CN114245347B - Geographic position routing method based on prediction and encounter history information in vehicle-mounted opportunity network - Google Patents
Geographic position routing method based on prediction and encounter history information in vehicle-mounted opportunity network Download PDFInfo
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Abstract
The invention discloses a geographic position routing method based on prediction and meeting history information in a vehicle-mounted opportunity network, which comprises the following steps: s1, each vehicle node in a vehicle-mounted opportunity network periodically broadcasts own geographic position information and judges whether the vehicle node is positioned at an intersection road section or not; s2, when the node receives the beacon packet from the intersection node, the node is indicated to run near the intersection; a node running near the intersection starts a prediction algorithm to judge the advancing direction of the data packet; s3, calculating an included angle between the current node and the target node, and when theta is less than or equal to 25 degrees, starting a routing method of the straight road section to select a next hop relay node; s4, when theta is more than 25 degrees, starting a routing method of an intersection road section, and judging whether the message needs to change the message transmission direction or not by the current node; if the direction is required to be changed, the information is firstly transmitted to the intersection node, otherwise, the information bypasses the intersection node and is transmitted forward. The invention can effectively improve the data transmission efficiency and reduce the message transmission time delay.
Description
Technical Field
The invention relates to the field of vehicle-mounted mobile opportunity networks, in particular to a geographic position routing method based on prediction and meeting history information in a vehicle-mounted opportunity network.
Background
With the steady development of communication technology, wireless communication technology is an important branch, and is the most common scheme for solving a plurality of communication tasks by current engineering personnel due to the characteristics of convenient and rapid access mode and strong mobility. In the aspect of urban traffic application, in order to provide a more convenient, safe and efficient traffic network system, the stability and the safety of information communication between vehicles and roadside solid state infrastructure are improved, and the intelligent traffic system is produced by combining modern information technologies such as wireless communication, internet and traffic systems and is rapidly developed and applied. The intelligent traffic system adopts wireless communication sensor network and other technologies to connect the infrastructure of the vehicles and the urban traffic road side and some management and control parts into a communication system, so as to collect, process and transmit the vehicle information in the traffic network and realize the efficient and orderly operation of the traffic system.
However, the wireless telephone communication network applied to the communication carrier is not suitable for an intelligent transportation system in which the nodes move at high speed due to the fact that the communication base stations are large in size and difficult to move. In order to construct a wireless communication network in a dedicated intelligent transportation system, a mobile self-organizing network is first thought of, and the mobile self-organizing network endows each movable terminal node in the network with a routing function of forwarding information, so that networking communication can be realized under the condition of no communication infrastructure, and the design idea of the mobile self-organizing network can be applied to network construction of the intelligent transportation system. The idea of the vehicle-mounted self-organizing network is derived from the mobile self-organizing network, is a workshop communication network with nodes moving at high speed, multi-hop, temporary, autonomous and without centers, is a subclass of the mobile self-organizing network, and plays a role in the initial construction of an intelligent traffic system. The vehicle-mounted self-organizing network is characterized in that network nodes are unevenly distributed in urban areas, the urban areas are highly concentrated, the remote areas are sparsely distributed, the network topology is irregularly changed due to rapid movement of vehicles, and the vehicle nodes are intermittently connected, so that communication paths of communication do not necessarily exist in real time in the communication network, and the vehicle-mounted self-organizing network also belongs to a mobile opportunity network.
The Mobile opportunistic network (Mobile Opportunistic Network, MON) evolved from a Mobile Ad-Hoc Networks (Mobile Ad-Hoc Networks, MANETs) and a Delay/interrupt tolerant network (Delay/Disruption Tolerant Network, DTN), featuring both. The mobile opportunity network is characterized in that nodes in the network continuously move as the name implies, so that the opportunity that the nodes carrying the message are contacted with the destination node is increased, and non-real-time information transmission is realized under the network condition that communication channels such as frequent disconnection of communication links and the like are intermittently connected. Mobile opportunistic network communication employs wireless mobile network technology, and nodes in the network serve as hosts and have router functions, so that wireless information communication and data transmission are realized without using traditional communication network infrastructures such as base stations and the like when networking. Conventional delay/interrupt tolerant networks use Store-Carry-Forward (Store-Forward) routing to accomplish data transmission. As a variation of the mobile ad hoc network and the delay/disruption tolerant network, the mobile opportunistic network has a wider service range, and is more suitable for occasions where users of portable mobile intelligent devices and traffic props equipped with intelligent sensing devices which are widely used at present use short-distance wireless communication technology for information communication.
The vehicle-mounted mobile opportunity network is generated by fusing a mobile opportunity network and a vehicle-mounted self-organizing network, and is mainly applied to a vehicle-mounted embedded system to form a novel wireless network of an intelligent traffic system. The main problem faced by the vehicle-mounted self-organizing network is still that the high-speed movement of the vehicle causes the communication links among the network nodes to be disconnected, and the network topology structure is frequently broken. In order to handle the intermittent connectivity of the vehicle-mounted mobile opportunity network nodes and the high delay of data transmission, the vehicle-mounted mobile opportunity network also adopts a data transmission strategy of 'store-carry-forward'. The vehicles embedded in the vehicle-mounted mobile opportunity network carry traffic messages during running, and information is transferred between the vehicles and the roadside infrastructure whenever the vehicles meet in a communication range or enter a roadside infrastructure service range, so that the problems of high delay, opportunistic and instability in the communication process in the vehicle-mounted mobile opportunity network are solved to a great extent. Researchers in the field are continuously innovatively explored, a plurality of routing protocols based on meeting history information and geographic position strategies are provided, and the feasibility of the algorithms is proved in practical application.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a geographic position routing method based on prediction and meeting history information in a vehicle-mounted opportunity network.
The technical scheme adopted for solving the technical problems is as follows:
The invention provides a geographic position routing method based on prediction and meeting history information in a vehicle-mounted opportunistic network, which is used for solving the problem of message transmission of vehicle nodes in an urban traffic road, wherein the urban traffic road mainly comprises two conditions of straight road sections and intersection road sections, and comprises the following steps:
s1, each vehicle node in the vehicle-mounted opportunity network periodically broadcasts own geographic position information, and if the vehicle node is judged to be in an intersection road section, the marker bit TAG of the vehicle node is set to be 1 in a beacon packet, and the nodes are also called intersection nodes;
S2, when a vehicle node running in the vehicle-mounted opportunity network receives a beacon packet from an intersection node, the node is indicated to run near the intersection; meanwhile, a node running near the intersection starts a prediction algorithm to judge whether the advancing direction of a data packet carried by the node needs to be changed when the node is at the intersection;
S3, calculating an included angle theta between the current node and the target node, and when the included angle theta is less than or equal to 25 degrees, starting a routing method of the straight road section, wherein the routing of the straight road section comprehensively selects a next hop relay node according to four parameters including a distance attenuation coefficient, a link stability coefficient, an encounter history coefficient and a direction weight coefficient;
S4, when theta is more than 25 degrees, starting a routing method of an intersection road section, wherein a current node can firstly judge whether the message passes through the intersection or not and needs to change the message transmission direction, and the direction comprises left turn or right turn; if the direction needs to be changed, the information is firstly transmitted to the intersection node, and then the intersection node determines what direction to transmit the information specifically; otherwise, the message bypasses the junction node and is delivered forward;
s5, repeating the steps S3-S4 until the message is delivered to the destination node.
Further, the method for judging whether the vehicle node is located at the intersection road section in the step S1 of the present invention specifically includes:
predicting whether a vehicle node is positioned at an intersection in advance, and whether the vehicle node changes when passing through the transmission direction of the intersection; the method comprises the following steps:
Each node in the network calculates a correlation coefficient between the node and the geographic position of the neighbor node to judge whether the node is positioned at an intersection or not; defining r a and c a as the abscissa and ordinate, respectively, of node a; the variables r and c represent all groups r a and c a, respectively; for the mean value of r Expressed as average value of c/>A representation; cov (r, c) denotes the covariance of r and c, σ r denotes the standard deviation of r; the correlation coefficient η is thus defined as:
Wherein, the eta rc value changes in the [0,1] interval, the closer the value is to 1, the more to 0 the node is positioned in the center of the straight road section, the more to 0 the position of the current node is not in linear correlation with the position of the neighbor node, so as to judge that the node is positioned at the intersection; setting a threshold value tau, and judging whether the node is positioned at the intersection by comparing the value tau with the threshold value tau; when eta rc is more than or equal to tau, the node is positioned on the straight road section; when η rc < the node is located at an intersection road segment.
Further, the threshold value τ of the present invention is set to τ=0.85.
Further, the method in step S1 of the present invention further comprises:
Periodically broadcasting a beacon packet by a node in the network, judging that the node which is positioned at an intersection road section sets a TAG field in the own beacon packet to be 1 so as to inform surrounding neighbors of the identity of an intersection coordination node of the node, and resetting the field to be 0 when the node leaves the intersection range;
the beacon message updates information according to a certain period, and when the beacon message is updated in one update period, the current speed a and the current direction theta a of the node a can be calculated, wherein the following formula is as follows:
Where, (r a,ca) is the coordinates of node a, (r a1,ca1) and (r a2,ca2) are the coordinates of node a at time 1 and time 2, and timestamp a1 and timestamp a2 are the timestamps of node a at time 1 and time 2.
Further, in step S3 of the present invention, the route of the straight road section comprehensively selects the next hop relay node according to four parameters including a distance attenuation coefficient, a link stability coefficient, an encounter history coefficient and a direction weight coefficient, and the specific method is as follows:
Distance attenuation coefficient F: the routing method of the straight road section uses the Nakagami fading channel model to evaluate factors of poor wireless signal receiving quality and unstable link quality caused by complex urban vehicle traffic environment. The probability that the receiving side successfully receives the number of the data packets sent by the sending side can be calculated by the model, namely the probability that the data packet signal is larger than the receiving threshold value Y thres, and the probability is calculated according to the following formula:
Wherein y is the transmission power of the signal; alpha is an attenuation parameter, which is about 3 within a communication range of 50m, about 1.5 within a communication range of 50m to 150m, and 1 outside the communication range of 150 m; y thres is the secondary path loss defined in the Friis model, inversely proportional to R 2 from the receive threshold at the transmitting node R; omega is the received power at a distance d from the transmit point determined by the Friis model and is inversely proportional to d 2.
The expected probability of successful receipt of the data packet from the sending point d is:
Wherein Z is an intermediate variable and is common sense without explanation; Γ (alpha) is a mathematical representation of a gamma function, the value of F is the probability value of the formula P F (d), and the data packet calculates the F value for the node participating in forwarding on the basis of the original forwarding in the process of each forwarding and is used for calculating the stability Q of the node later;
Link stability coefficient S: the link stability factor is defined based on the speed v of the vehicle; assuming that the vehicle speed meets the standard distribution, the probability distribution function F (v) of the probability density function F (v) of v should be:
where μ is the mean of the velocities v and σ 2 is the variance of the velocities.
And the probability density function of the data communication in the T time is as follows:
Where Deltav represents the relative speed between the connected initiating vehicle N 1 and receiving vehicle N 2; mu Δv is the mean value of the relative velocities; σ Δv is the standard deviation of the relative velocity, V0 is the intermediate variable;
Assuming that the coordinates of the vehicle nodes N 1 and N 2 in communication are (r 1,c1) and (r 2,c2), respectively, the distance between the two communication nodes can be expressed as If at a certain moment two vehicle nodes can mutually receive the beacon packet of each other, assuming that a connection is established at the moment, the effective duration of the connection is denoted as t duration, the calculation formula of the stability factor S of N 2 is:
Wherein,
Encounter history coefficient P: vehicles travel on fixed urban roads, so the fixed geographic position and road shape of the traffic roads cause the trajectories of the vehicles to have a certain rule, which also increases the chances of meeting each other between vehicles to some extent; the more frequently two nodes meet in a period of time, the larger the transmission probability value between the two nodes is, and the calculation formula is as follows:
Pi,j＝P'i,j+(1-P'i,j)×Pinit
Whenever nodes i and j meet once, their transmission probability values P i,j are updated according to the above formula, where P' i,j is the transmission probability value maintained before nodes i and j, P i,j is the new value after updating, P init is the initial probability value, P init E (0, 1); when two nodes do not meet for a long time, the transmission probability of the two nodes should be reduced, so that the meeting probability value is updated according to a formula P i,j＝P'i,j·γn, gamma represents the attenuation parameter, gamma epsilon (0, 1), and n is the number of time units from the last meeting to the current passing of the two nodes i and j;
The update of the transmission probability considers the transitivity of node meeting, namely node i and node j meet frequently, and node j and node k meet frequently; the calculation formula is as follows:
Pi,k＝P'i,k+(1-P'i,k)×P'i,j×P'j,k×β,β∈[0,1]
The value of the meeting history coefficient P is calculated by the formula;
Directional weight coefficient D: the direction weight factor D determines the sign of the node reliability Q and the weight size occupied by the direction; the direction weight factor D is divided into a direction factor D and a weight factor w; setting the direction of the destination node as the positive direction, setting the d value of the node in the positive direction as1, otherwise, setting the d value as-1; in order to ensure that the node does not exceed the communication range of the node in the process of transmitting the message as far as possible, and the node with the furthest distance in the transmission range can be selected to realize the maximum step length, the weight factor w divides the communication radius R into 4 areas according to the communication range of the transmission direction and is distributed with different weights; The weight of the communication range is 1,/> The weight of the communication range is 2,/>The weight of the communication range is 4,/>The weight of the communication range is 3,/>The weight of the communication range is the largest, the probability that the node in the area moves out of the communication range is lower and the distance between the node and the destination node is relatively close, the message transmission efficiency is improved, and the calculation formula of the direction weight coefficient D is as follows:
D＝d×w
Finally, comprehensively judging a reliability coefficient Q according to the four parameters of the distance attenuation coefficient F, the link stability coefficient S, the meeting history coefficient P and the direction weight coefficient D, and selecting a next hop relay node with a large Q value, wherein the formula is as follows:
Q＝D×(F+S+P)。
The invention has the beneficial effects that: according to the geographic position routing method based on the prediction and encounter history information in the vehicle-mounted opportunistic network, the history encounter coefficient is added, relay node selection is performed according to the history encounter information, and the purpose of improving the accuracy of message transmission and the success rate of message delivery is achieved; the geographic position of the vehicle node is judged in advance based on the predicted geographic strategy, so that the transmission direction of the message is changed more accurately and rapidly, and the average transmission delay is shortened. The method and the system greatly meet the road characteristics of urban environment and the rapidly-changing vehicle mobile opportunity network, and improve the routing performance.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is an overall flow chart of a routing method of an embodiment of the present invention;
FIG. 2 is pseudo code of a routing method of an embodiment of the present invention;
FIG. 3 is a method for determining direction weight coefficients according to an embodiment of the present invention;
Fig. 4 is a message passing path diagram of a method of early prediction according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1 and fig. 2, the geographic location routing method based on prediction and meeting history information in the vehicle-mounted opportunistic network is mainly divided into two cases of solving urban traffic roads, namely straight road sections and intersection road sections. The route of the straight road section comprehensively selects the next hop relay node according to four parameters mainly according to the distance attenuation coefficient, the link stability coefficient, the meeting history coefficient and the direction weight coefficient. When the road section of the intersection is crossed, the current node firstly judges whether the message passes through the intersection and needs to change the message transmission direction (left turn or right turn), if so, the message is firstly transmitted to the node of the intersection, then the node of the intersection determines what direction to transmit the message specifically, otherwise, the message is transmitted. By-passing the junction node, delivering forward, the method comprises the following steps:
s1, periodically broadcasting own geographic position information by each node in a vehicle-mounted opportunity network, and setting a marker bit TAG of the node to be 1 in a beacon packet if the node is judged to be in an intersection road section, wherein the nodes are also called intersection nodes;
S2, when a vehicle node running in the vehicle-mounted opportunity network receives a beacon packet from an intersection node, the node is indicated to run near the intersection. Meanwhile, a node running near the intersection starts a prediction algorithm to judge whether the advancing direction of a data packet carried by the node needs to be changed when the node is at the intersection;
s3, calculating an included angle theta between the current node and the target node, and when the included angle theta is less than or equal to 25 degrees, starting a routing method of the straight road section, wherein the routing of the straight road section comprehensively selects a next hop relay node according to four parameters, namely a distance attenuation coefficient, a link stability coefficient, an encounter history coefficient and a direction weight coefficient;
S4, when theta is more than 25 degrees, starting a routing method of an intersection road section, wherein the current node firstly judges whether the message passes through the intersection or not and needs to change the message transmission direction (left turn or right turn), if so, the message is firstly transmitted to the intersection node, then the intersection node determines what direction to specifically transmit the message, otherwise, the message bypasses the intersection node and is transmitted forward;
s5, repeating the steps S3-S4 until the message is delivered to the destination node.
The method for determining the direction weight coefficient in step S3 is shown in fig. 3. Setting the direction of the destination node as the positive direction, setting the d value of the node in the positive direction as 1, otherwise, setting the d value as-1. In order to ensure that the node does not exceed the communication range of the node in the process of transmitting the message as far as possible, and the node with the furthest distance in the transmission range can realize the maximum step length, the weight factor w divides the communication radius R into 4 areas according to the communication range of the transmission direction and is distributed with different weights.The weight of the communication range is 1,/>The weight of the communication range is 2,/>The weight of the communication range is 4,/>The weight of the communication range is 3. /(I)The weight of the communication range is maximum, the probability that the node in the area moves out of the communication range is low, the node is relatively close to the destination node, and the message transmission efficiency is improved.
Fig. 4 illustrates the advantage of the routing method of the intersection road segment in step S4, S and D are the source node and the destination node, respectively, the solid arrow is the forwarding path with the prediction function enabled, and the dotted arrow is the forwarding path without the prediction function enabled. The method reduces the forwarding times of the messages of the nodes at the crossing, improves the accuracy of message forwarding and greatly reduces the message forwarding times and average delay.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (4)
1. The geographic position routing method based on prediction and meeting history information in a vehicle-mounted opportunistic network is characterized by being used for solving the problem of message transmission of vehicle nodes in an urban traffic road, wherein the urban traffic road mainly comprises two conditions of straight road sections and intersection road sections, and comprises the following steps:
s1, each vehicle node in the vehicle-mounted opportunity network periodically broadcasts own geographic position information, and if the vehicle node is judged to be in an intersection road section, the marker bit TAG of the vehicle node is set to be 1 in a beacon packet, and the nodes are also called intersection nodes;
S2, when a vehicle node running in the vehicle-mounted opportunity network receives a beacon packet from an intersection node, the node is indicated to run near the intersection; meanwhile, a node running near the intersection starts a prediction algorithm to judge whether the advancing direction of a data packet carried by the node needs to be changed when the node is at the intersection;
S3, calculating an included angle theta between the current node and the target node, and when the included angle theta is less than or equal to 25 degrees, starting a routing method of the straight road section, wherein the routing of the straight road section comprehensively selects a next hop relay node according to four parameters including a distance attenuation coefficient, a link stability coefficient, an encounter history coefficient and a direction weight coefficient;
In the step S3, the route of the straight road section comprehensively selects the next hop relay node according to four parameters including a distance attenuation coefficient, a link stability coefficient, an encounter history coefficient and a direction weight coefficient, and the specific method is as follows:
distance attenuation coefficient F: the method for routing the straight road section uses a Nakagami fading channel model to evaluate factors of poor wireless signal receiving quality and unstable link quality caused by complex urban vehicle traffic environment; the probability that the receiving side successfully receives the number of the data packets sent by the sending side can be calculated by the model, namely the probability that the data packet signal is larger than the receiving threshold value Y thres, and the probability is calculated according to the following formula:
Wherein y is the transmission power of the signal; alpha is an attenuation parameter, 3 in a communication range 50m, 1.5 in a communication range 50 m-150 m, and 1 outside the communication range 150 m; y thres is the secondary path loss defined in the Friis model, inversely proportional to R 2 from the receive threshold at the transmitting node R; omega is the received power at a distance d from the transmitting point determined by the Friis model, inversely proportional to d 2;
the expected probability of successful receipt of the data packet from the sending point d is:
Wherein Z is an intermediate variable and is common sense without explanation; Γ (alpha) is a mathematical representation of a gamma function, the value of F is the probability value of the formula P F (d), and the data packet calculates the F value for the node participating in forwarding on the basis of the original forwarding in the process of each forwarding and is used for calculating the stability Q of the node later;
Link stability coefficient S: the link stability factor is defined based on the speed v of the vehicle; assuming that the vehicle speed meets the standard distribution, the probability distribution function F (v) of the probability density function F (v) of v should be:
Where μ is the mean of the velocities v and σ 2 is the variance of the velocities;
And the probability density function of the data communication in the T time is as follows:
Where Deltav represents the relative speed between the connected initiating vehicle N 1 and receiving vehicle N 2; mu Δv is the mean value of the relative velocities; σ Δv is the standard deviation of the relative velocity, V0 is the intermediate variable;
Assuming that the coordinates of the vehicle nodes N 1 and N 2 in communication are (r 1,c1) and (r 2,c2), respectively, the distance between the two communication nodes can be expressed as If at a certain moment two vehicle nodes can mutually receive the beacon packet of each other, assuming that a connection is established at the moment, the effective duration of the connection is denoted as t duration, the calculation formula of the stability factor S of N 2 is:
Wherein,
Encounter history coefficient P: vehicles travel on fixed urban roads, so the fixed geographic position and road shape of the traffic roads cause the trajectories of the vehicles to have a certain rule, which also increases the chances of meeting each other between vehicles to some extent; the more frequently two nodes meet in a period of time, the larger the transmission probability value between the two nodes is, and the calculation formula is as follows:
Pi,j＝P'i,j+(1-P'i,j)×Pinit
Whenever nodes i and j meet once, their transmission probability values P i,j are updated according to the above formula, where P' i,j is the transmission probability value maintained before nodes i and j, P i,j is the new value after updating, P init is the initial probability value, P init E (0, 1); when two nodes do not meet for a long time, the transmission probability of the two nodes should be reduced, so that the meeting probability value is updated according to a formula P i,j＝P'i,j·γn, gamma represents the attenuation parameter, gamma epsilon (0, 1), and n is the number of time units from the last meeting to the current passing of the two nodes i and j;
The update of the transmission probability considers the transitivity of node meeting, namely node i and node j meet frequently, and node j and node k meet frequently; the calculation formula is as follows:
Pi,k＝P′i,k+(1-P′i,k)×P′i,j×P′j,k×β,β∈[0,1]
The value of the meeting history coefficient P is calculated by the formula;
Directional weight coefficient D: the direction weight factor D determines the sign of the node reliability Q and the weight size occupied by the direction; the direction weight factor D is divided into a direction factor D and a weight factor w; setting the direction of the destination node as the positive direction, setting the d value of the node in the positive direction as1, otherwise, setting the d value as-1; in order to ensure that the node does not exceed the communication range of the node in the process of transmitting the message as far as possible, and the node with the furthest distance in the transmission range can be selected to realize the maximum step length, the weight factor w divides the communication radius R into 4 areas according to the communication range of the transmission direction and is distributed with different weights; The weight of the communication range is 1,/> The weight of the communication range is 2,/>The weight of the communication range is 4,/>The weight of the communication range is 3,The weight of the communication range is the largest, the probability that the node in the area moves out of the communication range is lower and the distance between the node and the destination node is relatively close, the message transmission efficiency is improved, and the calculation formula of the direction weight coefficient D is as follows:
D＝d×w
Finally, comprehensively judging a reliability coefficient Q according to the four parameters of the distance attenuation coefficient F, the link stability coefficient S, the meeting history coefficient P and the direction weight coefficient D, and selecting a next hop relay node with a large Q value, wherein the formula is as follows:
Q＝D×(F+S+P)
S4, when theta is more than 25 degrees, starting a routing method of an intersection road section, wherein a current node can firstly judge whether the message passes through the intersection or not and needs to change the message transmission direction, and the direction comprises left turn or right turn; if the direction needs to be changed, the information is firstly transmitted to the intersection node, and then the intersection node determines what direction to transmit the information specifically; otherwise, the message bypasses the junction node and is delivered forward;
s5, repeating the steps S3-S4 until the message is delivered to the destination node.
2. The geographic location routing method based on prediction and encounter history information in the vehicular opportunistic network according to claim 1, wherein the method for determining whether the vehicular node is at the intersection road section in step S1 is specifically as follows:
predicting whether a vehicle node is positioned at an intersection in advance, and whether the vehicle node changes when passing through the transmission direction of the intersection; the method comprises the following steps:
Each node in the network calculates a correlation coefficient between the node and the geographic position of the neighbor node to judge whether the node is positioned at an intersection or not; defining r a and c a as the abscissa and ordinate, respectively, of node a; the variables r and c represent all groups r a and c a, respectively; for the mean value of r Expressed as average value of c/>A representation; cov (r, c) denotes the covariance of r and c, σ r denotes the standard deviation of r; the correlation coefficient η is thus defined as:
Wherein, the eta rc value changes in the [0,1] interval, the closer the value is to 1, the more to 0 the node is positioned in the center of the straight road section, the more to 0 the position of the current node is not in linear correlation with the position of the neighbor node, so as to judge that the node is positioned at the intersection; setting a threshold value tau, and judging whether the node is positioned at the intersection by comparing the value tau with the threshold value tau; when eta rc is more than or equal to tau, the node is positioned on the straight road section; when η rc < τ, the node is located at the intersection road segment.
3. The geographical location routing method based on prediction and encounter history information in a vehicular opportunistic network of claim 2, wherein the threshold τ is set to τ = 0.85.
4. The method for routing geographic locations based on prediction and encounter history information in a vehicular opportunity network of claim 2, wherein the method in step S1 further comprises:
Periodically broadcasting a beacon packet by a node in the network, judging that the node which is positioned at an intersection road section sets a TAG field in the own beacon packet to be 1 so as to inform surrounding neighbors of the identity of an intersection coordination node of the node, and resetting the field to be 0 when the node leaves the intersection range;
the beacon message updates information according to a certain period, and when the beacon message is updated in one update period, the current speed a and the current direction theta a of the node a can be calculated, wherein the following formula is as follows:
Where, (r a,ca) is the coordinates of node a, (r a1,ca1) and (r a2,ca2) are the coordinates of node a at time 1 and time 2, and timestamp a1 and timestamp a2 are the timestamps of node a at time 1 and time 2.
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