CN108965004A - A kind of configured transmission optimization method of vehicular ad hoc network analysis model - Google Patents
A kind of configured transmission optimization method of vehicular ad hoc network analysis model Download PDFInfo
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- CN108965004A CN108965004A CN201810782394.9A CN201810782394A CN108965004A CN 108965004 A CN108965004 A CN 108965004A CN 201810782394 A CN201810782394 A CN 201810782394A CN 108965004 A CN108965004 A CN 108965004A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000005457 optimization Methods 0.000 title claims abstract description 17
- 238000003012 network analysis Methods 0.000 title claims abstract description 13
- 239000002245 particle Substances 0.000 claims abstract description 36
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- 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
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- 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
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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Abstract
The present invention provides a kind of configured transmission optimization methods of vehicular ad hoc network analysis model, belong to the studying technological domain of vehicular ad hoc network.The parameter optimization method includes network parameter initialization, calculates packet probability of acceptance PRP, calculates perception probability PA, judge whether there is the parameter combination for meeting security application minimum requirements and using modified particle swarm optiziation further multi-parameter optimized.This method is using the configured transmission in modified particle swarm optiziation optimization vehicular ad hoc network analysis model, it has the ability to adjust multiple network transmission parameters in real time to change in frequent vehicle-mounted net in network topology and network state, under the premise of guaranteeing vehicle-mounted net security application performance requirement, maximum network transmission capacity is approached as far as possible.
Description
Technical Field
The invention belongs to the technical field of research of vehicle-mounted ad hoc networks, and particularly relates to a transmission parameter optimization method of a vehicle-mounted ad hoc network analysis model.
Background
The vehicle-mounted ad hoc network is used as an important component of an intelligent traffic system, and safety applications such as road safety early warning and the like can be provided by periodically broadcasting beacon messages among vehicles. Broadcast transmission of beacon messages requires low latency and high reliability in order to guarantee the quality of service and performance of security applications. At present, many studies have been made and evaluated on broadcast performance indexes of the MAC layer, for example, the article "MAC and application-level broadcast reliability in VANETs with channel mapping" evaluates the packet reception probability PRP and the packet reception rate PRR, but these indexes cannot reflect the performance of security application or the perception of information.
To solve this problem, researchers have proposed perceptual probability as an indicator of the application layer, which is expressed in a time window TaThere is a probability that at least n data packets are successfully received at the receiving end. Other work has then adaptively optimized the parameters involved, such as transmission power and beacon generation rate. However, in the prior art, a single parameter is adjusted, the comprehensive influence of each transmission parameter on the network performance is not comprehensively considered, and a theoretical analysis model is not provided to ensure the performance requirement of the safety application.
Disclosure of Invention
Aiming at the actual requirements in the field of the existing vehicle networking, the invention provides a transmission parameter optimization method of a vehicle ad hoc network analysis model, namely, an improved particle swarm algorithm is adopted to simultaneously optimize a plurality of transmission parameters in the vehicle ad hoc network analysis model, so that the maximum network transmission capacity is approached as far as possible on the premise of ensuring that the safety application performance of the vehicle network meets the requirements.
The technical scheme of the invention is as follows:
a transmission parameter optimization method of a vehicle-mounted ad hoc network analysis model comprises the following steps:
step 1: setting network environment parameters including the number N of vehicles and the distance x between a receiving node and a sending node; randomly setting values of a plurality of groups of network transmission parameters, wherein the network transmission parameters comprise beacon generation rate lambda, backoff window W in CSMA protocol and data transmission rate RdAnd a communication distance R;
step 2: calculating the probability PRP of successfully receiving a single packet by a receiving node according to the network environment parameters and the network transmission parameters selected in the step 1;
and step 3: calculating the network broadcast performance index and the perception probability P of the application layerAI.e. the receiving node is x away from the transmitting node in the time window TaProbability of successful reception of at least n data packets:
wherein the parameters n and TaGiven by the security application;
and 4, step 4: verifying whether the result in step 3 meets the minimum requirement of the corresponding safety application, namely PA>ξ, if the current time is out of the corresponding safety application, the subsequent optimization process is carried out, if the current time is out of the corresponding safety application, the steps 1-4 are repeated until the maximum cycle number is reached, and the optimization process is exited;
and 5: the improved particle swarm algorithm is used for approaching the maximum network transmission capacity on the premise of ensuring that the network transmission parameters meet the corresponding safety application performance requirements;
step 5.1, the combination of the network transmission parameters in the step 1 is used as the position of each particle, and the particle is initialized;
step 5.2 calculating the perception probability P of the corresponding particle according to the particle position and by the step 2 and the step 3A；
Step 5.3 select particles whose perception probability meets the requirements of the corresponding safety application, i.e. PA>ξ, forming a set S;
step 5.4, selecting the particle which can enable the transmission capacity to take the maximum value in the set S as the current optimal particle, wherein the transmission capacity is defined as:
TC＝Nλ
step 5.5, all the particles move one step to the optimal node;
and 5.6, repeating the step 5.1 to the step 5.5 until the transmission parameters are converged or the cycle times reach the maximum value, wherein the obtained optimal particles are the parameter combination which can maximize the transmission capacity on the premise of meeting the corresponding safety application performance requirements.
The invention has the beneficial effects that: the invention provides a transmission parameter optimization method of a vehicle-mounted ad hoc network analysis model, which is characterized in that an improved particle swarm algorithm is adopted to optimize transmission parameters in the vehicle-mounted ad hoc network analysis model, so that a plurality of network transmission parameters can be adjusted in real time in a vehicle-mounted network with frequent network topology and network state changes, and the maximum network transmission capacity is approached as far as possible on the premise of ensuring the safety application performance requirement of the vehicle-mounted network.
Drawings
Fig. 1 is a flowchart of a transmission parameter optimization method for a vehicle ad hoc network analysis model designed by the present invention.
FIG. 2 is a flow chart of the particle swarm algorithm improved by the present invention.
Detailed Description
The following description will explain embodiments of the present invention in further detail with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a method for optimizing transmission parameters of a vehicle ad hoc network analysis model designed by the present invention, which is detailed as follows:
in S101, according to the actual situation, initializing network environment parameters including the number N of vehicles, the distance x between a receiving node and a sending node, and the like, and randomly setting values of a plurality of groups of network transmission parameters including a beacon generation rate lambda, a backoff window W in a CSMA protocol, and a data transmission rate RdCommunication distance R, etc.;
in S102, calculating the probability PRP of successfully receiving a single packet by a receiving node according to the parameters selected in S101;
(1) initialization P0＝1，P0A probability that at least one packet is to be sent for each vehicle;
(2) calculating the probability that a channel is occupiedWherein
(3) The service rate μ is calculated by the following formula:
μ＝1/Q′(1)
wherein L isHIs the length of the header, PLIs the packet average length, DIFS is the distributed inter-frame space, δ is the transmission delay;
(4) if it isOtherwise P0＝0；
(5) Repeating the steps (2) to (4) until P0Converging;
(6) calculating the probability that no hidden terminal is sending a packet
(7) Calculating the probability that the hidden terminal does not send the packet when the sending node sends the packet
(8) Calculating the probability P that other nodes in the receiving range do not send packets in the process of receiving the packets by the receiving nodeconc，
Wherein,
(9) calculating the probability P of successfully receiving the message when the distance is xF(x),
Wherein Γ (m) is a gamma function,
(10) computing
In S103, further calculating the performance index of the application layer network broadcast according to the obtained PRP, and sensing the probability PAI.e. the receiving node is x away from the transmitting node in the time window TaProbability of successful reception of at least n data packets:
wherein the parameters n and TaGiven by the security application;
in S104, it is verified whether any result in S103 meets the minimum requirements of the corresponding security application, i.e. PA>ξ, if the data is provided by corresponding safety application, the subsequent optimization process is carried out, if the data is not provided, S101-S104 are repeated until the maximum cycle number is reached and the optimization process is exited;
in S105, using an improved particle swarm algorithm to approach the maximum network transmission capacity on the premise of ensuring that the network transmission parameters can meet the corresponding security application performance requirements;
fig. 2 shows a flow chart of the improved particle swarm algorithm of the present invention, which is detailed as follows:
in S201, initializing the particles by taking the combination of a plurality of network transmission parameters as the positions of the particles in the particle swarm algorithm;
in S202, the perceptual probability P of the corresponding particle is calculated from the particle position using the same method as S102, S103A；
In S203, selecting particles with perception probability meeting the corresponding safety application requirement, namely PA>ξ, forming a set S;
in S204, the particle that maximizes the transmission capacity, which is defined as,
TC＝Nλ
in S205, all particles move one step to the optimal node;
in S206, S201 to S205 are repeated until the transmission parameter converges or the number of cycles reaches the maximum value, and the position vector of the obtained optimal particle is the parameter combination that can maximize the transmission capacity on the premise of meeting the corresponding safety application performance requirement.
Claims (1)
1. A transmission parameter optimization method of a vehicle-mounted ad hoc network analysis model is characterized by comprising the following steps:
step 1: setting network environment parameters including the number N of vehicles and the distance x between a receiving node and a sending node; randomly setting values of a plurality of groups of network transmission parameters, wherein the network transmission parameters comprise beacon generation rate lambda, backoff window W in CSMA protocol and data transmission rate RdAnd a communication distance R;
step 2: calculating the probability PRP of successfully receiving a single packet by a receiving node according to the network environment parameters and the network transmission parameters selected in the step 1;
and step 3: calculating the network broadcast performance index and the perception probability P of the application layerAI.e. the receiving node is x away from the transmitting node in the time window TaProbability of successful reception of at least n data packets:
wherein the parameters n and TaGiven by the security application;
and 4, step 4: verifying whether the result in step 3 meets the minimum requirement of the corresponding safety application, namely PA>ξ, if the current time is out of the corresponding safety application, the subsequent optimization process is carried out, if the current time is out of the corresponding safety application, the steps 1-4 are repeated until the maximum cycle number is reached, and the optimization process is exited;
and 5: the improved particle swarm algorithm is used for approaching the maximum network transmission capacity on the premise of ensuring that the network transmission parameters meet the corresponding safety application performance requirements;
step 5.1, the combination of the network transmission parameters in the step 1 is used as the position of each particle, and the particle is initialized;
step 5.2 calculating the perception probability P of the corresponding particle according to the particle position and by the step 2 and the step 3A；
Step 5.3 select particles whose perception probability meets the requirements of the corresponding safety application, i.e. PA>ξ, forming a set S;
step 5.4, selecting the particle which can enable the transmission capacity to take the maximum value in the set S as the current optimal particle, wherein the transmission capacity is defined as:
TC＝Nλ
step 5.5, all the particles move one step to the optimal node;
and 5.6, repeating the step 5.1 to the step 5.5 until the transmission parameters are converged or the cycle times reach the maximum value, wherein the obtained optimal particles are the parameter combination which maximizes the transmission capacity on the premise of meeting the corresponding safety application performance requirements.
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Cited By (2)
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| CN110569527A (en) * | 2019-06-28 | 2019-12-13 | 武汉理工大学 | automobile transmission ratio design and optimization method based on hybrid particle swarm algorithm |
| CN111314934A (en) * | 2020-02-14 | 2020-06-19 | 西北工业大学 | Network cooperative detection method for unified optimal decision |
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| CN104955056A (en) * | 2015-06-05 | 2015-09-30 | 大连理工大学 | Internet-of-vehicle road side unit deployment method based on particle swarm optimization |
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| CN104955056A (en) * | 2015-06-05 | 2015-09-30 | 大连理工大学 | Internet-of-vehicle road side unit deployment method based on particle swarm optimization |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110569527A (en) * | 2019-06-28 | 2019-12-13 | 武汉理工大学 | automobile transmission ratio design and optimization method based on hybrid particle swarm algorithm |
| CN110569527B (en) * | 2019-06-28 | 2023-09-05 | 武汉理工大学 | Automobile transmission gear ratio design and optimization method based on mixed particle swarm algorithm |
| CN111314934A (en) * | 2020-02-14 | 2020-06-19 | 西北工业大学 | Network cooperative detection method for unified optimal decision |
| CN111314934B (en) * | 2020-02-14 | 2021-08-10 | 西北工业大学 | Network cooperative detection method for unified optimal decision |
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