CN111585893A - Routing performance analysis method based on opportunity network in Internet of vehicles environment - Google Patents
Routing performance analysis method based on opportunity network in Internet of vehicles environment Download PDFInfo
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- CN111585893A CN111585893A CN202010366400.XA CN202010366400A CN111585893A CN 111585893 A CN111585893 A CN 111585893A CN 202010366400 A CN202010366400 A CN 202010366400A CN 111585893 A CN111585893 A CN 111585893A
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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
- H04L45/14—Routing performance; Theoretical aspects
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The invention discloses a routing performance analysis method based on an opportunity network in a car networking environment, which is used for carrying out quantitative research on the stability and the safety of data communication among vehicle-mounted nodes, and carrying out performance quality analysis and comparison on different routing algorithms under different preset values in the opportunity network, so that a more efficient routing algorithm which is superior to the existing routing algorithm and is more suitable for the complex and changeable environment of the current car networking is selected, the stability and the accuracy of communication among the nodes are further improved, the adverse effect caused by the uncertainty of the communication can be reduced as much as possible by analyzing the improved algorithm through the method, the resource utilization rate is improved, and corresponding technical support is provided for subsequent research.
Description
Technical Field
The invention belongs to the field of route performance analysis, and particularly relates to an opportunity network-based route performance analysis method in a vehicle networking environment.
Background
Vehicle-mounted technology is also developed vigorously today when large traffic data is applied more and more widely. The concept of the internet of vehicles is extended from the internet of things, and the definition of the internet of vehicles is different according to different industry backgrounds. The traditional definition of internet of vehicles refers to a system which realizes the extraction and effective utilization of attribute information and static and dynamic information of all vehicles on an information network platform by using an electronic tag loaded on the vehicle through identification technologies such as radio frequency identification and the like, and effectively supervises the running states of all vehicles according to different functional requirements and provides comprehensive services. The Internet of vehicles is used as a new application of new strength and information network platforms in the traffic industry, and the wide application prospect of the wireless sensor network in an intelligent traffic system is fully shown. Since the wireless sensor network technology is mainly applied to the internet of vehicles, the network is low in self security and easy to attack, once a hacker attacks some nodes in the internet of vehicles, the attacked nodes cannot be prevented from forging, tampering, sending or transmitting false information in the network. Therefore, the safety of the internet of vehicles is gradually emphasized, which is also the main background of the research.
Since most of the nodes in the currently researched vehicle networking are fast moving vehicles, the mobility of the nodes makes the structure of the vehicle networking network topology more complex, the distribution range of the nodes becomes wider, and the relative positions of the nodes become more flexible and uncontrollable. These uncertainties all affect the stability of the communication between the on-board nodes and the accuracy of the on-board data transmission.
Although the advantages of opportunistic networks are prominent, it is because of this unique transmission method that causes many uncertainties in the transmission process of information. For example, the transmission path is uncertain, the hop node selection is uncertain, and further, the relay node selection and the network resource utilization are also uncertain. Different options will cause corresponding delay and consumption of network resources.
Disclosure of Invention
The invention aims to overcome the defects and provide a routing performance analysis method based on an opportunity network in an internet of vehicles environment, which is used for carrying out quantitative research on the stability and safety of data communication among vehicle-mounted nodes and carrying out performance analysis and comparison on different routing algorithms under different preset values in the opportunity network, so that a more efficient routing algorithm which is better than the existing routing algorithm and is more suitable for the complex and changeable environment of the existing internet of vehicles is selected, and the stability and the accuracy of communication among nodes are improved.
In order to achieve the above object, the present invention comprises the steps of:
selecting a route under an opportunity network to perform performance analysis, wherein the performance analysis comprises the transmission delivery rate, the transmission delay and the route load rate of the route;
performing performance simulation on the routing algorithm on a simulation platform to obtain simulation results under different preset values, and visualizing the simulation results;
and step three, selecting a routing algorithm suitable for the current Internet of vehicles environment according to the performance analysis and simulation result of the routing.
In the first step, the transmission delivery rate is the proportion of the number of successfully received data packets in the total amount of the transmitted data within a specified time, and the calculation method is as follows:
wherein, success is the number of Successfully received data packets, and TotalData is the total amount of data to be transmitted.
In step one, the transmission delay is the time length of the packet data from the source node to the destination node.
In the first step, the route load rate is the route control data required by each packet data to reach the destination node, and the calculation method is as follows:
wherein, delayed is the total amount of the data packets, and successful received data packets.
In the second step, the algorithm for the simulation platform to perform performance simulation on the routing algorithm comprises a routing algorithm based on a forwarding strategy, a routing algorithm based on a prediction strategy and a routing algorithm based on a plan strategy.
The routing algorithm based on the forwarding strategy comprises Epidemic, Spray and Wait, Direct live and First Contact;
the routing algorithm based on the prediction strategy comprises Prophet, MaxProp and CAR;
routing algorithms based on planning strategies include Message forwarding.
Simulation software for performing performance simulation on the routing algorithm by the simulation platform adopts an ONE simulation tool to simulate an intelligent transportation system, and a Helsinki city map carried by the ONE simulation tool is selected as a simulation experiment map.
Compared with the prior art, the method and the device have the advantages that quantitative research is conducted on the stability and the safety of data communication among the vehicle-mounted nodes, and the performance of different routing algorithms is analyzed and compared under different preset values in the opportunity network, so that a more efficient routing algorithm which is superior to the existing routing algorithm and is more suitable for the complex and changeable environment of the existing Internet of vehicles is selected, the stability and the accuracy of communication among the nodes are improved, the bad influence caused by the uncertainty of the communication can be reduced as much as possible through the algorithm analyzed and improved by the method and the device, the resource utilization rate is improved, and corresponding technical support is provided for subsequent research.
Drawings
FIG. 1 is a transmission diagram of an opportunistic network;
FIG. 2 is a schematic flow chart of the Spray and Focus algorithm;
FIG. 3 is a diagram of routing packets in an opportunistic network;
FIG. 4 is a flow chart of the present invention;
FIG. 5 is a diagram of transmission success rates;
FIG. 6 is a schematic diagram of transmission delay;
fig. 7 is a schematic diagram of routing load.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, compared with the traditional end-to-end communication protocol, the invention cannot adapt to the characteristics of multi-hop, message delay, network interruption and the like, and the opportunistic network can better adapt to the communication problem in a specific extreme network environment. The opportunity network is a delay tolerant network and a network derived under a mobile ad hoc network, and is a network which can be ad hoc in different modes under the condition of network delay or interruption. Unlike a traditional wireless network, the node position and the transmission path of the wireless network cannot be predicted in advance, and the transmission between messages is realized by using the communicable opportunity formed by the movement between nodes, so that a special storage-carrying-forwarding routing mode is formed. The opportunity network is increasingly applied to the fields of network environments such as military, Internet of vehicles and the like and the field of actual environment change, and the intelligent and efficient development of future network communication is greatly promoted.
Referring to fig. 4, the present invention includes the steps of:
and step M1, determining the direction, selecting to analyze the routing performance under the opportunity network, and switching into the theme. The performance index of the routing algorithm in the invention mainly comprises three aspects: transmission delivery rate, transmission delay, and route load rate. Fig. 5, 6, and 7 are graphs of simulation results of these three indexes, respectively.
The transmission delivery rate is the proportion of the number of successfully received data packets in the total amount of the transmitted data in a specified time, and the calculation method is as follows:
wherein, success is the number of Successfully received data packets, and TotalData is the total amount of data to be transmitted. The successful delivery rate is the ratio of the number of successfully received data packets to the total amount of data to be transmitted within a predetermined time. A higher ratio indicates a higher transmission efficiency of data. Successful delivery rate is an important indicator of whether the routing model can properly deliver the corresponding packet.
The transmission delay is the time length of packet data from a source node to a destination node, and the routing performance is often evaluated by adopting average transmission delay. In an opportunistic network, high latency is allowed to exist, but reducing latency can better improve resource reuse and network operation efficiency.
The route load rate is the route control data required by each packet data to reach the destination node, and the calculation method is as follows:
wherein, delayed is the total amount of the data packets, and successful received data packets. The route load rate, i.e., the route overhead, refers to the route control data required for each packet data to reach the destination node. The lower the load rate, the less the share of overhead indicates the better the network performance.
Step M2: and performing performance simulation on the routing algorithm on a simulation platform to obtain simulation results under different preset values, visualizing the simulation results, and selecting a more efficient routing algorithm suitable for the current Internet of vehicles environment.
Example (b):
the embodiment selects a more effective routing algorithm through the performance comparison between the classical routing algorithm and an improved algorithm, and the improved algorithm can reduce adverse effects caused by communication uncertainty as much as possible, improve the resource utilization rate and provide corresponding technical support for subsequent research. The added Spray and Focus algorithm is an improved algorithm of the Sprayand Wait algorithm, and a routing strategy based on single copy is adopted, so that the phenomenon that a large number of copies are congested due to uncertainty of paths can be greatly reduced, and a network legacy copy self-reduction mechanism special for the algorithm of the self-copy self-reduction mechanism also greatly saves network resources and improves the message transmission rate.
Comparative example:
referring to fig. 2, the specific flow of the Spray and Focus algorithm is as follows:
step S1, in the course of the Spray, generating M message copies, the property of the node determines the size of the M message copies;
when solving for M, the average delay is
The average delay is β times the optimal delay.
Step S2, continuously sending the message copies generated in the step I to other nodes, and distributing half of the copies of all the nodes with the copies to new nodes when encountering the new nodes until all the nodes have at least one copy;
the forwarding mechanism is based on dichotomy and can achieve efficient forwarding;
and step S3, in the Focus process, each node carrying the copy adopts a routing strategy based on single copy, so that the relay node with high waiting utility transmits the message to the target node to finish the transmission.
At step S4, the transfer is complete and the data transfer ends.
Referring to fig. 3, routing packets in the opportunistic network are algorithms written on the simulation software platform, and are divided into several categories according to different message processing modes. The routing algorithm based on the forwarding strategy comprises Epidemic, spread and Wait, Direct Dlivery, First Contact and the like; the routing algorithm based on the prediction strategy comprises Prophet, MaxProp, CAR and the like; routing algorithm Message forwarding based on planning strategy, etc.
In the embodiment, an ONE simulation tool is used for simulating an intelligent transportation system, and a Helsinki city map carried by the ONE is selected as a simulation experiment map. And simulating that each vehicle runs on an urban traffic track with the area of 4000m3500m, wherein the moving speed is 0.5-1.5 m/s, and the simulation time is 12 h. The node adopts a broadcast mode, the transmission range is 10m, the buffer space is 5MB, and the transmission rate is 250 KB/s. The vehicle movement models include a random movement model, a bus-based movement model, a map route-based movement model, and a shortest path-based movement model.
Referring to fig. 5, 6 and 7, the invention realizes research and performance comparison of three indexes of successful delivery rate, transmission delay and routing load rate of different routing algorithms under different node numbers on an ONE simulation platform. The research result shows that: the number of nodes will affect each routing algorithm to a different extent. When the number of nodes is small, the difference of the three performance indexes is not obvious, and the numerical value floats between 5% and 15%. When the number of nodes is greatly increased, the difference of each route is more and more obvious. By integrating the simulation performance ratios of the five routing algorithms, the Spray and Focus routing algorithms have relatively highest delivery success rate and transmission delay as small as possible under the same node compared with other algorithms, and the routing load rate which is almost zero greatly saves the network space. The Spray and Focus algorithm under the opportunistic network is a relatively efficient routing protocol more suitable for the diverse complex environment of the internet of vehicles.
Claims (7)
1. A routing performance analysis method based on an opportunity network in a car networking environment is characterized by comprising the following steps:
selecting a route under an opportunity network to perform performance analysis, wherein the performance analysis comprises the transmission delivery rate, the transmission delay and the route load rate of the route;
performing performance simulation on the routing algorithm on a simulation platform to obtain simulation results under different preset values, and visualizing the simulation results;
and step three, selecting a routing algorithm suitable for the current Internet of vehicles environment according to the performance analysis and simulation result of the routing.
2. The method for analyzing routing performance based on the opportunistic network in the car networking environment as claimed in claim 1, wherein in the first step, the transmission delivery rate is a ratio of the number of successfully received data packets to the total amount of data to be transmitted in a specified time, and the calculation method is as follows:
wherein, success is the number of Successfully received data packets, and TotalData is the total amount of data to be transmitted.
3. The method for analyzing routing performance based on the opportunistic network in the car networking environment according to claim 1, wherein in the first step, the transmission delay is the time duration for packet data to reach the destination node from the source node.
4. The method for analyzing routing performance based on the opportunistic network in the car networking environment according to claim 1, wherein in the first step, the routing load rate is the routing control data required by each packet data to reach the destination node, and the calculation method is as follows:
wherein, delayed is the total amount of the data packets, and successful received data packets.
5. The routing performance analysis method based on the opportunity network in the car networking environment according to claim 1, wherein in the second step, the algorithm for the simulation platform to perform performance simulation on the routing algorithm comprises a forwarding policy-based routing algorithm, a prediction policy-based routing algorithm and a planning policy-based routing algorithm.
6. The opportunistic network based routing performance analysis method in the car networking environment according to claim 5, wherein the routing algorithm based on the forwarding policy comprises Epidemic, Spray and Wait, Direct live and First Contact;
the routing algorithm based on the prediction strategy comprises Prophet, MaxProp and CAR;
routing algorithms based on planning strategies include Message forwarding.
7. The routing performance analysis method based on the opportunity network in the internet of vehicles environment according to claim 1, wherein simulation software for performing performance simulation on the routing algorithm by the simulation platform adopts an ONE simulation tool to simulate an intelligent transportation system, and the simulation experiment map selects a Helsinki city map carried by the ONE simulation tool.
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| CN107135155A (en) * | 2017-06-01 | 2017-09-05 | 陕西师范大学 | A kind of opportunistic network routing method based on node social relationships |
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| CN101895954A (en) * | 2010-08-24 | 2010-11-24 | 重庆邮电大学 | Opportunistic network routing method based on incremental transmission of packet index |
| US20120238208A1 (en) * | 2011-03-17 | 2012-09-20 | Maik Bienas | Mobile radio communication devices and servers |
| CN102209029A (en) * | 2011-05-19 | 2011-10-05 | 北京工商大学 | Grouping strategy based opportunistic network routing algorithm |
| CN106850425A (en) * | 2016-12-21 | 2017-06-13 | 陕西师范大学 | Segmental routing method and a kind of network node based on markov decision process |
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