CN113301534A - Routing method applied to multi-intelligent-vehicle communication - Google Patents
Routing method applied to multi-intelligent-vehicle communication Download PDFInfo
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
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Abstract
The invention provides a routing method applied to multi-intelligent vehicle communication. The method improves the traditional routing protocol by utilizing an improved clustering method, aims to ensure the stability of intelligent vehicle communication, and establishes an efficient routing protocol in a dynamic environment so as to realize stable and efficient clustering, simplify routing and ensure service quality. The proposed protocol improves the overall network throughput and packet delivery rate, and compared with the traditional routing protocol, the routing load and average delay are smaller.
Description
Technical Field
The invention belongs to the technical field of communication, and relates to a routing method applied to multi-intelligent-vehicle communication.
Background
With the continuous development of unmanned driving, unmanned communication becomes more and more important. The traditional vehicle-mounted self-organizing network cannot meet the requirement of unmanned driving and has to be improved. The demand for the proposed new vehicular ad hoc network is that vehicles participating in becoming mobile nodes can be interconnected and a wide range of networks can be established for them. Furthermore, other vehicles can join when they leave the signal range or will leave the interconnected network, thereby requiring the creation of a moving network. It is observed that today's on-board ad hoc networks can only cover a small mobile network, which is practically limited by mobility and the number of connected vehicles. In order to solve these problems, the conventional routing protocols need to be improved, and there are many routing protocols currently under study for self-moving ad hoc networks, such as destination-ordered distance vectors, dynamic source routing and on-demand routing. Protocols such as greedy peripheral coordinated routing and greedy peripheral stateless routing are some geographical protocols proposed by researchers when vehicle attributes are considered, however, most routing algorithms are only applicable to a single radio access technology and a limited scale, and cannot be applied to reducing communication problems between vehicles. According to research, it is found that some important parameters affecting the performance of routing protocols are network size and vehicle speed, etc.
Therefore, designing a suitable routing protocol for large-scale vehicles is a challenging problem. Among the most significant difficulties is the lack of network scalability, resulting in a significant compromise in network sustainability. Furthermore, delays can pose a hazard in monitoring and security applications. In this regard, optimizing a vehicle network to achieve an equal distribution of network load and scalability may be created by intelligent clustering algorithms that help solve such problems.
Disclosure of Invention
In view of this, the present invention provides a routing method applied to multi-intelligent vehicle communication, and first proposes a dynamic sensing transmission range algorithm (DA-TRLD) for forming a mesh topology between dynamic vehicles, according to the characteristics of an actual communication scenario. Then, each vehicle is defined as a node. And clustering the target nodes by analyzing the corresponding edges of the target function and utilizing a clustering method (CA) to form a cluster. The direct communication between the node and the neighbor is limited by adopting a clustering technology, a gateway and a control channel are used for each cluster at the same time to reduce transmission energy and link failure, and the feasibility of the method is verified through theoretical analysis. Secondly, the method provided by the invention can simplify the routing, ensure the service quality, improve the overall network throughput and delivery rate by the provided protocol, and have smaller routing load and average time delay compared with the traditional routing protocol. And finally, combining the DA-TRLD and the CA and integrating the DA-TRLD and the CA into an AODV protocol to obtain a complete routing method AODV-CD based on an improved clustering method.
In order to achieve the purpose, the invention provides the following technical scheme:
a routing method applied to multi-intelligent vehicle communication comprises the following steps
Step 1) cluster formation;
step 2), maintaining a cluster;
and step 3) clustering routing.
The step 1 specifically comprises the following steps:
step 11) obtaining the minimized network overhead for the cluster limitation;
step 12) defining the vehicle as a node and distributing an identifier;
step 13) clustering according to the node state to form a cluster;
the step 2 specifically comprises the following steps:
step 21) sending messages to all nodes in the same direction to obtain CH and CM;
step 22) detecting CM, wherein the CM has more than one channel and is allocated to GW state;
the step 3 specifically comprises the following steps:
step 31) when a channel is selected, the channel packets will be sent to all the neighbour lists of the channel and they will become the CMs of the channel, which updates the routing table each time a HELLO message is received;
the invention has the beneficial effects that: the invention provides a routing method based on multi-intelligent vehicle communication, which firstly provides a dynamic perception transmission range algorithm (DA-TRLD) for forming a mesh topology among dynamic vehicles aiming at the characteristics of an actual communication scene. Then, each vehicle is defined as a node. And clustering the target nodes by analyzing the corresponding edges of the target function and utilizing a clustering method (CA) to form a cluster. The direct communication between the node and the neighbor is limited by adopting a clustering technology, a gateway and a control channel are used for each cluster at the same time to reduce transmission energy and link failure, and the feasibility of the method is verified through theoretical analysis. Secondly, the method provided by the invention can simplify the routing, ensure the service quality, improve the overall network throughput and delivery rate by the provided protocol, and have smaller routing load and average time delay compared with the traditional routing protocol. And finally, combining the DA-TRLD and the CA and integrating the DA-TRLD and the CA into an AODV protocol to obtain a complete routing method AODV-CD based on an improved clustering method.
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For the purpose of making the objects, aspects and advantages of the present invention more apparent, the invention will be described in further detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of an improved clustering algorithm
FIG. 2 is a flow chart of the Algorithm 2CH election method
FIG. 3 is a flow chart of algorithm 3GW election method
Fig. 4 is a schematic diagram of network throughput provided by the embodiment of the present invention.
Fig. 5 is a schematic diagram of a packet delivery rate according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a routing load according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of average delay provided in the embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention provides a routing method applied to multi-intelligent vehicle communication, and firstly provides a dynamic perception transmission range algorithm (DA-TRLD) for forming a mesh topology among dynamic vehicles according to the characteristics of an actual communication scene. Then, each vehicle is defined as a node. And clustering the target nodes by analyzing the corresponding edges of the target function and utilizing a clustering method (CA) to form a cluster. The direct communication between the node and the neighbor is limited by adopting a clustering technology, a gateway and a control channel are used for each cluster at the same time to reduce transmission energy and link failure, and the feasibility of the method is verified through theoretical analysis. Secondly, the method provided by the invention can simplify the routing, ensure the service quality, improve the overall network throughput and delivery rate by the provided protocol, and have smaller routing load and average time delay compared with the traditional routing protocol. The method comprises the following steps: 1) forming a cluster; 2) maintaining a cluster; 3) and clustering routing.
Step 1) cluster formation;
further, the step 1) comprises the following steps:
step 11) limiting the cluster in the AODV-CD within the dynamic radio range of the channel to obtain the minimized network overhead;
step 12) assigning the identifier to each vehicle, storing the routing table in a memory according to the state of each vehicle, having a channel table if the vehicle state is CH, and maintaining the channel table if the vehicle state is CM;
step 13) the cluster size, called degree, is determined by the neighbour list field in the HELLO packet, which consists of five parameters, such as the status, ID, Neighbour List (NL), difference (f1), distance (f2) and node speed (f3) fields, this process starts with exchanging HELLO-packets between all vehicles to announce their presence in the network, and then clustering the vehicles according to algorithm 1 to form clusters, each center allowing vehicles to join the cluster according to the degree of clustering (determined by the TRLD algorithm) in order to prevent control overhead and limit the number of centers in the cluster;
and carrying out feasibility evaluation on the algorithm cluster utilizing the dynamic sensing transmission range.
From the multi-objective nature of V2V, the objective function is normalized and evaluated without using the following equation:
ft＝f1·W1+f2·W2+f3·W3 (1)
where W1-W2-W3-0.5 show the assigned weights of the three objective functions f1, f2 and f3, respectively. For AODV-CD, the first parameter f1 is the difference of the cluster, the second parameter f2 is obtained from the summed distance applicable to all CMs and their CHs, and the third parameter f3 is a speed function, selecting the node with the lowest speed as the candidate to be CH or GW.
Where | t | is the total number of clusters formed. All vehicles present in the cluster are | CNi |, but do not include CH, and the absolute value of a given value is returned by the ABS function. The formation of clusters is represented by the lowest value of D, which is nearly equal to the user-specified ideality. Regarding the desirability requirements of the user, clustering is optimal if the value of D is zero. The calculation of the distance between CNi and CH is performed by the use of a formula.
CHi is the coordinate position of ith CH. Thus, CN j, I is the coordinate position of the jth CN that is actually a member of cluster I, and is similarly used to calculate the f2 target value. Just like f1, the lowest value of f2 is desirable. When the distance between the channel and its cluster member is short, the energy required to move the data is small, and the normalized comparison speed f3 can be obtained by using the following equation for adjusting the vehicle speed.
f3＝Sij/Smax (5)
In this calculation, Smax is a predetermined maximum speed, Sij is a comparison speed between the vehicles I and j, and is at a specific angle to the direction of travel of CHi and its neighboring directions
The cluster is feasible by dynamically sensing the transmission range algorithm through the analysis of the actual operation condition by a formula.
Step 2), maintaining a cluster;
step S21) sending HELLO messages to its neighbors according to its dynamic transmission range (obtained from algorithm 2) for all nodes in the same direction, the status of any node that does not receive a HELLO message will be CH, otherwise, CM is still responsible for controlling and monitoring node activity, CH is responsible for and performing packet transmission between nodes in the network, at this stage, when the current CH energy level is exhausted, CM selects the corresponding node with high energy level, and CM stores both new CH activity and old CH activity, the process continues for all incoming nodes of the network;
step S22) the CH-election process will be initiated by a vehicle with a CM status, to which vehicle the GW status will be assigned if there is more than one channel in the routing table of any vehicle, the GW node advertising its GHs to the channel controllers within radio range of the node, this progression being based on three important objective functions. The first parameter is the difference in clustering or difference f1, the second is the total distance of the CMs from its CHs or f2 and the last is the speed of the vehicle f3, in order to increase the lifetime of the cluster, preferably low, the GW packet consists of four fields such as source identification, list of channel identifications within the radio range of the gateway node, destination identification and message identification, by applying the clustering method to the AODV algorithm, RREQ messages will be sent to the CHs instead of being broadcast between all vehicles, so that routing information between CMs will be distributed by CH, RREP packets will be sent to vehicles if routes are available, otherwise RREQ messages will be received by CHs, in which respect the number of control messages that find a route will be greatly reduced, which results in a reduction of congestion and network overhead in the entire network, increasing the lifetime of the cluster, maintaining the cluster;
step 3), clustering routing;
further, the step 3) comprises the following steps:
step 31) if there is more than one channel in the routing table of any vehicle to which GW is to be assigned, this parameter being determined randomly in the initial phase, the total value of the objective function will be used for algorithm 3, when a channel is selected, the channel packets will be sent to all the neighbour lists of the channel and they will become CMs of the channel, therefore, every time a HELLO message is received, all CMs should update their routing table, each node only belongs to one cluster, and furthermore, the cluster size varies according to their transmission range;
the invention is further described below with reference to specific experimental results and simulations.
In order to evaluate the overall performance of the AODV-CD protocol presented herein more clearly, four performance indicators, namely, the routing load, the average end-to-end delay, the normalized routing overhead, and the packet transmission rate, are used for performance evaluation in the simulation, and the performance of the AODV protocol is compared in the simulation.
Consider the network topology for two parallel highways, with 4 lanes in each direction, in the middle of the highway. The position of the vehicle is designated by the position server, all vehicles in the same direction are considered as neighbors, the first stage of simulation belongs to scene initialization, the environment details and the moving mode of the vehicles on the expressway are defined by the SUMO, and the second stage is that the network simulator (NS-2) takes the input of the SUMO generation file.
The scenario set up comprises 40 to 500 different numbers of nodes, the scenario considered here comprises evaluating the performance index of the vehicle density in the network (which will result in an increase in the number of vehicles per cluster, an increase in the total number of clusters in the network), pure AODV algorithm with a constant transmission range value at 300 meters, AODV-CD with a dynamic transmission range, the performance index used in the result evaluation being throughput, packet delivery, routing overhead and average end-to-end delay, and the number of vehicles increasing with a network scale of 4 km and a simulation time of 500 s, the simulation being performed 20 times, the average value being used in the results to compare the performance of AODV-discs.
As can be seen from fig. 4, fig. 5, fig. 6 and fig. 7, compared with the AODV protocol, the AODV-CD has a larger packet delivery rate and network throughput, a smaller average end-to-end delay and routing load, and can transmit data more efficiently.
Claims (4)
1. The utility model provides a routing method for many intelligent car communication which characterized in that: the method provides a clustering method (CA) and a basic application dynamic sensing transmission range (DA-TRLD) combined with local traffic density; the CA and the DA-TRLD are combined and merged into the AODV protocol to obtain a complete improved AODV routing solution AODV-CD, so that stable and efficient clustering is realized, routing is simplified, service quality is guaranteed, the overall network throughput and delivery rate are improved through the proposed protocol, and compared with the traditional routing protocol, the routing load and the average time delay are smaller.
The mechanism comprises the following steps:
s1) Cluster formation
S2) Cluster maintenance
S3) cluster routing.
2. The routing method applied to multi-intelligent-vehicle communication of claim 1, wherein the step S1 comprises the following steps:
step S11) limiting the clusters in the AODV-CD within the dynamic radio range of the channel to obtain a minimized network overhead;
step S12) assigning an identifier to each vehicle, each vehicle storing a routing table in a memory according to its state, having a channel table if the vehicle state is CH, and maintaining the channel table if the vehicle state is CM;
step S13) cluster size is called degree, determined by the neighbor list field in the HELLO packet, which consists of five parameters, such as status, ID, Neighbor List (NL), difference (f1), distance (f2) and node speed (f3) fields, and this process starts with exchanging HELLO-packets between all vehicles to announce their presence in the network, and then clustering the vehicles according to algorithm 1 to form clusters, each center allowing vehicles to join the cluster according to the degree of clustering (determined by the TRLD algorithm) in order to prevent control overhead and limit the number of centers in the cluster.
3. The routing method applied to multi-intelligent-vehicle communication of claim 1, wherein the step S2 comprises the following steps
Step S21) sending HELLO messages to its neighbors according to its dynamic transmission range (obtained from algorithm 2) for all nodes in the same direction, the status of any node that does not receive a HELLO message will be CH, otherwise, CM is still responsible for controlling and monitoring node activity, CH is responsible for and performing packet transmission between nodes in the network, at this stage, when the current CH energy level is exhausted, CM selects the corresponding node with high energy level, and CM stores both new CH activity and old CH activity, the process continues for all incoming nodes of the network;
step S22) the CH election process will be initiated by a vehicle with a CM status, to which vehicle a GW status will be assigned if there is more than one channel in the routing table of any vehicle, the GW node advertising its GHs to the channel controllers within radio range of the node, this progression being based on three important objective functions. The first parameter is the difference in clustering or difference f1, the second is the total distance of the CMs from their CHs or f2 and the last is the speed of the vehicle f 3. to increase cluster life, preferably low, the GW packet consists of four fields such as source identification, list of channel identifications within radio range of the gateway node, destination identification and message identification, by applying the clustering method to the AODV algorithm, RREQ messages will be sent to the CHs instead of being broadcast between all vehicles, so that routing information between CMs will be distributed by CH, RREP packets will be sent to vehicles if routes are available, otherwise RREQ messages will be received by CHs, in which respect the number of control messages to find routes will be greatly reduced, which results in a reduction in congestion and network overhead throughout the network, increasing cluster life, maintaining the cluster.
4. The routing method applied to multi-intelligent-vehicle communication of claim 1, wherein the step S3 comprises the following steps
Step S31) if there is more than one channel in the routing table of any vehicle to which GW is to be assigned, this parameter is determined randomly in the initial phase, the total value of the objective function will be used for algorithm 3, when a channel is selected, the channel packets will be sent to all the neighbour lists of the channel and they will become CMs of the channel, therefore, every time a HELLO message is received, all CMs should update their routing table, each node belongs to only one CM, and furthermore, the cluster size varies according to their transmission range.
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| CN114189468A (en) * | 2021-11-02 | 2022-03-15 | 云端领航(北京)通信科技股份有限公司 | Multi-identification network system routing method based on identification clustering |
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| CN114189468A (en) * | 2021-11-02 | 2022-03-15 | 云端领航(北京)通信科技股份有限公司 | Multi-identification network system routing method based on identification clustering |
| CN114189468B (en) * | 2021-11-02 | 2024-04-12 | 云端领航(北京)通信科技股份有限公司 | Multi-identification network system routing method based on identification clustering |
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Application publication date: 20210824 |