CN-122027545-A - Multi-rate network intelligent route planning method based on minimum media occupation time

Abstract

The invention provides a multi-rate network intelligent route planning method based on minimum media occupation time, belonging to the fields of multi-rate networks and route planning. The method comprises the steps of firstly adopting an ant colony algorithm, introducing mobility of nodes, selecting a stable and optimal MPR set to obtain topology information of the whole network, then providing a network link weight based on media occupation time, and finally selecting an optimal route based on a simulated annealing algorithm optimized by multiple agents. The invention abandons the traditional route selection method based on the minimum hop count, defines the media occupation time to comprehensively evaluate the network link weight, reduces the time delay of the network, increases the throughput of the network, adopts the simulated annealing algorithm based on multi-agent optimization to select the route, improves the optimizing capability, adapts to the network dynamic property, and enhances the robustness of the network.

Inventors

• WANG YANGYANG
• ZHANG JINBO
• SONG ZHIQUN
• LIU LIZHE
• GUO XIAOBO
• GAN RUIMENG
• JIA ZEKUN

Assignees

• 中国电子科技集团公司第五十四研究所

Dates

Publication Date

20260512

Application Date

20260210

Claims (6)

1. 1. A multi-rate network intelligent route planning method based on minimum media occupation time is characterized by comprising the following steps: Step 1, selecting MPR nodes based on an ant colony optimization algorithm, sending topology messages and constructing a whole network topology table; Step 2, calculating the link media occupation time according to the network state information, wherein the link media occupation time is the time required by transmitting a data packet through a certain link; step 3, giving link weight based on media occupation time, converting multi-rate network route planning solution into multi-rate network route selection based on minimum transmission cost, and constructing a route planning objective function; And 4, solving a route planning objective function by adopting a simulated annealing algorithm based on multi-agent optimization, and selecting an optimal route.
2. 2. The intelligent routing method for the multi-rate network based on the minimum media occupation time according to claim 1, wherein the specific steps of the step 1 are as follows: Step 101, recording a one-hop neighbor node set of a source node as The two-hop neighbor node set is recorded as Nodes selected into MPR set Is marked as Otherwise , The objective function of selecting MPR sets is: (1) step 102, initializing the ant colony number antNum, wherein the current iteration number is Maximum iteration number iterMax, number of uncovered two-hop neighbor nodes uncoverN, and optimal objective function solution ; Step 103, ants are processed The node is calculated according to a path probability selection formula by starting from a source node And selecting the node with the highest probability as the MPR node as the probability of the MPR set, wherein a path probability selection formula is as follows: (2) In the formula, Representing the heuristic factor of the pheromone, Representing the heuristic factor of the weight value, Representing a heuristic factor of the speed of movement of the node, ; Representing nodes Current pheromone concentration; Representing nodes Weight of (i.e. current node) Covering the number of the two-hop neighbor nodes; Representing nodes Influence formula of moving speed on probability selection when node Is a relative movement speed of (2) When the node is represented To the source node at this time Node The probability as MPR node becomes large when When the node is represented Far from the source node, at this time Node The probability as MPR node becomes smaller; step 104, collecting the two-hop neighbor nodes The node associated with the current MPR node is set to the overlay state and recalculated , When (when) If not, returning to the step 103, otherwise, entering the step 105; Step 105, calculating the fitness value of the current iteration as If (if) Then ; Step 106, updating the pheromone concentration of each node, wherein the formula is expressed as follows: (3) (4) (5) In the formula, Indicating the rate of volatilization of the pheromone, , Representing pre-update nodes The concentration of the pheromone in the water, Representing updated nodes The concentration of the pheromone in the water, Representing the sum of the pheromone concentrations left by all ants passing through this node, Representing ants Freeing nodes in an iterative process The concentration of pheromones on the path, Is an initial pheromone concentration constant; step 107: When (when) Time, order = +1, Returning to step 103, otherwise, ending the iteration, and entering step 108; 108, after MPR node selection is completed, each node constructs a whole network topology table through interactive topology messages Node Link , Respectively representing the number of nodes and the number of links in the network, wherein two adjacent communication nodes in the network form a link, namely , , 。
3. 3. The intelligent routing method for the multi-rate network based on the minimum media occupation time according to claim 1, wherein the link media occupation time is calculated according to the network state information in the step 2 in the following calculation mode: link media occupancy time Defined as the transmission rate of Is a link of (2) Transmitting data packets The time required, expressed as: (6) (7) In the formula, Representing links Transmitting each of The average transmission overhead time delay of the (a) comprises the frame header lead code and the physical frame header transmission time of a physical layer, the overhead time delay of an MAC layer and the processing time delay of information; Indicating at normal rate The size of the transmitted data packet is as follows Is used for the time-varying operation of the device, Representing the selected link Is used for the transmission rate of (a), Indicating the payload size of the data packet.
4. 4. The intelligent routing method for multi-rate network based on minimum media occupation time according to claim 1, wherein the link weight in step 3 Expressed as: (8) In the formula, For the time taken up by the link media, Is a constant; Converting the multi-rate network routing plan solution based on the minimum link media occupation time into multi-rate network routing selection based on the minimum transmission cost, and expressing a routing plan objective function as follows: (9) In the formula, A set of routing paths representing data transmissions for any node i and node j in a network, each routing path being defined by one or more links Composition is prepared.
5. 5. The intelligent routing method for the multi-rate network based on the minimum media occupation time according to claim 1, wherein the specific steps of the step 4 are as follows: Step 401, setting an initial temperature Termination temperature The temperature reduction coefficient is Maximum iteration number Iter; Step 402, initializing multi-agent parameters, and multi-agent grids as follows Wherein Is of the size of Then , Is an agent, i.e. a solution to the routing objective function, each agent occupying a location of the grid , , Each agent has a certain energy, the value of which is equal to the objective function value of the route planning, namely the transmission cost of the route path, and the aim of the agent is to reduce the energy of the agent as much as possible; step 403, calculating , Is the temperature Optimal individuals on a time-multiple agent grid Is a function of the energy of the (c), At a temperature of Time of day Objective function values of the agent; Step 404 at temperature At the time of intelligent agent grid The intelligent agents compete and learn, wherein, The iteration times; Step 405, at the agent grid Upper calculation ; Step 406 if it meets Or (b) When it is, then output the optimal solution And the energy corresponding to the energy is the optimal value And stopping the iteration, otherwise, And proceeds to step 404.
6. 6. The method for intelligent routing of a multi-rate network based on minimum media occupancy time as recited in claim 5 wherein said competing and learning between intelligent agents in step 404 is: Step 4041, competition process of the agent: Assuming a temperature of Multiple agent grid Upper part Is an intelligent body Is a neighborhood of (a) The agent with the smallest energy, then the agent And The corresponding energy is respectively And The competitiveness of the agent is: (10) (11) ; In the formula, Is that A random number between the two random numbers, In order to set the parameters, depending on the degree of variation of the energy function argument of the agent, ; Step 4042, learning process of the agent: for the gaps on the agent grid caused by the agent discarded in the competition process, a new agent is constructed to fill in The agent is replaced by an agent Optimal agent in neighborhood Agent with random generation of temperature drop direction The composition is as follows: (12) In the formula, The step length is the searching step length; if the new solution meets the survival condition of the agent, namely the formula (10), filling the new solution into the vacant grid, otherwise, continuing to search, if the new solution is in the set times A satisfactory solution is not found yet, and a new agent is generated in the global scope until a suitable solution is found.

Description

Multi-rate network intelligent route planning method based on minimum media occupation time Technical Field The invention relates to the field of multi-rate networks and route planning, in particular to an intelligent multi-rate network route planning method based on minimum media occupation time. Background With the high-speed development of wireless communication technology and mobile internet, people have richer lives, and the demands for multimedia services are diversified and differentiated, and meanwhile, higher demands are also put on the performance of wireless networks. For the current data services and diversified multimedia services, the communication mechanism sets a series of new protocol standards for multi-rate and high-rate transmission, so that the wireless local area network has greatly progressed. The capability to support multi-rate transmission is common in existing IEEE 802.11 protocols. For example, the IEEE 802.11b protocol operating at a frequency of 2.4 GHz may support 4 data transmission rates including 1 Mbps, 2 Mbps, 5.5 Mbps, and 11 Mbps, and the IEEE 802.11a protocol operating at a frequency of 5 GHz may support 8 rates from 6 Mbps to 54 Mbps depending on channel conditions. In addition, due to the physical characteristics of the wireless channel, the wireless channel is extremely easy to be interfered by the environment, and the quality difference of the wireless link is increased in the complex environments such as cities, mountainous areas, forests and the like, so that a multi-rate network is formed. The advent of multi-rate networks presents great difficulties in the planning and selection of routes. This is because the propagation characteristics of wireless signals make an inherent trade-off between communication rate and effective communication distance, where high-rate communication is suitable for links that are closer together, and conversely, where only low-rate communication is possible over long-range links. Thus, using a high rate link for communication generally requires more hops to reach the destination node. Conventional wireless network routing protocols utilize hop count as a routing criteria (e.g., DSR, AODV, DSDV, etc.), and tend to select a link for long-distance transmission as the next hop during routing, thereby selecting a link with a lower transmission rate, and degrading network performance. In a multi-rate network environment, the advantages of a multi-rate network are not realized by using a traditional Ad Hoc routing protocol. Furthermore, due to the mobility of the nodes, the link conditions change over time, and in a single rate network, mobility may cause a link connection to break and the data packets need to be rerouted. In a multi-rate network, however, mobility may merely result in a change in the channel environment, with an increase or decrease in the rate of links in the network, and not necessarily in a change in network connectivity. Whether to reselect an appropriate route in this case is also an issue that conventional routing protocols cannot address. The existing network protocols all support the use of various available transmission rates for data communication, and if the multi-rate transmission capability and available rate resources in the network can be fully and reasonably utilized, the communication performance and connectivity of the network can be greatly improved. Thus, research on multi-rate transmission has attracted considerable attention from students, and in routing metrics, rational design and application of multi-rate transmission is of great value. Scholars have studied the impact of multi-rate, prioritization, coordination, etc. on geographic location opportunity routing throughput and have proposed a geographic location based multi-rate opportunity routing. The throughput parameter which can balance the data packet lifting and the time overhead is used, the node with larger progress is allocated with higher priority through rate self-adaption and next-hop node selection, and the network throughput is maximized, and the method is a node ordering algorithm. Although the method improves the network throughput, the method relies on hardware equipment with position information, and is high in cost and easy to interfere. Other scholars have proposed an opportunistic adaptive routing protocol. And carrying out self-adaptive forwarding path selection by utilizing path diversity, minimizing repeated transmission times, and selecting an optimal forwarding node based on a priority timer. By efficiently detecting and retransmitting lost packets and determining an appropriate transmission rate through adaptive rate control based on current network conditions, while achieving high throughput and fairness of the network, overhead on the network is challenging. Also, scholars have proposed an integrated architecture combining routing metrics and rate adaptation in multi-rate multi-hop wireless networks. The architecture take