CN111131031B - VANET cluster clustering optimization method based on mobility weight - Google Patents

VANET cluster clustering optimization method based on mobility weight Download PDF

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CN111131031B
CN111131031B CN201911375100.1A CN201911375100A CN111131031B CN 111131031 B CN111131031 B CN 111131031B CN 201911375100 A CN201911375100 A CN 201911375100A CN 111131031 B CN111131031 B CN 111131031B
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暴建民
吴晨杰
丁飞
米冠宇
任素菊
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
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    • H04L45/00—Routing or path finding of packets in data switching networks
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract

A VANET cluster clustering optimization method based on mobility weight utilizes mobility information of each vehicle, including node connection level, average speed and distance, and a clustering algorithm selects CH based on calculated weight value. To ensure and maintain cluster stability, SeCH was introduced to act as a leader when the PCH is about to leave, and the speed difference between PCH and SeCH was checked and updated periodically to ensure smooth transitions to meet the stringent requirements of VANET environmental security applications.

Description

VANET cluster clustering optimization method based on mobility weight
Technical Field
The invention relates to the technical field of vehicle-mounted self-organizing networks, in particular to a VANET cluster clustering optimization method based on mobility weight.
Background
Vehicular ad hoc networks (VANET) are one of the promising technologies for supporting resource sharing between adjacent vehicles traveling on roads in order to improve road traffic safety and provide infotainment services. Secure applications are the most important applications envisaged by VANET, requiring reliable and timely delivery. These applications have stringent quality of service (QoS) requirements in terms of transmission delay and packet loss rate, which cannot be guaranteed by conventional MAC protocols, especially under heavy traffic conditions. High mobility of vehicles and frequent network topology changes also affect delivery of safety critical applications, especially in high density networks. To solve this problem, it is particularly important to use clustering techniques to ensure good coordination between neighboring nodes.
Currently, relevant researchers have conducted a great deal of research to develop suitable CH selection algorithms. These algorithms all attempt to minimize cluster reconfiguration. However, existing cluster-based MAC approaches show that maintaining cluster stability is a serious challenge due to high mobility and dynamic topology changes of vehicles. While most existing clustering algorithms do not provide an adequate mechanism to handle situations when a selected CH moves out of the cluster because the CH may leave or merge with another CH. Furthermore, the CH is responsible for coordinating all CMs within a cluster, if for any reason it moves out of the cluster, the cluster structure will be affected and must be reconfigured, which will result in high communication overhead for the CCH. In addition, this operation may also result in a loss of channel access scheduling and may result in transmission collisions or a delay in the delivery of the security messages.
Scholars at home and abroad respectively conduct research and discussion on stability and the situation that cluster head nodes move out of a cluster, and propose an improvement method, and Hassanabaadi proposes a clustering algorithm based on mobility, wherein the algorithm uses affinity propagation called APROVE. Arkia also proposes a stability-based clustering scheme that uses an adaptive multi-metric algorithm. However, both approaches indicate that not all neighboring nodes fit in the same cluster. This assumption may affect some neighboring vehicles that are not suitable for receiving safety messages in the cluster. Furthermore, it may cause interference within two or more adjacent clusters and a less stable clustering process, thus affecting the reliability of the MAC protocol to efficiently transfer security information. For the case of CH moves out of the cluster, Mammu proposes a stability-based clustering algorithm. The minimum speed difference of the vehicle and the proximity of the candidate CH and its neighborhood are used to determine which vehicle is selected as the CH. The authors also emphasize the introduction of SeCH to improve cluster stability, but do not specifically provide the detailed procedures of their claims. Furthermore, the direction of motion of the vehicle is not taken into account for the cluster formation process, which also likely leads to unstable clusters.
Disclosure of Invention
The invention aims to provide a VANET cluster clustering optimization method based on mobility weight. To ensure and maintain cluster stability, SeCH was introduced to act as a leader when the PCH is about to leave, and the speed difference between the PCH and SeCH was periodically checked and updated to ensure a smooth transition to meet the stringent requirements of VANET environmental security applications.
A VANET cluster clustering optimization method based on mobility weight comprises the following steps:
step 1, vehicle XiWhen entering the networkThe neighbour relation is established by initially exchanging Hello messages with other vehicles within its communication range, if it receives a cluster join message MjcThen it indicates that there is an existing cluster in the vicinity that it will confirm and join the cluster; on the contrary, if XiAt time TxWithout receiving MjcThen the formation and election process of the clusters is needed;
step 2, calculate the fitness value to determine which vehicle will be the PCH, broadcast this new state information to X of all its neighborsj；
Step 3, when new state information is received, the vehicle executes CH election algorithm to determine the highest weight value betaiThe vehicle with the highest weight is elected as PCH;
step 4, after the PCH is selected in the step 3, setting the node ID as a cluster ID, sending a cluster head message, and modifying the member receiving the cluster head message into a CM state of a cluster member;
and 5, in the CM in the step 4, the CM with the highest weight value is allocated to be in a secondary cluster head SeCH state, and the cluster is formed.
Step 6, periodically updating the PCH and SeCH status messages to cope with the following situations:
1) the leader is transferred from the PCH to the SeCH; 2) merging clusters; 3) the PCH leaves the cluster.
Further, it is characterized in that: in step 1, if XiAt time TxWithout receiving MjcAs shown in equation (1):
Figure BDA0002340738030000031
nmax is the total number of vehicles in the transmission range, and the MAC layer is controlled by a distributed coordination function, where the vehicles utilize a minimum contention window CWminAnd maximum contention window size CWmaxA value; for each unsuccessful transmission, the vehicle will CWminThe value is doubled until the maximum value is reached.
Further, in step 3, the weight value βiThe calculation formula is as shown in formula (1) and (2) Shown in the figure:
βi＝(wv1*Ni(t))+(wv2*μnormal)+(wv3*ρnormal) (1)
wv1+wv2+wv3＝1 (2)
in the formula Ni(t) represents the node connectivity level, i.e. the number of neighbors of node i at time t in the cluster; mu.snormal、ρnormalRepresents the average velocity and distance of adjacent vehicles after normalization, and wv1、wv2、wv3A weighting factor associated with each parameter respectively.
Further, node connection level Ni(t) means that if the distance between potential node j and i is less than the transmission range of node i, the level is increased by 1, as shown in equation (3):
Figure BDA0002340738030000041
normalized mean velocity μ after processingnormalAnd distance ρnormalModeling is performed according to normal distribution of the mean and variance of the corresponding adjacent nodes, and the expressions are shown in formulas (4) and (5):
Figure BDA0002340738030000042
Figure BDA0002340738030000043
viindicates vehicle speed, npIndicating the node position, μavg、μdThe average speed and the average distance of all the adjacent vehicles are represented by formulas (6), (7), (8), respectively:
np＝(xi，yi) (6)
Figure BDA0002340738030000044
Figure BDA0002340738030000045
wherein the node position npThe coordinates are represented by x and y, derived from the position coordinates of the vehicle, δ d represents the total distance, δ t represents the total time covered, NiAnd (t) is the node connection level, and j 1.. n represents an adjacent vehicle in the transmission range.
Further, in step 4, when two or more vehicles with the same Form Cluster send forms simultaneously, collision will occur, resulting in no vehicle being able to Form a Cluster; in this case, the vehicle will start a new competition iteration until one wins; if a collision still occurs, the vehicle with the lowest id is selected and the election is won; subsequently, the selected vehicle will send a Form Cluster message to all vehicles; at this time, the vehicle changes its state to PCH and sets its ID as a cluster ID and transmits a cluster head message, and the member receiving the cluster head message modifies itself to a cluster member CM state.
The invention has the beneficial effects that: compared with the prior art, the enhanced VANET clustering algorithm based on the mobility weight adopts the technical scheme that:
1. the mobility information of each vehicle, including node connection level, average speed and distance, is used for determining the highest weight value, the node with the highest weight value is used as the PCH, and the number of times of change of the vehicle node state and the number of formed clusters are effectively reduced, so that the stability of the clusters is improved.
2. Most existing clustering algorithms focus on cluster head elections and are rarely concerned with other key issues such as cluster formation and maintenance. This greatly affects the stability of the cluster, which may affect the timely delivery of the security application. Therefore, the present invention introduces the SeCH to have leadership when the PCH is about to leave, periodically checking and updating the speed difference of the PCH and the SeCH, minimizing the delay of broadcasting these safety messages to the CM to ensure a smooth transition to meet the strict requirements of VANET environmental security applications.
Drawings
FIG. 1 is a diagram of a clustering node transformation model.
Fig. 2 is a flow chart of cluster formation and cluster head election.
Fig. 3 is a cluster head leadership transfer flow diagram.
Fig. 4(a) (b) is a graph comparing the cluster stability of the present invention with two clustering algorithms selected.
Fig. 5(a) (b) is a graph comparing the number of clusters formed by the present invention with two clustering algorithms selected.
FIG. 6(a) (b) is a comparison of end-to-end delay of the present invention and two selected clustering algorithms.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The embodiment provides a VANET cluster clustering optimization method based on mobility weight, which utilizes mobility information of each vehicle, including node connection level, average speed and distance, and a clustering algorithm selects a PCH based on the calculated weight value, so that cluster stability is improved. In order to maintain cluster stability, SeCH is introduced to act as a leader when PCH is about to leave, and the speed difference between PCH and SeCH is periodically checked and updated, so that the delay of broadcasting these safety messages to CM is minimized to ensure smooth transition to meet the strict requirements of VANET environmental security application.
As shown in fig. 1, comprises the following steps:
step 1, when the vehicle XiUpon entering the network, a neighbor relation is established by initially exchanging Hello messages with other vehicles within its communication range, if it receives a cluster join message MjcIt indicates that there is an existing cluster in the vicinity that it will acknowledge and join the cluster. On the contrary, if XiAt time TxAs shown in equation (1), M is not receivedjcThen a cluster formation and election process is required.
Figure BDA0002340738030000071
Nmax is the total number of vehicles in the transmission range, βiIt refers to the highest weight value and the MAC layer is controlled by a distributed coordination function, where the vehicle utilizes the minimum contention window CWminAnd maximum contention window size CWmaxThe value is obtained. For each unsuccessful transmission, the vehicle will CWminThe value is doubled until the maximum value is reached.
In the event that the vehicle finds more than one PCH in its transmission range, the vehicle needs to make a decision to determine the most appropriate PCH to join. This is accomplished by comparing the position and relative speed of the vehicle with the position and relative speed of the available PCH. If the position of the PCH is greater than the position of the vehicle, the vehicle accepts the joining of the PCH. This is because it is always better to receive safety information from the front vehicle, so that decisions can be made in a timely manner to avoid any dangerous situation. If two or more PCHs are in front of the vehicle at the same time, the vehicle will join the PCH at a relatively low speed.
Step 2, in step 1, each vehicle has information about its neighbour nodes, thus allowing calculation of an adaptation value to determine which vehicle will be the PCH, and this new state information will then be broadcast to X's of all its neighboursj. Steps 2-5 are specifically shown in FIG. 2.
Step 3, when receiving the information, the vehicle executes CH election algorithm to determine the highest weight value betaiThe vehicle with the highest weight is selected as CH. Weight value betaiThe calculation formula is shown in formulas (2) and (3):
βi＝(wv1*Ni(t))+(wv2*μnormal)+(wv3*ρnormal) (2)
wv1+wv2+wv3＝1 (3)
in the formula Ni(t) represents the node connectivity level, i.e., the number of neighbors of node i at time t in the cluster; mu.snormal、ρnormalRepresents the average velocity and distance of adjacent vehicles after normalization, and wv1、wv2、wv3A weighting factor associated with each parameter respectively.
Node connection level Ni(t) means that if the distance between potential node j and i is less than the transmission range of node i, the level is increased by 1, as shown in equation (4):
Figure BDA0002340738030000081
normalized mean velocity μ after processingnormalAnd distance ρnormalModeling is performed according to normal distribution of the mean and variance of the corresponding adjacent nodes, and the expressions are shown in formulas (5) and (6):
Figure BDA0002340738030000082
Figure BDA0002340738030000083
viindicates vehicle speed, npIndicating the node position, μavg、μdThe average speed and the average distance of all the adjacent vehicles are represented by formulas (7), (8), (9), respectively:
np＝(xi，yi) (7)
Figure BDA0002340738030000084
Figure BDA0002340738030000085
wherein the node position npThe coordinates are represented by x and y, and can be derived from the position coordinates of the vehicle, δ d represents the total distance, and δ t represents the total time covered. N is a radical ofiAnd (t) is the node connection level, and j 1.. n represents an adjacent vehicle in the transmission range.
Step 4, after PCH is selected in step 3, setting the node ID as a cluster ID and sending a message of becoming a cluster headThe member receiving the cluster head message modifies itself to the cluster member CM state. When two or more of them have the same TxWhen the vehicles send Form Cluster at the same time, collision will occur, so that no one vehicle can Form a Cluster. In this case, the vehicle will therefore start a new competition iteration until one wins. If a collision still occurs, the vehicle with the lowest id will be selected and the election won. Subsequently, the selected vehicle will send a Form Cluster message to all vehicles. At this time, the vehicle changes its state to PCH and sets its ID to cluster ID and sends a cluster head message, and the member receiving the cluster head message modifies itself to a cluster member CM state.
And step 5, in the CM in the step 4, the CM with the highest weight value is allocated to be in a secondary cluster head (SeCH) state, so that the cluster is formed.
Step 6, after the cluster is formed, the PCH and SeCH status messages need to be periodically updated to deal with the following situations:
1) the leader is transferred from the PCH to the SeCH; 2) merging the clusters; 3) the PCH leaves the cluster.
The three-way case described above will trigger a maintenance mechanism, as shown in fig. 3, where the vehicle that is about to arrive at the destination or is about to exit must be slowed down. Thus, speed is used as the primary criterion for determining whether a PCH is eligible to continue its leadership. In addition, the PCH needs to regularly maintain status information of its SeCH to ensure that it is actively responsible when needed. This is due to the fact that the SeCH may have left the cluster when the PCH attempts to exit. While in the course of this update, the PCH continues its leader whenever it determines that the current speed of the PCH is greater. It will update the state information of the SeCH and share this information with all CMs.
And the vehicle may be forced to decelerate due to some accident occurring on the road, for example, due to traffic jam caused by an accident or road congestion. In this case, the PCH does not relinquish its responsibility. And when the current speed of SeCH is greater than the speed of PCH without any event on the road, PCH slows down and is about to exit. The PCH passes the leadership responsibility and changes its status to CM. The SeCH then changes its state to PCH and specifies its ID as CID. The new SeCH is then determined by the new PCH using the same procedure as in fig. 1, without performing the cluster formation and CH selection algorithm. The updated status information is then broadcast to all CMs within the cluster.
To analyze the validation examples based on the enhanced mobility weight-based VANET clustering algorithm, simulations were performed using a network simulator version 3 (3.21), and micro traffic simulators, called city traffic Simulators (SUMO), were used to generate movement patterns of vehicles of different densities, which had real movement trajectories. For DSRC communications, experiments used a WAVE module that defined the ieee802.11p standard for PHY and MAC layers. The scene setting is established at 5KM by 5KM square urban road, with 3 lanes in each direction. The vehicles on each road were traveling in the same direction with an average speed of 15 m/s and an average deviation of the free traffic flow of 3 m/s. Further, the weight factor value associated with each metric of the CH election process is arbitrarily defined according to the importance of each metric. The weight values for the number of neighboring nodes are 0.4, while the weight values for the remaining metrics, including the location and average velocity of the neighboring nodes, are each 0.3. The weighting factors provide flexibility to adjust the effective contribution of each metric. For example, in a city environment, the number of neighboring nodes is more important, and the weight value associated with the number of nodes may be made larger. The simulated traffic density was varied to show different behaviors of the proposed clustering algorithm, including 50 and 100 vehicles.
In order to verify the superiority of the clustering algorithm provided by the invention, two main clustering methods are selected, the same one is subjected to multiple experiments respectively, and the average value of the recommendation index is calculated. And selecting a stability-based SB algorithm and a threshold-based TB algorithm for comparison experiments. Each method performs 10 experiments and calculates and records the times of changing the state of each vehicle in the cluster, the number of formed clusters and the average value of transmission delay, and finally compares the average value with the result of the invention. The superiority of the clustering algorithm can be seen in detail in fig. 4-6.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.

Claims (5)

1. A VANET cluster clustering optimization method based on mobility weight is characterized in that: the method comprises the following steps:
step 1, vehicle XiUpon entering the network, a neighbor relation is established by initially exchanging Hello messages with other vehicles within its communication range, if it receives a cluster join message MjcThen it indicates that there is an existing cluster in the vicinity that it will confirm and join the cluster; on the contrary, if XiAt time TxWithout receiving MjcThen the formation and election process of the clusters is needed;
in the event that the vehicle finds more than one primary cluster head PCH within its transmission range, determining the most appropriate PCH to join by comparing the position and relative speed of the vehicle with the position and relative speed of the available PCHs; if the position of the PCH is greater than the position of the vehicle, the vehicle accepts to join the PCH; if two or more PCHs are in front of the vehicle at the same time, the vehicle will join a relatively lower speed PCH;
step 2, calculate the fitness value to determine which vehicle will be the PCH, broadcast this new state information to X of all its neighborsj；
Step 3, when new state information is received, the vehicle executes CH election algorithm to determine the highest weight value betaiThe vehicle with the highest weight is elected as PCH;
step 4, after the PCH is selected in the step 3, setting the vehicle ID as a cluster ID, sending a cluster head message, and modifying the member receiving the cluster head message into a CM state of the cluster member;
step 5, in the CM of the step 4, the CM with the highest weight value is allocated to be in a secondary cluster head SeCH state, and the cluster is formed;
step 6, periodically updating the PCH and SeCH status messages to cope with the following situations:
1) the leader is transferred from the PCH to the SeCH; 2) merging clusters; 3) PCH leaves the cluster;
when the three situations occur, a maintenance mechanism is triggered, and the speed is used as a standard for determining whether the PCH is suitable for continuing the leadership; the PCH needs to regularly maintain state information of its SeCH to ensure that it actively assumes responsibility when needed; in this process, whenever it determines that the current speed of the PCH is greater, the PCH continues its leader, updates the state information of the SeCH and shares this information with all CMs;
under the condition that the traffic jam vehicle is forced to decelerate, the PCH does not give up the responsibility of the traffic jam vehicle; and when the current speed of the SeCH is greater than the speed of the PCH without any event occurring on the road, the PCH slows down and is about to exit; the PCH breaks away from the leadership responsibility and changes the status thereof into CM; then the SeCH changes the state of the SeCH into a PCH and designates the ID of the SeCH as a cluster ID; then determining a new SeCH from the new PCH without performing cluster formation and CH election algorithms; the updated status information is then broadcast to all CMs within the cluster.
2. The VANET cluster clustering optimization method based on mobility weight as claimed in claim 1, characterized in that: in step 1, if XiAt time TxWithout receiving MjcAs shown in equation (1):
Figure FDA0003585279520000021
nmax is the total number of vehicles in the transmission range, and the MAC layer is controlled by a distributed coordination function, where the vehicles utilize a minimum contention window CWminAnd maximum contention window size CWmaxA value; for each unsuccessful transmission, the vehicle will CWminThe value is doubled until the maximum value is reached.
3. The VANET cluster clustering optimization method based on mobility weight as claimed in claim 1, characterized in that: in step 3, weight value betaiThe calculation formula is shown in formulas (2) and (3):
βi＝(wv1*Ni(t))+(wv2*μnormal)+(wv3*ρnormal) (2)
wv1+wv2+wv3＝1 (3)
in the formula Ni(t) represents the node connectivity level, i.e. the number of neighbors of node i at time t in the cluster; mu.snormal、ρnormalRepresents the average velocity and distance of adjacent vehicles after normalization, and wv1、wv2、wv3A weighting factor associated with each parameter respectively.
4. The VANET cluster clustering optimization method based on mobility weight as claimed in claim 3, characterized in that: node connection level Ni(t) means if the distance between the potential node j and i is less than the transmission range of the node i, the level is increased by 1;
normalized mean velocity μ after processingnomalAnd distance ρnomalModeling is performed according to normal distribution of the mean and variance of the corresponding adjacent nodes, and the expressions are shown in formulas (4) and (5):
Figure FDA0003585279520000031
Figure FDA0003585279520000032
viindicates vehicle speed, npIndicating the node position, μavg、μdThe average speed and the average distance of all the adjacent vehicles are represented by formulas (6), (7), (8), respectively:
np＝(xi，yi) (6)
Figure FDA0003585279520000033
Figure FDA0003585279520000034
wherein node position npThe coordinates are represented by x and y, derived from the position coordinates of the vehicle, δ d represents the total distance, δ t represents the total time covered, NiAnd (t) is the node connection level, and j 1.. n represents an adjacent vehicle in the transmission range.
5. The VANET cluster clustering optimization method based on mobility weight as claimed in claim 1, characterized in that: in step 4, when two or more of them have the same TxWhen the vehicles send Form Cluster at the same time, collision occurs, so that no vehicle can Form a Cluster; in this case, the vehicle will start a new competition iteration until one wins; if a collision still occurs, the vehicle with the lowest id is selected and the election is won; subsequently, the selected vehicle will send a Form Cluster message to all vehicles; at this time, the vehicle changes its state to PCH and sets its ID as a cluster ID and transmits a cluster head message, and the member receiving the cluster head message modifies itself to a cluster member CM state.
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