CN113316193A - CAM message equalization reconstruction algorithm based on distributed cooperation - Google Patents

CAM message equalization reconstruction algorithm based on distributed cooperation Download PDF

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CN113316193A
CN113316193A CN202110587830.9A CN202110587830A CN113316193A CN 113316193 A CN113316193 A CN 113316193A CN 202110587830 A CN202110587830 A CN 202110587830A CN 113316193 A CN113316193 A CN 113316193A
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CN113316193B (en
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肖广兵
刘欣雨
孙宁
马浩
吕立亚
张涌
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Nanjing Forestry University
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    • H—ELECTRICITY
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    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0925—Management thereof using policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0958—Management thereof based on metrics or performance parameters
    • H04W28/0967—Quality of Service [QoS] parameters
    • H04W28/0975—Quality of Service [QoS] parameters for reducing delays
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
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Abstract

The invention provides a CAM message equalization reconstruction algorithm based on distributed cooperation, wherein the network load equalization degree reaches more than 0.8, and the CAM message equalization reconstruction algorithm is characterized in that all vehicles of a fleet are correspondingly connected end to form a closed virtual logic ring; dividing the fleet into a plurality of mutually overlapped game groups along the virtual logic ring, wherein each game group comprises three vehicles which are respectively defined as a head vehicle, a middle vehicle and a tail vehicle in a clockwise direction; the middle vehicle of each game group firstly carries out interaction and negotiation of strategy information with the front vehicle and the rear vehicle in the current game group, and meanwhile, the front vehicle and the rear vehicle are respectively used as the middle vehicles in the forward and backward game groups to carry out information interaction with other vehicles, so that the interaction and negotiation of information between adjacent game groups are realized; and each vehicle finally continuously corrects and updates own strategy information according to the feedback information interacted on the virtual logic ring until all vehicles in all game groups reach the agreement.

Description

CAM message equalization reconstruction algorithm based on distributed cooperation
Technical Field
The invention relates to a method for realizing distributed interactive sharing of road environment information by an intelligent fleet through periodically broadcasting Cooperative Awareness Messages (CAM).
Background
The intelligent motorcade is a typical application mode of future unmanned driving, and has relatively small safe distance, so that the road traffic capacity can be effectively improved, and the intelligent motorcade becomes a research hotspot in the field of current intelligent traffic. In an intelligent fleet, each vehicle and surrounding adjacent vehicles construct a Vehicular Ad hoc NETwork (VANET), distributed interactive sharing of road environment information is achieved through periodic broadcasting of Cooperative Awareness Messages (CAM), the Cooperative Awareness capability of the traffic environment conditions is enhanced, and the purpose of reducing traffic accidents is achieved. Considering that a single vehicle has a limited broadcast coverage, vehicles at greater distances have difficulty reliably receiving the CAM messages they broadcast. This may cause continuous data packet loss, which may cause the vehicle to only sense the incomplete road environment information, thereby reducing the safety of vehicle driving. A common solution is relay forwarding, as shown in fig. 1, where CAM messages are embedded in packets periodically broadcast by other neighboring vehicles, and are gradually transmitted from the head vehicle to the tail vehicle in the form of forwarding attachments. With vehicle v in figure 13For example, when it is scheduled to broadcast, it is necessary to insert the CAM message generated by itself into the data packet, and attach the CAM message generated by other vehicles, so as to help other nearby vehicles reconstruct the lost CAM message. However, relay forwarding causes the total amount of forwarded messages in the network to grow rapidly along the direction of delivery, in particular from the source node v1Relatively close vehicle v2The number of messages forwarded is 1 and vehicles v which are further away from the source noden-1The number of forwarded messages is then n-2. This is likely to cause unbalanced network load and even cause channel blocking.
At present, the solution to the problem of unbalanced communication load of the fleet can be divided into three categories: dynamic clustering algorithm based on load balancing, restrictive flooding broadcast and route optimization. (1) The dynamic clustering algorithm based on load balancing is characterized in that the number of cluster heads in a network is limited, and different nodes are dynamically selected to serve as the cluster heads, so that the balanced distribution of network loads among different cluster heads (nodes) is realized; (2) the method comprises the following steps of (1) restrictive flooding broadcast, namely, the purpose of network load balancing is achieved by limiting a flooding range and flooding times; (3) route optimization, namely, a proper path is selected by sensing the link state, analyzing link indexes and the like to improve the reliability of network communication; however, the problems of optimal cluster heads, optimal path selection, data duplication generated by flooding broadcast, and the like in the above measures are difficult to solve in a VANET with a complex topology structure.
Currently, three general solutions are available for the research on the network load balancing problem of the intelligent fleet: clustering algorithm, flooding algorithm and route optimization.
The clustering algorithm can effectively manage nodes and optimize data, and is widely applied to the VANET. Aiming at the problem of network load imbalance, an energy balance dynamic clustering algorithm is provided, the probability of the node becoming a cluster head is calculated according to the residual energy of the node and the number of times of sending data packets, and meanwhile, the node with excessively low residual energy enters a sleep state, so that the balanced consumption of the nodes in the cluster is realized. However, the selection of the optimal cluster head in the above method often requires the node to obtain global information, which is difficult to implement in a VANET with a dynamically changing topology structure. Luyi et al propose a clustering algorithm combined with network coding, after performing coding operation on data packets, discard invalid data packets to reduce network traffic, and use cluster heads as broadcast agents in a clustering structure of a strip topology, so as to achieve better energy consumption balance. However, the encoding and broadcasting method has the disadvantages of occupying a frequency band, having a high synchronization requirement, and the like, and is not sufficient in VANET.
Compared with the complexity of the clustering algorithm, the flooding algorithm is a common communication algorithm in the VANET due to the characteristics of simple structure and easy code realization. The improved flooding algorithm is provided by Zhao navy and the like, and the hop count of each node is reduced by evaluating the effective hop distance between nodes, so that the quantity of forwarded messages is effectively reduced. An improved flooding algorithm facing the mobile ad hoc network is provided from pagey and the like, a source node request area and an expected area of a destination node are calculated through node position information, a nominated broadcast retransmission neighbor list is set, the area limitation of routing packet number transmission is realized, the forwarding times of redundant messages are effectively reduced, and the network performance is improved. However, the inevitable data forwarding in the flooding broadcast is likely to cause broadcast storm, network delay and other consequences, which is not feasible in VANET.
Route optimization becomes a typical algorithm in the VANET because it can adapt to various network environments quickly and accurately. The Zhang-Ming proposes a load-balanced multi-path routing algorithm, and adopts a path polling method to distribute data on one path to a plurality of paths for transmission according to factors such as node load, link stability and the like, so as to better solve the problem of unbalanced network load. However, considering that the topology structure in VANET changes drastically and the channel quality is poor, the above method is not suitable. Presto proposes a routing optimization scheme for splitting data streams, and distributes traffic to parallel paths according to the granularity, so as to achieve the purpose of load balancing. However, the optimal traffic distribution on the parallel paths in the above scheme needs to comprehensively consider the global traffic state, which makes it difficult to implement an optimal decision in the VANET.
Disclosure of Invention
Compared with relay forwarding, the method not only improves the network load balance degree to be more than 0.8, but also can obtain lower message forwarding amount and shorter transmission delay, thereby achieving the purposes of shortening the network delay and balancing the communication load.
In order to achieve the purpose, the invention adopts the technical scheme that: a CAM message equalization reconstruction algorithm based on distributed cooperation is characterized in that a fleet composed of n vehicles is formed, odd numbered vehicles are sequentially connected into a virtual line, even numbered vehicles are sequentially connected into another virtual line, the two virtual lines are correspondingly connected end to end, and finally a closed virtual logic ring is formed;
dividing the fleet into a plurality of mutually overlapped game groups along the virtual logic ring, wherein each game group comprises three vehicles which are respectively defined as a head vehicle, a middle vehicle and a tail vehicle in a clockwise direction; the head car in the current group is the middle car of the forward game group, and the tail car is the middle car of the backward game group; if n is odd, the divided game groups are { (v)1,v3,v5)，(v3,v5,v7)，(v5,v7,v9)…(vn-4,vn-2,vn)，(vn-2,vn,vn-1)，(vn,vn-1,vn-3)…(v4,v2,v1)，(v2,v1,v3) }; if n is even number, the divided game groups are { (v)1,v3,v5)，(v3,v5,v7)，(v5,v7,v9)…(vn-5,vn-3,vn-1)，(vn-3,vn-1,vn)，(vn-1,vn,vn-2)…(v4,v2,v1)，(v2,v1,v3)}；
The linear network topology of a smart fleet is represented by an undirected graph G ═ (V, L), where the set V ═ Vi|i≤n,i∈Z+Vertex v iniRepresenting vehicles in a smart fleet, set L ═ Lij|vi,vjE.g. edge l in VijIndicating a vehicle viAnd vjA wireless communication link therebetween;
each vehicle needs to negotiate with a head vehicle and a tail vehicle respectively in a current game group as a middle vehicle, and then the head vehicle and the tail vehicle negotiate again in a forward game group and a backward game group respectively as the middle vehicle, so that information interaction and sharing are realized by gradual iteration along a virtual logic ring; because the virtual logic ring is a closed network connected end to end, when the CAM message reconstruction strategy is iterated step by step to the source vehicle, all vehicles in the fleet are agreed, namely, the balance distribution of the CAM message reconstruction task among the vehicles is realized.
As a further improvement to the above-described CAM message equalization reconstruction algorithm based on distributed cooperation, in the CAM broadcasting phase, the vehicle viFirstly, strategy information interaction and negotiation are carried out between the front vehicle and the rear vehicle in the current game group, and meanwhile, the front vehicle and the rear vehicle are respectively used as middle vehicles in the forward game group and the backward game group to carry out information interaction with other vehicles, so that information interaction and negotiation between adjacent game groups are realized; therefore, the information of each vehicle is transmitted to other vehicles in the fleet in a small group form on the virtual logic ring, and finally the strategy information of the vehicle is continuously corrected and updated according to the feedback information interacted on the virtual logic ring until all the vehicles in the game small groups reach the same condition, namely, the balance distribution of CAM message reconstruction tasks among the vehicles is realized.
As a further improvement to the CAM message equalization reconstruction algorithm based on distributed cooperation, a vehicle v is providediGenerating a collaboration offer at time t
Figure BDA0003088350470000041
Comprising a vehicle viProposed strategy of
Figure BDA0003088350470000042
And bidding strategy
Figure BDA0003088350470000043
Wherein the vehicle viAppending suggestions to other vehicle messages at time t
Figure BDA0003088350470000044
A set of (i) i
Figure BDA0003088350470000045
Is a proposed strategy; vehicle viWith a suggestion for each message
Figure BDA0003088350470000046
Bid of
Figure BDA0003088350470000047
A set of (i) i
Figure BDA0003088350470000048
A bidding strategy; the higher the bid price, the higher the priority with which its suggested message is attached;
in generating collaboration offers
Figure BDA0003088350470000051
Rear, vehicle viDifferent rewards will be awarded based on the deviation of the self-cooperation proposal from the other vehicle cooperation proposals, the basic principle being that the smaller the deviation from the other vehicle cooperation proposals, the smaller the vehicle viThe more prizes earned and vice versa; and each vehicle needs to continuously and interactively share the respective cooperation proposal in the virtual logic ring, and the cooperation proposal is dynamically adjusted according to the deviation until the cooperation proposals generated by the vehicles are the same, namely the cooperation proposal is agreed.
As a further improvement to the CAM message equalization reconstruction algorithm based on distributed cooperation, a vehicle v is providediAnd vjThe cooperation proposals generated at the time t are respectively
Figure BDA0003088350470000052
And
Figure BDA0003088350470000053
Figure BDA0003088350470000054
and
Figure BDA0003088350470000055
co-operative deviation therebetween
Figure BDA0003088350470000056
As shown in formula (6):
Figure BDA0003088350470000057
if the vehicle viAnd vjIf the generated cooperation proposals are the same, the cooperation deviation is 0, otherwise, the cooperation deviation is not 0;
a certain game subgroup (v) on the virtual logical ringi，vj，vk) Wherein v isi，vj，vkRespectively a front car, a middle car and a tail car in the current game group;
Figure BDA0003088350470000058
is the front vehicle viThe cooperation proposal generated at the moment t defines the game bonus obtained by the cooperation proposal
Figure BDA0003088350470000059
Is composed of
Figure BDA00030883504700000510
And
Figure BDA00030883504700000511
the difference between them is shown in formula (7):
Figure BDA00030883504700000512
if the front vehicle viV for middle vehiclejIs smaller than the middle vehicle vjAnd tail car vkA cooperative deviation therebetween, then the preceding vehicle viWill receive a positive reward, i.e.
Figure BDA00030883504700000513
On the contrary, the front vehicle viWill receive a negative reward, i.e.
Figure BDA00030883504700000514
As a further improvement to the CAM message equalization reconstruction algorithm based on distributed cooperation, games are rewarded
Figure BDA00030883504700000515
And suggesting strategies
Figure BDA00030883504700000516
The following message reception rates are combined to define a utility function as shown in equation (8)
Figure BDA00030883504700000517
Figure BDA00030883504700000518
The state where all the cooperation proposals of each vehicle are agreed is called as a nash balance point, and at this time, the utility function value corresponding to each vehicle is the maximum, and the cooperation proposal set of the nash balance point is also the global optimal scheme for cooperatively reconstructing the CAM message.
The invention has the beneficial effects that: the invention provides a CAM message equalization reconstruction algorithm based on distributed cooperation, which is characterized in that all vehicles in an intelligent motorcade are connected into a closed virtual ring and are divided into a plurality of game groups with overlapped heads and tails. Because each game group comprises vehicles in the adjacent game groups, the vehicles can transmit the strategy information in the current game group to the adjacent game groups, and the information interaction and negotiation between the adjacent game groups are realized. Therefore, the information of each vehicle is transmitted to other vehicles in the fleet in a small group form on the virtual logic ring, the strategy information of the vehicle is continuously corrected and updated according to the feedback information interacted on the virtual logic ring, and finally the balanced distribution of the CAM message reconstruction task among the vehicles is realized. Experimental simulation results show that compared with relay forwarding, the CAM message balance reconstruction algorithm based on distributed cooperation not only improves the network load balance degree to be more than 0.8, but also can obtain lower message forwarding amount and shorter transmission delay, and achieves the purposes of shortening network delay and balancing communication load.
Drawings
FIG. 1 is a schematic diagram of periodic broadcast of CAM messages in relay forwarding;
FIG. 2 is a schematic diagram of a closed virtual logic ring constructed when n is an odd number;
FIG. 3 is a schematic diagram of a closed virtual logic ring constructed when n is an even number;
FIG. 4 is a schematic diagram of a gaming group partitioned for odd (left) and even (right) numbers n, respectively;
FIG. 5 is a schematic diagram of the variation of cooperation bias between vehicles with information interaction turns in the inventive algorithm;
FIG. 6 is a comparison graph of network load balancing for a fleet of smart vehicles under different communication algorithms;
FIG. 7 is a graph of message forwarding variation for a fleet of smart vehicles under different communication algorithms;
FIG. 8 is a graph of the variation of message reception rate for an intelligent fleet under different communication algorithms;
fig. 9 is a graph of the variation of the average delay of message delivery for a smart fleet under different communication algorithms.
Detailed Description
1 System model
Consider a smart fleet of n vehicles, each equipped with a wireless transceiver for broadcast communications. The broadcast coverage area is defined as a circular coverage area with the current vehicle center of gravity as the origin and r as the radius. The distance between adjacent vehicles is d ═ l + s, wherein l is the length of the vehicle body, and s is the minimum safe distance. The linear network topology of a smart fleet is represented by an undirected graph G ═ (V, L), where the set V ═ Vi|i≤n,i∈Z+Vertex v iniRepresenting vehicles in a smart fleet, set L ═ Lij|vi,vjE.g. edge l in VijIndicating a vehicle viAnd vjA wireless communication link therebetween.
The channel is divided into successive time slices after GPS synchronization. Vehicles in the smart fleet use a Self-organized Time Division multiplexing (STDMA) mechanism to Access and control channels. After each vehicle occupies a time slot through channel competition, CAM information is periodically generated and broadcast, and the CAM information and surrounding adjacent vehicles interactively share respective running states and sensed local road environment information, including current vehicle speed, acceleration, overtaking warning and the like. Considering that the broadcasting coverage of vehicles is limited, vehicles at a long distance cannot reliably receive the CAM information broadcasted by the current vehicle. In addition, the blocking and attenuation of electromagnetic waves by the vehicle body, building, and the like also increases the unreliability of the wireless communication link between vehicles. Because the CAM message contains information related to safe driving, the unreliable communication link may cause continuous CAM message packet loss, so that the vehicle can only sense the incomplete road environment information, and traffic safety accidents are easily caused.
In order to assist other vehicles to reconstruct the lost CAM message, after periodically broadcasting the CAM message, each vehicle can selectively attach part of the received CAM message in the data packet according to the packet loss situation of the adjacent vehicle so as to assist the adjacent vehicle to reconstruct the packet loss.
Definition 1 (message reception rate) set vehicle viThe message reconstruction task to be executed at the moment t is
Figure BDA0003088350470000071
All vehicles in the intelligent fleet need to perform a set of message reconstruction tasks at time t, i.e.
Figure BDA0003088350470000072
Is a collaborative reconstruction strategy. Set MjFor vehicles vjCAM messages that are broadcast and have not yet exceeded their validity period, | Ni(Mj,St) L is vehicle viIn collaborative reconstruction of policies StLower received set MjThe total number of CAM messages in. Definition of a vehicle viFor vjOf the message reception rate rij(St) Is a vehicle viIn collaborative reconstruction of policies StThe lower receive source vjCAM message total and set MjThe ratio of the total number of the messages is shown in formula (1):
Figure BDA0003088350470000081
based on equation (1), defining the average message receiving rate of the intelligent motorcade at t moment as
Figure BDA0003088350470000082
The concrete formula is shown as (2):
Figure BDA0003088350470000083
where | V | represents the number of vehicles in the smart fleet.
Definition 2 (load balance) of vehicle viThe number of messages to be forwarded at time t is
Figure BDA0003088350470000084
The number of messages to be forwarded by all vehicles in the intelligent motorcade at the time t is integrated into
Figure BDA0003088350470000085
For vehicle viIn other words, the ratio of the number of messages to be forwarded at time t to the total number of messages to be forwarded at time t for all vehicles in the fleet may be expressed as:
Figure BDA0003088350470000086
to describe the difference in the attached quantity of CAM messages of different vehicles, the load balance degree sigma of the vehicles in the intelligent fleet is evaluated by adopting Jain's fairness index, and the specific formula is shown in (4):
Figure BDA0003088350470000087
where | V | represents the number of vehicles in the smart fleet,
Figure BDA0003088350470000088
indicating a vehicle viThe number of messages to be forwarded at time t and all vehicles in the fleet at time tThe ratio of the total number of messages. According to equation (4), the load balancing takes a maximum value of 1 if and only if all vehicles forward the same number of CAM messages; when only one vehicle is used for forwarding, the load balance degree obtains the minimum value
Figure BDA0003088350470000089
In order to comprehensively consider the reliability and the balance of the interactive sharing of the CAM messages in the intelligent fleet, an objective function shown in the formula (5-1) and the formula (5-2) is defined:
Figure BDA0003088350470000091
equation (5-1) represents the goal of seeking to maximize message acceptance rate in the CAM message accompanying forwarding process in a smart fleet.
Figure BDA0003088350470000092
Equation (5-2) represents the goal of seeking maximum load balancing during CAM message accompanying forwarding in a smart fleet.
2CAM message equalization reconstruction algorithm
The invention provides a CAM message equalization reconstruction algorithm based on distributed cooperation. And finally, the balance distribution of the CAM message reconstruction task among the vehicles is realized through the interaction and the correction of the CAM message reconstruction strategy among the adjacent game groups.
The balanced distribution of CAM message reconstruction tasks within a smart fleet may be expressed as follows: in the CAM broadcast phase, vehicle viFirstly, the strategy information is interacted and negotiated with the front vehicle and the rear vehicle in the current game group in which the game player is positioned. Meanwhile, the front vehicle and the rear vehicle are respectively used as middle vehicles in the forward game group and the backward game group to carry out information interaction with other vehicles, and therefore the game system is practicalAnd (4) performing information interaction and negotiation between adjacent game groups. Therefore, the information of each vehicle is transmitted to other vehicles in the fleet in a small group form on the virtual logic ring, and finally the strategy information of the vehicle is continuously corrected and updated according to the feedback information interacted on the virtual logic ring, so that the balanced distribution of the CAM message reconstruction task among the vehicles is realized. Since all vehicles are connected into a closed virtual logical ring, if the vehicles in all game teams are agreed, the CAM message reconstruction task can be considered to be distributed evenly in the intelligent fleet.
Considering an intelligent fleet composed of n vehicles, odd numbered vehicles are sequentially connected into a virtual line, even numbered vehicles are sequentially connected into another virtual line, and the two virtual lines are correspondingly connected end to finally form a closed virtual logic ring as shown in fig. 2 and 3.
In order to distribute the CAM message reconstruction tasks among vehicles in a balanced manner, the intelligent vehicle fleet needs to be divided into a plurality of mutually overlapped game groups along a virtual logic ring so as to realize the interaction and correction of vehicle strategy information. Wherein, every game subgroup contains three vehicles, according to the clockwise respectively define head car, well car and tail car. The leading car in the current group is the middle car of the forward gaming group and the trailing car is the middle car of the backward gaming group. Taking fig. 4 as an example, in an intelligent fleet comprising n vehicles, if n is odd, the divided game groups are { (v)1,v3,v5)，(v3,v5,v7)，(v5,v7,v9)…(vn-4,vn-2,vn)，(vn-2,vn,vn-1)，(vn,vn-1,vn-3)…(v4,v2,v1)，(v2,v1,v3)}. If n is even number, the divided game groups are { (v)1,v3,v5)，(v3,v5,v7)，(v5,v7,v9)…(vn-5,vn-3,vn-1)，(vn-3,vn-1,vn)，(vn-1,vn,vn-2)…(v4,v2,v1)，(v2,v1,v3)}. Specifically, when n is 9, the divided game subgroup set is { (v)1,v3,v5),(v3,v5,v7)，(v5,v7,v9)，(v7,v9,v8)，(v9,v8,v6)，(v8,v6,v4)，(v6,v4,v2)，(v4,v2,v1)，(v2,v1,v3)}. Wherein (v)1,v3,v5)，(v3,v5,v7)，(v5,v7,v9) And (v)8,v6,v4)，(v6,v4,v2) The method is a game group consisting of three vehicles which are continuous on two virtual lines and meet the grouping principle. And (v)7,v9,v8)，(v9,v8,v6)，(v4,v2,v1)，(v2,v1,v3) The game groups are divided at the head-tail connection part of the two virtual lines according to the grouping principle.
3 cooperative gaming
In a distributed network environment, vehicles in the intelligent fleet can only obtain incomplete local information, and the generated CAM message reconstruction strategies may be different. In order to eliminate the difference among the vehicles, each vehicle needs to negotiate with the head car and the tail car respectively in the game group to reach a consistency, and then the head car and the tail car negotiate again in the game group in which the vehicles are located respectively, and the information interaction sharing is realized by gradually iterating along the virtual logic ring. Since the virtual logical ring is a closed network connected end to end, when the CAM message reconstruction strategy is iteratively interacted to the source vehicle step by step, it can be considered that all vehicles in the smart fleet have agreed.
Without provision for vehicles viGenerating a collaboration offer at time t
Figure BDA0003088350470000101
Comprising a vehicle viProposed strategy of
Figure BDA0003088350470000102
And bidding strategy
Figure BDA0003088350470000111
Wherein the vehicle viAppending suggestions to other vehicle messages at time t
Figure BDA0003088350470000112
A set of (i) i
Figure BDA0003088350470000113
Figure BDA0003088350470000114
Is a proposed strategy; vehicle viWith a suggestion for each message
Figure BDA0003088350470000115
Bid of
Figure BDA0003088350470000116
A set of (i) i
Figure BDA0003088350470000117
As a bidding strategy. The higher the bid price, the higher the priority with which its suggested message is attached.
In generating collaboration offers
Figure BDA0003088350470000118
Rear, vehicle viDifferent rewards will be awarded based on the deviation of the self-cooperation proposal from the other vehicle cooperation proposals. The basic principle is that the smaller the deviation of the proposal in cooperation with other vehicles, the less the vehicle viThe more prizes are earned and vice versa. To maximize the reward, each vehicle needs to share their respective cooperative offer in a virtual logical loop with each other and dynamically adjust to the extent of the deviation until an agreement is reached.
Definition 3 (in)As deviation) is provided with a vehicle viAnd vjThe cooperation proposals generated at the time t are respectively
Figure BDA0003088350470000119
And
Figure BDA00030883504700001110
definition of
Figure BDA00030883504700001111
And
Figure BDA00030883504700001112
co-operative deviation therebetween
Figure BDA00030883504700001113
As shown in formula (6):
Figure BDA00030883504700001114
in the formula
Figure BDA00030883504700001115
Each represents viAnd vjThe bias between bidding strategies and proposal strategies in collaborative proposals,
Figure BDA00030883504700001116
the representatives take priority of suggested strategies for contingent bidding into account.
If the vehicle viAnd vjIf the generated collaboration proposals are the same, the collaboration bias is 0, otherwise, the collaboration bias is not 0.
Definition 4 (game awards) consider a certain game subgroup (v) on a virtual logical ringi，vj，vk) Wherein v isi，vj，vkRespectively a front car, a middle car and a tail car in the current game group.
Figure BDA00030883504700001117
Is the front vehicle viGenerated at time tDefine the game award it receives
Figure BDA00030883504700001118
Is composed of
Figure BDA00030883504700001119
And
Figure BDA00030883504700001120
the difference between them is shown in formula (7):
Figure BDA00030883504700001121
if the front vehicle viV for middle vehiclejIs smaller than the middle vehicle vjAnd tail car vkA cooperative deviation therebetween, then the preceding vehicle viWill receive a positive reward, i.e.
Figure BDA00030883504700001122
On the contrary, the front vehicle viWill receive a negative reward, i.e.
Figure BDA00030883504700001123
Since each game group on the virtual logic ring is overlapped with the game groups before and after the game group, the cooperation proposal of each vehicle is gradually interactively shared in the virtual logic ring, and finally, the cooperation deviation is eliminated. But eliminating the collaboration bias is not equivalent to the collaboration proposal being optimal. For example, all vehicles agree not to forward any CAM message, when the cooperation bias is 0, all vehicles do not receive a negative reward, but the cooperation proposal is not optimal and cannot reconstruct any lost CAM message. In response to the foregoing, games can be awarded
Figure BDA0003088350470000121
And suggesting strategies
Figure BDA0003088350470000122
The following message reception rates are combined to define a utility function as shown in equation (8)
Figure BDA0003088350470000123
Each vehicle also needs to maximize the utility function in updating the iterative collaboration proposal.
Figure BDA0003088350470000124
The state in which all the collaborative proposals agree is called the nash balance point according to the definition of nash balance. At this time, the utility function value corresponding to each vehicle is the largest, and any vehicle adjustment to the service offer will reduce the utility function value.
Theorem 1 when all vehicles agree in a cooperative game, the set of their cooperative proposals is a nash equilibrium point in the cooperative reconstruction of CAM messages.
Certificate is provided
Figure BDA0003088350470000125
A set of proposed strategies for all vehicles at time t,
Figure BDA0003088350470000126
is set AtThe bidding strategy corresponding to each proposed strategy. The cooperation proposal which is not arranged at the Nash balance point is
Figure BDA0003088350470000127
The corresponding proposal strategy and bidding strategy are respectively
Figure BDA0003088350470000128
And
Figure BDA0003088350470000129
according to the definition of Nash equilibrium, the cooperative proposal is agreed at the Nash equilibrium point, and any vehicle cannot change its attached suggestion or bid price to obtain a larger utility function value, namely the utility function of each vehicle at Nash equilibriumThe equilibrium point is already locally maximized. Further, at the Nash balance point, the sum of the game awards earned by all vehicles is 0, i.e., the sum of the game awards earned by all vehicles is 0
Figure BDA00030883504700001210
The sum of utility functions of all vehicles can be calculated according to the formula (8), and an equation shown in the formula (9) can be further obtained.
Figure BDA0003088350470000131
Due to utility function in
Figure BDA0003088350470000132
Where Nash equilibrium is reached, the combination of equation (9) can conclude that the objective function is also at
Figure BDA0003088350470000133
The nash equilibrium is reached.
Theorem 1 demonstrates that all vehicles are Nash balanced when they reach consensus in cooperative gambling, but this is not equivalent to the cooperative proposal where the Nash balance corresponds to being the best solution for cooperatively reconstructing the CAM message. In other words, each vehicle arrives according to local information
Figure BDA0003088350470000134
Is not always satisfied
Figure BDA0003088350470000135
For this reason, theorem 2 further demonstrates that the set of collaborative proposals at nash equilibrium is also a globally optimal solution for the collaborative reconstruction of CAM messages.
Theorem 2 when all vehicles agree in the cooperative game, the corresponding set of cooperative proposals is the optimal solution for cooperatively reconstructing the CAM message.
Proving consideration of any one of the cooperative game offers generated in the cooperative game
Figure BDA0003088350470000136
Figure BDA0003088350470000137
The utility of each vehicle is maximized when equilibrium is reached, resulting in the inequality (10):
Figure BDA0003088350470000138
Figure BDA0003088350470000139
Figure BDA00030883504700001310
utility function at this time
Figure BDA00030883504700001311
Is totally made of
Figure BDA00030883504700001312
If so, the inequality is obtained:
Figure BDA00030883504700001313
suppose viIs a game group (v)i，vj，vk) The head car of (1) then
Figure BDA00030883504700001314
Can convert inequality (11) to inequality (12):
Figure BDA0003088350470000141
taking into account the inequality (12)
Figure BDA0003088350470000142
Can take any value, will
Figure BDA0003088350470000143
And
Figure BDA0003088350470000144
respectively substituting to obtain:
Figure BDA0003088350470000145
substituting equation (13) into equation (7) yields the vehicle v at the Nash equilibrium pointiThe game award obtained is:
Figure BDA0003088350470000146
considering again the definition of nash balance, the inequality (15) can be obtained:
Figure BDA0003088350470000147
by substituting equation (14) into inequality (15), inequality (16) shown below can be obtained:
Figure BDA0003088350470000148
because of the inequality (16)
Figure BDA0003088350470000149
Is arbitrary and can make
Figure BDA00030883504700001410
Inequality (17) is obtained:
Figure BDA00030883504700001411
converting inequality (17) to equation (18):
Figure BDA00030883504700001412
from which it can be demonstrated
Figure BDA00030883504700001413
Is the optimal solution of equation (2), i.e., the set of cooperation proposals corresponding to the nash equilibrium points is the maximum solution of equation (2).
4 simulation experiment
In order to evaluate the performances of the CAM message equalization reconstruction algorithm based on distributed cooperation, NS2 software is adopted to carry out simulation experiments on the interactive sharing of CAM messages in the intelligent fleet and carry out performance comparison analysis with the following algorithms.
(1) Relay forwarding
When each vehicle is scheduled periodically for broadcasting, not only the self-generated CAM message needs to be inserted into the data packet, but also the CAM message of the preceding vehicle needs to be forwarded so as to help other vehicles reconstruct the lost CAM message.
(2) Cooperation reconstruction algorithm based on geographical position information
And each vehicle carries out priority sequencing on the vehicle and the neighbor vehicles according to the geographical position between the vehicle and the packet-losing vehicle, and the vehicle which has smaller distance from the packet-losing vehicle has higher priority to carry out CAM message reconstruction.
(3) Non-uniform clustering algorithm based on competition
Each vehicle participates in cluster head election by broadcasting information such as self ID, competition range, surplus energy and the like, the vehicle with the largest surplus energy successfully conducts the election and broadcasts a final cluster head message, other vehicles add similar cluster heads according to the strength of the message, and finally the intelligent vehicle fleet is divided into a plurality of clusters with different geometric sizes. The cluster vehicle selectively reconstructs the packet loss message according to the residual energy information, and the cluster vehicle transmits the packet loss message through the relay vehicle.
As shown in fig. 5, the cooperation bias between vehicles in the CAM message equalization reconstruction algorithm based on distributed cooperation will gradually decrease and stabilize to 0 after several information interactions, and the cooperation proposal of each vehicle in the virtual logic ring is agreed. This is because each game group on the virtual logic ring overlaps with the game groups before and after the game group, and the cooperation proposal of each vehicle is gradually interactively shared in the virtual logic ring, and finally the cooperation deviation is eliminated.
In order to evaluate the performance of each algorithm in the aspect of network load balancing, Jain's fairness indexes are adopted for comparative analysis. Fig. 6 shows the network load balance of the intelligent fleet under different communication algorithms. Compared with a relay forwarding and competition-based heterogeneous clustering broadcasting scheme, the network load balance degree of the intelligent motorcade under the CAM message balance reconstruction algorithm based on distributed cooperation can reach more than 0.8, and particularly when the number of vehicles is increased, the network load balance degree is higher. In the cooperative reconstruction algorithm based on the geographic position information, the network load balance degree is higher when the number of vehicles is higher, but the network load balance degree is lower when the number of vehicles is lower in a mode of reconstructing the CAM message by means of the geographic position information.
Fig. 7 shows the variation of message forwarding amount of the intelligent vehicle fleet under different communication algorithms, and compared with relay forwarding, the message forwarding amount of the intelligent vehicle fleet is significantly reduced under the other three communication algorithms. This is because each vehicle inevitably needs to forward the CAM message of the preceding vehicle in relay forwarding, so that the message forwarding amount increases rapidly in the transmission direction. In the competition-based non-uniform clustering and geographical position-based cooperative reconstruction algorithm, each vehicle selectively reconstructs CAM messages according to the residual energy and geographical position information of each vehicle, and the message forwarding amount is small. In the CAM message equalization reconstruction algorithm based on distributed cooperation, each vehicle finally bears respective CAM message reconstruction task under the condition of consistent decision, so that the rapid increase of message forwarding amount is avoided.
The message receiving rate is defined as the ratio of the number of messages successfully received by the vehicles in the intelligent motorcade to the total message forwarding amount. As shown in fig. 8, the CAM message equalization reconstruction algorithm based on distributed cooperation not only enables the messages of each game group to be negotiated and unified on the virtual logic ring, but also performs equalization distribution on the CAM message recovery task borne by each vehicle, thereby avoiding forwarding of a large number of redundant messages and obtaining a high message receiving rate. The non-uniform clustering algorithm based on competition shows a higher message receiving rate when the number of vehicles is lower, but the message receiving rate fluctuates with the increase of the number of vehicles and the influence of the clustering strategy, and the stability is reduced. The duplication and forwarding of a large number of messages in the relay forwarding easily cause broadcast storms and frequent message packet loss, so that the message receiving rate is rapidly reduced along with the increase of the number of vehicles. In the cooperative reconstruction algorithm based on the geographic position information, because negotiation is not performed between adjacent vehicles, when each vehicle detects a packet loss message in a broadcast range, the vehicle tends to arrange the reconstruction priority of the vehicle at the first according to the geographic position, so that the adjacent vehicles broadcast the packet loss message at the same time to generate collision easily, and the message receiving rate is low.
The message delivery delay is defined as the maximum value of the time consumed by each message to reach all vehicles in the smart fleet, and the average delay is the average of the sum of the message delivery delays in the smart fleet. As shown in fig. 9, since message delivery is affected by the clustering strategy in the contention-based non-uniform clustering algorithm, a large amount of time is consumed in cluster head election and message forwarding to vehicles within a cluster, and thus message delivery delay is high. The CAM message equalization reconstruction algorithm based on distributed cooperation not only transmits the messages of each vehicle on the virtual logic ring in the form of game groups after negotiation and unification, thereby avoiding the forwarding of a large amount of redundant messages, but also has lower average delay. In the cooperative reconstruction algorithm based on the geographic position information, when each vehicle detects a packet loss message in a broadcast range, the vehicle tends to arrange the self-reconstruction priority at the first according to the geographic position, so that the CAM message reconstruction can be carried out without waiting time, and the average delay is low.

Claims (5)

1. The CAM message equalization reconstruction algorithm based on distributed cooperation is characterized in that: firstly, sequentially connecting odd-numbered vehicles into a virtual line, then sequentially connecting even-numbered vehicles into another virtual line, and then correspondingly connecting the heads and the tails of the two virtual lines to finally form a closed virtual logic ring;
dividing the fleet into a plurality of mutually overlapped game groups along the virtual logic ring, wherein each game group comprises three vehicles which are respectively defined as a head vehicle, a middle vehicle and a tail vehicle in a clockwise direction; the head car in the current group is the middle car of the forward game group, and the tail car is the middle car of the backward game group; if n is odd, the divided game groups are { (v)1，v3，v5)，(v3，v5，v7)，(v5，v7，v9)…(vn-4，vn-2，vn)，(vn-2，vn，vn-1)，(vn，vn-1，vn-3)…(v4，v2，v1)，(v2，v1，v3) }; if n is even number, the divided game groups are { (v)1，v3，v5)，(v3，v5，v7)，(v5，v7，v9)…(vn-5，vn-3，vn-1)，(vn-3，vn-1，vn)，(vn-1，vn，vn-2)…(v4，v2，v1)，(v2，v1，v3)}；
The linear network topology of a smart fleet is represented by an undirected graph G ═ (V, L), where the set V ═ Vi|i≤n，i∈Z+Vertex v iniRepresenting vehicles in a smart fleet, set L ═ Lij|vi，vjE.g. edge l in VijIndicating a vehicle viAnd vjA wireless communication link therebetween;
each vehicle needs to negotiate with a head vehicle and a tail vehicle respectively in a current game group as a middle vehicle, and then the head vehicle and the tail vehicle negotiate again in a forward game group and a backward game group respectively as the middle vehicle, so that information interaction and sharing are realized by gradual iteration along a virtual logic ring; because the virtual logic ring is a closed network connected end to end, when the CAM message reconstruction strategy is iterated step by step to the source vehicle, all vehicles in the fleet are agreed, namely, the balance distribution of the CAM message reconstruction task among the vehicles is realized.
2. The distributed collaboration based CAM message equalization reconstruction algorithm as claimed in claim 1 wherein: in the CAM broadcast phase, vehicle viFirstly, strategy information interaction and negotiation are carried out between the front vehicle and the rear vehicle in the current game group, and meanwhile, the front vehicle and the rear vehicle are respectively used as middle vehicles in the forward game group and the backward game group to carry out information interaction with other vehicles, so that information interaction and negotiation between adjacent game groups are realized; therefore, the information of each vehicle is transmitted to other vehicles in the fleet in a small group form on the virtual logic ring, and finally the strategy information of the vehicle is continuously corrected and updated according to the feedback information interacted on the virtual logic ring until all the vehicles in the game small groups reach the same condition, namely, the balance distribution of CAM message reconstruction tasks among the vehicles is realized.
3. The distributed collaboration based CAM message equalization reconstruction algorithm as claimed in claim 2 wherein: provided with vehicles viGenerating a collaboration offer at time t
Figure FDA0003088350460000021
Comprising a vehicle viProposed strategy of
Figure FDA0003088350460000022
And bidding strategy
Figure FDA0003088350460000023
Wherein the vehicle viAppending suggestions to other vehicle messages at time t
Figure FDA0003088350460000024
A set of (i) i
Figure FDA0003088350460000025
Is a proposed strategy; vehicle viWith a suggestion for each message
Figure FDA0003088350460000026
Bid of
Figure FDA0003088350460000027
A set of (i) i
Figure FDA0003088350460000028
A bidding strategy; the higher the bid price, the higher the priority with which its suggested message is attached;
in generating collaboration offers
Figure FDA0003088350460000029
Rear, vehicle viDifferent rewards will be awarded based on the deviation of the self-cooperation proposal from the other vehicle cooperation proposals, the basic principle being that the smaller the deviation from the other vehicle cooperation proposals, the smaller the vehicle viThe more prizes earned and vice versa; and each vehicle needs to continuously and interactively share the respective cooperation proposal in the virtual logic ring, and the cooperation proposal is dynamically adjusted according to the deviation until the cooperation proposals generated by the vehicles are the same, namely the cooperation proposal is agreed.
4. A distributed collaboration based CAM message equalization reconstruction algorithm as claimed in claim 3 wherein:
provided with vehicles viAnd vjThe cooperation proposals generated at the time t are respectively
Figure FDA00030883504600000210
And
Figure FDA00030883504600000211
Figure FDA00030883504600000212
and
Figure FDA00030883504600000213
co-operative deviation therebetween
Figure FDA00030883504600000214
As shown in formula (6):
Figure FDA00030883504600000215
Figure FDA00030883504600000216
each represents viAnd vjThe bias between bidding strategies and proposal strategies in collaborative proposals,
Figure FDA00030883504600000217
priority on behalf of a suggested policy considering collateral bidding;
if the vehicle viAnd vjIf the generated cooperation proposals are the same, the cooperation deviation is 0, otherwise, the cooperation deviation is not 0;
a certain game subgroup (v) on the virtual logical ringi，vj，vk) Wherein v isi，vj，vkRespectively a front car, a middle car and a tail car in the current game group;
Figure FDA0003088350460000031
is the front vehicle viThe cooperation proposal generated at the moment t defines the game bonus obtained by the cooperation proposal
Figure FDA0003088350460000032
Is composed of
Figure FDA0003088350460000033
And
Figure FDA0003088350460000034
the difference between them is shown in formula (7):
Figure FDA0003088350460000035
if the front vehicle viV for middle vehiclejIs smaller than the middle vehicle vjAnd tail car vkA cooperative deviation therebetween, then the preceding vehicle viWill receive a positive reward, i.e.
Figure FDA0003088350460000036
On the contrary, the front vehicle viWill receive a negative reward, i.e.
Figure FDA0003088350460000037
5. The distributed collaboration based CAM message equalization reconstruction algorithm as claimed in claim 4 wherein:
game winning syncope
Figure FDA0003088350460000038
And suggesting strategies
Figure FDA0003088350460000039
The following message reception rates are combined to define a utility function as shown in equation (8)
Figure FDA00030883504600000310
Figure FDA00030883504600000311
The state where all the cooperation proposals of each vehicle are agreed is called as a nash balance point, and at this time, the utility function value corresponding to each vehicle is the maximum, and the cooperation proposal set of the nash balance point is also the global optimal scheme for cooperatively reconstructing the CAM message.
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