CN106850437B - Complex network routing method based on gravitational field - Google Patents
Complex network routing method based on gravitational field Download PDFInfo
- Publication number
- CN106850437B CN106850437B CN201710042660.XA CN201710042660A CN106850437B CN 106850437 B CN106850437 B CN 106850437B CN 201710042660 A CN201710042660 A CN 201710042660A CN 106850437 B CN106850437 B CN 106850437B
- Authority
- CN
- China
- Prior art keywords
- node
- path
- nodes
- data packet
- neighbor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- H04L45/12—Shortest path evaluation
- H04L45/125—Shortest path evaluation based on throughput or bandwidth
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The research of an efficient dynamic routing method is very important for improving the network throughput and relieving the traffic congestion degree. Therefore, from the perspective of a gravitation theory, the invention deeply analyzes the aggregation effect of the nodes on the traffic flow in the network traffic transmission process, considers the smoothness degree of the nodes and the neighbor nodes thereof and the length of a transmission path, establishes a gravitation model of the nodes on the traffic flow, and further provides a dynamic routing method of the gravitational field considering the node aggregation capability.
Description
Technical Field
The invention relates to the field of communication, in particular to a complex network routing method based on a gravitational field.
Background
At present, the research on the structural and dynamic characteristics of real complex systems by using complex networks has become a hot spot and a key problem in various fields. Network traffic congestion, a common and complex dynamic phenomenon, widely exists in various real-world systems, and is represented by phase change of a traffic flow state on a network, namely, a free flow changes into a congestion phase. It has been found that for a given routing strategy, when the amount of traffic load generated per unit time is greater than a certain value, the network traffic flow state immediately falls into a congested state from free flow. Meanwhile, for any complex network, when the network structure and related function indexes are kept unchanged, the network has to have a maximum network throughput.
The generation of traffic congestion depends on the adopted network routing strategy, and for the routing algorithm with lower transmission performance, the network is easy to be congested and the congestion degree is very serious. Therefore, how to improve the throughput of the network and alleviate the traffic congestion degree of the network by exploring an efficient routing strategy is one of the key approaches to solving the traffic congestion problem. For this reason, researchers have conducted intensive research into routing strategies and have proposed many effective routing methods. Random walk is a well-known local routing method that reveals some basic dynamics. However, the routing method is difficult to reflect the traffic flow characteristics of a real complex system, and the routing performance is low.
Therefore, the invention researches the network transmission process by using the gravitational field theory, establishes a gravitational model of the nodes on the flow in the transmission process by considering the gathering capacity of the nodes and the neighboring nodes, and further provides a gravitational field dynamic routing method considering the gathering capacity of the nodes.
Disclosure of Invention
On the basis, the application provides a complex network routing method based on a gravitational field, and the method comprises the following steps:
1) at each time step, aiming at any node v, if the cache queue of the node v is not empty, a data packet is indicated to wait for transmission, so that all neighbor nodes of the node v are obtained;
2) for each neighbor node i of v, calculating the shortest path from the node i to a target node t of the node v and obtaining a node set P (i, t) on the path, and
3) calculating the attraction G of the shortest paths corresponding to all the neighbor nodes of the node v to the node vitAs set forth in the following formula:
where n denotes the number of nodes on the path, j 1,2jvThe gravity of the node j to the node v data packet on the path is represented by the following formula:
wherein, cjThe transmission capability of the node j is represented, namely the maximum number of data packets which can be sent in unit time; q. q.sjNumber of packets stored in buffer queue representing node j, thereby
The smoothness degree of the node j is reflected; djvRepresents the shortest distance between node j and node v; alpha and gamma are adjustable parameters; h isjRepresents the average degree of openness of the neighbor nodes of node j, which is for the u neighbor nodes of node jThe average degree of smoothness is:
4) obtaining a set L of paths in descending order according to the size of the path gravitation [ L ═ L-1 L2 ... Li]Wherein i is the number of neighbor nodes of the node v, and the path L with the largest path gravitation1The neighbor node of the middle corresponding node v is e;
5) if no data packet of other paths is ready to enter the node e at the same time, the node e is taken as the next routing node transmitted by the data packet of the node v;
6) if a data packet of another path node M is ready to enter the node e at the same time, calculating the weight coefficients M of the node v and the node M respectively, wherein the calculation formula is as follows:
k is an adjustable parameter, k is more than or equal to 0 and less than or equal to 1 and is used for balancing the weight between the length of the path and the waiting time; l is the shortest path length from the current node to the target node; q is the number of data packets stored in the current node; c. CeThe maximum number of data packets which can be sent in unit time of the node e is obtained; respectively obtaining the weight coefficients M of the node v and the node M through a calculation formula of the weight coefficient MvAnd Mm；
7) Comparison MvAnd MmThe node with the large weight coefficient is used as a priority node to enter the path, and the node e is used as the next routing node for transmitting the priority node data packet;
8) the node with small weight coefficient returns to the step 4) to select the path L with the second largest gravity of the entering path2。
Preferably, α and γ in the step 3) are constants of 1 or more.
Preferably, the value range of the coefficient k in the step 6) is 0-1.
Drawings
Fig. 1 is a process of a routing method according to the first embodiment.
Fig. 2 is a route performance curve of the algorithm of the present invention.
Detailed Description
The invention will now be further described with reference to the embodiments and the accompanying drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto.
The first embodiment is as follows:
the aggregation capability of the nodes is a result of the joint action of the traffic states of the nodes and the traffic states of the neighbor nodes. Each node has a certain aggregate capacity that represents an attractive force to traffic flow on the network, whereby the node may excite an attractive field model to the traffic flow. Assuming that the node where the current data packet to be sent is located is v, the attraction of the node j to the data packet at the node v is defined as follows:
wherein, FjvRepresenting the attraction of node j to the packet at node v; c. CjThe transmission capability of the node j is represented, namely the maximum number of data packets which can be sent in unit time; q. q.sjNumber of packets stored in buffer queue representing node j, thereby
The smoothness degree of the node j is reflected; djvRepresents the shortest distance between node j and node v; alpha and gamma are adjustable parameters, and alpha and gamma are constants more than or equal to 1; h isjThe average degree of openness of the neighboring nodes of the node j is represented, and for the u neighboring nodes of the node j, the average degree of openness is:
formula (1) shows that the attraction of the node j to the data packet at the node v, the smoothness of the node and the neighbor nodesIs in direct proportion to the degree of openness of the node j and is in inverse proportion to the shortest distance between the nodes j and v, and expresses remarkable physical significance.
The transmission of data packets is not determined by the traffic state of a single node, and an efficient routing strategy must take into account the traffic state of more nodes. For any data packet, a reliable path is selected as much as possible to transmit in the transmission process, so the path is the research object of the routing strategy. For this purpose, let the node where the data packet is currently located be v, and denote the set of all nodes on the shortest path from the neighbor node i to the target node t by P (i, t) and
from this, the following gravity model of the path for packets in node v can be defined:
the above equation shows that the attraction of a path to a packet is the average of the attractions of all nodes on the path to the packet. Therefore, the present disclosure provides a dynamic routing policy of a gravitational field considering node aggregation capability, where a specific routing process is as follows:
1) at each time step, aiming at any node v, if the cache queue of the node v is not empty, a data packet is indicated to wait for transmission, so that all neighbor nodes of the node are obtained;
2) for each neighbor node i of v, calculating the shortest path from i to the target node t of the data packet and obtaining a node set P (i, t) of the path;
3) according to the formulas (1) and (2), the gravity G of the shortest path corresponding to all the neighbor nodes to the data packet is calculatedit；
4) Obtaining a set L of paths in descending order according to the size of the path gravitation [ L ═ L-1 L2 ... Li]Wherein i is the number of neighbor nodes of the node v, and the path L with the largest path gravitation1The neighbor node of the middle corresponding node v is e; and by said path L1The neighbor node e of the correspondent node v being passed as the current data packetThe next routing node.
Example two:
the technical solution in the first embodiment is also not without defects, and as can be seen from formulas (1) - (2), the path L with the greatest attraction force in the entire nodal network must be a relatively "clear" and "unobstructed" path, and this path is equivalent to a "highway" in the entire nodal network. Therefore, according to the calculation results of the formulas (1) to (2), all nodes carrying data packets around the current node v will choose to transmit from the path L, so that the path L will become a highly congested path instantly, and other paths will be "blank" temporarily, resulting in waste of node paths. This means that the neighbor node e of the corresponding node v calculated in the first embodiment may become the next routing node of two, three or more nodes at the same time, which may instead become further congestion.
In order to solve the above-mentioned defects, if at the same time, a data packet of another path node M is ready to enter the node e, the node e needs to be "current-limited" to avoid congestion, a weight coefficient M is introduced, and the calculation formula is as follows:
k is an adjustable parameter, k is more than or equal to 0 and less than or equal to 1, and the k is used for balancing the weight between the length of a path and the time for waiting for the node e to process a data packet; l is the shortest path length from the current node to the target node; q is the number of data packets stored in the current node; c. CeThe maximum number of data packets that can be sent in unit time of the node e. The calculation formula shows that the weight coefficient M is larger, the node e belongs to the node which needs to be transmitted preferentially and carries the data packets with long distance or has larger data packet quantity, and the balance of the transmission distance and the data packet quantity is adjusted through the parameter k.
On the basis of the above, if a packet of another path node M is ready to enter the node e at the same time, the weight coefficients M of the node v and the node M are calculated respectivelyvAnd Mm；
Comparison MvAnd MmThe node with the large weight coefficient is used as a priority node to enter the path, and the node e is used as the next routing node for transmitting the priority node data packet;
the node with small weight coefficient returns to the step 4) to select the path L with the second largest gravity of the entering path2。
Network throughput is an important indicator for evaluating the performance of routing policies. For this purpose, an order state parameter is introduced here for measuring the network traffic phase change process, i.e. the network traffic state changes from free flow to congested phase, and the parameter is defined as follows:
wherein W (t) is the total number of data packets remaining in the network at time t; r is the number of newly added data packets in unit time, and R.t is the total number of data packets generated in the network by the time t. When R is less than or equal to RcThe value of the order parameter η is close to 0; however, when R > RcAt that time, the η value will suddenly become larger and gradually approach 1 as R increases, the network falls into congestion. Therefore, the network traffic state is shown in the condition that R is RcIs subjected to a phase transition, RcI.e. the throughput of the network.
To evaluate the effectiveness of the algorithm, a BA scale-free network model was used herein (network size N100 and m)04), the transmission capacity of the node is equal to 1, i.e. c is equal to 1, without loss of generality. Fig. 2 shows the routing performance obtained by performing simulation experiments on different values of α and γ. The result shows that the network traffic flow has phase change at the load R-16, thereby illustrating the maximum traffic bearing capacity R of the network under the control of the gravitational field routing strategy considering the node aggregation capacityc16. Meanwhile, no matter what the values of the adjustable parameters alpha and gamma are, the throughput and the congestion degree of the network are almost unchanged, which shows that the two parameters have no influence on the transmission performance of the routing algorithm. Therefore, the routing algorithm is stable and reliable.
As described above, the present invention can be preferably realized. Variations, modifications, substitutions, integrations and variations of these embodiments may be made without departing from the principle and spirit of the invention, and still fall within the scope of the invention.
Claims (1)
1. A gravity field-based complex network routing method is characterized by comprising the following steps:
1) at each time step, aiming at any node v, if the cache queue of the node v is not empty, a data packet is indicated to wait for transmission, so that all neighbor nodes of the node v are obtained;
2) for each neighbor node i of the node v, calculating the shortest path from the node i to a target node t of the node v and obtaining a node set P (i, t) on the path, and
3) calculating the attraction G of the shortest paths corresponding to all the neighbor nodes of the node v to the node vitAs set forth in the following formula:
where n denotes the number of nodes on the path, j 1,2jvThe gravity of the node j to the node v data packet on the path is represented, and the calculation formula is as follows:
wherein, cjThe transmission capability of the node j is represented, namely the maximum number of data packets which can be sent in unit time; q. q.sjNumber of packets stored in buffer queue representing node j, thereby
Reflect and make a stand ofThe degree of openness of node j; djvRepresents the shortest distance between node j and node v; alpha and gamma are adjustable parameters, and alpha and gamma are constants more than or equal to 1; h isjThe average degree of openness of the neighboring nodes of the node j is represented, and for the u neighboring nodes of the node j, the average degree of openness is:
4) obtaining a set L of paths in descending order according to the size of the path gravitation [ L ═ L-1 L2 ... Li]Wherein i is the number of neighbor nodes of the node v, and the path L with the largest path gravitation1The neighbor node of the middle corresponding node v is e;
5) if no data packet of other paths is ready to enter the node e at the same time, the node e is taken as the next routing node transmitted by the data packet of the node v;
6) if a data packet of another path node M is ready to enter the node e at the same time, calculating the weight coefficients M of the node v and the node M respectively, wherein the calculation formula is as follows:
k is an adjustable parameter, k is more than or equal to 0 and less than or equal to 1 and is used for balancing the weight between the length of the path and the waiting time;lthe length of the shortest path from the current node to the target node is obtained; q is the number of data packets stored in the current node; c. CeThe maximum number of data packets which can be sent in unit time of the node e is obtained; respectively obtaining weight coefficients Mv and Mm of the node v and the node M through a calculation formula of the weight coefficient M;
7) comparing the size of Mv and Mm, taking the node with large weight coefficient as a priority node to enter the path, and taking a node e as the next routing node for transmitting the priority node data packet;
8) the node with small weight coefficient returns to the step 4) to select the gravity of the entering pathTwo major paths L2。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710042660.XA CN106850437B (en) | 2017-01-20 | 2017-01-20 | Complex network routing method based on gravitational field |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710042660.XA CN106850437B (en) | 2017-01-20 | 2017-01-20 | Complex network routing method based on gravitational field |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106850437A CN106850437A (en) | 2017-06-13 |
| CN106850437B true CN106850437B (en) | 2021-05-11 |
Family
ID=59120698
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710042660.XA Active CN106850437B (en) | 2017-01-20 | 2017-01-20 | Complex network routing method based on gravitational field |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106850437B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107733797A (en) * | 2017-09-06 | 2018-02-23 | 甜旅（上海）财务咨询有限公司 | A kind of complex network method for routing based on gravitational field |
| CN114070773B (en) * | 2021-10-13 | 2023-04-25 | 杭州电子科技大学 | Space network routing strategy based on shortest path length |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326942A (en) * | 2013-06-19 | 2013-09-25 | 华南理工大学 | Reliable routing protocol used for vehicle-mounted Ad Hoc network |
| CN104821921A (en) * | 2015-04-23 | 2015-08-05 | 苏州中晟宏芯信息科技有限公司 | Routing mechanism for heterogeneous many-core task scheduling based on artificial potential field |
-
2017
- 2017-01-20 CN CN201710042660.XA patent/CN106850437B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103326942A (en) * | 2013-06-19 | 2013-09-25 | 华南理工大学 | Reliable routing protocol used for vehicle-mounted Ad Hoc network |
| CN104821921A (en) * | 2015-04-23 | 2015-08-05 | 苏州中晟宏芯信息科技有限公司 | Routing mechanism for heterogeneous many-core task scheduling based on artificial potential field |
Non-Patent Citations (2)
| Title |
|---|
| Improved routing strategy based on gravitational field theory;宋海权等;《Chin. Phys. B》;20151231;第24卷(第10期);108901-1-108901-8 * |
| 顾及节点聚集能力的引力场动态路由方法;宋海权等;《计算机应用研究》;20161231;第33卷(第12期);3562-3564 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106850437A (en) | 2017-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gelenbe et al. | Autonomous smart routing for network QoS | |
| CN103561426A (en) | Probability route improving method in delay-tolerance mobile sensor network based on node activeness | |
| CN106850437B (en) | Complex network routing method based on gravitational field | |
| Egaji et al. | A comparison of Mamdani and Sugeno fuzzy based packet scheduler for MANET with a realistic wireless propagation model | |
| CN116390164A (en) | Low orbit satellite network trusted load balancing routing method, system, equipment and medium | |
| CN114979014B (en) | Data forwarding path planning method and device and electronic equipment | |
| Danladi et al. | DyRED: An enhanced random early detection based on a new adaptive congestion control | |
| Oužecki et al. | Reinforcement learning as adaptive network routing of mobile agents | |
| CN114205782A (en) | Optimal time delay caching and routing method, device and system based on cloud edge cooperation | |
| Yu et al. | A multipath routing protocol using congestion control in wireless multimedia sensor networks | |
| Royyan et al. | Bio-inspired scheme for congestion control in wireless sensor networks | |
| CN108712336A (en) | A kind of local message dynamic routing algorithm improving scales-free network transmission capacity | |
| CN115473854B (en) | Intelligent flow control method for multi-mode network | |
| CN107733797A (en) | A kind of complex network method for routing based on gravitational field | |
| He et al. | Deephop on edge: Hop-by-hop routing bydistributed learning with semantic attention | |
| CN112511445B (en) | Shortest path route generating method based on load weighting | |
| CN100583836C (en) | A self-adapted local routing method in Scale-Free network | |
| CN113096396A (en) | Path selection method based on gravitational field theory | |
| JP5361001B2 (en) | ROUTING CONTROL DEVICE, ROUTING CONTROL METHOD, AND PROGRAM | |
| Wu et al. | Traffic dynamics based on dynamic local routing protocol in a finite buffer network | |
| CN105897870A (en) | Congestion control and load balancing method oriented to network video caching system | |
| CN108768874B (en) | Method for relieving blocking performance of avionics network based on command efficiency loss | |
| Chan et al. | A hybrid congestion control for TCP over high speed networks | |
| Kusumawardani | Active queue management (aqm) and adaptive neuro fuzzy inference system (anfis) as intranet traffic Control | |
| Kaur et al. | An Efficient and Reliable Routing Protocol using bio-inspired techniques for congestion control in WSN |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210421 Address after: 233000 Anhui Bengbu Wuhe County Economic Development Zone Industrial Acceleration Center 3, 4 level Applicant after: WUHE WEILI AGRICULTURAL TECHNOLOGY Co.,Ltd. Address before: 610036, Sichuan, Chengdu, Jinniu District Province, No. 2 West prosperity Road, Tianyuan building, 2 floor Applicant before: SICHUAN ZHONGDA YUNKE TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |