CN113518327B - Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method - Google Patents
Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method Download PDFInfo
- Publication number
- CN113518327B CN113518327B CN202110839760.1A CN202110839760A CN113518327B CN 113518327 B CN113518327 B CN 113518327B CN 202110839760 A CN202110839760 A CN 202110839760A CN 113518327 B CN113518327 B CN 113518327B
- Authority
- CN
- China
- Prior art keywords
- community
- vehicle
- vehicle node
- node
- nodes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/12—Messaging; Mailboxes; Announcements
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Multimedia (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a social attribute-aware vehicle-mounted ad hoc network opportunistic routing method which comprises the following steps that firstly, vehicle nodes periodically record own historical destination information, a social attribute set of the vehicle nodes is generated, and communities are formed among the vehicle nodes with the same social attribute; secondly, all vehicle nodes in the community continuously update the community state, and the community state update is commonly maintained, so that the community gradually tends to be stable; thirdly, the self-carried data packet is transmitted to a destination node to form a decentralised self-organizing vehicle-mounted opportunity network. Compared with the prior art, the invention has the beneficial effects that: the vehicle-mounted self-networking opportunity routing method forms social attribute information by utilizing the vehicle node state information, adopts a local data analysis method, belongs to offline operation, effectively reduces the data interaction amount in the vehicle networking, and reduces the network load. The community has strong correlation with the geographic position, and can meet the data distribution requirement with strong purposefulness and regionalization.
Description
Technical Field
The invention relates to a routing protocol in a vehicle-mounted self-organizing network, belongs to a fusion technology of an opportunity network and the vehicle-mounted self-organizing network, and particularly relates to a social attribute-aware vehicle-mounted self-organizing network opportunity routing method.
Background
The internet of things (Internet of Vehicle, ioV) is derived from the extension of the concept of internet of things (Internet of Things, ioT), and aims to realize the perception collaborative development of Vehicle-to-Vehicle (V2V), vehicle-to-peoples (V2P), vehicle-to-Infrastructure (V2I) and Vehicle-internet of things (V2N), realize the intelligent management of traffic, achieve the perception collaborative development of Vehicle-road-person-cloud, and construct a highly collaborative internet of vehicles ecological system. As an important application scenario of a wireless Ad-Hoc Network (MANET), a vehicular Ad-Hoc Network (VANET) is a solution for implementing networking communication between vehicle nodes. The characteristics of frequent movement, high movement speed, wide movement range and changeable network topology structure of the vehicle nodes in the vehicle-mounted self-organizing network lead the routing protocol in the traditional wireless self-organizing network not to be well used for solving the data transmission problem among the vehicle nodes. However, the characteristics of the opportunistic network just meet the application scene of the internet of vehicles.
The opportunistic network (Opportunistic Network) reduces the requirements on communication links between nodes, allows the communication links to be intermittently connected, and mainly utilizes meeting opportunities caused by frequent movement of a large number of nodes to realize data transmission. Related studies of opportunistic Networks were originally originated in early Mobile Ad-Hoc Networks (MANET) and Delay/break tolerant Networks (DTN). The mobile self-organizing network is a special mobile wireless network, the MANET does not need the support of network infrastructure, nodes in the network are used as hosts and have the related functions of routers, and the nodes are used as peer entities for wireless connection and data communication; the DTN mainly adopts a Store-Carry-Forward (Store-Carry-Forward) strategy to complete the task of data transmission in the network. Compared with MANET and DTN, the coverage of opportunistic network is wider, and the scene of short distance data transmission by common users carrying mobile intelligent devices and vehicles equipped with intelligent sensors in daily life is more focused.
Therefore, by combining the routing protocol and the opportunity routing idea of the traditional vehicle-mounted self-organizing network, the design of the opportunity routing transmission protocol working method suitable for the vehicle-mounted self-organizing network has important practical significance for improving the communication capacity and the communication quality between vehicle nodes.
Disclosure of Invention
In order to solve the problem that the vehicle-mounted self-organizing network in the background art cannot well solve the data transmission problem between vehicle nodes, and combine the opportunity network with the routing protocol of the traditional vehicle-mounted self-organizing network to improve the communication capacity and the communication quality between the vehicle nodes, the invention provides a social attribute-aware vehicle-mounted self-organizing network opportunity routing method.
In order to achieve the above purpose, the invention provides a social attribute-aware vehicle-mounted ad hoc network opportunistic routing method, which has the technical scheme that:
a social attribute-aware vehicular ad hoc network opportunistic routing method comprises the following steps,
s1, community initialization: the vehicle nodes periodically record historical destination information of the vehicle nodes, a social attribute set of the vehicle nodes is generated, and communities are formed among the vehicle nodes with the same social attribute;
S2, community maintenance: all vehicle nodes in the community continuously update the community state, and the community state update is commonly maintained, so that the community gradually tends to be stable;
s3, a data forwarding stage: transmitting the self-carried data packet to a destination node to form a self-organized self-centering vehicle-mounted opportunity network;
the vehicle node obtains the running state, the position and the stay time of the vehicle node by using a GPS, a vehicle-mounted sensor, intelligent mobile equipment or an electronic map; the vehicle node is equipped with an on-board system, a storage device, and a short-range communication device to provide computing, storage, and short-range communication capabilities.
Further, the community initialization stage, the community maintenance stage and the data forwarding stage realize functions of each stage through one or more methods of a vehicle node social attribute generation method, a vehicle node state transition method, a vehicle node community formation method, a vehicle node community maintenance method, a community correlation and community dispersion method and a data forwarding method.
Further, the vehicle node social attribute generation method comprises the following steps:
a1, a vehicle node v obtains position coordinate information of the vehicle node v by using GPS equipment, and generates a position coordinate p (x, y) for each destination;
A2, periodically recording N destination positions p and residence time t visited by the vehicle node v in a period of time to form a historical destination record set PT of the vehicle node v v ；
A3, obtaining a new set PT' after coordinate aggregation and time aggregation through a K-means clustering algorithm v
A4, removing the areas with shorter access time according to the access habit of the vehicle node to obtain k interested areas as social attribute marks Q of the vehicle node v, wherein the generation of the social attribute set of the vehicle node v is expressed as follows:
Atter v ＝{Q 1 ,Q 2 ,Q 3 …,Q k }。
further, according to the access habit of the vehicle node, the removing the area with the shorter access time is specifically: calculating and obtaining a time length threshold t of accessing the region of interest according to the access habit of the vehicle node 0 By t 0 Removing areas with shorter total access duration; wherein, the duration threshold t of the region of interest 0 The method comprises the following steps:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing an average residence time of the vehicle node v; sigma represents the standard deviation of the residence time of the vehicle node v; μ is an adjustable coefficient representing the difference in behavior activity of different vehicle nodes.
Further, the states of the vehicle NODEs include a FREE NODE state free_node, a to-join community state wait_node, and a joined community state; the state of the vehicle node is related to the current position of the vehicle node, and the state transition method of the specific vehicle node comprises the following conditions:
B1, if the vehicle NODE is in a region which is not interested in the vehicle NODE, the state bit of the vehicle NODE is FREE_NODE; in this state, the vehicle node does not seek to join the community, nor does it generate interaction with other nodes about the community;
b2, the vehicle NODE is in an area of interest of the vehicle NODE, and a community of the area is not added yet, and the state bit of the vehicle NODE is WAIT_NODE; in the state, the vehicle node periodically broadcasts a Join message, seeks to Join the community, or receives Join message from other nodes to form a new community;
b3, the vehicle node is in the area of interest of the vehicle node and has joined the community, and the vehicle node periodically broadcasts a Combine message; in this state, if other communities exist in the area are found, the two communities are combined; the vehicle node also receives Join message messages from other vehicle nodes to be joined in the community state, and brings the Join message messages into own communities.
Further, the community formation method between the vehicle nodes includes the steps of:
c1, according to the history journey of a vehicle node v which is not added into a community in a detection period, obtaining k interest areas of the vehicle node v, namely obtaining k social attribute information of the vehicle node v;
C2, the vehicle NODE v is in one of the k interest areas S, the vehicle NODE v is in the WAIT_NODE state, and broadcasts a Join message to the surrounding, so as to seek to Join the community;
c3, or forming a new community by: the vehicle NODE v receives the Join message of the vehicle NODE u which has not joined the community in the communicable range, and the vehicle NODE u is also in the WAIT_NODE state, the vehicle NODE u and the vehicle NODE v will form a new community C S ；
C4, forming a new community C in the step C3 S After that, the vehicle NODE u changes its own state to the UNITED_NODE state, and adds the community C to its own community attribute list S Status information of (2); the vehicle node u returns a Create message to inform the vehicle node v to join the community;
and C5, in the step C2 or the step C4, after the vehicle node v receives the Create message, newly generated community state information is added into a community list of the vehicle node v, and the vehicle node v joins the community.
Further, the community maintenance method between the vehicle nodes comprises the following maintenance methods of maintenance conditions: a maintenance method for joining a vehicle NODE in a WAIT_NODE state into a community, a maintenance method for merging communities, a maintenance method for updating community state information and a maintenance method for leaving the community by the vehicle NODE.
Further, the maintenance method for joining the vehicle NODE in the wait_node state into the community comprises the following steps: the vehicle NODE v is in the interest area of the vehicle NODE v, periodically broadcasts Join message to the surrounding to wait for joining the community, the vehicle NODE u in the communicable range of the vehicle NODE v is in UNITED_NODE, and returns a permission message after receiving the Join message, allowing the vehicle NODE v to Join the community C S And updating own community state information; the maintenance method for the combination of communities comprises the following steps: the vehicle NODE v is in a region S which is added into the community and is in a UNITED_NODE state, and periodically broadcasts a Combine message to the surrounding, and performs community merging with other vehicle NODEs in the UNITED_NODE state in the region S; the maintenance method for community state information update comprises the following steps: the vehicle NODEs join in communities and the combination of communities to change the state of the communities, and the vehicle NODEs in the UNITED_NODE state broadcast Update message to show the states of the vehicle NODEs and the communities; the vehicle node receiving the Update message compares and analyzes the two community states, and updates and maintains community state information; the maintenance method for the vehicle node to leave the community comprises the following steps: and when the behavior habit of the vehicle node v changes in a new detection period, sending an Update message to inform other nodes in the community of updating community information, selecting a proper relay node to forward the message, deleting the community information, and leaving the community.
Further, the community relativity and community dispersion method comprises community relativity reflecting the frequent degree and the stable degree of the vehicle node v accessing the community C and community dispersion reflecting the distance relativity between the vehicle node v and the target community C; the higher the community correlation is, the higher the viscosity between the vehicle node and the community C is, and the vehicle node is used as a core node in the community C; the lower the degree of dispersion, the greater the likelihood that the vehicle node v approaches the target community C, the closer the social attributes of the vehicle node v and the vehicle node within the target community C, and the easier the vehicle node v and the vehicle node within the target community C meet.
Further, the data forwarding method comprises a data forwarding method of the vehicle node in the target community and a data forwarding method of the vehicle node outside the target community; the data forwarding method of the vehicle node in the target community comprises the following steps: detecting whether a vehicle node which also belongs to a community to which a target node belongs exists in a vehicle node set in a communication range, and if a plurality of nodes which belong to the target community exist, selecting the vehicle node with the largest community correlation degree for data forwarding; otherwise, the maximum duration is reserved by utilizing the data packet, and if the vehicle node does not reach the target community before reaching the maximum duration or does not meet the node with larger community correlation, the vehicle node with the smallest community dispersion from the target community is selected for forwarding; the data forwarding method of the vehicle node outside the target community comprises the following steps: and selecting the vehicle node with smaller community dispersion from the target node community as a relay node for data forwarding.
Compared with the prior art, the invention has the advantages that: the invention combines the opportunity network with the routing protocol of the traditional vehicle-mounted self-organizing network, creatively provides a vehicle node social attribute generation method, a vehicle node state conversion method, a vehicle node community formation method, a vehicle node community maintenance method, a vehicle node community correlation degree and community dispersion degree method and a data forwarding method, reduces the occupation of resources, such as storage space and calculation amount, reduces network load, and improves the communication quality and capacity between vehicle nodes. The vehicle-mounted ad hoc network opportunistic routing working method realized by the method mainly utilizes the running state information and the geographic position information of the vehicle nodes to form the social attribute information of the vehicle nodes, adopts a local data analysis method, belongs to offline operation, and can effectively reduce the data interaction amount in the vehicle networking and reduce the network load. The communities have strong correlation with the geographic positions, and can meet the data distribution requirements of strong purposiveness and regionality in reality.
Drawings
FIG. 1 is a schematic diagram of the vehicular ad hoc network opportunistic routing workflow of the present invention;
FIG. 2 is a Gaussian distribution confidence probability distribution diagram;
FIG. 3 is a vehicle node state transition diagram;
FIG. 4 is a vehicle node generating social attribute process;
FIG. 5 is a flow of two vehicle nodes forming a community;
FIG. 6 is a vehicle node joining community flow;
FIG. 7 is a community merge flow;
FIG. 8 is a community state update flow;
FIG. 9 is a vehicle node leave community flow;
fig. 10 is a data forwarding flow.
Detailed Description
In order to make the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail with reference to fig. 1 to 10.
A social attribute-aware vehicle-mounted ad hoc network opportunistic routing working method is shown in figure 1, and comprises the following steps:
s1, community initialization: the vehicle node periodically records its own historical destination information, including destination coordinates, access duration, access intervals, and the like. A set of social attributes of the vehicle node is generated in accordance with a social attribute generation mechanism. Communities may be formed between vehicle nodes having similar social attributes.
S2, community maintenance: aiming at the conditions that the vehicle nodes join and leave communities, the communities are combined, and the like, the corresponding processing mechanisms are adopted to update the community state information so as to maintain the stability of the communities.
S3, a data forwarding stage: by combining the thought of opportunity transmission, the vehicle node transmits the data packet to the destination node as far as possible in a storage-carrying-forwarding mode by carrying the data packet, and selects a proper vehicle node from the neighbor nodes according to the community relativity and the community dispersion as a relay node to forward the data packet, and transmits the data packet to the destination node through two paths.
The working premise of the vehicle-mounted ad hoc network opportunistic routing working method is that all vehicle nodes have the following functions and can obtain the following information:
(1) The vehicle node can obtain the running state, the position, the stay time and the like of the vehicle by using related tools such as a GPS, an on-vehicle sensor, an intelligent mobile device or an electronic map and the like. The vehicle nodes are all provided with vehicle-mounted systems and have certain computing capacity; the storage device has certain storage capacity; the short-range communication equipment meeting the condition of the Internet of vehicles is provided with short-range communication capability.
(2) When the vehicle nodes are in the interest areas, the state information of the vehicle nodes can be exchanged in a mode of periodically broadcasting message messages, wherein the state information comprises geographic position information, interest areas, interest degree information, community state information and the like of the vehicle nodes.
(3) The vehicle nodes can manage the community topology structure in a mode of periodically broadcasting message in a communication range.
On the premise that the vehicle-mounted ad hoc network opportunistic routing has the above work, the community initialization stage, the community maintenance stage and the data forwarding stage realize the functions of each stage through one or more of a vehicle node social attribute generation method, a vehicle node area correlation, a vehicle node state transition method, a vehicle node community formation method, a vehicle node community maintenance method, a vehicle node community correlation and community dispersion method and a data forwarding method.
The method for generating the social attribute of the vehicle node comprises the following steps:
the vehicle node v may acquire position coordinate information of the vehicle node v using a GPS device, and generate a position coordinate p (x, y) for each destination. Wherein x and y represent coordinates of a destination of the vehicle node v on a map, and can be obtained by referring to longitude and latitude data in reality, and in experiments, the coordinates can be represented by (x, y) because a two-dimensional electronic map is adopted.
Defining a detection period delta T according to user behavior habit and activity degree of the vehicle node, periodically recording N destination positions p and residence time T visited by the vehicle node v in a period of time, and forming a historical destination record set PT of the vehicle node v v ：
PT v ＝{(p 1 ,t 1 ),(p 2 ,t 2 ),(p 3 ,t 3 ),…,(p N ,t N )}#(1)
The values of the position coordinates p (x, y) generated each time the vehicle node v accesses a certain destination region of interest may be different, but in close proximity. This is in reality manifested as the vehicle node stopping somewhere in the vicinity of the destination. Then, a cluster analysis is required for the set of historical access destinations to find K regions of interest to the vehicle node v. The degree of dispersion of the historical destination coordinates of the vehicle node v may reflect the degree of activity of the vehicle node to a certain extent, so the standard deviation of the historical destination coordinates of the vehicle node v determines the value of the parameter K. The higher the liveness the greater the value of K for the vehicle node, the more social attributes it has.
The average of the historical destination location coordinates is:
normalizing the position coordinates:
wherein p is max (x) And p max (y) respectivelyValues representing the maximum p (x) and p (y), p min (x) And p min (y) represents the smallest p (x) and p (y) values, respectively. The average of the coordinates after normalization is:
the value of the parameter K is obtained by normalizing the standard deviation of the coordinates:
where K ε Z represents the K regions of interest to vehicle node v. The value of the number parameter K of the classes required for clustering is thus obtained.
From the collection PT v Extracting destination sets of vehicle nodes v in a detection period delta T:
P v ＝{p 1 ,p 2 ,p 3 ,…,p N }#(8)
the K-means clustering algorithm is a widely used simple and efficient clustering algorithm, and is used for collecting P v Obtaining central positions of K areas of interest of the vehicle node v through cluster analysis:
the parameter K is an important input parameter of the K-means clustering algorithm and is the number of interesting areas of the vehicle node v, and the number of the K-means clustering algorithm is determined.
Since the region center position generated by K-means is a randomly generated position, it may not be the set P v So one needs to be foundThe physical locations actually visited correspond to their regions of interest, i.e. in the set P v Find the closest set
Position coordinates of each element in the map. Defining the Euclidean distance L between two positions p1,p2 The method comprises the following steps:
calculating each position p to a clustering center p in each clustering area c Find the distance p from each cluster center c Position coordinate p with shortest distance L Then modify the position p L Is effective to L th The coordinates p of all other positions of (a) are the coordinates p L Wherein L is th The range is adjustable according to the degree of the bloom of the area where the vehicle node is located. Therefore, besides the unified coordinate identification formed by aggregation of some position coordinates with obvious aggregation characteristics, some coordinates with larger discrete degree exist, but positions which are possibly interesting for the vehicle nodes exist independently. So that a new history destination record set PT 'after the coordinate aggregation is finally obtained' v ：
PT′ v ＝{(p 1 ,t 1 ),(p 1 ,t 2 ),(p 2 ,t 3 ),…,(p n ,t N )|K<n<N}#(11)
Collection PT' v The method comprises the steps that destination areas and duration information accessed by a user in one detection period are contained, then the residence duration of a vehicle node v in each area is summed up respectively, the total access duration of the vehicle node v to each area in one detection period is obtained, and a new interest area set is obtained:
PT″ v ＝{(p 1 ,t′ 1 ),(p 2 ,t′ 2 ),(p 3 ,t′ 3 ),…,(p n ,t′ n )|K<n<N}#(12)
but however，PT” v The number of the included areas may be large, and all the areas cannot be selected as the social attribute of the vehicle node, so that some areas with shorter access time are eliminated according to the access habit of the vehicle node.
Modeling historical destination residence time by using a Gaussian distribution model, and calculating average residence time of a vehicle node v:
and then calculating the standard deviation of the residence time of the vehicle node v:
the confidence interval range of the gaussian distribution is determined by the standard deviation σ obtained by equation (14), and the region of interest for the selected vehicle node v is determined from this interval. However, since there is a very large difference in the behavior activities of different vehicle nodes, introducing an adjustable coefficient μ results in a minimum dwell time of confidence intervals of Gaussian distribution, i.e., a threshold dwell time t 0 The method comprises the following steps:
the value of the adjustable coefficient μmay be finely adjusted according to the vehicle activity level, and in conjunction with fig. 2, the confidence interval range of the gaussian distribution is as follows:
The access situation of a vehicle node to a certain area can be classified into the following 4 types:
(1) The access frequency is low, the duration is short, and it can be judged that the vehicle node v may not be interested in the area;
(2) The access frequency is low, the duration time is long, and whether the vehicle node v is interested in the area or not is judged according to the total access time length in one detection period;
(3) The access frequency is high, the duration time is short, and whether the vehicle node v is interested in the area or not is judged according to the total access time length in one detection period;
(4) The access frequency is high and the duration is long, and it can be determined that the vehicle node v is interested in this area.
Excluding all total access durations less than t 0 After the regions of (a), k destination regions are obtained as social attribute marks of the vehicle nodes v, and the higher the liveness is, the larger the value of k is, and the more social attributes are possessed. The character string combination of the region of interest position information generates a region of interest ID denoted as Q, and the social attribute set generated by the vehicle node v is denoted as:
Attr v ＝{Q 1 ,Q 2 ,Q 3 ,…,Q k }#(17)
according to the social attribute generation method, the social attribute of the vehicle node is generated through the history travel information of the vehicle node, all data are acquired and processed locally, and finally generated information is anonymous information, so that the privacy of a user is not revealed, and the privacy safety of the user is protected to the greatest extent. By controlling parameters such as detection period, the required historical data volume can be reduced, the required storage space can be reduced, the calculated amount can be reduced, and the node load can be reduced.
The regional correlation of the vehicle nodes is:
the social attribute of the vehicle node is determined by the area frequently accessed by the vehicle node, so as to reflect the vehicle node v and the area Q to the greatest extent j Is also representative of the social relevance of the vehicle node v in the region Q j The following vehicle node attributes are defined to describe the influence of the vehicle node v in the region Q j In a community correlation WCD (v, Q) j )。
To facilitate description of the vehicle node-to-region correlation problem, F (v, Q j ) Is the vehicle node v and the region Q j Correlation between access frequencies of (i) a vehicle node v accesses an area Q j The number of times of access to all areas within the detection period Δt is proportional to the number of times of access to all areas as shown in the formula (18):
wherein m (v, Q) j ) Indicating that the vehicle node is within DeltaT for region Q j Is used for the number of accesses of (a),
representing the total number of times the vehicle node v accesses all areas within Δt.
Definition of T (v, Q) j ) Representing vehicle node v and region Q j Is a correlation between affinities of (a):
wherein t (v, Q) j ) Indicating that vehicle node v is within ΔT and within region Q j Is used for the total duration of the retention time of (a),
representing the total length of time that the vehicle node v remains in all regions within Δt.
Definition of R (v, Q) j ) Representing vehicle node v access area Q j Is defined as the correlation of the time intervals between two adjacent access areas Q of the vehicle node j Is not shown). In the opportunistic transmission process, the time interval is a very important factor affecting the opportunistic transmission, by which it can be predicted that the future vehicle node v accesses the area Q j Is a possibility of (1).
Wherein r (v, Q) j ) Representing the nearest two adjacent vehiclesNode v and region Q j The time interval between the encounters is such that,
representing the total duration of the time interval between two nearest neighbor encounters of the vehicle node v with other regions.
Finally, the three weighing vehicle nodes v and the region Q j The parameters of the correlation between them are combined into a regional correlation:
WCD(v,Q j )＝αF(v,Q j )+βT(v,Q j )+γR(v,Q j )#(21)
wherein α, β, and γ are three coefficients that are dynamically adjustable according to the analysis result of the vehicle node, and satisfy:
α+β+γ＝1#(22)
the area correlation degree of the vehicle nodes reflects the correlation degree of the vehicle nodes and the physical area, and the vehicle nodes with higher area correlation degree access areas relatively frequently and stably, and belong to core nodes in the areas. The regenerated set of social attributes of the vehicle node v is represented as:
Attr v ＝{(Q 1 ,D 1 ),(Q 2 ,D 2 ),(Q 3 ,D 3 ),…,(Q k ,D k )}#(23)
D j ＝WCD(v,Q j ),j＝1,2,3,…,k#(24)#
the state transition method of the vehicle node comprises the following steps:
the change of the positions of the vehicle nodes can enable the vehicle nodes to be in different working states, different data interaction behaviors are generated, and the vehicle nodes are divided into 3 states (shown in table 1) by combining the relationship between the positions of the vehicle nodes and communities: free node state, to-be-joined community state, and joined community state. The state transition diagram between the states is shown in fig. 3. The state of the vehicle node is closely related to the current position of the vehicle node, and the following 3 situations are classified according to whether the vehicle node goes out of the interest area of the vehicle node and whether the vehicle node has joined the community:
(1) When the vehicle node is in a region which is not interested by the vehicle node, the state bit of the vehicle node is as follows: free_node. The vehicle nodes do not seek to join the community nor do they interact with other nodes about the community.
(2) When the vehicle node is in the area of interest of the vehicle node, if the vehicle node has not joined the community of the area, the state bit of the vehicle node is: wait_node. The vehicle nodes periodically broadcast Join message messages, seek to Join the community, or receive Join message messages from other nodes to form a new community.
(3) When the vehicle node is in the area of interest of the vehicle node, if the vehicle node has joined the community, the vehicle node periodically broadcasts a Combine message, and if other communities exist in the area, the two communities are combined. Meanwhile, the vehicle nodes also receive Join message messages from other vehicle nodes to be added into the community state, and bring the Join message into own communities.
TABLE 1
The community formation method between the vehicle nodes comprises the following steps:
the new community is formed by two vehicle NODEs that are both in the WAIT NODE state. K regions of interest concerning the vehicle node v, that is, k pieces of social attribute information possessed by the vehicle node v are obtained from the historical travel of the vehicle node v in one detection period, as shown in the formula (23). The more lively the vehicle node, the more social attributes it has.
When the vehicle NODE v is in one of the k areas S, the vehicle NODE v is in wait_node state because the vehicle NODE v has not joined the community yet, and broadcasts a Join message to the surroundings seeking to Join the community.
Some key fields in the Join message are listed in table 2:
type: the type of the message;
·N v : the number of social attributes possessed by the vehicle node v;
·
the social attribute ID of the vehicle node v with respect to the area S;
·
community relevance of vehicle node v with respect to region S.
TABLE 2
If the vehicle NODE u within the communication range of the vehicle NODE v receives the Join message and the vehicle NODE u is also in the wait_node state, the vehicle NODE u and the vehicle NODE v form a new community C s 。
When the vehicle node u receives the Join message, it first compares
And->
Generating ID information of a new community->
/>
Because the more relevant vehicle nodes of the community are more closely tied to this area, the new location information is offset toward the more relevant vehicle nodes of the community. Meanwhile, the regional judgment and the position coordinate aggregation are facilitated when the two vehicle nodes access the region S again.
New community C s After generation, the vehicle NODE u changes its own state to the UNITED_NODE state, and Adding community C to own community attribute list s State information of (a), comprising: community ID, community creation time, and community capacity. Wherein the community ID is defined by
The generation, community creation time is generated by the vehicle node u, and the community capacity is 2. Final vehicle node u joins community C s 。
And then the vehicle node u returns a Create message to inform the vehicle node v to join the community.
Some key fields in the Create message are listed in table 3:
type: message type;
·
community C s Is a unique identifier ID of (a);
·
community C s Is generated by the method;
·
community C s Content, i.e. the number of nodes involved.
TABLE 3 Table 3
After receiving the Create message, the vehicle node v adds a newly generated community C into its own community list s Status information, the vehicle node v also joins the community C s As shown in table 4.
TABLE 4 Table 4
The above-described method of forming communities between vehicle nodes is different from other methods of clustering or partitioning communities in that nodes in the same community do not indicate that they are currently in the same physical area. The vehicle nodes in the same community have similar social attributes, which can be expressed in aspects of similar consumption level, similar consumption field, similar living habit and the like. The community is formed by utilizing the social attribute of the vehicle nodes, and in the message delivery and distribution scene with stronger directivity, larger benefit returns can be obtained with smaller resource consumption and faster speed.
The community maintenance method between the vehicle nodes comprises the following steps:
the vehicle nodes firstly acquire the social attribute of the vehicle nodes, and then the vehicle nodes similar to other social attributes form communities. Similar communities will gradually merge to form communities that are stable over a long period of time and that have a greater coverage. However, the change of the social relationship, living state or working state of the person often leads to the change of the behavior rule, and simultaneously leads to the change of the social attribute of the vehicle node, and the change of the community of the vehicle node. Thus, for community changes, maintenance is divided into the following cases:
(1) Vehicle NODEs in WAIT NODE state join the community
When the vehicle NODE v is in the interest area, periodically broadcasting Join message to the surrounding, and when the vehicle NODE u in the communication range of the vehicle NODE v is in UNITED_NODE, returning a permission message after receiving the Join message, allowing the vehicle NODE v to Join the community C s And updates own community state information.
Some key fields in the permission message are listed in table 5:
type: message type;
·
community C of vehicle node u s An ID of (2);
·
community C of vehicle node u s Generating time;
·
Community C of vehicle node u s Number of nodes in.
TABLE 5
After receiving the permission message, the vehicle NODE v updates its own community state information and changes from the wait_node state to the united_node.
(2) Merging of communities
When the vehicle NODE v is not in its own region of interest, the own state is updated to the free_node state. When the vehicle NODE v is in a community-added area S, the vehicle NODE v is in a united_node state, and periodically broadcasts a Combine message to the surrounding, and performs community merging with other NODEs in the area S which are also in the united_node state.
Some key field information in Combine message messages are listed in table 6:
type: the type of the message;
·
vehicle node v in community C s Is a community centrality of (a);
·
community C of vehicle node v s An ID of (2);
·
community C of vehicle node v s Is generated by the method;
·
community C of vehicle node v s Is a node number of (a) in the network.
TABLE 6
If there is a vehicle NODE u in the area S, which is also in the united_node state, after receiving the Combine message, the community information from the vehicle NODE v
And about community C by oneself s Information of->
Comparison is performed:
And by comparing the number of the vehicle nodes in the communities, the communities with smaller capacity are integrated into communities with larger capacity, so that communities with larger coverage range are formed. The newly established community is integrated into an earlier established community, and the stability of the existence of the community is maintained.
Suppose that after comparison, vehicle node u chooses to join the society of vehicle node vIf the area is in the area, the vehicle node u modifies the community state list of the vehicle node u and updates the community state list
And->
Is a value of (2). And then returning an Update message to inform the vehicle node v that a new vehicle node is added, and updating the community state list.
Some key fields in Update message messages are listed in table 7:
type: message type;
action: representing specific actions including joining communities, joined communities, updating capacity, etc.;
·
community C s An ID of (2);
·
community C s Is generated by the method;
·
community C s Number of nodes in.
TABLE 7
After receiving Update message, vehicle node v updates community C in community state list s C of (2) s Values. Finally, the vehicle node u deletes the original community
Is added to the community of vehicle node v +.>
And new community C s Comprises a community->
Is favorable for the original capacity belonging to the community +.>
Is accessed again by the vehicle node of community C s When incorporating a new community C s . Meanwhile, the method is also beneficial to combining other communities with smaller capacity, and larger and more stable communities are formed.
(3) Community status information update
The vehicle NODEs u and v are in the same area and are in the state of united_node, but the community state is changed because the two vehicle NODEs respectively generate information interaction processes with other NODEs, allow the other NODEs to join the community or be combined with other communities.
The vehicle NODE in the united_node state will broadcast Update message indicating its own NODE state and community state. The vehicle node receiving the Update message will compare and analyze the two community states. Community C for vehicle nodes u and v p If the values are different, entering a community merging process; if C p The values are the same, but C n A larger value overrides a smaller value if the values are different.
(4) Vehicle nodes leave the community
When the vehicle node v changes due to behavior habits, and the interest area changes in a new detection period, the community information which can not be accessed any more is deleted. However, other vehicle nodes in the community do not necessarily meet the vehicle node v, the vehicle node needs to send an Update message to inform other nodes in the community to Update community information, select an appropriate relay node to forward the message, and then delete the community information.
According to the community maintenance method, corresponding processing mechanisms are respectively formulated aiming at the joining of the vehicle nodes into the community and the leaving of the community, and the combination of the communities and the updating of the community state so as to maintain the long-term stability of the communities.
The community relativity and community dispersion method comprises the following steps:
the vehicle nodes v that have joined the community possess their own social attribute combinations and community state list:
Attr v ＝{(Q 1 ,D 1 ),(Q 2 ,D 2 ),(Q 3 ,D 3 ),…,(Q k ,D k )}#(27)
when the vehicle node v joins the community C in the area S, the value of the community correlation WCD of the vehicle node v about the area S can be used as the value of the community correlation of the vehicle node v about the community C to obtain a set
C v ＝{(C 1 ,D 1 ),(C 2 ,D 2 ),(C 3 ,D 3 ),…,(C k ,D k )}#(28)
The community relativity reflects the frequency and the stability of the vehicle node v accessing the community C, so that the vehicle node with higher community relativity in the community C has higher viscosity with the community C and can be understood as a core node in the community C. And the node with higher community correlation is selected as the relay node, so that the forwarding of data to or in the community C is facilitated.
To facilitate the forwarding of data across communities, the concept of inter-community dispersion is introduced, and the community dispersion of vehicle node v for community C is defined as:
wherein L (C) i C) represents community C i Euclidean distance to community C:
The value of the community dispersion BCD (v, C) reflects the distance correlation between the vehicle node v and the target community C, and the closer the community set of the vehicle node v is to the target community C, or the lower the degree of dispersion about the target community is, the smaller the value of the BCD (v, C) is, the greater the possibility that the vehicle node v approaches the target community C is, the closer the social properties of the vehicle node v and the vehicle node in the target community C are, and the easier the vehicle node v and the target community C meet; conversely, the greater the value of BCD (v, C), the less likely it is that the vehicle node v approaches the target community C, the greater the social attribute of the vehicle node v and the vehicle node within the target community C differ, and the less likely it is that the vehicle node v will meet.
Obviously, vehicle nodes with smaller BCD (v, C) values are suitable as relay nodes for forwarding data packets to community C.
The community relativity and community dispersion method reflects the closeness of vehicle nodes accessing the community, and simultaneously represents the probability of meeting other vehicle nodes in the community. The community dispersion reflects the probability that the vehicle node meets the destination community, that is, the probability that the vehicle node can successfully deliver the data packet to the destination community. The community relativity and the community dispersion fully play the advantages of opportunistic routing transmission based on communities, reduce the number of data packet copies in a network, reduce network load and improve the success rate of data packet delivery.
The data forwarding method comprises the following steps:
(1) Data forwarding mechanism for current node in destination community
When a vehicle node receives a data packet, if the community in which a destination node is located is also a community of the current vehicle node, firstly detecting whether a vehicle node which also belongs to the community to which the destination node belongs exists in a vehicle node set in a communicable range, and if a plurality of nodes which belong to the destination community are detected, selecting the vehicle node with the largest community relativity for data forwarding; otherwise, the maximum duration is reserved by using the community correlation calculation data packet of the vehicle node, and if the vehicle node does not reach the target community before reaching the maximum duration or does not meet the node with larger community correlation, the vehicle node with the minimum community dispersion from the target community is selected for forwarding.
Defining the maximum reserved time period RT (v, D) of the vehicle node v for the data packet D as follows:
wherein D is 1 Is an adjustable parameter of the data packet and is adjustable according to the importance and urgency of the data packet; delta is a time adjustment factor that prevents the value of RT (v, D) from being too large, resulting in a long-term storage space occupation of the packet. WCD (v, C) D ) The community correlation of the vehicle node v with respect to the community of the destination node of D is obtained from the community state list because the community list of v contains the community of the destination node of D, and the meeting time interval R (v, C) D ) R (v, Q) in the same formula (20) D ) Because the vehicle node has joined the zone Q D Community C of (2) D So area Q D And community C D Is consistent; BCD (v, C) D ) Is a vehicle node v and a destination community C D Although the vehicle node v already belongs to the destination community C D However, the value of the community dispersion can still measure the dispersion degree of the active area of the vehicle node about the destination community, and the smaller the community dispersion degree is, the closer the active area of the vehicle node v is to the destination community, the longer the retention time of the data packet is, and the possibility of forwarding the data packet to the destination community is increased.
(2) Data forwarding mechanism for current node outside destination community
When the current node and the destination node belong to the same community, the data is preferentially forwarded to the vehicle node with higher community correlation in the community, and then forwarded to the destination node. When the current node is out of the target community, firstly selecting a vehicle node with smaller community dispersion from the target community as a relay node, and simultaneously calculating the retention time of the data packet, wherein the current node is not in the target community, so that the community correlation WCD (v, C D ) The value of (2) and the encounter time interval R (v, C D ) So node v not within the destination community is concerned withThe maximum reserved period RT (v, D) of the data packet D is:
wherein ε is a time adjustment factor that prevents the value of RT (v, D) from becoming too large; n is the number of neighbor nodes of the vehicle node v,
is the historical average retention time of the vehicle node v. When the number of the neighbor nodes of the vehicle node v is larger, the maximum reserved time length of the vehicle node v is shorter, and the neighbor nodes are more suitable for completing the forwarding task, so that the storage cost of the vehicle node v can be saved; when there are fewer vehicle nodes in the neighborhood of the vehicle node v, which are close to the community of the destination node, the vehicle node v may be more suitable for completing the forwarding task, so the maximum reservation duration is longer.
The data forwarding method combines the thought of opportunity transmission, fully utilizes the movement rule and the meeting rule of the vehicle nodes, and establishes a community-based data forwarding method.
According to the vehicle-mounted ad hoc network opportunistic routing working process, the working front-mentioned vehicle node social attribute generation method, the vehicle node area correlation, the vehicle node state conversion method, the vehicle node community formation method, the vehicle node community maintenance method, the vehicle node community correlation degree and community dispersion method and the data forwarding method, the vehicle-mounted ad hoc network opportunistic routing method firstly generates a vehicle node social attribute set, then communities are formed among vehicle nodes with the same social attribute, all vehicle nodes in the communities commonly maintain community state update, and finally the decentralized and self-organizing vehicle-mounted opportunistic network is formed. The specific working mode is as follows:
1. Community initialization phase
The community initialization stage is mainly divided into two sub-stages: the first stage is that a vehicle node generates self social attribute information through analyzing a self history destination set; the second phase is to form a stable community between vehicle nodes with similar social attribute features.
As shown in fig. 4, when the vehicle node v starts a new detection period, position coordinate information and access duration information of all the destinations accessed in the period are recorded. When the detection period is over, a history destination set PT of the vehicle node v is generated v Then obtaining a new set PT after coordinate aggregation and time aggregation through a K-means clustering algorithm v And (3) a step of performing the following steps. Calculating and obtaining a time length threshold t of accessing the region of interest according to the access habit of the vehicle node 0 By t 0 Removing the areas with shorter total access time length to finally obtain the interest areas of the vehicle node v, namely the social attribute of the vehicle node v combined with Attr v . The first phase is completed.
As shown in fig. 5, when the vehicle NODE is in its own region of interest, if the status bit is wait_node, it indicates that the vehicle NODE is looking for an opportunity to Join the community, and the vehicle NODE periodically broadcasts Join message messages to vehicle NODEs within the communicable range, and also listens for whether to receive Join message messages broadcast from other vehicle NODEs that are also in wait_node. If the vehicle node receives the Join message from other vehicle nodes, the Join message is not broadcasted any more, then the community state information is generated according to the vehicle node data in the received Join message, the community state list is updated, and the Create message is returned to inform the opposite vehicle node that a new community is generated. The second phase is completed so far, and the community initialization phase is also completed.
After the community initialization stage is completed, some vehicle nodes have mutually generated some communities, but the communities have smaller capacity, the position IDs of the communities cannot reflect the social characteristics of the area to the greatest extent, and the communities gradually tend to be stable only after the community maintenance stage.
2. Community maintenance phase
The community maintenance stage is to continuously update the community state, so that the community gradually tends to be stable. Aiming at three situations of joining a community by a vehicle NODE in a WAIT_NODE state, merging the communities, updating the community state and leaving the community by the vehicle NODE, respectively adopting a targeted processing scheme.
(1) Vehicle NODEs in WAIT NODE state join the community
Fig. 6 illustrates a process in which a vehicle NODE in a wait_node state joins a community, wherein the process is distinguished from a community formation process in that if the vehicle NODE does not receive a Create message after broadcasting a Join message, but receives a permission message, it indicates that a community exists in the area, and the vehicle NODE has been allowed to Join the community, the vehicle NODE updates its own community state.
(2) Community merge
As shown in FIG. 7, a vehicle NODE in UNITED_NODE periodically forwards a Combine message containing information about its own community state, and compares two communities C after receiving the Combine message with a vehicle NODE in UNITED_NODE n Whether or not the values are the same, larger C n C with smaller value coverage n Value, large community is merged with small community to enable stable increase of community capacity, if C n The value is also the same, smaller C t C with larger value coverage t And (3) forming a long-time community and combining the newly formed communities, so that the communities gradually tend to be stable. And finally returning an Update message to inform the opposite party to Update the community state list.
(3) Community status update
FIG. 8 shows a community state Update flow between vehicle NODEs, in which a vehicle NODE in the UNITED_NODE state periodically broadcasts Update message, then detects whether Update message from other vehicle NODEs is received, and compares community state C of the other vehicle NODEs when Update message from other vehicle NODEs is received p If the values are the same, entering a community merging flow if the values are different, so that communities C of the two are caused p Tend to be unified, if C p If the values are the same, continue to compare C n Whether or not the values are the same, larger C n C with smaller value coverage n Value, large community and combinationAnd small communities can enable the capacity of communities to steadily increase if C n The value is also the same, smaller C t C with larger value coverage t And (3) forming a long-time community and combining the newly formed communities, so that the communities gradually tend to be stable.
(4) Vehicle nodes leave the community
The community set of vehicle nodes is a subset of the social attribute set, and after the vehicle nodes join the community in the area represented by a certain social attribute, the ID of the community will cover the ID of the social attribute. Thus, community C corresponding to each social attribute p The value is the same as the value of the social attribute Q.
As shown in fig. 9, each time a vehicle node completes one detection cycle, it is compared whether the social attribute set generated by the nearest region of interest and the original social attribute set are changed, and if a certain community is no longer included in the social attribute set of the vehicle node, it is necessary to notify the vehicle node in the community to change the community state information. Because the vehicle node may not be interested in the community any more, the vehicle node sends an Update message to the destination community, and forwards the message according to the message forwarding rule from outside the community to inside the community. And the vehicle node of the target community receiving the Update updates the state value of the corresponding community in the community state list while forwarding the data packet. Finally, the updated information of the community is gradually spread to the vehicle nodes of the whole community.
3. Data forwarding phase
The network is a data transmission path, the communities are established to improve the transmission efficiency of data in the network, and fig. 10 specifically illustrates the data forwarding rule in the vehicle-mounted opportunistic network based on the SAVOR protocol.
After generating/receiving the data packet, the vehicle node firstly judges whether the own community list contains the community of the destination node, and if so, the vehicle node belongs to a forwarding scene of the data in the community. Detecting whether a vehicle node with the same community exists in the neighbor nodes by the vehicle nodes, and if so, preferentially selecting the vehicle node with the largest community correlation as a relay node; otherwise, calculating the maximum reserved time length of the data packet, and taking charge of carrying and forwarding the data packet by the user.
If the vehicle node does not belong to the target node community, the vehicle node belongs to a scene of community outward-inward transmission, the vehicle node preferentially selects the vehicle node with the smallest community dispersion from the target community in the neighbor nodes as a relay node, and meanwhile calculates the maximum retention time of the data packet, and forwards the data packet to the target community as possible.
The above examples illustrate only one embodiment of the invention, which is described in more detail and is not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. A social attribute-aware vehicular ad hoc network opportunistic routing method is characterized in that: comprises the steps of,
s1, community initialization: the vehicle nodes periodically record historical destination information of the vehicle nodes, a social attribute set of the vehicle nodes is generated, and communities are formed among the vehicle nodes with the same social attribute;
s2, community maintenance: all vehicle nodes in the community continuously update the community state, and the community state update is commonly maintained, so that the community gradually tends to be stable;
s3, a data forwarding stage: transmitting the self-carried data packet to a destination node to form a self-organized self-centering vehicle-mounted opportunity network;
the vehicle node obtains the running state, the position and the stay time of the vehicle node by using a GPS, a vehicle-mounted sensor, intelligent mobile equipment or an electronic map; the vehicle node is equipped with an on-board system, a storage device and a short-range communication device to have computing capability, storage capability and short-range communication capability;
the community initialization stage is realized through a vehicle node social attribute generation method, a vehicle node state conversion method and a community formation method among vehicle nodes; the community maintenance stage is realized by a community maintenance method among vehicle nodes; the data forwarding stage is realized through community relativity, a community dispersion method and a data forwarding method;
The vehicle node social attribute generation method comprises the following steps:
a1, a vehicle node v obtains position coordinate information of the vehicle node v by using GPS equipment, and generates a position coordinate p (x, y) for each destination;
a2, periodically recording N destination positions p and residence time t visited by the vehicle node v in a period of time to form a historical destination record set PT of the vehicle node v v ；
A3, obtaining a new set PT' after coordinate aggregation and time aggregation through a K-means clustering algorithm v ；
A4, removing the areas with shorter access time according to the access habit of the vehicle node to obtain k interested areas as social attribute marks Q of the vehicle node v, wherein the generation of the social attribute set of the vehicle node v is expressed as follows: atter v ＝{Q 1 ,Q 2 ,Q 3 ...,Q k }；
The states of the vehicle NODEs comprise a FREE NODE state FREE_NODE, a community state waiting to be added WAIT_NODE and a community state already added;
the community formation method between the vehicle nodes comprises the following steps:
c1, according to the history journey of a vehicle node v which is not added into a community in a detection period, obtaining k interest areas of the vehicle node v, namely obtaining k social attribute information of the vehicle node v;
C2, the vehicle NODE v is in one of the k interest areas S, the vehicle NODE v is in the WAIT_NODE state, and broadcasts a Join message to the surrounding, and a joining community is sought, including a newly built community or joining an existing community;
and C3, forming a new community by the following method: vehicle node v receives Join message messages of vehicle node u which has not joined the community within a communicable rangeIf the vehicle NODE u is also in the WAIT NODE state, then the vehicle NODE u and the vehicle NODE v will form a new community C S And vehicle node v returns a Create message informing vehicle node u to join the new community C S ；
C4, joining the existing community by the following method: when the vehicle node v broadcasts a Join message to the surrounding, and receives the permission message, the vehicle node v joins the community existing in the permission message.
2. The vehicular ad hoc network opportunistic routing method according to claim 1, wherein: the removing of the area with shorter access time according to the access habit of the vehicle node specifically comprises the following steps: calculating and obtaining a time length threshold t of accessing the region of interest according to the access habit of the vehicle node 0 By t 0 Removing areas with shorter total access duration; wherein, the duration threshold t of the region of interest 0 The method comprises the following steps:
wherein t represents the average residence time of the vehicle node v; sigma represents the standard deviation of the residence time of the vehicle node v; μ is an adjustable coefficient representing the difference in behavioral activity of different vehicle nodes.
3. The vehicular ad hoc network opportunistic routing method according to claim 2, wherein: the state of the vehicle node is related to the current position of the vehicle node, and the state transition method of the specific vehicle node comprises the following conditions:
b1, if the vehicle NODE is in a region which is not interested in the vehicle NODE, the state bit of the vehicle NODE is FREE_NODE; in this state, the vehicle node does not seek to join the community, nor does it generate interaction with other nodes about the community;
b2, the vehicle NODE is in an area of interest of the vehicle NODE, and a community of the area is not added yet, and the state bit of the vehicle NODE is WAIT_NODE; in the state, the vehicle node periodically broadcasts a Join message, seeks to Join the community, or receives Join message from other nodes to form a new community;
b3, the vehicle node is in the area of interest of the vehicle node and has joined the community, and the vehicle node periodically broadcasts a Combine message; in this state, if other communities exist in the area are found, the two communities are combined; the vehicle node also receives Join message messages from other vehicle nodes to be joined in the community state, and brings the Join message messages into own communities.
4. The vehicular ad hoc network opportunistic routing method of claim 3, wherein: the community maintenance method between the vehicle nodes comprises the following maintenance methods of the maintenance conditions: a maintenance method for updating community state information and a maintenance method for leaving communities by vehicle nodes.
5. The vehicular ad hoc network opportunistic routing method according to claim 4, wherein:
the maintenance method for community state information update comprises the following steps: the vehicle NODE joins the community and the combination of the community to change the state of the community, and the vehicle NODE in the UNITED_NODE state broadcasts Update message to indicate the state of the self NODE and the state of the community; the vehicle node which receives the Update message compares and analyzes the two community states, and updates and maintains the community state information;
the maintenance method for the vehicle node to leave the community comprises the following steps: and when the behavior habit of the vehicle node v changes in a new detection period, sending an Update message to inform other nodes in the community of updating community information, selecting a proper relay node to forward the message, deleting the community information, and leaving the community.
6. The vehicular ad hoc network opportunistic routing method according to claim 4, wherein: the community relativity reflects the relativity degree of the vehicle nodes and the physical area, and the community relativity degree The higher the vehicle node, the more frequent and stable the access community, belonging to the core node in the community; the social attribute of the vehicle node is determined by the area frequently accessed by the vehicle node, so as to reflect the vehicle node v and the area Q to the greatest extent j Is also representative of the social relevance of the vehicle node v in the region Q j The influence of the community correlation is defined as:
WCD(v,Q j )＝βF(v,Q j )+βT(v,Q j )+γR(v,Q j )
wherein F (v, Q) j ) Is the vehicle node v and the region Q j Correlation between access frequencies of T (v, Q) j ) Representing vehicle node v and region Q j Correlation between affinities of R (v, Q) j ) Representing vehicle node v access area Q j Correlation of time intervals of (a);
α, β, and γ are three coefficients that are dynamically adjustable according to the vehicle node analysis result, and satisfy:
α+β+γ＝1。
7. the vehicular ad hoc network opportunistic routing method according to claim 4, wherein: the community dispersion reflects the probability that the vehicle node meets the target community, namely the probability that the vehicle node can successfully deliver the data packet to the target community;
the community dispersion of the vehicle node v for community C is defined as:
wherein Ci represents the ith community,
cv represents a community set to which the vehicle node v belongs,
L (Ci, C) represents the Euclidean distance of community Ci from community C,
di represents the regional relevance of the ith community of vehicle nodes,
the value of the community dispersion BCD (v, C) reflects the distance correlation between the vehicle node v and the target community C, and the closer the community set of the vehicle node v is to the target community C, or the lower the degree of dispersion about the target community is, the smaller the value of the BCD (v, C) is, the greater the possibility that the vehicle node v approaches the target community C is, the closer the social properties of the vehicle node v and the vehicle node in the target community C are, and the easier the vehicle node v and the target community C meet; conversely, the greater the value of BCD (v, C), the less likely it is that the vehicle node v approaches the target community C, the greater the social attribute of the vehicle node v and the vehicle node within the target community C differ, and the less likely it is that the vehicle node v will meet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110839760.1A CN113518327B (en) | 2021-07-23 | 2021-07-23 | Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110839760.1A CN113518327B (en) | 2021-07-23 | 2021-07-23 | Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113518327A CN113518327A (en) | 2021-10-19 |
| CN113518327B true CN113518327B (en) | 2023-05-02 |
Family
ID=78067743
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110839760.1A Active CN113518327B (en) | 2021-07-23 | 2021-07-23 | Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113518327B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107509229A (en) * | 2017-08-03 | 2017-12-22 | 华南理工大学 | A kind of car networking chance method for routing calculated based on vehicle node centrality |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150338226A1 (en) * | 2014-05-22 | 2015-11-26 | Telogis, Inc. | Context-based routing and access path selection |
| CN107318141B (en) * | 2016-04-27 | 2021-04-09 | 华为技术有限公司 | Data transmission method, equipment and system |
| CN110784886B (en) * | 2019-11-11 | 2022-05-17 | 华中师范大学 | Mobile opportunity network message transmission method based on node social attributes |
-
2021
- 2021-07-23 CN CN202110839760.1A patent/CN113518327B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107509229A (en) * | 2017-08-03 | 2017-12-22 | 华南理工大学 | A kind of car networking chance method for routing calculated based on vehicle node centrality |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113518327A (en) | 2021-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Senouci et al. | Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues | |
| Ren et al. | A mobility-based scheme for dynamic clustering in vehicular ad-hoc networks (VANETs) | |
| Qi et al. | SDN-enabled social-aware clustering in 5G-VANET systems | |
| Al-Kharasani et al. | An adaptive relay selection scheme for enhancing network stability in VANETs | |
| Khakpour et al. | Using clustering for target tracking in vehicular ad hoc networks | |
| Althamary et al. | A survey on multi-agent reinforcement learning methods for vehicular networks | |
| Pourghebleh et al. | Towards efficient data collection mechanisms in the vehicular ad hoc networks | |
| Saleh et al. | A reliable routing protocol for vehicular ad hoc networks | |
| Meneguette et al. | SERVitES: An efficient search and allocation resource protocol based on V2V communication for vehicular cloud | |
| Hou et al. | Data forwarding scheme for vehicle tracking in named data networking | |
| Cárdenas et al. | A probability-based multimetric routing protocol for vehicular ad hoc networks in urban scenarios | |
| Padmanaban et al. | An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system | |
| Arkian et al. | FcVcA: A fuzzy clustering-based vehicular cloud architecture | |
| Mujahid et al. | Cluster-based location service schemes in VANETs: current state, challenges and future directions | |
| Yadav et al. | Secure and reliable routing protocols for VANETs | |
| Rivoirard et al. | CBL: A clustering scheme for VANETs | |
| CN107509229A (en) | A kind of car networking chance method for routing calculated based on vehicle node centrality | |
| Kaisar et al. | Decentralized content sharing in mobile ad-hoc networks: A survey | |
| Heidari et al. | Survey on location information services for Vehicular Communication Networks | |
| Sakthipriya et al. | A reliable communication scheme for VANET communication environments | |
| Zhang et al. | A survey of abnormal traffic information detection and transmission mechanisms in VSNs | |
| Kumar et al. | Epidemic and transmission priority based data dissemination model in vehichular adhoc networks (VANETs) | |
| Wahid et al. | Software-Defined Networks and Named Data Networks in Vehicular Ad Hoc Network Routing: Comparative Study and Future Directions | |
| CN113518327B (en) | Social attribute-aware vehicle-mounted ad hoc network opportunistic routing method | |
| Ullah et al. | A social utility-based dissemination scheme for emergency warning messages in vehicular social networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |