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CN111615060A - Regional multicast method based on bus track and positioning information - Google Patents

Regional multicast method based on bus track and positioning information Download PDF

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Publication number
CN111615060A
CN111615060A CN202010356702.9A CN202010356702A CN111615060A CN 111615060 A CN111615060 A CN 111615060A CN 202010356702 A CN202010356702 A CN 202010356702A CN 111615060 A CN111615060 A CN 111615060A
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vehicle
bus
vehicles
message
node
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樊秀梅
林苗苗
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Xian University of Technology
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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
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
    • 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]

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Abstract

The invention discloses a region group broadcasting method based on bus track and positioning information, which introduces the track information of buses, firstly establishes a track tree and an encounter model of bus nodes, and then calculates the message forwarding capacity c of the bus nodes to a target region according to an encounter graphpAnd selecting the node with higher message forwarding capability to forward the message to the destination area. And in the second stage, the stability indexes are used for estimating the stability of two vehicles, a vehicle set is established on each street of the target area, and the purpose of multicasting is achieved by establishing and maintaining the vehicle set. The simulation is carried out under a combined platform of an experimental platform SUMO, OMNET + + and Veins, and the analysis of experimental results shows that: according to the number of vehiclesThe aim is increased, the algorithm can reduce the transmission overhead of the whole network under the condition of maintaining high data packet delivery rate, and the expected aim is achieved.

Description

Regional multicast method based on bus track and positioning information
Technical Field
The invention belongs to the technical field of vehicle node communication in the Internet of vehicles, and relates to a regional multicast method based on bus tracks and positioning information.
Background
The internet of vehicles is a large system network which is based on an in-vehicle network, an inter-vehicle network and a vehicle-mounted mobile internet and performs wireless communication and information exchange between vehicles-X (X: vehicles, roads, pedestrians, the internet and the like) according to an agreed communication protocol and a data interaction standard, is an integrated network capable of realizing intelligent traffic management, intelligent dynamic information service and intelligent vehicle control, and is a typical application of the internet of things technology in the field of traffic systems. The internet of vehicles is a very challenging technology, can support a plurality of applications of safety information and entertainment information, and can realize a plurality of functions including automatic driving, traffic information sharing, vehicle safety and the like under the development of V2X communication. Compared with communication modes such as 5G and the like, the Internet of vehicles has great advantages in terms of hardware deployment and cost, and roadside units can be used for forming fog nodes in the Internet of vehicles for fog calculation, so that the burden of cloud calculation is reduced, and the Internet of vehicles can become a data exchange platform in a smart city and play a great role in many fields.
In the internet of vehicles, geo-casting is a special geographical group cast (geocast) that delivers messages from a source to all nodes in a given target geographical area. This is the basic service content for a variety of car networking applications, regional multicast can form regional broadcast to transmit commercial information, advertisements, etc. related to geographic location, or send urgent messages to designated areas, etc. for practical applications, many applications in car networking are designed to transmit messages in specific geographic areas by regional multicast in order to reduce the risk of car accidents and avoid traffic congestion. Geo-multicasting has many applications with entertainment information. It may form an area broadcast to deliver commercials etc. related to geographical location, e.g. to send information about parking positions and free spaces within the range for vehicles arriving in a certain geographical area or distribution of location based commercials (starbucks send coupons to the surroundings). In addition, it is also an important application to send an urgent message to a designated area, for example, to send a warning message to vehicles in a surrounding area when a traffic accident occurs.
Regional multicast has been well studied in WSNs. The geo-multicasting algorithm of the WSN can be classified into a data transmission-based protocol, a route creation-based protocol, and a geographical location-based protocol.
However, in the car networking technology in the prior art, the problems of vehicle mobility and vehicle track in the urban car networking are not considered, so that the accuracy of vehicle information is low, the information transmission cost is high, and the efficiency is low.
Disclosure of Invention
The invention aims to provide a regional multicast method based on bus tracks and positioning information, and solves the problems of low accuracy of vehicle information, high information transmission cost and low efficiency caused by the fact that vehicle mobility and vehicle tracks in urban internet of vehicles are not considered in the prior art.
The technical scheme adopted by the invention is that a regional multicast method based on public transport tracks and positioning information comprises the following steps:
step 1, establishing an encounter model between buses in urban traffic through bus historical track analysis, and improving packet transfer by reducing uncertainty of vehicle mobility by using track information of the vehicles and by using line coincidence between the buses and encounter probability between different time periods;
step 2, in the first stage of the regional multicast, according to the local encounter between vehicles, effectively using vehicle tracks, and forwarding the data packet to the vehicle with higher node forwarding capability through the bus encounter model established in the step 1;
and 3, in the second stage of the regional multicast, estimating the stability of the two vehicle nodes by using the stability index, establishing a vehicle set on each street of the target region, and using a vehicle set establishing and maintaining mechanism to ensure that the vehicle passing through the target region can deliver the message by one hop when the message needs to be transmitted.
The invention is also characterized in that:
the process of establishing the bus meeting model in the step 1 is as follows: generalizing all bus node encounters into a geographical encounter graph g (v) { x (v), e (v) }, maintained by vehicle nodes v, where x (v) represents a set of vertices, e (v) represents a set of edges, the geographical encounter graph maintains all trajectories known to vehicle nodes v and all possible encounters between geographical locations on those trajectories; wherein an absolute time at which a vehicle v arrives at a given location I is defined as
Figure BDA0002473710870000031
E (v) comprises two types of edges,
Figure BDA0002473710870000032
is a one-way edge, which means that the vehicle a is on its trajectory from the vertex
Figure BDA0002473710870000033
Travel to the top
Figure BDA0002473710870000034
The bidirectional edge e represents the trajectory between the vehicles a and b that may meet and
Figure BDA0002473710870000035
assume that v has obtained a set of vehicle trajectories ψ (v), for each Ta∈ψ(v),
Figure BDA0002473710870000036
When the meeting mode of the vehicles is analyzed, each track records the ID number of an intersection and the passing time of vehicle nodes, in order to solve the time deviation problem of bus running, the time of a bus passing a certain intersection is assumed to fluctuate up and down within a certain range, the time fluctuation condition accords with normal distribution, the time of the bus reaching the intersection I is assumed to be t, and the upper limit and the lower limit of the t are respectively set as tl,thTherefore t ∈ [ tl,th]And one variable follows a normal distribution, then the probability density function is:
Figure BDA0002473710870000037
in the formula: μ -is the expectation of a normal distribution function,
σ -variance as a Normal distribution function
For two vehicles with mutually intersected tracks, the meeting probability is judged by the approaching degree of arrival time when the vehicles arrive at the track intersection, and the meeting between buses is divided into the following 3 cases:
in the first case, two vehicles are on the same road segment and travel in the same direction, e.g., bus A and bus C at r2Meet on road segment, assume A arrives at crossing obeys
Figure BDA0002473710870000038
C compliance to crossing
Figure BDA0002473710870000039
Then the probability of encounter
Figure BDA0002473710870000041
In the formula: -as time threshold, determined by bus speed and communication range
μA,σAExpectation and variance obeying normal distribution for time of bus A reaching the intersection
μB,σB-expectations and variances obeying a normal distribution for the time of arrival of bus B at the intersection;
in the second case, two vehicles travel in different directions on the same road section, e.g. bus A and bus B at r4Meet on the road section, the time when the bus A reaches the intersection 1 is
Figure BDA0002473710870000042
The time when the road reaches the intersection 2 is
Figure BDA0002473710870000043
The time when the bus B reaches the intersection 1 is
Figure BDA0002473710870000044
The time when the road reaches the intersection 2 is
Figure BDA0002473710870000045
Wherein
Figure BDA0002473710870000046
Setting the other two times as the arrival timesAt the time of the period of time,
Figure BDA0002473710870000047
and
Figure BDA0002473710870000048
the density function of (a) is:
Figure BDA0002473710870000049
Figure BDA00024737108700000410
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure BDA00024737108700000411
Expectation and variance, μ, obeying a normal distribution2,σ2Time for bus B to reach the 2-intersection
Figure BDA00024737108700000412
Subject to the expectation and variance of a normal distribution,
therefore, the encounter probability is:
Figure BDA00024737108700000413
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure BDA00024737108700000414
Expectation and variance, μ, obeying a normal distribution2,σ2Time for bus B to reach the 2-intersection
Figure BDA00024737108700000415
Expectation and variance obeying a normal distribution;
in the third case, two vehicles in different directions meet at the intersection, for example, the bus B and the bus C meet at the intersection I2, and the meeting probability is:
Figure BDA0002473710870000051
in the formula: -a time threshold, determined by the bus speed and the communication range, smaller than in the first case, muC,σCTime for bus C to reach the 2 crossing
Figure BDA0002473710870000052
Expectation and variance, μ, obeying a normal distributionB,σBTime for bus B to reach the 2-intersection
Figure BDA0002473710870000053
Obeying the expectation and variance of a normal distribution.
The step 2 specifically comprises the following steps:
step 2.1, firstly initializing a map and a vehicle track;
step 2.2, judging whether a bus node exists in the neighbor nodes of the source node, if so, establishing a geographical encounter model according to the step 1, calculating the message forwarding capacity of each node, if not, forwarding the message by virtue of a common vehicle, and if so, selecting the common vehicle node closest to the target area center in the neighbor vehicle nodes;
step 2.3, calculating the message forwarding capability of the nodes, and selecting the node with the strongest message forwarding capability, namely cpForwards the message;
and 2.4, judging whether a node reaches a target area, if so, ending the process, and if not, continuing to execute the step 2.2.
Calculating message Forwarding capability of Each node c in step 2.2pThe process comprises the following steps:
the arrival conditions of the vehicle track to the target area are divided into direct arrival and indirect arrival, wherein the direct arrival means that the vehicle track directly drives into the target area; the indirect arrival means that the vehicle passes through the meeting with other vehicles, and the tracks of the other vehicles arrive at the target area; vehicle v1At the purpose ofThe message forwarding capability over an area is defined as the probability that its extended vehicle trajectory's coverage area overlaps with the destination area, vehicle v1Having multiple possible extended paths to reach its destination area, βiIndicating i paths, p (β), within the destination areai) To be βiIs contained in v1Probability of in-reach of cpThe calculation formula of (2) is as follows:
Figure BDA0002473710870000061
calculation of cpIt is necessary to obtain a path expansion set omega for all possible destination areasv＝{ηi1,2 … n, wherein ηiRepresents the set of extended paths that each can reach, via path ηi∈ΩvCalculation βiIs contained in v1Is within the coverage of (β)i)。
Step 3 is specifically implemented according to the following steps:
step 3.1, calculating the stability index of the vehicles on the street of the target area, and establishing and maintaining a vehicle set on the street;
and 3.2, the vehicles in the vehicle set send regional broadcast grouping messages to other vehicles in the target area.
Assuming that the vehicle j is a neighbor node of the vehicle i and they are on the same street in step 3.1, the stability estimation index between the vehicles j and i is calculated as follows:
Figure BDA0002473710870000062
in the formula: di,j-maintaining a distance for a link between two vehicles
Figure BDA0002473710870000063
-vector velocity of vehicle i, j
If the vehicle is approaching, there are two situations, vehicle i and j are traveling towards each other; vehicles i and j travel in the same direction, with the rear vehicle at a faster speed, in both cases the link maintenance distance between the vehicles is:
Di,j＝R+di,j(8)
in the formula: r-is the communication range of the vehicle
di,jAs distance between vehicles i, j
If the vehicles are driven separately, there are two situations where vehicles i and j are far away from each other; the vehicles i and j travel in the same direction, the vehicle in front is faster, and the link between the vehicles maintains the distance:
Di,j＝R-di,j(9)
in the formula: r-is the communication range of the vehicle
di,jIs the distance between the vehicles i, j.
The specific process in step 3.2 is as follows: in the internet of vehicles, due to the limitation of street width, the broadcast only needs to select one neighbor node as the next relay node in the broadcast direction, assuming viIs the first vehicle in the street segment that successfully receives the broadcast message, viCalculating a stability estimation index of each neighbor vehicle and selecting the neighbor vehicle with the highest stability estimation index as a next vehicle set vehicle; then viForwarding a broadcast message containing the selected neighbor node ID and to be sent and setting a wait timer, if the selected neighbor node successfully receives the message, it repeats the selection process and forwards the message to the next selected vehicle set vehicle; if v isiIf the message of selecting the neighbor node is not received, the neighbor node is indicated to unsuccessfully forward the message, and when v isiV after the wait timer ofiRecalculating the stability estimation index, and reselecting the vehicle of the next vehicle set; if one vehicle node in the vehicle set detects that one neighbor node in the set has left the communication range of the vehicle node through the beacon message, the link between the two vehicle nodes is broken, the vehicle reselects the vehicle node with the highest stability in the direction to join the vehicle set, and then sends a message to inform the newly joined vehicle.
The invention has the beneficial effects that: the method introduces the track information of the bus, firstly establishes a track tree and an encounter model of the bus nodes, calculates the message forwarding capacity of the bus nodes to a target area according to an encounter graph, and selects nodes with higher message forwarding capacity to forward the message to the target area; and in the second stage, the stability indexes are used for estimating the stability of two vehicles, a vehicle set is established on each street of a target area, the purpose of multicasting is achieved by establishing and maintaining the vehicle set, and the problems of low accuracy of vehicle information, high information transmission cost and low efficiency caused by the fact that vehicle mobility and vehicle tracks in the urban Internet of vehicles are not considered in the prior art are solved.
Drawings
FIG. 1 is a calculation diagram of node forwarding capability in a region multicast method based on bus tracks and positioning information according to the present invention;
FIG. 2 is a diagram of an encounter pattern between vehicles in a regional multicast method based on bus tracks and positioning information according to the present invention;
FIG. 3 is a bus meeting map in a regional multicast method based on bus trajectory and positioning information according to the present invention
FIG. 4 is a flowchart of a first stage algorithm in a regional multicast method based on bus track and positioning information according to the present invention;
FIG. 5 is a flow chart of vehicle set setup in a regional multicast method based on bus trajectory and positioning information according to the present invention;
FIG. 6 is a vehicle cluster maintenance diagram in a regional multicast method based on bus tracks and positioning information according to the present invention;
FIG. 7 is a comparison graph of delivery rates of different numbers of vehicles in a regional multicast method based on bus tracks and positioning information and other two algorithms according to the invention;
FIG. 8 is a comparison chart of the transmission overhead of different numbers of vehicles in a region multicast method based on bus tracks and positioning information and other two algorithms according to the invention;
FIG. 9 is a comparison graph of delivery rates of different message generation rates of a regional multicast method based on bus track and positioning information and other two algorithms according to the present invention;
FIG. 10 is a comparison graph of the transmission overhead of the different message generation rates of a regional multicast method based on bus track and positioning information and other two algorithms according to the present invention;
fig. 11 is a comparison graph of the data packet transmission rate of different numbers of vehicles according to the regional multicast method based on the bus track and the positioning information and other two algorithms.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a regional multicast method based on bus tracks and positioning information, which comprises the following steps:
step 1, establishing a bus meeting model: establishing an encounter model between buses in urban traffic through historical bus trajectories, dividing the encounter model into a day-of-week mode and an intra-week mode by using line superposition between the buses and encounter probability between different time periods, and reducing uncertainty of vehicle mobility by using trajectory information of the vehicles to improve packet transfer;
step 2, in the first stage of the regional multicast, according to the local encounter between vehicles, effectively using vehicle tracks, and forwarding the data packet to the vehicle with higher node forwarding capability through the bus encounter model established in the step 1;
and 3, in the second stage of the regional multicast, estimating the stability of the two vehicle nodes by using the stability index, establishing a vehicle set on each street of the target region, and using a vehicle set establishing and maintaining mechanism to ensure that the vehicle passing through the target region can deliver the message by one hop when the message needs to be transmitted.
The process of establishing the bus meeting model in the step 1 is as follows: generalizing all bus node encounters into a geographical encounter graph g (v) { x (v), e (v) }, maintained by vehicle nodes v, where x (v) represents a set of vertices, e (v) represents a set of edges, the geographical encounter graph maintains all trajectories known to vehicle nodes v and all possible encounters between geographical locations on those trajectories; wherein a vehicle v is defined toThe absolute time of arrival at a given location I is
Figure BDA0002473710870000091
E (v) comprises two types of edges,
Figure BDA0002473710870000092
is a one-way edge, which means that the vehicle a is on its trajectory from the vertex
Figure BDA0002473710870000093
Travel to the top
Figure BDA0002473710870000094
The bidirectional edge e represents the trajectory between the vehicles a and b that may meet and
Figure BDA0002473710870000095
assume that v has obtained a set of vehicle trajectories ψ (v), for each Ta∈ψ(v),
Figure BDA0002473710870000096
In analyzing the encounter pattern of the vehicle, each trajectory records the ID number of the intersection and the time when the vehicle node passes, due to the habit of the driver and the influence of other factors. Suppose in<7:00-8:00>In between, the vehicle v has 3 tracks, then it can be recorded as<T2,T4,T6>,T2＝<(I1,7:00),(I2,7:15)>,T4＝<(I2,7:15),(I3,7:50)>,T6＝<(I3,7:50),(I4,8:00)>In this way, a trajectory tree of nodes can be built. In order to solve the problem of time deviation of bus operation, the time of a bus passing a certain intersection is assumed to fluctuate up and down within a certain range, the time fluctuation condition accords with normal distribution, the time of the bus reaching the intersection I is assumed to be t, and the upper limit and the lower limit of the t are respectively set as tl,thTherefore t ∈ [ tl,th]And one variable follows a normal distribution, then the probability density function is:
Figure BDA0002473710870000101
in the formula: μ -is the expectation of a normal distribution function,
σ -variance as a Normal distribution function
For two vehicles with mutually intersected tracks, the meeting probability is judged by the approaching degree of arrival time when the vehicles arrive at the track intersection, and the meeting between buses is divided into the following 3 cases:
in the first case, two vehicles are on the same road segment and travel in the same direction, as shown in fig. 2, e.g., a bus a and a bus C at r2Meet on road segment, assume A arrives at crossing obeys
Figure BDA0002473710870000102
C compliance to crossing
Figure BDA0002473710870000103
Then the probability of encounter
Figure BDA0002473710870000104
In the formula: -as time threshold, determined by bus speed and communication range
μA,σAExpectation and variance obeying normal distribution for time of bus A reaching the intersection
μB,σB-expectations and variances obeying a normal distribution for the time of arrival of bus B at the intersection;
in the second case, two vehicles travel in different directions on the same road section, e.g. bus A and bus B at r4When the road sections meet, as shown in fig. 3, the time when the bus A reaches the intersection 1 is
Figure BDA0002473710870000105
The time when the road reaches the intersection 2 is
Figure BDA0002473710870000106
The time when the bus B reaches the intersection 1 is
Figure BDA0002473710870000107
The time when the road reaches the intersection 2 is
Figure BDA0002473710870000108
Wherein
Figure BDA0002473710870000111
The other two times are set as the expected times of arrival,
Figure BDA0002473710870000112
and
Figure BDA0002473710870000113
the density function of (a) is:
Figure BDA0002473710870000114
Figure BDA0002473710870000115
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure BDA0002473710870000116
Expectation and variance, μ, obeying a normal distribution2,σ2Time for bus B to reach the 2-intersection
Figure BDA0002473710870000117
Subject to the expectation and variance of a normal distribution,
therefore, the encounter probability is:
Figure BDA0002473710870000118
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure BDA0002473710870000119
Expectation and variance, μ, obeying a normal distribution2,σ2-isThe time when the bus B arrives at the 2-intersection
Figure BDA00024737108700001110
Expectation and variance obeying a normal distribution;
in the third case, two vehicles in different directions meet at the intersection, for example, the bus B and the bus C meet at the intersection I2, and the meeting probability is:
Figure BDA00024737108700001111
in the formula: -a time threshold, determined by the bus speed and the communication range, smaller than in the first case, muC,σCTime for bus C to reach the 2 crossing
Figure BDA00024737108700001112
Expectation and variance, μ, obeying a normal distributionB,σBTime for bus B to reach the 2-intersection
Figure BDA00024737108700001113
Obeying the expectation and variance of a normal distribution.
The step 2 specifically comprises the following steps:
step 2.1, firstly initializing a map and a vehicle track;
step 2.2, judging whether a bus node exists in the neighbor nodes of the source node, if so, establishing a geographical encounter model according to the step 1, calculating the message forwarding capacity of each node, if not, forwarding the message by virtue of a common vehicle, and if so, selecting the common vehicle node closest to the target area center in the neighbor vehicle nodes;
step 2.3, calculating the message forwarding capability of the nodes, and selecting the node with the strongest message forwarding capability, namely cpForwards the message;
and 2.4, judging whether a node reaches a target area, if so, ending the process, and if not, continuing to execute the step 2.2.
As shown in fig. 4, step 2Calculating message forwarding capability of each node c in 2pThe process comprises the following steps:
the arrival conditions of the vehicle track to the target area are divided into direct arrival and indirect arrival, wherein the direct arrival means that the vehicle track directly drives into the target area; the indirect arrival means that the vehicle passes through the meeting with other vehicles, and the tracks of the other vehicles arrive at the target area; vehicle v1The message forwarding capability at the destination area is defined as the probability that its extended vehicle trajectory's coverage area overlaps with the destination area, vehicle v1Having multiple possible extended paths to reach its destination area, βiIndicating i paths, p (β), within the destination areai) To be βiIs contained in v1Probability of in-reach of cpThe calculation formula of (2) is as follows:
Figure BDA0002473710870000121
calculation of cpIt is necessary to obtain a path expansion set omega for all possible destination areasv＝{ η i1,2 … n, wherein ηiRepresents the set of extended paths that each can reach, via path ηi∈ΩvCalculation βiIs contained in v1Is within the coverage of (β)i)。
As shown in FIG. 1, a vehicle v1Having three possible extension paths to reach its destination area, we use β respectively1,β2,β3Indicating three paths within the destination area, whether or not to divide βiIs contained in v1Is probabilistic, let the probability be p (β)i). Then, cpCan be calculated as:
Figure BDA0002473710870000122
step 3 is specifically implemented according to the following steps:
step 3.1, calculating the stability index of the vehicles on the street of the target area, and establishing and maintaining a vehicle set on the street;
and 3.2, the vehicles in the vehicle set send regional broadcast grouping messages to other vehicles in the target area.
Assuming that the vehicle j is a neighbor node of the vehicle i and they are on the same street in step 3.1, the stability estimation index between the vehicles j and i is calculated as follows:
Figure BDA0002473710870000131
in the formula: di,j-maintaining a distance for a link between two vehicles
Figure BDA0002473710870000132
-vector velocity of vehicle i, j
The vehicle node stability indicator is used to measure the link stability between two vehicles, taking into account the duration of the link.
If the vehicle is approaching, there are two situations, vehicle i and j are traveling towards each other; vehicles i and j travel in the same direction, with the rear vehicle at a faster speed, in both cases the link maintenance distance between the vehicles is:
Di,j＝R+di,j(8)
in the formula: r-is the communication range of the vehicle
di,jAs distance between vehicles i, j
If the vehicles are driven separately, there are two situations where vehicles i and j are far away from each other; the vehicles i and j travel in the same direction, the vehicle in front is faster, and the link between the vehicles maintains the distance:
Di,j＝R-di,j(9)
in the formula: r-is the communication range of the vehicle
di,jIs the distance between the vehicles i, j.
As shown in fig. 5, the specific process in step 3.2 is: in the internet of vehicles, due to the limitation of street width, the broadcast only needs to select one neighbor node as the next relay in the broadcast directionNode, let viIs the first vehicle in the street segment that successfully receives the broadcast message, viCalculating a stability estimation index of each neighbor vehicle and selecting the neighbor vehicle with the highest stability estimation index as a next vehicle set vehicle; then viForwarding a broadcast message containing the selected neighbor node ID and to be sent and setting a wait timer, if the selected neighbor node successfully receives the message, it repeats the selection process and forwards the message to the next selected vehicle set vehicle; if v isiIf the message of selecting the neighbor node is not received, the neighbor node is indicated to unsuccessfully forward the message, and when v isiV after the wait timer ofiRecalculating the stability estimation index, and reselecting the vehicle of the next vehicle set; if one vehicle node in the vehicle set detects that one neighbor node in the set has left the communication range of the vehicle node through the beacon message, the link between the two vehicle nodes is broken, the vehicle reselects the vehicle node with the highest stability in the direction to join the vehicle set, and then sends a message to inform the newly joined vehicle.
Since the vehicles in the vehicle network have high moving directions, the positions of the vehicles in the vehicle carrier set are changed continuously, and the links between the vehicles in the carrier set may be broken along with the movement of the vehicles, and new vehicles need to be added into the carrier set. In a general case, the vehicles in the vehicle carrier set have two neighboring nodes in the group and are in different directions. If one vehicle node in the vehicle carrier set detects through the beacon message that one neighbor node in the set has left its communication range, the vehicle reselects the vehicle node with the highest stability in that direction to join the vehicle carrier set, and then sends a message to notify the newly joined vehicle. As shown in FIG. 6, initially, viAnd vjIn the same vehicle carrier set, v over timejAway from viCommunication range of, link breakage between two nodes, viRecalculating the stability indicators of its neighboring nodes and selecting a new vehicle vkA vehicle as a vehicle carrier set.
In order to verify the correctness and feasibility of the algorithm, the performance of the proposed method is simulated and verified by OMNeT + + and SUMO simulation software, the SUMO software is open and is convenient for road network introduction, a map of the Western region is selected in the experiment, 5736 road segments and 2722 road intersections are counted, 1200 vehicle nodes are set at most, and other simulation parameters are shown in Table 1.
TABLE 1 bus node movement trajectory parameter
Content providing method and apparatus Numerical value
Simulation area 4000*4000m
Simulation time 43200s
Vehicle node type Car,Bus
Number of vehicle nodes 200-1200
Vehicle running speed 0m/s～30m/s
Scene update time 0.1s
Node caching 300M
Communication range 200m
Network bandwidth 2M/s
In the simulation, each vehicle has message generation capability, the target area of the message is square, and the target area is randomly selected on the whole road network. Simulations were averaged 100 times for each parameter configuration execution. The comparison algorithm adopted in the first stage is Epidemic routing algorithm, and the vehicle can blindly forward the message whenever meeting other vehicles which do not receive the message, and the main disadvantage of the algorithm is high transmission overhead. In the NTR algorithm, NTR firstly establishes a track tree according to track data of each vehicle node. With such a trajectory tree, the future position of each vehicle is predicted and the probability of contact with all other vehicles is derived. The comparison algorithm used in the second phase is the LINGER algorithm, which distributes messages in the vehicle network with a minimum of protocol overhead. In the first stage of the algorithm, the algorithm is evaluated from two aspects, namely the delivery rate is the ratio of the number of data packets received by a destination node to the number of data packets sent by a source node; transmission overhead: the information is the network energy consumed by data and exchange and data packet forwarding in the network transmission process. In the second stage of the algorithm we use packet transmission rate as the main performance indicator. The packet transmission rate is defined as the ratio of the number of vehicles in the target area that receive the message to the number of vehicles that pass through the target area.
Fig. 7 depicts a data packet delivery rate comparison graph of three different algorithms as the number of vehicles increases, and in general, as the number of vehicles increases, the algorithms BTGR and NTR presented herein both present a higher delivery rate, while the Epidemic routing algorithm presented a decreasing trend, because the number of data packets increases sharply due to flooding after the number of vehicle nodes increases, and the data cache of each vehicle is fixed at 300M, thereby causing network congestion, data being unable to be forwarded, and a decrease in delivery rate. In the case of less than 400 nodes, the delivery rate is slightly less than NTR because there are too few nodes, and there are fewer encounters between buses, resulting in fewer reachable links.
FIG. 8 depicts a graph of network transmission total routing cost versus vehicle node number, where Epidemic causes the cost to grow explosively due to infection, which is far more different than the other two methods. The method provided by the invention has good effect on saving the routing overhead, and the transmission overhead is reduced because the message is only broadcasted in the target area.
Fig. 9 depicts the effect of different message generation rates on delivery rate (800 vehicles), and it has been found that BTGR is superior to other methods when the message arrival rate is greater than two messages per hour. Epidemic has the best performance when network traffic is relatively slow (less than two messages per hour). This is because Epidemic actively replicates messages and makes full use of network capacity, but as traffic continues to increase, Epidemic transmission is less efficient because network capacity is always limited.
Fig. 10 illustrates the effect of different message generation rates on the transmission overhead, and it can be seen that the transmission overhead of BTGR is always lower than other algorithms. As the message generation rate increases, the transmission overhead for all algorithms becomes smaller. The main reason is that in the case of a stable number of vehicles, the node that is always connected remains stable. As more messages are generated, they compete for the limited resources of the contacting node. Unnecessary transmissions are reduced and thus the average transmission overhead is also reduced.
FIG. 11 illustrates the second stage of the effect of different vehicle data on packet transmission rate, as the vehicle traverses the destination area street, even if the first vehicle member in the set of vehicles fails to send a message in time, the members of the set of other vehicles may pass a message to the other vehicle member as it travels on the street, increasing the packet transmission rate.
The invention relates to a regional multicast method based on bus tracks and positioning information, which has the advantages that: firstly, establishing a track tree and an encounter model of the bus nodes, calculating the message forwarding capacity of the bus nodes to a target area according to an encounter graph, and selecting nodes with higher message forwarding capacity to forward messages to the target area; in the second phase of geo-multicasting, the stability index is used to estimate the stability of two vehicles, creating a set of vehicles on each street in the destination area. Through the vehicle set, when a vehicle passing through a target area needs to transmit a message, the message can be delivered through one hop, so that the remarkable expense generated by multi-hop broadcasting is avoided, if the vehicle which does not receive the message cannot obtain the message from the vehicle which just passes through, the vehicle can still receive the message from the subsequent vehicle set which the vehicle will encounter, so that the message receiving rate is improved, and the transmission expense of the whole network can be reduced under the condition of maintaining a high data packet delivery rate by the algorithm, so that the expected target is achieved.

Claims (7)

1. A regional multicast method based on bus tracks and positioning information is characterized by comprising the following steps:
step 1, establishing an encounter model between buses in urban traffic through bus historical track analysis, and improving packet transfer by reducing uncertainty of vehicle mobility by using track information of the vehicles and by using line coincidence between the buses and encounter probability between different time periods;
step 2, in the first stage of the regional multicast, according to the local encounter between vehicles, effectively using vehicle tracks, and forwarding the data packet to the vehicle with higher node forwarding capability through the bus encounter model established in the step 1;
and 3, in the second stage of the regional multicast, estimating the stability of the two vehicle nodes by using the stability index, establishing a vehicle set on each street of the target region, and using a vehicle set establishing and maintaining mechanism to ensure that the vehicle passing through the target region can deliver the message by one hop when the message needs to be transmitted.
2. The regional multicast method based on the bus track and the positioning information as claimed in claim 1, wherein the process of establishing the bus encounter model in step 1 is as follows:generalizing all bus node encounters into a geographical encounter graph g (v) { x (v), e (v) }, maintained by vehicle nodes v, where x (v) represents a set of vertices, e (v) represents a set of edges, the geographical encounter graph maintains all trajectories known to vehicle nodes v and all possible encounters between geographical locations on those trajectories; wherein an absolute time at which a vehicle v arrives at a given location I is defined as
Figure FDA0002473710860000011
E (v) comprises two types of edges,
Figure FDA0002473710860000012
is a one-way edge, which means that the vehicle a is on its trajectory from the vertex
Figure FDA0002473710860000013
Travel to the top
Figure FDA0002473710860000014
The bidirectional edge e represents the trajectory between the vehicles a and b that may meet and ri a∈Ta,
Figure FDA0002473710860000015
Assume that v has obtained a set of vehicle trajectories ψ (v), for each Ta∈ψ(v),
Figure FDA0002473710860000016
When the meeting mode of the vehicles is analyzed, each track records the ID number of an intersection and the passing time of vehicle nodes, in order to solve the time deviation problem of bus running, the time of a bus passing a certain intersection is assumed to fluctuate up and down within a certain range, the time fluctuation condition accords with normal distribution, the time of the bus reaching the intersection I is assumed to be t, and the upper limit and the lower limit of the t are respectively set as tl,thTherefore t ∈ [ tl,th]And one variable follows a normal distribution, then the probability density function is:
Figure FDA0002473710860000021
in the formula: μ -is the expectation of a normal distribution function,
σ -variance as a Normal distribution function
For two vehicles with mutually intersected tracks, the meeting probability is judged by the approaching degree of arrival time when the vehicles arrive at the track intersection, and the meeting between buses is divided into the following 3 cases:
in the first case, two vehicles are on the same road segment and travel in the same direction, e.g., bus A and bus C at r2Meet on road segment, assume A arrives at crossing obeys
Figure FDA0002473710860000022
C compliance to crossing
Figure FDA0002473710860000023
Then the probability of encounter
Figure FDA0002473710860000024
In the formula: -as time threshold, determined by bus speed and communication range
μA,σAExpectation and variance obeying normal distribution for time of bus A reaching the intersection
μB,σB-expectations and variances obeying a normal distribution for the time of arrival of bus B at the intersection;
in the second case, two vehicles travel in different directions on the same road section, e.g. bus A and bus B at r4Meet on the road section, the time when the bus A reaches the intersection 1 is
Figure FDA0002473710860000025
The time when the road reaches the intersection 2 is
Figure FDA0002473710860000026
The time when the bus B reaches the intersection 1 is
Figure FDA0002473710860000027
The time when the road reaches the intersection 2 is
Figure FDA0002473710860000028
Wherein
Figure FDA0002473710860000029
The other two times are set as the expected times of arrival,
Figure FDA00024737108600000210
and
Figure FDA00024737108600000211
the density function of (a) is:
Figure FDA0002473710860000031
Figure FDA0002473710860000032
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure FDA0002473710860000039
Expectation and variance, μ, obeying a normal distribution2,σ2Time for bus B to reach the 2-intersection
Figure FDA00024737108600000310
Subject to the expectation and variance of a normal distribution,
therefore, the encounter probability is:
Figure FDA0002473710860000033
in the formula: mu.s1,σ1Time for bus A to reach the 1 intersection
Figure FDA0002473710860000034
Expectation and variance, μ, obeying a normal distribution2,σ2Time for bus B to reach the 2-intersection
Figure FDA0002473710860000035
Expectation and variance obeying a normal distribution;
in the third case, two vehicles in different directions meet at the intersection, for example, the bus B and the bus C meet at the intersection I2, and the meeting probability is:
Figure FDA0002473710860000036
in the formula: -a time threshold, determined by the bus speed and the communication range, smaller than in the first case, muC,σCTime for bus C to reach the 2 crossing
Figure FDA0002473710860000037
Expectation and variance, μ, obeying a normal distributionB,σBTime for bus B to reach the 2-intersection
Figure FDA0002473710860000038
Obeying the expectation and variance of a normal distribution.
3. The regional multicast method based on the bus track and the positioning information as claimed in claim 2, wherein the step 2 specifically comprises the following steps:
step 2.1, firstly initializing a map and a vehicle track;
step 2.2, judging whether a bus node exists in the neighbor nodes of the source node, if so, establishing a geographical encounter model according to the step 1, calculating the message forwarding capacity of each node, if not, forwarding the message by virtue of a common vehicle, and if so, selecting the common vehicle node closest to the target area center in the neighbor vehicle nodes;
step 2.3, calculating the message forwarding capability of the nodes, and selecting the node with the strongest message forwarding capability, namely cpForwards the message;
and 2.4, judging whether a node reaches a target area, if so, ending the process, and if not, continuing to execute the step 2.2.
4. The regional multicast method based on bus track and positioning information as claimed in claim 3, wherein the message forwarding capability c of each node is calculated in step 2.2pThe process comprises the following steps:
the arrival conditions of the vehicle track to the target area are divided into direct arrival and indirect arrival, wherein the direct arrival means that the vehicle track directly drives into the target area; the indirect arrival means that the vehicle passes through the meeting with other vehicles, and the tracks of the other vehicles arrive at the target area; vehicle v1The message forwarding capability at the destination area is defined as the probability that its extended vehicle trajectory's coverage area overlaps with the destination area, vehicle v1Having multiple possible extended paths to reach its destination area, βiIndicating i paths, p (β), within the destination areai) To be βiIs contained in v1Probability of in-reach of cpThe calculation formula of (2) is as follows:
Figure FDA0002473710860000041
calculation of cpIt is necessary to obtain a path expansion set omega for all possible destination areasv＝{ηi1,2 … n, wherein ηiRepresents the set of extended paths that each can reach, via path ηi∈ΩvCalculation βiIs contained in v1Is within the coverage of (β)i)。
5. The regional multicast method based on the bus track and the positioning information as claimed in claim 1, wherein the step 3 is implemented according to the following steps:
step 3.1, calculating the stability index of the vehicles on the street of the target area, and establishing and maintaining a vehicle set on the street;
and 3.2, the vehicles in the vehicle set send regional broadcast grouping messages to other vehicles in the target area.
6. The regional multicast method based on bus track and location information as claimed in claim 5, wherein in step 3.1, it is assumed that the vehicle j is a neighbor node of the vehicle i and they are on the same street, and the stability estimation index between the vehicles j and i is calculated according to the following formula:
Figure FDA0002473710860000051
in the formula: di,j-maintaining a distance for a link between two vehicles
Figure FDA0002473710860000052
-vector velocity of vehicle i, j
If the vehicle is approaching, there are two situations, vehicle i and j are traveling towards each other; vehicles i and j travel in the same direction, with the rear vehicle at a faster speed, in both cases the link maintenance distance between the vehicles is:
Di,j＝R+di,j(8)
in the formula: r-is the communication range of the vehicle
di,jAs distance between vehicles i, j
If the vehicles are driven separately, there are two situations where vehicles i and j are far away from each other; the vehicles i and j travel in the same direction, the vehicle in front is faster, and the link between the vehicles maintains the distance:
Di,j＝R-di,j(9)
in the formula: r-is the communication range of the vehicle
di,jIs the distance between the vehicles i, j.
7. The regional multicast method based on the bus track and the positioning information as claimed in claim 5, wherein the specific process in the step 3.2 is as follows: in the internet of vehicles, due to the limitation of street width, the broadcast only needs to select one neighbor node as the next relay node in the broadcast direction, assuming viIs the first vehicle in the street segment that successfully receives the broadcast message, viCalculating a stability estimation index of each neighbor vehicle and selecting the neighbor vehicle with the highest stability estimation index as a next vehicle set vehicle; then viForwarding a broadcast message containing the selected neighbor node ID and to be sent and setting a wait timer, if the selected neighbor node successfully receives the message, it repeats the selection process and forwards the message to the next selected vehicle set vehicle; if v isiIf the message of selecting the neighbor node is not received, the neighbor node is indicated to unsuccessfully forward the message, and when v isiV after the wait timer ofiRecalculating the stability estimation index, and reselecting the vehicle of the next vehicle set; if one vehicle node in the vehicle set detects that one neighbor node in the set has left the communication range of the vehicle node through the beacon message, the link between the two vehicle nodes is broken, the vehicle reselects the vehicle node with the highest stability in the direction to join the vehicle set, and then sends a message to inform the newly joined vehicle.
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