CN109640295B - Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene - Google Patents
Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene Download PDFInfo
- Publication number
- CN109640295B CN109640295B CN201910097641.6A CN201910097641A CN109640295B CN 109640295 B CN109640295 B CN 109640295B CN 201910097641 A CN201910097641 A CN 201910097641A CN 109640295 B CN109640295 B CN 109640295B
- Authority
- CN
- China
- Prior art keywords
- rsu
- candidate node
- node set
- vehicles
- node
- 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]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/18—Communication route or path selection, e.g. power-based or shortest path routing based on predicted events
-
- 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]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
In the infrastructure vehicle networking, the whole network can be in a connected state due to the assistance of the infrastructure RSU, namely, vehicles in the range of the RSU can be connected through vehicle-to-vehicle communication or RSU node forwarding. However, since the vehicle may have various behaviors such as acceleration and deceleration, turning, sudden stop, driving off a road network, and the like during the driving process, that is, the degree of freedom is high, the RSU node not only needs to be updated in real time when performing management control on the area where the RSU node is located, but also needs to timely guide the vehicle nodes in the area to be communicated when the topology structure changes, thereby causing RSU communication congestion and data loss problems. Therefore, analyzing and solving connectivity predictions of the nodes of the internet of vehicles in urban road scenes is an effective method for solving the accessibility problem of the internet of vehicles. The invention provides a candidate node set construction method for connected prediction of an infrastructure Internet of vehicles in an urban scene, and relates to a connected candidate node set construction model and a connected candidate node set construction algorithm.
Description
Technical Field
The invention relates to the field of vehicle networking, in particular to a candidate node set construction method for connectivity prediction of infrastructure vehicle networking in an urban scene.
Background
In the infrastructure vehicle networking, with the help of the infrastructure RSU, the whole network can be in a connected state, namely vehicles in the range of the RSU can be connected through vehicle-to-vehicle communication or RSU node forwarding, vehicles crossing the RSU can be connected through the RSU as a routing gateway, and the communication between the vehicles crossing the RSU can be carried out according to a traditional routing mode because the position of the RSU is fixed. The RSU node is not only updated in real time when performing management control on the area, but also needs to guide the vehicle nodes in the area to communicate in time when the topology structure changes, so as to ensure the real-time performance and stability of communication, i.e. accessibility, and the load balance can avoid communication congestion and data loss.
In summary, no candidate node set for performing connectivity prediction when weak connection is present for nodes in a network part of an infrastructure vehicle networking in an urban scene is currently studied. Therefore, the overall connectivity of the vehicle networking network with the infrastructure in the urban scene is maintained, the load balance of the RSU is reduced, and the effective transmission of the vehicle networking data is effectively guaranteed.
Disclosure of Invention
The purpose of the invention is as follows:
the research method of the invention aims at the overall connectivity of the vehicle networking network with infrastructure in an urban scene, reduces the load balancing problem of RSU, constructs a candidate node set facing the connectivity prediction, and can still maintain the overall connectivity of the vehicle networking network when part of nodes of the vehicle networking network are in weak connection.
No relevant research has been carried out on this problem.
Therefore, the invention specifically provides the following technical scheme:
a candidate node set construction method for connectivity prediction of an infrastructure-based Internet of vehicles in an urban scene is characterized by comprising the following steps of:
The method for constructing the model by the connected candidate node set comprises the following steps:
in the infrastructure vehicle networking, because the RSU node exists to assist the connection, when the temporary disconnection condition occurs, the RSU node can be used as a bridging node to play a role similar to a switch in the network, so that the disconnected sub-network is reconnected. However, data that can be originally transmitted by the vehicle ad hoc network is forwarded by the RSU node, so that the load of the RSU node increases sharply, and the efficiency of data transmission between the RSU node and the relatively static RSU node is not high due to the high-speed characteristic of the vehicle. Therefore, after the weak connection is detected at the time t, an effective method needs to be adopted to obtain the selected node, so that an effective connection edge can be obtained from the selected node preferentially after the connection is disconnected at the time t + in the future, and the RSU node is prevented from fulfilling the role of the switch.
Two nodes v not communicated at time t in Internet of vehiclesi，vjThe possibility of connectivity occurring in a short delta t time is defined as the node attraction VertexAttraction, denoted VA (v)i，vj) The mathematical expression is (1):
wherein, Relu function is the activation function commonly used in deep learning, Relu (x) max (0, x), the function image is shown in FIG. 2Relu function image,
the coefficients are to conform to the centrality characteristics of the nodes. VecDis (x)i，xj) The method is a vector distance calculation function trained in a predictive measurement model of the communication strength between vehicle nodes based on the analysis of the vehicle networking space-time data in the urban scene (the predictive measurement model of the communication strength between vehicle nodes based on the analysis of the vehicle networking space-time data in the urban scene (applicant: university, patent application number: 201810824233.1) applied by inventor of Chengdu et al in 2018, 7, 25 and 7 months)i，xjIs a node vi，vjThe corresponding attribute vector.
Two-node set S in Internet of vehiclesa，SbA pair of points with the greatest gravity between the nodes is defined as a connecting candidate node CandidatePoint and is marked as CP (S)a，Sb) The model is as follows:
according to the computational formula of CandidatePoint, when weak connection is disconnected, a node set S which needs to be preferentially detected is equal to CP (BV)a，(BVb))∪CP(BVb，(BVa))∪CP((BVa)，(BVb) And the elements in the set are<vi，vj>The node gravity degree ordering between two points is consistent with the priority order of the connected candidate set.
The construction algorithm of the connected candidate node set comprises the following steps:
in the infrastructure-based vehicle networking system, all RSU information and node sets V (t), edge sets E (t), connection strength sets W (t), corresponding weak connection point sets BV (t) and weak connection edge sets BE (t) at the time t are set to obtain a connected candidate node set construction algorithm, and the specific algorithm process is shown as algorithm 1.
Advantageous effects
The invention aims to provide a candidate node set for connectivity prediction, which is constructed in order to maintain the overall connectivity of an Internet of vehicles network when infrastructure Internet of vehicles network nodes present weak connection under the condition that the road network conditions and the structure in actual traffic of an urban scene are very complex.
In the urban scene, under the conditions that various behaviors with high degrees of freedom such as acceleration and deceleration, turning, sudden stop, driving off a road network and the like are possible in the driving process of a vehicle, the communication capacity of the whole network, the communication congestion of RSU nodes and data loss are caused, the method for constructing the connected prediction candidate node set can be used for keeping the whole infrastructure vehicle networking network connected all the time.
Drawings
Fig. 1 intersection network topology
FIG. 2Relu function image
FIG. 3 Algorithm for constructing connected candidate node set
FIG. 4 is a flow chart of the method of the present invention
Detailed Description
The disclosure of the "predictive metric model of connectivity strength between vehicle nodes based on analysis of spatiotemporal data of the internet of vehicles in urban scenes" (applicant: university, patent application No. 201810824233.1) by chengjijun et al, filed by the inventor on 25/7/2018, can be considered as an integral part of the present description.
The specific implementation process of the invention is shown in fig. 4, and comprises the following 2 aspects:
connecting candidate node set construction model
Second, connecting candidate node set construction algorithm
①
Connected candidate node set construction model
In the infrastructure vehicle networking, because the RSU node exists to assist the connection, when the temporary disconnection condition occurs, the RSU node can be used as a bridging node to play a role similar to a switch in the network, so that the disconnected sub-network is reconnected. However, data that can be originally transmitted by the vehicle ad hoc network is forwarded by the RSU node, so that the load of the RSU node increases sharply, and the efficiency of data transmission between the RSU node and the relatively static RSU node is not high due to the high-speed characteristic of the vehicle. Therefore, after the weak connection is detected at the time t, an effective method needs to be adopted to obtain the selected node, so that an effective connection edge can be obtained from the selected node preferentially after the connection is disconnected at the time t + in the future, and the RSU node is prevented from fulfilling the role of the switch.
As shown in the road condition network topology of fig. 1, e is detected1，e2，e3For weak connections, the reason is node v1To steer away from the network, an upward v results2The nodes and the connected network are disconnected from the underlying network, causing a temporary disconnection of the network. We need to be on both sides of the neighbor node, v2And v3，v4The connection possibility of the RSU node is calculated and predicted so as to determine whether to be listed in an alternative node or directly use the RSU node as a temporary bridging point to perform auxiliary connection when the RSU node is disconnected.
The number k of the groups in the weak connection detection is set to 2, that is, the graph G may be divided into two subgraphs, i.e. a and b, and the vertices on the two sides of the weak connection form a boundary node set BVaBV andbwhereas the set of neighbors in the graph is denoted (ξ), when ξ is a node then (ξ) represents the set of all neighbors of ξ, when ξ is a set, (ξ) represents the set of all neighbors to the node in ξ, where
So our goal is to aim at (BV)a，(BVb))，(BVb，(BVa))，((BVa)，(BVb) Three groups of nodes perform correlation calculation to determine whether any candidate nodes meeting the requirements appear.
Two nodes v not communicated at time t in Internet of vehiclesi，vjThe possibility of connectivity occurring in a short delta t time is defined as the node attraction VertexAttraction, denoted VA (v)i，vj) The mathematical expression is (1):
wherein Relu function is an activation function commonly used in deep learning, Relu (x) max (0, x), and the function image is shown in FIG. 2Relu function image,
the coefficients are to conform to the centrality characteristics of the nodes. VecDis (x)i，xj) The method is a vector distance calculation function trained in a predictive measurement model of the communication strength between vehicle nodes based on the analysis of the vehicle networking space-time data in the urban scene (the predictive measurement model of the communication strength between vehicle nodes based on the analysis of the vehicle networking space-time data in the urban scene (applicant: university, patent application number: 201810824233.1) applied by inventor of Chengdu et al in 2018, 7, 25 and 7 months)i，xjIs a node vi，vjThe corresponding attribute vector.
According to a predictive measurement model of the communication strength between vehicle nodes based on the analysis of the spatiotemporal data of the Internet of vehicles in an urban scene (the inventor of Chengjun et al, applied in 2018, 7, 25.7, the predictive measurement model of the communication strength between vehicle nodes based on the analysis of the spatiotemporal data of the Internet of vehicles in an urban scene (applicant: university, patent application No. 201810824233.1), a communication coefficient ConnexFactor (v) for determining whether the nodes are communicated or noti，vj，t)＝[Distance(vi，vj)≤Range]Dependent on the distance between nodes, hence the rootIt is important to estimate the position at time t + at from the state at time t of the node. Because of the short delta-t time, a basic displacement calculation formula can be adopted
Is processed, and the Distance between two nodes after delta t is recorded as Distance (v'i，v′j) The transient connectivity that occurs in the middle of Δ t is not considered because the time is too short to be of practical significance.
Two-node set S in Internet of vehiclesa，SbA pair of points with the greatest gravity between the nodes is defined as a connecting candidate node CandidatePoint and is marked as CP (S)a，Sb) The model is as follows:
according to the computational formula of CandidatePoint, when weak connection is disconnected, a node set S which needs to be preferentially detected is equal to CP (BV)a，(BVb))∪CP(BVb，(BVa))∪CP((BVa)，(BVb) And the elements in the set are<vi，vj>The node gravity degree ordering between two points is consistent with the priority order of the connected candidate set.
②
Connected candidate node set construction algorithm
In the infrastructure-based vehicle networking system, all RSU information and node sets V (t), edge sets E (t), connection strength sets W (t), corresponding weak connection point sets BV (t) and weak connection edge sets BE (t) at time t are set to obtain a connected candidate node set construction algorithm, the specific algorithm process is shown as algorithm 1, and the algorithm flow chart is shown as figure 3.
The technical scheme is that a neural network model based on tensor factor aggregation is constructed for predicting the communication strength between vehicle nodes aiming at communication prediction of the vehicle networking with infrastructure in an urban scene, and the method is provided.
And constructing a connected candidate node set for connected prediction by taking the connection strength as the weight of the edge, so that a new bridging node can be effectively selected, the load balance of the RSU is effectively reduced, and the connection of the whole Internet of vehicles network with the infrastructure in an urban scene is fundamentally facilitated.
Claims (1)
1. A candidate node set construction method for connectivity prediction of an infrastructure-based Internet of vehicles in an urban scene is characterized by comprising the following steps of:
step 1, constructing a connected candidate node set construction model;
step 2, constructing an algorithm by connecting candidate node sets;
the method for constructing the model by the connected candidate node set comprises the following steps:
two nodes v not communicated at time t in Internet of vehiclesi，vjThe possibility of connectivity occurring in a short delta t time is defined as the node attraction VertexAttraction, denoted VA (v)i，vj) The mathematical expression is (1):
wherein, Relu function is the activation function commonly used in deep learning, Relu (x) max (0, x),
the coefficient is in order to accord with the centrality characteristic of the node; VecDis (x)i，xj) Is a trained vector distance calculation function, x, in the existing prediction measurement model of the communication strength between vehicle nodes based on the analysis of the spatio-temporal data of the Internet of vehicles in the urban scenei，xjIs a node vi，vjA corresponding attribute vector;
range represents the minimum communication distance between nodes;
|(vi) | represents viThe number of the neighbor nodes;
|(vj) | represents vjThe number of the neighbor nodes;
after a time of Δ t, node vi、vjAre respectively moved to v'iAnd v'jFrom Dis tan ce (v'i，v′j) Recording the distance between two nodes of delta t; two-node set S in Internet of vehiclesa，SbA pair of points with the greatest gravity between the nodes is defined as a connecting candidate node CandidatePoint and is marked as CP (S)a，Sb) The model is as follows:
the construction algorithm of the connected candidate node set comprises the following steps:
in the infrastructure-based vehicle networking system, all RSU information and node sets V (t), edge sets E (t), connection strength sets W (t), corresponding weak connection point sets BV (t) and weak connection edge sets BE (t) at the time t are set to obtain a connected candidate node set construction algorithm, and the specific algorithm process is as shown in algorithm 1: inputting: all RSU information and node sets V (t), edge sets E (t), communication strength sets W (t) corresponding to the weak connection point sets BV (t), the weak connection edge sets BE (t), and i ═ 1;
the method comprises the following steps: there are RSU not accessed in the RSU informationiRsu will beiOf weak attachment point BV [ i ]][0]And BV [ i ]][1]Respectively assign to BVa、BVbCalculating two independent critical point sets (BV)a) And (BV)b) And assigned to NeiBV respectivelyaAnd NeiBVb(ii) a Solving for BVaAnd NeiBVbCandidate node set cp0(ii) a Solving for BVbAnd NeiBVaCandidate node set cp1(ii) a Solving NeiBVaAnd NeiBVbCandidate node set cp2(ii) a Will { cp0，cp1，cp2Assign CP, i + 1;
step two: if i is less than len (RSU), turning to the step one; otherwise, outputting;
and (3) outputting: and the candidate node set CP corresponds to the RSU at the time t.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910097641.6A CN109640295B (en) | 2019-01-31 | 2019-01-31 | Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910097641.6A CN109640295B (en) | 2019-01-31 | 2019-01-31 | Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109640295A CN109640295A (en) | 2019-04-16 |
| CN109640295B true CN109640295B (en) | 2020-08-14 |
Family
ID=66064490
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910097641.6A Active CN109640295B (en) | 2019-01-31 | 2019-01-31 | Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109640295B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112689267B (en) * | 2020-12-16 | 2022-05-13 | 同济大学 | Dynamic evolution method for open type unmanned vehicle group in expressway scene |
| CN115356986B (en) * | 2022-08-22 | 2024-08-30 | 电子科技大学 | Be-SA algorithm-based industrial robot absolute positioning precision improvement method |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102724663A (en) * | 2011-03-29 | 2012-10-10 | 上海永畅信息科技有限公司 | Relay-based cooperative communication system for Internet of Vehicles |
| CN102737503B (en) * | 2012-06-20 | 2014-10-29 | 东南大学 | Communication connectivity analysis method for bus dynamic scheduling under internet of vehicles environment |
| CN103747063A (en) * | 2013-12-27 | 2014-04-23 | 哈尔滨工业大学 | Vehicle networking information broadcast based on MTPA algorithm |
| CN103702387B (en) * | 2014-01-08 | 2017-02-08 | 重庆邮电大学 | Social network-based vehicle-mounted self-organization network routing method |
| WO2015160859A1 (en) * | 2014-04-14 | 2015-10-22 | Sirius Xm Radio Inc. | Systems, methods and applications for using and enhancing vehicle to vehicle communications including synergies and interoperation with satellite radio |
| CN104598727B (en) * | 2015-01-07 | 2017-08-25 | 哈尔滨工业大学 | The V2V chain-circuit time delay dynamic prediction method towards in the VANETs of urban road environmental consideration intersection |
| CN104851282B (en) * | 2015-04-24 | 2017-11-07 | 同济大学 | City car networking data transfer path system of selection based on connective mechanism |
| US10531353B2 (en) * | 2015-10-05 | 2020-01-07 | Qualcomm Incorporated | Minimization of resource allocation delay for V2X application |
| CN105722176B (en) * | 2016-01-29 | 2019-04-02 | 同济大学 | There is the connectivity method of the large scale scale heterogeneous network of the car networking of infrastructure in City scenarios |
| CN106972898A (en) * | 2017-03-15 | 2017-07-21 | 北京大学 | Car networking data transmission scheduling method based on channel estimating |
| WO2018174763A1 (en) * | 2017-03-24 | 2018-09-27 | Telefonaktiebolaget Lm Ericsson (Publ) | Resource allocation methods and nodes with self-adapting to different synchronizations |
| CN107196835B (en) * | 2017-05-31 | 2020-08-14 | 同济大学 | Construction method of communication base component for interconnection and intercommunication of large-scale internet of vehicles |
-
2019
- 2019-01-31 CN CN201910097641.6A patent/CN109640295B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109640295A (en) | 2019-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | V2VR: reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability | |
| Li et al. | Adaptive transmission optimization in SDN-based industrial Internet of Things with edge computing | |
| CN112700639B (en) | Intelligent traffic path planning method based on federal learning and digital twins | |
| CN110557732B (en) | Vehicle edge computing network task unloading load balancing system and balancing method | |
| Oubbati et al. | SEARCH: An SDN-enabled approach for vehicle path-planning | |
| CN109640295B (en) | Candidate node set construction method for communication prediction oriented in infrastructure Internet of vehicles in urban scene | |
| Araujo et al. | CARTIM: A proposal toward identification and minimization of vehicular traffic congestion for VANET | |
| CN105208616A (en) | Road topology based adaptive multi-copy routing method in vehicular ad hoc network | |
| CN114499648B (en) | Unmanned aerial vehicle cluster network intelligent multi-hop routing method based on multi-agent cooperation | |
| CN108769926B (en) | Group perception layer-based car networking privacy protection method and car networking framework | |
| Fabian et al. | Programmable objective function for data transportation in the Internet of Vehicles | |
| CN104935659A (en) | Access algorithm based on service quality perception in vehicle area network | |
| CN103200041B (en) | Delay-disruption tolerant network node collision probability Forecasting Methodology based on historical data | |
| CN112929850A (en) | Internet of vehicles data returning method facing edge computing environment | |
| Alghamdi | Route optimization to improve QoS in multi-hop wireless sensor networks | |
| CN109982291B (en) | Method for detecting weak connection of Internet of vehicles with infrastructure in urban scene | |
| CN111404815A (en) | Constrained routing method based on deep learning | |
| Khan et al. | A game theory approach for smart traffic management | |
| Cheng et al. | Research on the prediction-based clustering method in the community of medical vehicles for connected health | |
| Paul et al. | Real-time routing for ITS enabled fog oriented VANET | |
| Prakash et al. | Path Load Balancing Adaptive Gateway Discovery in MANET-Internet Integration Using PSO. | |
| CN106850437B (en) | Complex network routing method based on gravitational field | |
| Christakos | Sensor networks applied to the problem of building evacuation: An evaluation in simulation | |
| Liu | Intelligent processing in vehicular ad hoc networks: a survey | |
| CN107634906B (en) | Cluster-based multi-attribute decision VDTN routing method |
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 |