A Low-Latency Routing Optimization Method and System for Vehicular Ad Hoc Networks

Abstract

The present invention discloses a low-latency routing optimization method and system for vehicle ad-hoc networks. The method is as follows: constructing a latency calculation model for vehicle ad-hoc networks to describe the latency optimization problem and minimize the average latency of all vehicle users; according to the constructed model, designing a clustering routing algorithm based on the synchronous optimization of the number of user jumps and cluster size, by constructing a cluster mechanism, introducing two parameters of jump penalty weight and channel fading coefficient, establishing a cluster-derived mechanism and a cluster preprocessing mechanism for evaluating and calculating the routing priority, and realizing the low-latency routing optimization of vehicle ad-hoc networks. The system is used to implement the low-latency routing optimization method for vehicle ad-hoc networks, including a vehicle ad-hoc network latency calculation unit and a clustering routing algorithm unit. The clustering routing algorithm unit includes three modules: routing priority evaluation and calculation, cluster-derived mechanism, and cluster preprocessing mechanism. The present invention can efficiently and reliably achieve latency routing optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of routing optimization for vehicular ad hoc networks, and particularly to a low-latency routing optimization method and system for vehicular ad hoc networks. Background Art

[0002] With the continuous development of vehicle networking technology, vehicular ad hoc networks (VANETs) are widely used in intelligent transportation systems to improve driving safety and traffic efficiency. The construction of VANETs plays an important role in reducing traffic accidents, alleviating traffic congestion, realizing driving assistance, and providing various networking services for in-vehicle users. However, VANETs have high requirements for real-time performance and reliability. Therefore, low-latency routing optimization is one of the key technologies for achieving efficient communication. Low-latency routing optimization aims to reduce the latency of data packet transmission by selecting the best path.

[0003] Currently, common routing protocols in VANETs include topology-based routing protocols, mobility prediction-based routing protocols, and location-based routing protocols. However, these protocols have some problems. For example, topology-based routing protocols often require high signaling overhead. Once the network scale is large, it is difficult to establish reliable routing paths, and the difficulty of congestion control will also increase. Mobility prediction-based routing protocols require vehicles to have fast real-time computing capabilities, and their universality is poor. Location-based routing protocols have opportunistic characteristics. In addition, problems such as signal interference, vehicle mobility, and network topology changes exist in VANETs, which pose challenges to latency routing optimization.

[0004] Therefore, it is necessary to develop more efficient and reliable latency routing optimization algorithms according to the characteristics of VANETs to meet the requirements of real-time performance and reliability. Summary of the Invention

[0005] The purpose of the present invention is to provide a more efficient and reliable low-latency routing optimization method and system for vehicular ad hoc networks to meet the requirements of real-time performance and reliability.

[0006] The technical solution for achieving the purpose of the present invention is: A low-latency routing optimization method for vehicular ad hoc networks, comprising the following steps:

[0007] First, construct a vehicular ad hoc network latency calculation model to describe the latency optimization problem, that is, to minimize the average latency of all vehicle users;

[0008] Secondly, based on the vehicle ad-hoc network delay calculation model, a clustering routing algorithm based on the synchronous optimization of the number of user jumps and cluster size is designed. This algorithm constructs a cluster mechanism, introduces two parameters, namely the jump penalty weight and the channel fading coefficient, and establishes a cluster derivation mechanism and a cluster preprocessing mechanism for evaluating and calculating the routing priority, so as to realize the low-delay routing optimization of the vehicle ad-hoc network.

[0009] A low-delay routing optimization system for a vehicle ad-hoc network, which is used to implement the low-delay routing optimization method for the vehicle ad-hoc network. The system includes a vehicle ad-hoc network delay calculation unit and a clustering routing algorithm unit. The clustering routing algorithm unit includes a routing priority evaluation and calculation module, a cluster derivation mechanism module, and a cluster preprocessing mechanism module, where:

[0010] The vehicle ad-hoc network delay calculation unit is used to describe the problem of delay optimization, that is, to minimize the average delay of all vehicle users.

[0011] The clustering routing algorithm unit, based on the vehicle ad-hoc network delay calculation unit, designs a clustering routing algorithm based on the synchronous optimization of the number of user jumps and cluster size. This algorithm constructs a cluster mechanism, introduces two parameters, namely the jump penalty weight and the channel fading coefficient, and establishes a cluster derivation mechanism and a cluster preprocessing mechanism for evaluating and calculating the routing priority, so as to realize the low-delay routing optimization of the vehicle ad-hoc network.

[0012] A mobile terminal includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the low-delay routing optimization method for the vehicle ad-hoc network.

[0013] A computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the steps in the low-delay routing optimization method for the vehicle ad-hoc network.

[0014] Compared with the prior art, the significant advantages of the present invention are:

[0015] (1) By introducing two parameters, namely the jump penalty weight and the channel fading coefficient, a cluster derivation mechanism for evaluating and calculating the routing priority is designed, effectively reducing the queue delay and transmission delay of vehicle users.

[0016] (2) By designing a cluster preprocessing mechanism, the average scale of the user cluster and the efficiency of cluster division are effectively improved, further reducing the user delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall block diagram of the clustering routing algorithm in the present invention.

[0018] Figure 2 It is a flow chart of a cluster derivation system optimized synchronously based on the number of user jumps and cluster size.

[0019] Figure 3 It is the overall flow chart of a clustering routing algorithm optimized synchronously based on the number of user jumps and cluster size. Specific implementation manners

[0020] It is easy to understand that according to the technical solution of the present invention, without changing the essence of the present invention, those of ordinary skill in the art can imagine various implementation manners of the present invention. Therefore, the following specific implementation manners and drawings are only illustrative descriptions of the technical solution of the present invention, and should not be regarded as all of the present invention or as a limitation or restriction on the technical solution of the present invention.

[0021] Now, various exemplary embodiments of the present invention will be described in detail with reference to the drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0022] The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present invention and its application or use.

[0023] The technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification.

[0024] In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0025] The present invention provides a low-latency routing optimization method and system for a vehicular ad hoc network, specifically including a vehicular ad hoc network latency calculation model and a clustering routing algorithm optimized synchronously based on the number of user jumps and cluster size. The vehicular ad hoc network latency calculation model describes the latency optimization problem, that is, minimizing the average latency of all vehicle users. The clustering routing algorithm optimized synchronously based on the number of user jumps and cluster size is based on this vehicular ad hoc network latency calculation model to establish a cluster system and improve the network latency performance.

[0026] A low-latency routing optimization method for a vehicular ad hoc network according to the present invention includes the following steps:

[0027] First, construct a vehicular ad hoc network latency calculation model to describe the latency optimization problem, that is, minimizing the average latency of all vehicle users;

[0028] Secondly, based on the vehicle ad-hoc network delay calculation model, a clustering routing algorithm based on synchronous optimization of user jump count and cluster size is designed. This algorithm constructs a cluster mechanism, introduces two parameters, namely the jump penalty weight and the channel fading coefficient, and establishes a cluster derivation mechanism and a cluster preprocessing mechanism for evaluating and calculating the routing priority, so as to achieve low-delay routing optimization of the vehicle ad-hoc network.

[0029] As a specific example, the construction of the vehicle ad-hoc network delay calculation model is as follows:

[0030] Define the user reception delay in the vehicle ad-hoc network , including data transmission delay and user queuing delay; define the total number of vehicles in the traffic scenario, and the total number of roadside units (RSUs) is ; define the cluster set distribution of vehicle user node n as the cluster head (CH) node; define the cluster set distribution of roadside unit node m as the cluster head (CH) node; the cluster head (CH) node is the source node for each-hop transmission.

[0031] After giving the above definitions, the low-delay optimization problem is transformed into the problem of minimizing the average delay of all vehicle users:

[0032] (1)

[0033] (2)

[0034] (3)

[0035] (4)

[0036] Among them, equations (2) and (3) represent the maximum capacity limit of the cluster is the maximum number of sub-members that the cluster set can accommodate; represents the reception delay of vehicle user node t, is composed of , , in three parts; represents the reception delay of the cluster head node of point t; represents When the node is the cluster head of the cluster, the queue delay caused by in-cluster transmission to deliver data to node t; represents the number of sub-members of the cluster with the node as the cluster head; The transmission delay between the node and node t.

[0037] As a specific example, the clustering routing algorithm based on synchronous optimization of user jump count and cluster size includes a routing priority evaluation and calculation module, a cluster derivation system module, and a cluster preprocessing system module, where:

[0038] The routing priority evaluation and calculation module, aiming at the low-delay optimization goal, introduces two parameters, the hop penalty weight and the channel fading coefficient, to score the priority of potential wireless links, serving as the basis for finding the optimal solution of relay nodes;

[0039] The cluster derivation system module, based on the greedy algorithm, conducts the partitioning of clusters and the selection of relay nodes to construct the basic framework of the clustering routing algorithm;

[0040] The cluster preprocessing system module optimizes and improves the cluster derivation system, introduces the cluster maximum capacity limit condition, improves the forwarding efficiency of relay nodes, and further reduces the reception delay of vehicle user nodes.

[0041] As a specific example, the routing priority evaluation and calculation module is to give a criterion for selecting the optimal link in the cluster derivation system based on the greedy algorithm, and then comprehensively consider the performance factors of vehicle user delay to define the routing priority evaluation and calculation function , and the calculation formula is:

[0042] (5)

[0043] (6)

[0044] Where, is the total number of vehicle users in the scenario, represents the communication signal-to-noise ratio between vehicle user nodes, represents the wireless transmission power of node m; represents the channel fading coefficient of the wireless link between nodes m and n, indicating the attenuation and distortion degree experienced during transmission; represents the noise power, represents the current hop count;

[0045] Aiming at the low-delay optimization goal, introduce the hop penalty weight , and , when the hop count of the sending node m of a certain hop is very high, through give a lower priority to the corresponding link of this hop.

[0046] As a specific example, the cluster-derived system module partitions the clusters and selects relay nodes based on the greedy algorithm to construct the basic framework of the clustering routing algorithm, as follows:

[0047] First, define the transmission queue set , which represents the in-vehicle user nodes in the scenario that have obtained the broadcast data. This set of nodes can complete the next relay forwarding process;

[0048] Secondly, define the reception queue set , which represents the in-vehicle user nodes in the scenario that have not obtained the broadcast data. This set of nodes can be used as the target of the clustering routing planning in the next step;

[0049] Based on the greedy algorithm, in each selection, find the link with the highest priority in the link that can transfer from to from these two sets of nodes;

[0050] When evaluating the routing priority, establish an evaluation result table to store the scores of all alternative links.

[0051] As a specific example, the cluster-derived system process of the greedy algorithm includes the following steps:

[0052] Step 1.1: In the set scenario, obtain the information of the vehicle user nodes and the roadside unit nodes, add all the roadside units to the initialization members of the transmission queue set , and take all the vehicle user nodes as the initialization members of the reception queue set . Calculate the link priority evaluation scores of all roadside units and all vehicle user nodes according to Equation (5), and fill the evaluation result table;

[0053] Step 1.2: In each greedy selection, select the link with the highest current existing priority evaluation, and use the sending node S of this link as the cluster head CH node of the receiving node R;

[0054] Step 1.3: Define to represent the maximum scale threshold that the cluster can accommodate, and judge whether the number scale of the sub-members exceeds the threshold:

[0055] (7)

[0056] where refers to the set of cluster members with the sending node s as the CH;

[0057] Step 1.4. Determine whether the sub - members of S reach the maximum cluster scale. If so, it means that the relay capacity of node S reaches the maximum. Then remove node S from the sending queue set and update the evaluation result table.

[0058] Step 1.5. At this time, the receiving node R can obtain the broadcast data, become a relay forwarding node for the next - hop transmission, and transfer the receiving node R from the receiving queue set to the sending queue set.

[0059] Step 1.6. Repeat steps 1.2 to 1.5 until it is determined that the receiving queue set is an empty set, indicating that all nodes are configured and the clusters are also allocated.

[0060] As a specific example, in the cluster pre - processing system module, the steps of the cluster pre - processing system are as follows:

[0061] Step 2.1. Define a set of standby cluster sub - members , and initialize it to be the same as the receiving queue set .

[0062] Step 2.2. When the greedy algorithm makes each selection, after finding the link with the highest current priority, regard the receiving node R as a standby cluster sub - member of the sending node S, rather than a real cluster sub - member. At this time, the receiving node R is still stored in the receiving queue set, and remove the receiving node R from the set of standby cluster sub - members .

[0063] Step 2.3. According to Equation (7), judge whether the number of standby cluster sub - members of the sending node S reaches the maximum scale threshold that the cluster can accommodate . If the maximum scale has been reached, remove the sending node from the sending queue and update the evaluation result table.

[0064] Step 2.4. The standby cluster sub - members of the sending node S become real cluster sub - members, and the sending node S becomes the cluster - head CH node.

[0065] Step 2.5. Incorporate the real cluster sub - members of the sending node S into the sending queue set and update it in a timely manner.

[0066] Step 2.6. Judge whether the set of standby cluster sub - members is an empty set: If there are still sub - members in it, return to step 2.2; if it has become an empty set, go to the cluster derivation system process steps of the greedy algorithm.

[0067] The present invention also provides a low-latency routing optimization system for vehicular ad-hoc networks, which is used to implement the low-latency routing optimization method for vehicular ad-hoc networks. The system includes a vehicular ad-hoc network latency calculation unit and a clustering routing algorithm unit. The clustering routing algorithm unit includes a routing priority evaluation calculation module, a cluster group derivation mechanism module, and a cluster group preprocessing mechanism module, where:

[0068] The vehicular ad-hoc network latency calculation unit is used to describe the latency optimization problem, that is, to minimize the average latency of all vehicle users;

[0069] Based on the vehicular ad-hoc network latency calculation unit, the clustering routing algorithm unit designs a clustering routing algorithm that synchronously optimizes based on the number of user jumps and the cluster group size. By constructing a cluster group mechanism, introducing two parameters of a jump penalty weight and a channel fading coefficient, and establishing a cluster group derivation mechanism and a cluster group preprocessing mechanism for routing priority evaluation calculation, the low-latency routing optimization of the vehicular ad-hoc network is realized.

[0070] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the low-latency routing optimization method for vehicular ad-hoc networks is implemented.

[0071] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the low-latency routing optimization method for vehicular ad-hoc networks are implemented.

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings.

[0073] Embodiment 1

[0074] In this embodiment, a vehicular ad-hoc network latency calculation model is first constructed, and the specific steps are as follows:

[0075] Step 1: Define the user reception latency in the vehicular ad-hoc network , which mainly includes data transmission latency and user queuing latency. The specific calculation formula is:

[0076] (8)

[0077] Step 2: Define the total number of vehicles in the traffic scenario, and the total number of roadside units (RSUs) is . Define the cluster set distribution of the vehicle user node n as the CH node (source node for each-hop transmission):

[0078] (9)

[0079] If it is an empty set, it means that there are no sub-members in the cluster of the vehicle user node n. If there is a sub-member t, define the variable , indicating that the cluster head node of the vehicle user node t is n.

[0080] Define the cluster set distribution of the road test unit node m as the CH node (source node for each-hop transmission):

[0081] (10)

[0082] If it is an empty set, it means that there are no sub-members in the cluster of the road test unit node m. If there is a sub-member t, define the variable , indicating that the cluster head node of the vehicle user node t is m.

[0083] Step 3: After giving the above definitions, the low-latency optimization problem can be transformed into the problem of minimizing the average latency of all vehicle users:

[0084] (11)

[0085] (12)

[0086] (13)

[0087] (14)

[0088] Specifically, equations (12) and (13) represent the maximum capacity limit of the cluster group which is the maximum number of sub-members that the cluster group set can accommodate. represents the receiving latency of the vehicle user node t, which consists of , , three parts. represents the receiving latency of the cluster head node of point t. represents the node as the cluster head of the cluster group. When data is transmitted within the cluster and passed to node t, the resulting queue latency. represents the node as the number of sub-members of the cluster group with the cluster head. represents the transmission latency between the node and node t.

[0089] The focus of the present invention lies in a clustering routing algorithm that synchronously optimizes based on the number of user jumps and cluster size, such as Figure 1 shown in the overall block diagram of the clustering routing algorithm that synchronously optimizes based on the number of user jumps and cluster size in the present invention, mainly including three modules: routing priority evaluation calculation, cluster derivation mechanism, and cluster preprocessing mechanism:

[0090] For the routing priority evaluation calculation, aiming at the low-latency optimization goal, two parameters, namely the jump penalty weight and the channel fading coefficient, are introduced to score the priority of potential wireless links, serving as the basis for finding the optimal solution of relay nodes;

[0091] The cluster derivation mechanism module is based on the greedy algorithm to perform the partitioning of clusters and the selection of relay nodes, thereby constructing the basic framework of this routing algorithm;

[0092] The cluster preprocessing mechanism module can optimize and improve the cluster derivation mechanism. By introducing the maximum cluster capacity limit condition, it can improve the forwarding efficiency of relay nodes and further reduce the reception latency of vehicle user nodes.

[0093] Specifically, the routing priority evaluation calculation is to give a criterion for selecting the optimal link in the cluster derivation mechanism based on the greedy algorithm, and then comprehensively consider the performance factors of vehicle user latency to define the routing priority evaluation calculation function , and its calculation formula is:

[0094] (15)

[0095] (16)

[0096] Among them, the total number of vehicle users in the scenario is , represents the communication signal-to-noise ratio between vehicle user nodes, represents the wireless transmission power of node m, represents the channel fading coefficient of the wireless link between nodes m and n, indicating the attenuation and distortion degree experienced during the transmission process, represents the noise power, represents the jump penalty weight, represents the current number of hops. Aiming at the low-latency optimization goal, the jump penalty weight is introduced, and , so when the hop count of the sending node m of a certain hop is very high, a lower priority can be given to the corresponding link of this hop through this weight, so that the cluster derivation mechanism based on the greedy algorithm can avoid preferentially selecting such links as much as possible, reduce the number of nodes that need to relay and forward, and generally reduce the average forwarding hop count of all vehicle receiving users, achieving low-latency optimization.

[0097] Specifically, the flow chart of the cluster derivation mechanism is as Figure 2 shown. First, define the sending queue set , which represents the on-vehicle user nodes that have obtained the broadcast data in the scenario. This set of nodes can complete the next process of relay and forwarding. Further, define the receiving queue set , which represents the on-vehicle user nodes that have not obtained the broadcast data in the scenario. This set of nodes can be used as the target of the next clustering routing plan. Based on the greedy algorithm, in each selection, only the highest-priority link in the two types of sets that can be transferred from to needs to be found. Therefore, when evaluating the routing priority, an evaluation result table is established to store the scores of all available links. The flow chart of the cluster derivation mechanism of the greedy algorithm has the following steps:

[0098] Step 1: In a certain scenario, obtain the information of vehicle user nodes and roadside unit nodes, add all roadside units to the initialization members of the sending queue set , add all vehicle user nodes to the initialization members of the receiving queue set , calculate the link priority evaluation scores of all roadside units and all vehicle user nodes according to formula (15), and fill the evaluation result table;

[0099] Step 2: In each greedy selection, select the link with the highest current existing priority evaluation, and use the sending node S of this link as the CH node of the receiving node R;

[0100] Step 3: Define to represent the maximum scale that a cluster can accommodate, and make a formula judgment on whether the number scale of sub-members exceeds the threshold:

[0101] (17)

[0102] Step 4: Judge whether the sub-members of S have reached the maximum cluster scale. If they have reached, it means that the relay capacity of node S has reached the maximum. Then remove node S from the sending queue set and update the evaluation result table;

[0103] Step 5: At this time, node R can obtain the broadcast data, become a relay forwarding node, and perform the next-hop transmission. Therefore, the receiving node R is transferred from the receiving queue set to the sending queue set;

[0104] Step 6: Repeat Steps 2 - 5 until it is determined that the receiving queue set is an empty set, indicating that all nodes are configured and the cluster has been allocated.

[0105] Specifically, the cluster preprocessing system module is to solve the problem of low efficiency in cluster derivation system division in a traffic scenario where the density distribution of vehicle user nodes is uniform. The steps of the cluster preprocessing system are as follows:

[0106] Step 1: Define the set of standby cluster sub - members , and initialize it to be the same as the receiving queue set ;

[0107] Step 2: When the greedy algorithm makes each selection, after finding the link with the highest priority currently, regard the receiving node R as a standby cluster sub - member of the sending node S, rather than a real cluster sub - member. At this time, R is still stored in the receiving queue set, and R is removed from the set of standby cluster sub - members ;

[0108] Step 3: According to formula (17), determine whether the number of standby cluster sub - members of node S has reached , which represents the maximum scale that the cluster can accommodate. If it has reached the maximum scale, remove the sending node from the sending queue and update the evaluation result table;

[0109] Step 4: The standby cluster sub - members of node S become real cluster sub - members, and node S becomes the CH;

[0110] Step 5: Incorporate the real cluster sub - members of node S into the sending queue set and update it in a timely manner;

[0111] Step 6: Determine whether the set of standby cluster sub - members is an empty set. If there are still sub - members in it, return to Step 2; if it has become an empty set, go to the cluster derivation system process step of the greedy algorithm.

[0112] As Figure 3 shown in the overall flowchart of the present invention, the specific embodiments are introduced as follows:

[0113] Step 1: Define the set of standby cluster sub - members , and initialize it to be the same as the receiving queue set ;

[0114] Step 2: When the greedy algorithm makes each selection, after finding the link with the highest current priority, regard the receiving node R as a standby cluster subgroup member of the sending node S, rather than a real cluster subgroup member. At this time, R is still stored in the receiving queue set, and R is removed from the standby cluster subgroup member set; from the set;

[0115] Step 3: According to formula (14), judge whether the number of standby cluster subgroup members of node S has reached which represents the maximum scale that the cluster can accommodate. If the maximum scale has been reached, remove the sending node from the sending queue and update the evaluation result table;

[0116] Step 4: The standby cluster subgroup members of node S become real cluster subgroup members, and node S becomes the CH;

[0117] Step 5: Incorporate the real cluster subgroup members of node S into the sending queue set and update in a timely manner;

[0118] Step 6: Judge whether the standby cluster subgroup member set is an empty set. If there are still subgroup members inside, return to Step 2; if it has become an empty set, go to the cluster derivation mechanism process step of the greedy algorithm.

[0119] Step 7: Judge whether the receiving queue set is an empty set. If it is an empty set, it means that all nodes have been allocated; if there are still subgroup members inside, find the link with the highest priority and use the sending node S of this link as the CH node of the receiving node R;

[0120] Step 8: Define which represents the maximum scale that the cluster can accommodate, and make a formula judgment on whether the number scale of subgroup members exceeds the threshold:

[0121] (18)

[0122] Step 9: Judge whether the subgroup members of S have reached the maximum cluster scale. If it has reached, it means that the relay capacity of node S has reached the maximum, then remove the S node from the sending queue set and update the evaluation result table;

[0123] Step 10: At this time, node R can obtain the broadcast data, become a relay forwarding node, and perform the next-hop transmission. Therefore, transfer the receiving node R from the receiving queue set to the sending queue set;

[0124] Step 11: Repeat Step 7 - Step 9 until it is judged that the receiving queue set is an empty set, which means that all nodes are configured and the clusters are also allocated.

[0125] Embodiment 2

[0126] Combined with specific parameters, the low-latency routing optimization method for vehicle ad-hoc networks in this embodiment is as follows:

[0127] S0: Parameter initialization. The number of vehicle nodes is 100 or 200; the vehicle speed is set to 15 km / h or 60 km / h, and the hop penalty weight = 6, and the corresponding channel fading coefficient is 0.3. The maximum cluster capacity limit is = 10. The initialization of the standby cluster subgroup member set is the same as the receive queue set;

[0128] S1:; Use the routing priority evaluation function to find the link with the highest current priority;

[0129] S2: Use the cluster preprocessing mechanism to regard the receive node R of this link as a standby cluster subgroup member of the send node S; According to the formula judge whether the number of standby cluster subgroup members of node S has reached the maximum scale of 10. If it has reached, remove the send node from the send queue and update the evaluation result table; At this time, the standby cluster subgroup members of node S become real cluster subgroup members; Incorporate the real cluster subgroup members into the send queue set;

[0130] S3: Judge whether the standby cluster subgroup member set is an empty set. If there are still subgroup members, return to step S2; If it has become an empty set, go to the cluster derivation mechanism process of the greedy algorithm.

[0131] S4: Based on the cluster derivation mechanism, judge whether the receive queue set is an empty set. If it is an empty set, it means that all nodes have been allocated; If there are still subgroup members, find the link with the highest priority and use the send node S of this link as the CH node of the receive node R;

[0132] S5: Judge whether the subgroup members of S have reached the maximum cluster scale. If they have reached, the relay capacity of node S reaches the maximum. Remove the S node from the send queue set and transfer the receive node R from the receive queue set to the send queue set;

[0133] S6: Repeat steps S4 - S5 until it is judged that the receive queue set is an empty set, indicating that all nodes are configured and the clusters are also allocated.

[0134] When the size of the vehicle node set is 100 and the average vehicle speed is 15 km / h, the end-to-end delay and packet arrival rate between vehicle nodes obtained by using the present invention are 5% and 96% respectively; when the average vehicle speed becomes 60 km / h, the end-to-end delay and packet arrival rate obtained are 5% and 95% respectively; when the size of the vehicle node set is 200 and the average vehicle speed is 15 km / h, the end-to-end delay and packet arrival rate obtained by using the present invention are 7% and 94% respectively.

[0135] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

[0136] It should be understood that in order to streamline the present invention and help those skilled in the art understand various aspects of the present invention, in the above description of the exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as meaning that the features included in the exemplary embodiments are all essential technical features of the claims of this patent.

Claims

1. A low-latency routing optimization method for vehicular ad hoc networks, characterized in that It includes the following steps: First, construct a vehicle ad-hoc network delay calculation model to describe the delay optimization problem, that is, to minimize the average delay of all vehicle users; Secondly, based on the vehicle ad-hoc network delay calculation model, design a clustering routing algorithm that synchronously optimizes based on the number of user jumps and cluster size. This algorithm constructs a cluster system, introduces two parameters of jump penalty weight and channel fading coefficient, and establishes a cluster derivation system and a cluster preprocessing system for evaluating and calculating routing priorities to achieve low-delay routing optimization of the vehicle ad-hoc network; The construction of the vehicle ad-hoc network delay calculation model is specifically as follows: User reception delay defined in vehicular ad hoc networks , including data transmission delay and user queuing delay; Define the total number of vehicles in the traffic scenario , and the total number of roadside units RSU is ; Define the cluster set distribution of vehicle user node n as the cluster head CH node ; Define the cluster set distribution of roadside unit node m as the cluster head CH node ; The cluster head CH node is the source node for each-hop transmission; After giving the above definitions, transform the low-delay optimization problem into the problem of minimizing the average delay of all vehicle users: （1） （2） （3） （4） Among them, formulas (2) and (3) represent the maximum capacity limit of the cluster group which is the maximum number of sub - members that the cluster group set can accommodate; represents the receiving delay of vehicle user node t, which consists of , , three parts; represents the receiving delay of the cluster - head node of point t; represents when the cluster - group with the node as the cluster - head has in - cluster transmission to deliver data to node t, the resulting queue delay; represents the number of sub - members of the cluster - group with the node as the cluster - head; represents the transmission delay between the node and node t; The routing priority evaluation and calculation module comprehensively considers the performance factor of vehicle user delay and defines a routing priority evaluation and calculation function , and the calculation formula is as follows: （5） （6） wherein, is the total number of vehicle users in the scenario, represents the communication signal-to-noise ratio between vehicle user nodes, represents the wireless transmission power of node m; represents the channel fading coefficient of the wireless link between nodes m and n, indicating the degree of attenuation and distortion experienced during transmission; represents the noise power, represents the current hop count; represents the hop penalty weight, and ; The process of the cluster derivation system of the greedy algorithm includes the following steps: Step 1.

1. In the set scenario, obtain the information of vehicle user nodes and road test unit nodes, add all road test units to the sending queue set as the initial members, and use all vehicle user nodes as the receiving queue set as the initial members, calculate the link priority evaluation scores of all road test units and all vehicle user nodes according to Equation (5), and fill the evaluation result table; Step 1.2: Each time a greedy selection is made, select the link with the highest current existing priority evaluation, and use the sending node S of this link as the cluster head CH node of the receiving node R; Step 1.

3. Define which represents the maximum scale threshold that the cluster can accommodate, and determines whether the number scale of the sub - members exceeds the threshold: （7） Among them refers to the set of cluster members with the sending node s as the CH; Step 1.4: Judge whether the sub-members of S have reached the maximum cluster scale. If it has reached, it means that the relay capacity of node S has reached the maximum, then remove the S node from the sending queue set and update the evaluation result table; Step 1.5: At this time, the receiving node R can obtain the broadcast data, become a relay forwarding node, and perform the next-hop transmission, and transfer the receiving node R from the receiving queue set to the sending queue set; Step 1.6: Repeat steps 1.2 to 1.5 until it is judged that the receiving queue set is an empty set, indicating that all nodes are configured and the clusters are also allocated; In the cluster preprocessing system module, the steps of the cluster preprocessing system are as follows: Step 2.1: Define the set of standby cluster group sub - members , initialize to be the same as the receive queue set ; Step 2.

2. When the greedy algorithm makes each selection, after finding the link with the highest current priority, the receiving node R is regarded as a standby cluster sub-member of the sending node S, rather than a real cluster sub-member. At this time, the receiving node R is still stored in the receiving queue set, and the receiving node R is removed from the standby cluster sub-member set ; Step 2.

3. Determine whether the number of standby cluster sub - members of the sending node S has reached the maximum scale threshold that the cluster can accommodate according to Equation (7). If the maximum scale has been reached, remove the sending node from the sending queue and update the evaluation result table; Step 2.4: The standby cluster subgroup members of the sending node S become real cluster subgroup members, and the sending node S becomes the cluster head CH node; Step 2.5: Incorporate the real cluster subgroup members of the sending node S into the sending queue set and update it in time; Step 2.6, determine whether the set of standby cluster group sub - members is an empty set: If there are still sub - members in it, return to Step 2.2; if it has become an empty set, go to the cluster group derivation process steps of the greedy algorithm.

2. The low-latency routing optimization method for vehicle ad-hoc networks according to claim 1, characterized in that The clustering routing algorithm that synchronously optimizes based on the number of user jumps and cluster size includes three processing processes: routing priority evaluation calculation, cluster derivation, and cluster preprocessing, where: The routing priority evaluation calculation is specifically as follows: For the low-delay optimization goal, introduce two parameters of jump penalty weight and channel fading coefficient to score the priority of potential wireless links as the basis for finding the optimal solution of relay nodes; The cluster derivation is specifically as follows: Based on the greedy algorithm, perform cluster partitioning and selection of relay nodes to construct the basic framework of the clustering routing algorithm; The cluster preprocessing is specifically as follows: Optimize and improve the cluster derivation system, introduce the maximum cluster capacity limit condition, improve the forwarding efficiency of relay nodes, and further reduce the receiving delay of vehicle user nodes.

3. The low-latency routing optimization method for vehicular ad hoc networks according to claim 2, characterized in that The cluster derivation is based on the greedy algorithm, performs cluster partitioning and selection of relay nodes, and constructs the basic framework of the clustering routing algorithm, specifically as follows: First, define the set of transmission queues , representing in-vehicle user nodes that have obtained broadcast data within the scenario. This set of nodes can complete the next relay forwarding process; Next, define the set of receiving queues , which represents the on-vehicle user nodes in the scenario that have not yet received broadcast data. This set of nodes can be used as the target for the next-level clustering routing plan; Based on the greedy algorithm, at each selection, find a link with the highest priority from within these two types of node sets that can transfer to in the links; When performing routing priority evaluation, establish an evaluation result table to store the scores of all selectable links.

4. A low-latency routing optimization system for vehicular ad hoc networks, characterized in that This system is used to implement the low-latency routing optimization method for vehicle ad-hoc networks as described in any one of claims 1 to 3. The system includes a vehicle ad-hoc network latency calculation unit and a clustering routing algorithm unit. The clustering routing algorithm unit includes a routing priority evaluation calculation module, a cluster group derivation mechanism module, and a cluster group preprocessing mechanism module, where: The vehicle ad-hoc network latency calculation unit is used to describe the latency optimization problem, that is, to minimize the average latency of all vehicle users; Based on the vehicle ad-hoc network latency calculation unit, the clustering routing algorithm unit designs a clustering routing algorithm that synchronously optimizes based on the number of user jumps and the cluster group size. By constructing a cluster group mechanism, introducing two parameters, namely the jump penalty weight and the channel fading coefficient, and establishing a cluster group derivation mechanism and a cluster group preprocessing mechanism for routing priority evaluation calculation, the low-latency routing optimization of the vehicle ad-hoc network is realized.

5. A mobile terminal, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the low-latency routing optimization method for vehicle ad-hoc networks as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the low-latency routing optimization method for vehicle ad-hoc networks as described in any one of claims 1 to 3.

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