. Connection probability niThe calculation formula of (2) is as follows:
Figure BDA0003301986250000061
wherein, local-area refers to node niAs the center of a circle, r is the radius communication range, kmaxIs a default node maximum value, q is that k has been reachedmaxThe number of nodes of (c), f (E)i) Is a function, for convenient handling, this scheme f (E)i)＝1。
As shown in fig. 3, the specific process of generating the random geometric graph model network by the homogeneous space network initialization includes: step C1) randomly distributing N nodes in the 1 × 1 area S; step C2) randomly selecting a node, and forming a communication area by taking the node as the center of a circle and u as the radius; step C3) defining the connection probability p, and connecting the node with other nodes in the connection area according to the connection probability p; step C4) repeats step C2 and step C3 until all nodes are traversed to.
In step S2, time step i is initialized to 0, then at each time step i, the network generates R units of load, the source node and the target node of which are determined randomly, the load is transmitted from the source node to the target node according to the routing strategy, and at each time step i, the load can be transmitted only one step forward, namely, from one node to its neighboring nodes, and when the load reaches its target node, the load is automatically deleted from the network.
In step S3, since the length of the connecting edge connecting the node i and the node j is:
Figure BDA0003301986250000062
wherein (x)i,yi) And (x)j,yj) Two-dimensional coordinates of nodes i and j respectively;
in the spatial network, for an arbitrary node pair (s, t), the length of a path between the node s and the node t is defined as the sum of the lengths of all connected sides passed by the path, and the path is expressed as:
P(s→t)＝(s≡x(0),x(1)…,x(n)≡t)
the path length is:
Figure BDA0003301986250000063
therefore, the shortest path length between any pair of nodes (s, t) in the present invention is:
Figure BDA0003301986250000064
in step S4, for a node pair (S, t) consisting of any source node and any destination node, the load is transmitted along the direction of the shortest path length min L (S → t) between the two nodes.
In order to verify the validity of the algorithm of the invention, a phase transition point R is used at which the network changes from a free flow state to a congested statecTo measure the throughput of the network. Comparing traditional routing mode with least hop count with routing strategy provided by the invention in homogeneous and heterogeneous space networkcSize of (A), RcThe larger the routing strategy is, the more efficient the routing strategy is, and the network transmission performance can be better improved. In the traditional routing strategy, the betweenness of any node v is expressed as follows:
Figure BDA0003301986250000071
wherein sigmastRepresenting the number of paths of least number of hops, σ, from any node s to node tst(v) Representing the number of paths through node v in the path of least number of hops from node s to node t.
In the routing strategy of the shortest path length provided by the invention, the betweenness of any node v is expressed as follows:
Figure BDA0003301986250000072
wherein sigma'stNumber of paths, σ ', representing the shortest path length from node s to node t'st(v) The number of paths representing the length of the shortest path between node s and node t through node v.
When R is<RcIn time, the network is in a free flow state, and the congestion phenomenon cannot occur; when R is>RcWhen the network is congested, it is assumed that the traditional routing strategy is adoptedCongestion appears at the middle node v, and Rg (v)/N (N-1) at the time>C. Studies have shown that congestion occurs first at the node with the largest value of intervention. Thus, R in the conventional least hop routing strategycCan be expressed as:
Figure BDA0003301986250000073
r in the routing strategy provided by the inventioncCan be expressed as:
Figure BDA0003301986250000074
under the condition that the average degree k is larger than or equal to 4 and different node scales, the maximum network throughput R of the routing strategy is larger than or equal to that of the traditional routing strategycComparing fig. 4 and fig. 5, wherein fig. 4 and fig. 5 are simulation results under the LAEE model network and the stochastic geometry model network, respectively, it can be found that the routing policy proposed by the present invention has a larger R in any spatial networkcThe value shows that the routing strategy of the shortest path length is superior to the routing strategy of the least hop number in the traditional sense, and the throughput of the network can be improved.
Under the condition that the number of nodes N is 1000 and different average degrees, the maximum network throughput R of the routing strategy is 1000cComparing fig. 6 and fig. 7, wherein fig. 6 and fig. 7 are simulation results under the LAEE model network and the stochastic geometry model network, respectively, it is also better that the routing strategy proposed by the present invention.
In conclusion, the novel routing strategy provided by the invention can effectively improve the maximum throughput of the network, and has good reference significance for traffic transportation, wireless communication networks and the like in real life.

Claims (10)

1. A space network routing strategy based on shortest path length is characterized by comprising the following steps:
step S1: initializing a space network;
step S2: configuring a flow model;
step S3: calculating the shortest path length between the node pairs;
step S4: and (4) load transmission.
2. The shortest path length-based spatial network routing strategy according to claim 1, wherein the spatial network in step S1 includes a heterogeneous spatial network and a homogeneous spatial network, and the heterogeneous spatial network initializes to generate a LAEE model network; and initializing the homogeneous space network to generate a random geometric graph model network.
3. The shortest path length-based spatial network routing strategy according to claim 2, wherein the heterogeneous spatial network initializes a specific process of generating a LAEE model network, comprising the following steps:
step A1: randomly distributing N nodes in a1 multiplied by 1 area S;
step A2: each node is used as a circle center, r is used as a radius to form a communication area, all nodes in the communication area are potential neighbors of the node at the circle center, and the node closest to the node at the circle center among the potential neighbors is defined as a sink node;
step A3: and carrying out network topology evolution from the sink node.
4. The shortest path length-based spatial network routing strategy according to claim 3, wherein the specific process of network topology evolution in step A3 includes the following steps:
step B1: let the sink node communicate with a within r0The potential neighbors are connected to form an initial network T0；
Step B2: at time step i, define the network TiThe node with the most isolated potential neighbors is v, and a node n is selected from the potential neighborsiJoining a network TiPerforming the following steps;
step B3: selecting a network TiA nodes in the network are connected with II on the basis of the principle of preferential connectioniWill connect probabilitiesa nodes and n nodesiConnecting;
step B4: step B2 and step B3 are repeated until all nodes within the area S are joined to the network T.
5. The shortest path length-based spatial network routing strategy of claim 4, wherein the connection probability is piiThe calculation formula of (2) is as follows:
Figure FDA0003301986240000011
wherein, local-area refers to node niAs the center of a circle, r is the radius communication range, kmaxIs a default node maximum value, q is that k has been reachedmaxThe number of nodes of (c), f (E)i) Is a function.
6. The shortest path length-based spatial network routing strategy according to claim 2, wherein the homogeneous spatial network initializes a specific process of generating a stochastic geometry model network, and comprises the following steps:
step C1: randomly distributing N nodes in a1 multiplied by 1 area S;
step C2: randomly selecting a node, and forming a communication area by taking the node as a circle center and u as a radius;
step C3: defining a connection probability p, and connecting the node with other nodes in the communication area according to the connection probability of p;
step C4: and repeating the step C2 and the step C3 until all the nodes are traversed.
7. The shortest path length-based spatial network routing policy of claim 1, wherein the specific process of step S2 is as follows: at each time step i, the network T generates R unit loads, and the loads are transmitted to the target node by the source node according to the routing strategy; at each time step i, the load can only be transmitted one step forward, from one node to a neighboring node, and when the load reaches the target node, the load is automatically removed from the network.
8. A spatial network routing strategy based on shortest path length according to claim 1, wherein, for any node pair (s, t), the length of a path between node s and node t is defined as the sum of the lengths of all the connected sides passed by the path, and the path is expressed as:
P(s→t)＝(s≡x(0),x(1)…,x(n)≡t)
the path length is:
Figure FDA0003301986240000021
9. the shortest path length-based spatial network routing strategy according to claim 8, wherein the shortest path length between any node pair (s, t) is calculated by the following formula:
Figure FDA0003301986240000022
10. the shortest path length-based spatial network routing strategy according to claim 9, wherein the length of the connecting edge connecting node i and node j is:
Figure FDA0003301986240000023
wherein (x)i,yi) And (x)j,yj) Two-dimensional coordinates of nodes i and j, respectively.
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CN115002021A (en) * 2022-04-08 2022-09-02 杭州电子科技大学 Efficient space network routing strategy
CN115550240A (en) * 2022-11-24 2022-12-30 云账户技术(天津)有限公司 Network routing method, system, electronic device and readable storage medium

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