CN110113725B - Internet of vehicles node excitation method based on game model - Google Patents
Internet of vehicles node excitation method based on game model Download PDFInfo
- Publication number
- CN110113725B CN110113725B CN201910360153.XA CN201910360153A CN110113725B CN 110113725 B CN110113725 B CN 110113725B CN 201910360153 A CN201910360153 A CN 201910360153A CN 110113725 B CN110113725 B CN 110113725B
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- stage
- value
- tolerance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000005284 excitation Effects 0.000 title claims abstract description 17
- 230000007246 mechanism Effects 0.000 claims abstract description 27
- 230000006855 networking Effects 0.000 claims abstract description 8
- 230000008901 benefit Effects 0.000 claims description 22
- 230000009471 action Effects 0.000 claims description 4
- 230000007774 longterm Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010921 in-depth analysis Methods 0.000 description 1
- 230000008450 motivation Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 239000011541 reaction mixture Substances 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention discloses a node excitation method of a car networking based on a game model, and provides a new excitation mechanism based on the game model, which rewards nodes with high contribution degree and punishs nodes with low contribution degree on the basis of credit values of the nodes, so that the problem of inconsistent contribution degree among the nodes is solved, and the enthusiasm of all the nodes is improved. The method can stimulate the nodes of the vehicle network to actively participate in the data packet forwarding task of the network, improves the delivery rate of the data packet, reduces the average end-to-end time delay and improves the overall network performance of the vehicle networking on the basis of the original AODV protocol.
Description
Technical Field
The invention belongs to the technical field of vehicle networking node communication, and relates to a game model-based vehicle networking node excitation method.
Background
With the continuous development of economy, the living standard of people is steadily improved. In order to facilitate travel, more and more families use automobiles as transportation tools. By the last half of 2018, the number of Chinese vehicles kept has exceeded 3 hundred million, with 2.17 million cars. The automobile brings convenience to people and also causes a plurality of social problems. Such as frequent traffic accidents, traffic jams and air pollution. Therefore, a technology of vehicle communication is urgently required to alleviate the above-described problems. In recent years, the advent of internet of vehicles technology has provided solutions to these problems.
The internet of vehicles has evolved from a vehicle-mounted ad hoc network, in which each vehicle can be regarded as a vehicle node in the network, and the interaction between the vehicles requires the nodes to communicate with each other. When a vehicle node attempts to communicate with a vehicle node located outside its communication range, relay node assistance is required. However, in a real situation, energy and resources of the vehicle node are limited, and participating in packet forwarding consumes memory, bandwidth, energy and the like of the vehicle node itself. Since each vehicle node is rational, i.e., maximizes its own revenue. Therefore, some vehicle nodes refuse to forward data relayed by other vehicle nodes in order to save resources of the vehicle nodes, and show selfishness of the vehicle nodes, and the nodes are called selfishness nodes. Studies have shown that even a small fraction of selfish nodes (10% -40%) in the network can cause a significant drop in network performance (16% -32%). Therefore, how to effectively stimulate cooperation among nodes in the internet of vehicles, and guaranteeing the availability and high performance of the network are important challenges facing the current research field of the internet of vehicles.
Currently, mechanisms for motivating selfish node cooperative communication in ad hoc networks are divided into two categories. The first type is a payment-based incentive mechanism. The mechanism mainly adopts a virtual electronic currency mode to stimulate the cooperation among nodes, and a typical representation is a Nuglet Counter strategy. The strategy is based on a counter value, when the node sends own data packet, the counter value is decreased, and when the node forwards the data packet of the neighbor node, the counter value is increased, but the node must maintain the value of the counter as a positive number to send the own data packet. Therefore, if a node wants to send its own packet, it must cooperate. The disadvantage of this mechanism is that additional equipment is usually required to ensure the mechanism is successfully implemented, but it is difficult to implement in a practical network environment, thus restricting the development of the mechanism.
The second type is a reputation based incentive mechanism. The mechanism has the idea that the trust degree of the node is evaluated through the credit of the node, whether the node is credible or not is judged according to the trust degree, and the trust degree is used as a decision basis for routing selection. A disadvantage of this mechanism is that the calculation of the reputation is complex and the calculated value of the reputation may not be consistent for different schemes.
Disclosure of Invention
The invention aims to provide a game model-based method for exciting nodes of the Internet of vehicles, which solves the problem of poor network performance caused by poor interaction enthusiasm of each node of the Internet of vehicles in the prior art.
The technical scheme adopted by the invention is that a node excitation method of the Internet of vehicles based on a game model comprises the following steps:
step 1: establishing a single-stage game model of adjacent nodes of the Internet of vehicles network, and then extending to establish an infinite repeated game model;
step 2: on the basis of the repeated game model in the step 1 and the pinpoint relative strategy, a fault tolerance strategy is adopted among the nodes;
and step 3: based on the credit values of the nodes, the nodes with high contribution degree are rewarded, the nodes with low contribution degree are punished, the problem of inconsistent contribution degree among the nodes is solved, and the enthusiasm of all the nodes is improved.
In the step 1, the whole Internet of vehicles network G (V, E) is composed of N rational nodes, G represents any connected graph, V represents a set of nodes, and E represents a link set; the system time is formed by discrete time slots t, and in each time slot, a node can make rational selection; the infinite repeated game model is characterized in that each node is interacted for multiple times and mutually cooperates to transmit data packets, so that the overall network performance of the Internet of vehicles is improved.
The fault tolerance strategy in the step 2 specifically comprises the following steps:
for a pair of adjacent nodes A and B in the Internet of vehicles, the strategy adopted for any node is as follows:
an initial stage: the attitude of the node A and the attitude of the node B are friendly, namely, the cooperative attitude is adopted to actively forward the data packet of the other side;
a tolerance stage: when the kth time slot, the node A adopts an uncooperative attitude, then in a certain time, the node A is set as T, and the node B adopts a tolerant attitude, namely the node A still forwards the data packet, and the node B is set as tolerant time;
a judging stage: if the attitude of the node A is still unfriendly after the tolerance stage, namely the node A is selected to be not doing the action, the node B broadcasts the node A out to inform other nodes, and then the node A enters a punishment stage and is set as P;
a punishment stage: in the punishment period, other nodes refuse to forward the data packet sent by the node A, and the node A must help the neighbor nodes to forward the data packet for free in the punishment period;
a forgetting stage: when the punishment stage is finished, the node A can re-enter the network, and the non-operation history can be forgotten;
the values of the tolerance time T and the penalty period P are determined according to the following rules:
firstly, determining the tolerance time T, wherein the node B always selects a cooperation strategy within the tolerance time T, but the neighbor node A adopts an uncooperative attitude, and according to the knowledge of the game theory, the income f of the node B in the tolerance stage B The following formula:
wherein delta is a presentation factor, the value range of delta is (0, 1), the presentation factor is regarded as the endurance degree of the node cooperation, the greater the value of delta is, the more endurance the node represents, beta represents the consumption generated by the node forwarding data
During this period, the node B forwards data to the node a without compensation, but if the node B does not forward its own data and T is established, the node B needs to make the expected profit U B Greater than or equal to zero, i.e.:
that is, the value of the tolerance time T is a value at which the above equation is established.
Finally, the value of the penalty period P is determined. Since node a still does not cooperate after the elapse of the tolerance time from the kth slot, a penalty is imposed on node a. If the benefit obtained in the tolerant stage is less than the consumption in the penalty period, since the nodes are rational, starting from the next stage, in order to maximize the benefit of the nodes, the attitude of the nodes can be changed from non-cooperation to cooperation, as shown in the following formula:
the formula (7) is satisfied, and the value of the penalty period P corresponds to this.
The reward and penalty mechanism based on the reputation value in step 3 is specifically as follows:
in the initialization stage, each node comprises a weight value table, the initial value of the weight value is initialized to 0, and when the node participates in forwarding once, the weight value is added by 1;
in each time slot t, the table is updated once, and all nodes are sorted according to the weight value;
when both the node B and the node C require the node a to forward respective data packets, the node a preferentially forwards the data packet of the node with the larger weight value according to the size of the weight value in the weight value table, and then forwards the data packet of the node with the smaller weight value.
The invention has the beneficial effects that a new excitation mechanism is provided based on a game model, the nodes of the vehicle network can be excited to actively participate in the data packet forwarding task of the network, the delivery rate of the data packet is improved on the basis of the original AODV protocol, the average end-to-end time delay is reduced, and the overall network performance of the vehicle network is improved.
Drawings
FIG. 1 is a single-stage node forwarding game model diagram of a node excitation method of a vehicle networking based on a game model;
FIG. 2 is a graph comparing a data packet delivery rate and a selfish node ratio of a node excitation method of the Internet of vehicles based on a game model;
FIG. 3 is a comparison graph of average end-to-end delay and selfish node proportion of the node excitation method of the Internet of vehicles based on the game model;
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a node excitation method of a car networking based on a game model, which comprises the following steps:
step 1: establishing a single-stage game model of adjacent nodes of the Internet of vehicles network, and then extending to establish an infinite repeated game model;
step 2: on the basis of the repeated game model in the step 1 and the pinpoint relative strategy, a fault tolerance strategy is adopted among the nodes;
and 3, step 3: based on the credit values of the nodes, the nodes with high contribution degree are rewarded, the nodes with low contribution degree are punished, the problem of inconsistent contribution degree among the nodes is solved, and the enthusiasm of all the nodes is improved.
In the step 1, the whole Internet of vehicles network G (V, E) is composed of N rational nodes, G represents any connected graph, V represents a set of nodes, and E represents a link set; the system time is formed by discrete time slots t, and in each time slot, a node can make rational selection; the infinite repeated game model is characterized in that each node is interacted for multiple times and mutually cooperates to transmit data packets, so that the overall network performance of the Internet of vehicles is improved.
The fault tolerance strategy in the step 2 specifically comprises the following steps:
for a pair of adjacent nodes A and B in the Internet of vehicles, the strategy adopted for any node is as follows:
an initial stage: the attitude of the node A and the attitude of the node B are friendly, namely, the cooperative attitude is adopted to actively forward the data packet of the other side;
a tolerance stage: when the kth time slot is the time slot, the node A adopts an improper attitude, the T is set within a certain time, the node B adopts a tolerant attitude, namely, the node A still forwards the data packet, and the tolerant time is set;
a judging stage: if the attitude of the node A is still unfriendly after the tolerance stage, namely the node A is selected to be not doing the action, the node B broadcasts the node A out to inform other nodes, and then the node A enters a punishment stage and is set as P;
a punishment stage: in the punishment period, other nodes refuse to forward the data packet sent by the node A, and the node A must help the neighbor nodes to forward the data packet for free in the punishment period;
a forgetting stage: when the penalty stage is finished, the node A can reenter the network, and the history of the non-operation of the node A can be forgotten;
the values of the tolerance time T and the penalty period P are determined according to the following rules:
firstly, determining the tolerance time T, wherein the node B always selects a cooperation strategy within the tolerance time T, but the neighbor node A adopts an uncooperative attitude, and according to the knowledge of the game theory, the income f of the node B in the tolerance stage B The following formula:
wherein, delta is a presentation factor, the value range of delta is (0, 1), which is regarded as the endurance degree of node cooperation, the bigger the value of delta, the more endurance the node represents, beta represents the consumption generated by node forwarding data
During this period, the node B forwards data for the node a without any compensation, but if T is established, the node B needs to make the expected profit U B Greater than or equal to zero, i.e.:
that is, the value of the tolerance time T is a value at which the above equation is established.
Finally, the value of the penalty period P is determined. Since node a still does not cooperate after the elapse of the tolerance time from the kth slot, a penalty is imposed on node a. If the benefit obtained by the tolerant phase is less than the consumption of the penalty period, since the nodes are rational, starting from the next phase, the attitude of the nodes can be changed from non-cooperative to cooperative in order to maximize the benefit thereof, as shown in the following formula:
the formula (7) is satisfied, and the value of the penalty period P corresponds to this.
The reward and penalty mechanism based on the reputation value in step 3 is specifically as follows:
in the initialization stage, each node comprises a weight value table, the initial value of the weight value is initialized to 0, and when the node participates in forwarding once, the weight value is added by 1;
in each time slot t, the table is updated once, and all nodes are sorted according to the weight;
when both the node B and the node C require the node a to forward respective data packets, the node a preferentially forwards the data packet of the node with the larger weight value according to the size of the weight value in the weight value table, and then forwards the data packet of the node with the smaller weight value.
The method comprises the following steps: establishing a single-stage game model of adjacent nodes, analyzing strategies adopted by the nodes, and extending and establishing an infinite repeated game model;
1) The whole Internet of vehicles network G (V, E) is composed of N rational nodes, G represents any connected graph, V represents a node set, and E represents a link set;
2) The links between the nodes are bi-directional, i.e. a link (w, z) is a subset of link E if and only if nodes w, z are within transmission range of each other;
3) The system time is formed by discrete time slots t, and in each time slot, a node can make rational selection;
4) All nodes show two behavior strategies, one is a cooperation strategy, namely the nodes are willing to relay and forward data for other nodes and is represented by C; the other is a selfish strategy, namely, the relay forwarding data is refused and is represented by D;
5) The node benefit generally consists of two parts, one part is the benefit obtained by the node sending data and being forwarded by the neighbor node, and is represented by alpha, and the other part is the consumption generated by the node forwarding data, and is represented by beta. The benefit of the node can then be expressed as: f = α - β;
as can be seen from FIG. 1, A, B, D1 and D2 are four nodes in the Internet of vehicles. D2 and A, A and B, B and D1 are all adjacent nodes, and the dotted line represents the transmission range of the vehicle node signal. In the Internet of vehicles, nodes communicate with each other in two ways, if adjacent nodes are within the transmission range of each signal, the nodes communicate directly, such as adjacent nodes A and B in the graph; another is that because the communication counterpart is out of the transmission range of the signal, the communication is realized by relay forwarding of the neighboring node, such as the communication between a and D1 in the figure must be forwarded by the node B. Assuming that the node A wants to send the data packet to the node D1, the data packet must be forwarded through the adjacent node B due to the limited signal transmission range; similarly, node B and D2 communicate on the premise of node a's forwarding. Therefore, a revenue matrix is obtained, in which the adjacent nodes a and B only play a single game, as shown in table 1.
TABLE 1 revenue matrix for single stage node forwarding game
| Neighboring nodes A and B | C (Cooperation) | D (Do not do) |
| C (Cooperation) | (α-β,α-β) | (-β,α) |
| D (Do not do) | (α,-β) | (0,0) |
As shown in table 1, α represents the benefit obtained by the node sending a packet and being forwarded by the neighboring node, β represents the consumption of the node in forwarding data, and β is much smaller than α. From the above, this is a typical single prisoner dilemma game, because for each node, taking the bad strategy by itself is the best choice regardless of what strategy the opponent of the game takes. Because the gains are greatest for the nodes themselves when taking the undesirable policy. In the whole single-stage node forwarding game process, all the nodes are rational individuals, and each node can seek the maximization of the benefit of the node in the game process.
Therefore, nash equilibrium solution (no-match ) of the game can be obtained, that is, both game sides refuse to forward data packets, and at this time, the node a and the node D1 cannot communicate, and the same is true for the node B and the node D2. The method is not willing to see, so that a repeated game model is introduced, and the nodes are stimulated to cooperate with each other to forward data packets, so that the overall network performance of the Internet of vehicles is improved.
Step two: on the basis of repeated games, a fault-tolerant strategy is provided on the basis of a needle-tip relative strategy;
the reason for causing the misbehavior among the nodes is that the nodes adopt the misbehavior strategy and do not cause corresponding punishment, and the punishment can reduce the income of the nodes. If a penalty mechanism is designed for the nodes, the short-term benefit obtained by the nodes not cooperating is less than the loss suffered by the penalty stage. Since each node is rational, i.e., maximizes its own benefits. Then the node will carefully consider the effect of its own behavior on the subsequent game from the next stage. At the moment, the multi-time interaction between the network node and the neighbor node is not a series of independent single-stage node forwarding games any more, but is expanded into an infinitely repeated message forwarding game.
According to the principle of the repeated game theory, the expected total profit of the node i is as follows:
in the formula 1, the reaction mixture is,
representing the income of the node i in the single-stage game of the time slot t, wherein delta is a revealing factor, the value range of the revealing factor is (0, 1), the revealing factor is regarded as the endurance degree of the node cooperation, and the greater the value of delta, the more endurance the node shows and the more importance to the long-term benefit; otherwise, the node focuses more on the anterior aspectBenefits; generally, the temporarily constructed network presence factor is relatively small, and the stably operating network presence factor is relatively large.
According to the theory of repeated game, two punishment strategies are commonly used by people, namely a pinpoint relative strategy and a cool strategy. In the repeated game, the best strategy is a needle-front relative strategy (TFT, tit-For-Tat), namely a one-to-one strategy. The policy may be described as: both parties participating in the game know the strategy adopted by the other party, if one party selects cooperation in the first stage, the other party in the next stage also selects a cooperation strategy, if one party in a certain stage adopts a non-cooperation strategy, corresponding to the next stage, the other party directly adopts a non-cooperation strategy, and the steps are circulated in sequence. In conclusion, the strategy modes of the two parties of the game are one report and another report. See formula (2):
assuming that the node i adopts the non-operation strategy in the second time slot, the strategies adopted among the nodes are alternated as shown in formula (3), and the network is very easy to be unstable since you come and go, so that the strategy needs to be improved.
If the attitude of the third time slot is still the inoperability attitude on the basis that the node i adopts the inoperability strategy in the second time slot, the node j will not show weakness, and can immediately adopt the inoperability to show the own attitude, as shown in formula (4). In the past, the connection performance of the network will be reduced sharply.
Through step analysis, if the dispute relative strategy is directly applied to the internet of vehicles, the strategy is found to be too strict. A fault tolerant TFT strategy (TTFT) is therefore proposed herein. Assuming that a pair of adjacent nodes A and B exist in the network, the strategy adopted for any node is as follows:
1) An initial stage: the attitude of the node A and the attitude of the node B are friendly, namely, the cooperative attitude is adopted to actively forward the data packet of the other side;
2) A tolerance stage: when the node A adopts an uncooperative attitude at the kth time slot, the node B adopts a tolerant attitude within a certain time (set as T), namely the node A still forwards the data packet, and the tolerant time is set;
3) A judging stage: if the attitude of the node A is still unfriendly after the tolerance stage, namely the node A is selected to be not doing any action, the node B broadcasts the node A out to inform other nodes, and then the node A enters a punishment stage (set as P);
4) A punishment stage: in the punishment period, other nodes refuse to forward the data packet sent by the node A, and the node A must help the neighbor nodes to forward the data packet (provide forwarding service without compensation) in the punishment period;
5) A forgetting stage: when the penalty stage is finished, the node A can reenter the network, and the history of the non-operation of the node A can be forgotten;
according to the strategy idea, the values of the tolerance time T and the penalty period P need to be determined. Firstly, determining tolerance time T, wherein within the tolerance time T, the node B always selects a cooperation strategy, but the neighbor node A adopts an uncooperative attitude, and according to the knowledge of a game theory, in a tolerance stage, the income of the node B is shown in a formula (5):
during this period, node B forwards data for node a without compensation, but data of node B is not forwarded, and if T is satisfied, the expected benefit of node B needs to be greater than or equal to zero, that is:
that is, the value of the tolerable time T is a value at which the equation (6) holds.
Finally, the value of the penalty period P is determined. Since node a still does not cooperate after the elapse of the tolerance time from the kth slot, a penalty is imposed on node a. If the benefit obtained in the tolerance phase is less than the consumption in the penalty period, since the nodes are rational, starting from the next phase, in order to maximize the benefit of the nodes, the attitude of the nodes can be changed from non-cooperation to cooperation, see formula (7):
equation (7) is satisfied, and the value of penalty period P corresponds to this.
Step three: based on the credit values of the nodes, the problem of inconsistent contribution degrees among the nodes is solved;
an in-depth analysis of the fault-tolerant TFT strategy proposed above shows that the contribution of the strategy to the nodes is not differentiated, i.e. node B forwards 10 data packets and node C forwards 1 data packet equally. Since the fault-tolerant TFT strategy encourages the nodes to limit the behavior of the nodes by adopting a penalty means, the nodes have to consider the influence of the current behavior on the future benefits. Therefore, on the basis of repeated games, the method provides a credit value-based mechanism to reward the nodes with high contribution degree so as to improve the enthusiasm of the nodes.
The basic idea is as follows: in the initialization stage, each node contains a weight value table with the format of (node name, weight value). The node name represents the node name which helps the node to forward the data packet, the initial value of the weight is initialized to 0, the forwarding is carried out once, and the weight is added with 1. For example, node B and node C both help node a forward the packet, but do not contribute to the same extent, such as: node B helps a forward 10 times, while node C helps a forward 1 time. The table is updated once in each time slot t and sorted according to the weight value. When the node B and the node C both require the node a to forward respective data packets, the node a will forward the data packet of the node with the larger weight preferentially according to the size of the weight in the weight table, and then forward the data packet of the node with the smaller weight. Through the analysis, the enthusiasm of the nodes can be greatly improved according to the credit mechanism, and the contribution degrees of the nodes are successfully distinguished.
In order to verify the correctness and feasibility of the mechanism, simulation is realized on the operating system Ubuntu 16.04 by using Network Simulation software NS2 (Network Simulation Version 2). The NS2 is a network simulation software which is oriented to objects, driven by events and powerful in function and is jointly written by C + + and OTcl languages.
The simulated environment is shown in the following table:
TABLE 2 Primary parameter settings for node trajectories
The performance index parameters are as follows:
1) Packet Delivery rate (Packet Delivery Ratio): the ratio of the data packets received by the destination vehicle node to the data packets sent by the source vehicle node directly reflects the reliability of the routing protocol, and the higher the delivery rate of the data packets, the better the reliability of the routing protocol. As shown in formula 3-1:
2) Average End-to-End Delay (Average End-to-End Delay): the ratio of the time consumed by the source vehicle node to send the data packet to the destination vehicle node to the number of the data packets sent in the time period is referred to. Usually, the end-to-end delay is used to reflect whether the network is unobstructed, and the smaller the delay is, the better the network is unobstructed. As shown in formulas 3-2:
example (b):
the experimental results are divided into two groups. And respectively inspecting two parameters of the delivery rate of the data packet and the average end-to-end time delay, and analyzing the change of each parameter along with the increase of the ratio of selfish nodes in the network under the two conditions of an original AODV protocol, an AODV protocol (0.6 AODV) added with an excitation mechanism and with a presentation factor of 0.6 and an ADOV protocol (0.8 AODV) added with the excitation mechanism and with a presentation factor of 0.8 to obtain the performance of the excitation mechanism.
As shown in fig. 2, when the proportion of selfish nodes in the network increases, the packet delivery rate changes in three cases, namely, AODV protocol without adding incentive mechanism, AODV protocol with discount factor of 0.6, and AODV protocol with discount factor. From the simulation chart, the three conditions have a common point that the packet delivery rate is reduced along with the increase of the proportion of selfish nodes in the network. When the number of selfish nodes in the network is more and more, the selfish nodes refuse to relay and forward the data packet, so that the delivery rate of the data packet is reduced. However, when an incentive mechanism is added to the AODV protocol, it can be found that the packet delivery rate decreases slowly, and the packet delivery rate at the discount factor of 0.8 is higher than that at the discount factor of 0.6. The reason is that: the motivation mechanism is added, so that the cooperation enthusiasm among the nodes is improved, the state of the selfish node is changed from non-cooperation to cooperation, the overall network cooperation is improved, and the higher the expression factor is, the higher the endurance degree of the node is, and the higher the data packet delivery rate is.
As shown in fig. 3, when the proportion of selfish nodes in the network increases, the average end-to-end delay changes under three conditions, namely, the AODV protocol without adding the excitation mechanism, the AODV protocol with the presence factor of 0.6, and the AODV protocol with the presence factor. It can be seen from the figure that as the ratio of selfish nodes increases, the overall trend of average end-to-end delay increases, and the delay value added to the incentive mechanism AODV protocol is higher than that of the original AODV protocol. This is because the excitation mechanism has a process of repeating the game between the nodes, which takes a relatively long time and increases the value of the delay. It can also be observed that the delay value of the impression factor 0.8 is higher than the delay value of the impression factor 0.6 as a whole, because the impression factor represents the tolerance of the node, the higher the impression factor is, the higher the tolerance of the node is, so that the node pays more attention to future benefits, and the delay value is higher.
Claims (1)
1. A node excitation method of a car networking based on a game model is characterized by comprising the following steps:
step 1: establishing a single-stage game model of adjacent nodes of the Internet of vehicles network, and then extending to establish an infinite repeated game model;
step 2: on the basis of the repeated game model in the step 1 and the pinpoint relative strategy, a fault tolerance strategy is adopted among the nodes;
and step 3: on the basis of the credit values of the nodes, the nodes with high contribution degree are rewarded, the nodes with low contribution degree are punished, the problem of inconsistent contribution degree among the nodes is solved, and the enthusiasm of all the nodes is improved;
in the step 1, the whole vehicle networking network G (V, E) is composed of N rational nodes, G represents any connected graph, V represents a set of nodes, and E represents a link set; the system time is formed by discrete time slots t, and in each time slot, nodes can make rational selection; the infinite repeated game model is characterized in that each node is interacted for multiple times and mutually cooperates to forward a data packet, so that the overall network performance of the Internet of vehicles is improved;
the fault tolerance strategy in the step 2 specifically comprises the following steps: for a pair of adjacent nodes A and B in the Internet of vehicles, the strategy adopted for any node is as follows:
an initial stage: the attitude of the node A and the attitude of the node B are friendly, namely, the node A and the node B adopt a cooperative attitude to actively forward a data packet of the other party;
a tolerance stage: when the node A adopts an uncooperative attitude at the kth time slot, the tolerance time is set as T within a certain time, and the node B adopts the tolerant attitude, namely the data packet is still forwarded to the node A;
a judging stage: if the attitude of the node A is still unfriendly after the tolerance stage, namely the node A is selected to be not doing the action, the node B broadcasts the node A out to inform other nodes, and then the node A enters a punishment period and is set as P;
a punishment stage: in the punishment period, other nodes refuse to forward the data packet sent by the node A, and the node A must help the neighbor nodes to forward the data packet for free in the punishment period;
a forgetting stage: when the penalty stage is finished, the node A can reenter the network, and the history of the non-operation of the node A can be forgotten;
the values of the tolerance time T and the penalty period P are determined according to the following rules:
firstly, determining tolerance time T, wherein within the tolerance time T, the node B always selects a cooperation strategy, but the neighbor node A adopts an uncooperative attitude, and according to the knowledge of a game theory, in a tolerance stage, the income f of the node B B The following formula:
wherein, delta is a presentation factor, the value range of delta is (0, 1), which is regarded as the endurance degree of node cooperation, the bigger the value of delta is, the endurance of the node is, beta represents the consumption generated by the node forwarding data;
during this period, the node B forwards data for the node a without any compensation, but if T is established, the node B needs to make the expected profit U B Greater than or equal to zero, i.e.:
that is, the value of the tolerance time T is a value for establishing the above equation, and α is a benefit obtained by forwarding the data sent by the node by the neighboring node;
and finally, determining the value of a penalty period P, applying a penalty to the node A because the node A still does not cooperate after the tolerant time from the k-th time slot, if the benefit obtained in the tolerant period is less than the consumption of the penalty period, and because the nodes are all rational, starting from the next period, in order to maximize the benefit of the nodes, the attitude of the nodes can be changed from non-cooperation to cooperation, and the following formula is shown:
satisfying the above formula, the corresponding is the value of the penalty period P;
the reward and penalty mechanism based on the reputation value in step 3 specifically includes:
in the initialization stage, each node comprises a weight value table, the initial value of the weight value is initialized to 0, and when the node participates in forwarding once, the weight value is added by 1;
in each time slot t, the table is updated once, and all nodes are sorted according to the weight;
when the node B and the node C both require the node A to forward respective data packets, the node A preferentially forwards the data packets of the nodes with large weights according to the weights in the weight table, and then forwards the data packets of the nodes with small weights;
the method for calculating the expected total profit of the node comprises the following steps:
wherein,
representing the income of the single-stage game of the node i in the time slot t, wherein delta is a discount factor, the value range of the factor is (0, 1), the factor is regarded as the endurance degree of node cooperation, and the greater the value of delta, the more the endurance the node shows and the more the long-term benefit is emphasized; otherwise, the more the node pays attention to the pre-eye benefit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910360153.XA CN110113725B (en) | 2019-04-30 | 2019-04-30 | Internet of vehicles node excitation method based on game model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910360153.XA CN110113725B (en) | 2019-04-30 | 2019-04-30 | Internet of vehicles node excitation method based on game model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110113725A CN110113725A (en) | 2019-08-09 |
| CN110113725B true CN110113725B (en) | 2022-11-11 |
Family
ID=67487731
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910360153.XA Active CN110113725B (en) | 2019-04-30 | 2019-04-30 | Internet of vehicles node excitation method based on game model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110113725B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115230433B (en) * | 2022-08-23 | 2024-05-14 | 重庆大学 | Electric vehicle passenger cabin and power battery cooperative heating control method and device |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109040075A (en) * | 2018-08-08 | 2018-12-18 | 中国联合网络通信集团有限公司 | Management method, server and the system of wireless mobile sensor network interior joint |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9934048B2 (en) * | 2016-03-29 | 2018-04-03 | Intel Corporation | Systems, methods and devices for dynamic power management of devices using game theory |
-
2019
- 2019-04-30 CN CN201910360153.XA patent/CN110113725B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109040075A (en) * | 2018-08-08 | 2018-12-18 | 中国联合网络通信集团有限公司 | Management method, server and the system of wireless mobile sensor network interior joint |
Non-Patent Citations (4)
| Title |
|---|
| Meijuan Kou ; Yanlin Zhao ; Hanyu Cai ; Xiumei Fan.Study of a Routing Algorithm of Internet of Vehicles Based on Selfishness.《2018 IEEE International Conference on Smart Internet of Things (SmartIoT)》.2018, * |
| 基于博弈论的移动Ad_Hoc网络节点合作策略研究;张健;《信息科技辑》;20140331;论文第34-50页 * |
| 基于重复博弈的机会网络路由算法研究;谭永银;《信息科技辑》;20170331;论文第59-60页 * |
| 机会网络中的节点激励策略研究;于季弘;《信息科技辑》;20130331;论文第45-48页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110113725A (en) | 2019-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Han et al. | Coalition games with cooperative transmission: a cure for the curse of boundary nodes in selfish packet-forwarding wireless networks | |
| Milan et al. | Achieving cooperation in multihop wireless networks of selfish nodes | |
| Saad et al. | Coalition formation games for distributed cooperation among roadside units in vehicular networks | |
| CN103916912B (en) | Wireless Heterogeneous Networks are based on the node cooperation motivational techniques of non-cooperative game | |
| Cai et al. | An incentive-compatible routing protocol for two-hop delay-tolerant networks | |
| CN105847149B (en) | Wireless Delay Tolerant Network method for routing based on multitiered network | |
| CN102984200B (en) | Method applicable for scene with multiple sparse and dense vehicular ad hoc networks (VANETs) | |
| Deng et al. | Cooperative downloading in vanets-lte heterogeneous network based on named data | |
| Cai et al. | A survey on routing algorithms for opportunistic mobile social networks | |
| CN107181793A (en) | The transportation service information forwarding mechanism discussed based on dynamic game | |
| Mao et al. | A fair credit-based incentive mechanism for routing in DTN-based sensor network with nodes’ selfishness | |
| CN110113725B (en) | Internet of vehicles node excitation method based on game model | |
| CN111641923B (en) | A dual-mode interest tag forwarding system and method for social vehicle networking based on fog computing | |
| Cao et al. | Interaction trust-driven data distribution for vehicle social networks: A matching theory approach | |
| Han et al. | A cartel maintenance framework to enforce cooperation in wireless networks with selfish users | |
| CN103824195A (en) | Excitation method based on three-round bargaining in opportunity network | |
| Wang et al. | Performance evaluation of passive clustering based techniques for inter-vehicle communications | |
| Dhurandher et al. | Energy aware routing for efficient green communication in opportunistic networks | |
| CN111866810B (en) | A kind of vehicle networking spectrum allocation method and device | |
| Kaushik et al. | Enhanced node cooperation technique for outwitting selfish nodes in an ad hoc network | |
| Li et al. | An incentive aware routing for selfish opportunistic networks: A game theoretic approach | |
| CN106453082A (en) | Fair delay-tolerant network node cooperation stimulation method based on credit strategy | |
| Ma et al. | A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks | |
| Guerber et al. | Transmission opportunities: a new approach to improve quality in V2V networks | |
| Wang et al. | Popular content distribution in vehicular networks using coalition formation games |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221009 Address after: No. 30728 Xi'an Square, Yanta District, Xi'an City, Shaanxi Province, 710000 Applicant after: Xi'an Zhixing Changjia Network Technology Co.,Ltd. Address before: 710048 No. 5 Jinhua South Road, Shaanxi, Xi'an Applicant before: XI'AN University OF TECHNOLOGY |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |