CN109862537B - Accessibility method in Internet of vehicles community - Google Patents
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Abstract
Because the vehicle nodes in the internet of vehicles move quickly and the topology of the internet of vehicles changes highly dynamically, the internet of vehicles is easy to generate data aggregation, delay and other phenomena, thereby bringing great challenges to the communication and stability of the internet of vehicles to a great extent. However, a good car networking routing strategy needs to maintain not only fast connectivity but also stability of the network, i.e., to ensure accessibility of the network. Therefore, analyzing and understanding the access inside the car networking community is an urgent problem to be solved. Aiming at the problems, the invention provides a method for detecting the internal communication of the car networking community and keeping the internal communication stable, and provides the accessibility of the car networking community.
Description
The invention is further research and development of a prior patent document of networking of vehicles community dynamic evolution method (applicant: college of congressions, patent application number 2019101555842)) applied by inventor of chengdu et al on 3/1 of 2019, which can be regarded as a part of the present specification.
Technical Field
The invention relates to the field of Internet of vehicles, in particular to an accessibility method in an Internet of vehicles community.
Background
Accessibility is one of the most important characteristics of the network to realize interconnection and interworking, and mainly comprises connectivity and stability in the network. Connectivity mainly solves whether point-to-point routing in the network is reachable; the key point of stability is to optimize the network structure and routing strategy, and avoid the network efficiency problem caused by information congestion and transmission delay. The following description focuses on connectivity and stability in the car networking accessibility method.
(1) Connectivity
The research on connectivity of the internet of vehicles is divided into qualitative analysis and quantitative measurement analysis. Qualitative analysis generally refers to the influence of the distribution condition of vehicles on roads or the inherent characteristics of the internet of vehicles on connectivity, and quantitative measurement is to study the specific advantages and disadvantages of different connectivity strategies by comparing indexes such as average data delay or packet loss rate. Jin et al consider the distribution of vehicles on a road as a poisson distribution, and combined with the characteristics of vehicle road constraints, study the effect of vehicle density and communication range on connectivity. In the literature, a two-dimensional random graph model is further adopted for modeling on the basis that vehicle nodes conform to Poisson distribution, the correlation degree between the vehicle density and the minimum wireless transmission distance is qualitatively and quantitatively analyzed, and guidance is provided for the position deployment of key nodes in the internet of vehicles according to the correlation degree. In addition, the MCEGR method mentioned in the first chapter is a compromise for connectivity within and among the car networking communities, but there is a problem that MCEGR is a two-hop routing method, the community size is not very large, and the application range is limited.
(2) Stability of
The stability is one of key indexes for ensuring the continuous communication of the self-organizing network with the topology structure rapidly changing in the Internet of vehicles, and is an important component of the accessibility of the Internet of vehicles. Regarding accessibility schemes based on community evolution, stability of communities is a major concern. Morales et al propose a vehicle-mounted network self-adaptive community clustering algorithm, which predicts the position of a vehicle node at the next moment according to the motion track, the current speed and position, the road condition and the like of the vehicle node, and performs community division by combining the current network topology and the predicted network topology, thereby considering the trend of future changes of the network and improving the duration and stability of the community.
In summary, the methods have problems in connectivity and stability, when the network size is large and the topology structure is highly dynamic, due to lack of adaptive routing, the network connectivity may be insufficient because a part of connections are lost and cannot be recovered quickly, and also the network stability may be poor because data aggregation causes network congestion, causing a part of nodes to exceed the upper limit of the communication load and cannot work normally. Aiming at the problems, the invention provides a communication method (communication access Scheme for vehicle Networking service Networking.
Disclosure of Invention
The purpose of the invention is as follows:
the research method of the invention aims at the accessibility problems caused by the phenomena of fast movement of vehicle nodes in the Internet of vehicles, high dynamic change of the network topology of the Internet of vehicles, easy data aggregation and delay of the Internet of vehicles and the like, sets corresponding excitation functions and penalty functions by utilizing the theory of learning automata and through information exchange and competition deployed among community nodes, adaptively adjusts the forwarding probability of different routes, and achieves the Nash equilibrium state, thereby realizing the purposes of optimizing data transmission in the network as a whole and improving the accessibility of the network of the Internet of vehicles.
The existing problems related to connectivity and stability of the car networking community are that when the network scale is large and the topological structure is highly dynamic, due to lack of adaptive routing, rapid recovery cannot be achieved due to partial connection loss, so that the network connectivity is insufficient, or network congestion is caused due to data aggregation, so that partial nodes exceed the upper limit of communication load and cannot work normally, so that the network stability is poor. The final conclusion is then: the Internet of vehicles is used as a dynamic self-organizing network, the community structure of the Internet of vehicles is difficult to detect in real time only by using a static community discovery method, different routing forwarding probabilities which are adaptively adjusted need to be established, and the purpose of improving the accessibility of the interior of the Internet of vehicles in the community is achieved.
Therefore, the invention specifically provides the following technical scheme: the accessibility method in the Internet of vehicles community specifically comprises the following steps:
step 1. definition of relevant Properties
Step2, accessibility method in Internet of vehicles community
Step 2.1 initialization and update of node information table
Step 2.2 Community head node screening
Step 2.3 common node to head node message forwarding
Step 2.4 message forwarding from head node to ordinary node
Step 2.5 Community accessibility routing Algorithm
Advantageous effects
The invention aims to provide a method for improving community connectivity of the Internet of vehicles and stabilizing the connectivity of the Internet of vehicles under the condition of considering high dynamic complex conditions of the Internet of vehicles.
On the basis of a dynamic evolution method of the vehicle networking community, (the inventor of Chengdu et al applies a dynamic evolution method of the vehicle networking community (applicant: university of the same society, patent application number 2019101555842)) in 3, 1, 2019, the invention provides an accessibility method in the vehicle networking community.
Description of the attached tables
TABLE 1 node information Table field
TABLE 2 node viForward behavior probability vector table of
TABLE 3 head node vchrCommunity distribution of (1) into a probability vector table
Drawings
FIG. 1 shows a flow chart of the community head node and gateway node screening (Algorithm 1 flow chart)
FIG. 2 shows a flowchart of adjusting LA forwarding behavior probability of a node in a community (Algorithm 2 flowchart)
FIG. 3 flow chart of reachability routing method in community (Algorithm 3 flow chart)
FIG. 4LA is a graph of the operation of the LA on each node within the community
FIG. 5 is a flow chart of the method of the present invention
Detailed Description
The specific implementation process of the invention is shown in fig. 5, and includes the following 6 aspects:
(ii) definition of relevant Properties
Initialization and update of node information table
Third, community head node screening
Message forwarding from common node to head node
Message forwarding from head node to ordinary node
Sixth, reach routing algorithm in community
①
Correlation property definition
For nodes in the vehicle networking community, the accessibility scheme adopted by the invention endows the nodes with different roles, namely a community head node, a community gateway node and a community common node, and the definition is as follows:
define 1 Community head node set (CHSet): community CiThe head node (CH) of (1) is a set of nodes with large community centripetal force in the community, if in the community CiThe existence node u satisfies the mathematical expression (1):
wherein η is head node selection factor, generally η is (0.75, 1)]One value of (1), community CiIn which a node u satisfying the above condition is added to CiCHSet of (1). The node in the CHSet is the current community CiThe communication quality of the nodes is better, and the nodes of the CHSet are usually selected as relay nodes for communication in the community.
Define 2 Community common node (CM): all nodes within a community except the head node may be referred to as community common nodes.
Generally, since the car networking is a highly dynamic network, the role of each node changes with the change of the movement and topology of the node and the communication situation, and the roles of the common nodes and the head nodes may be interchanged according to the requirement of network connectivity.
Defining 3 Node Connectivity Probability (NCP) refers to the credibility of Node Connectivity in the car networking.
If node u is adjacent to node v and within the respective wireless communication range, their Direct Node Connectivity Probability (DNCP) is the mathematical expression (2):
where dist (u, v) represents the distance between nodes u and v, and TR represents the maximum communication radius of the node. When the distance between the nodes is larger than the maximum communication radius of the nodes, the communication probability between the nodes is 0; otherwise, the probability of connectivity between nodes increases as the distance between nodes decreases.
If two nodes communicate indirectly, i.e. the nodes u and v can form a node communication path through other nodes, if this path is denoted as NodePathi＝{e1,e2,…,enIn which e1＝u,en＝v,n>2, n represents the number of nodes on the path, then nodes u and v are in NodePathiThe upper Node Connectivity Probability (PNCP) is:
i.e. the multiplication of the direct node connectivity probabilities of the u and v communication paths. Since there may be multiple Node communication paths between u and v, defining the Indirect Node Communication Probability (INCP) between nodes u and v as the maximum value of the communication probability on all Node communication paths:
INCP(u,v)＝max(PNCP(NodePathi)) (4)
in summary, the node connectivity probability between nodes is defined as the maximum value of DNCP and INCP:
NCP(u,v)＝max(DNCP(u,v),INCP(u,v)) (5)
②
node information table initialization and update
In a network communication layer of the Internet of vehicles, each node is provided with a node information table, and fields contained in the table comprise the ID of the node, the current time, the speed, the acceleration, the longitude and latitude of the position, the community attribution, the role of the node, the ID of the head node of the community of the node, the ID of a gateway node and the like. Specifically, the results are shown in Table 1.
In table 1, the node ID is a unique identifier of the node in the car networking, whether the node is an RSU is determined by the type of the node itself, and the timestamp represents the current time, and the basic information of the node such as speed, acceleration, longitude and latitude can be obtained through the sensor. For node community attribution, the calculation process is as follows: at the initial moment, each node needs to broadcast a Neighbor Node Detection Message (NNDM) to nodes within the wireless signal propagation range, and a node receiving the NNDM needs to reply a confirmation message. After that, a dynamic evolution method of the vehicle networking community is adopted, (this part of inventions is referred to as the dynamic evolution method of the vehicle networking community (applicant: university of the same society, patent application number 2019101555842), which is applied by inventor of Chengdu et al on 3/1 in 2019), to determine the Node community attributioniThe number of nodes is m, and its NAL is:
wherein, neip,qWhen 0 denotes community CiNode v inpAnd vqThere is no edge in between, otherwise, v is representedpAnd vqDirect connectivity probability of. Community merging process based on node similarity and evolution process based on incrementIn (3), NAL is exchanged among nodes, so that each node knows the node adjacency information of the community to which the node belongs.
After the Community structure at the current time is determined, a Community Adjacency List (CAL) of each Community, that is, adjacency Community information of the Community, may be obtained in a broadcast manner similar to the node adjacency list.
③
Community head node screening
After the Community structure at the current time is determined, a Community Adjacency List (CAL) of each Community, that is, adjacency Community information of the Community, may be obtained in a broadcast manner similar to the node adjacency list. The screening of the community head nodes can be implemented by referring to the content of definition 1, the specific steps are shown as algorithm 1, and the specific flow chart is shown as fig. 1.
Through the steps in the algorithm 1, the head node of the community is screened out, and the content of the CHSet is stored in the node information table of each node, so as to provide information support for the establishment of the subsequent reachability route.
④
Generic node to head node message forwarding
For the communication between nodes in a community with a larger Internet of vehicles, if the target node is in the direct communication range of the source node, the communication is directly established according to the information in the adjacency list. If the target node is not in the direct communication range of the source node, in order to realize the overall orderliness and accessibility of the communication in the community, the common node is required to firstly select to forward the message to a certain node in the CHSet. In the selection process, each common node in the community has a group of forwarding behavior probability vector tables. E.g. node viThe forwarding behavior probability vector table of (2) is shown as follows:
in Table 2, each row represents a common node viThe forwarding behavior probability vector. Forwarding probability PFrIs meant for a forwarding event, i.e. viV need to be co-communitypWhen sending information, selecting head node as vchsAnd the next hop node is vnextqThe probability of (c).
⑤
Head node to generic node message forwarding
When the common node forwards the message to the head node, the head node needs to select an optimal one of the routes to the common node that it maintains. Similar to the selection process in "message forwarding from a common node to a head node", there is also a group of forwarding behavior vector tables on the community head nodes. E.g. head node vchrThe intra-community forwarding behavior probability vector table of (3) is shown in table 3.
Similarly, in Table 3, each row represents a head node vchsThe forwarding behavior probability vector. PF (particle Filter)rDenotes vchsTo the same community node vpWhen sending information, the next hop node is vnextqThe probability of (c).
In order to ensure the network accessibility of the car networking network topology under the condition of high dynamic change, the forwarding probability PF in the above-mentioned forwarding behavior probability vector can be dynamically changed along with the progress of communication, and the changing process is determined by a feedback mechanism of a Learning Automaton (LA) deployed on a community common node or a community head node.
⑥
Community accessibility routing algorithm
In order to quantify the feedback of the environment to the LA, the present invention takes NCP, node Residual Energy Rate (RER), and data Delay (Delay) as the main indicators of the LA input. Here, the definitions of RER and Delay are shown in formulas (7) and (8).
The node RER represents the ratio of the communication load that the node can continue to carry to the maximum communication load:
where EA represents the maximum communication load of the node and EU represents the communication load already occupied by the node.
Delay at a node represents the time Delay at that node due to data processing:
wherein, TreceiveRepresenting a node viTime stamp of when data packet is received, TACKRepresents the node viA timestamp when the data packet is forwarded.
The NCP is mainly related to the relative distance between nodes, the RER is the guarantee that the nodes can continuously forward data, and the Delay is the description of the history information of the communication Delay between the nodes. Combining the above three factors, defining a new scale Opportunity to Forward Evaluation (OFE)
OFE＝f(NCP,RER,Delay) (9)
I.e., OFE is a function of NCP, RER, and Delay.
For a common node viTo community head node vchrAnd the next hop is vnextqThe forwarding behaviors of (a) are:
η、
and ψ are adjustment coefficients for NCP, RER, and Delay, respectively.
For community head node vchsTo the target node vpAnd the next hop node is vnextqThe forwarding behaviors of (a) are:
in the same way, η'),
And psi' are also the adjustment coefficients for NCP, RER, and Delay, respectively.
The forwarding behavior vector number of forwarding LA in the community deployed on a community node u (common node or community head node) of the Internet of vehicles is set as r, and LA forwarding probability is initialized as follows:
i.e. the probability of each forwarding action at the time LA is the same when t-0. At a later time, the LA cannot immediately obtain the NCP, RER and Delay values at the current time, while these data at the previous time are available. Thus, the OFE of the ith forwarding behavior is selected at time tiAnd the average opportunity forwarding evaluation factor OFE on the LAavgBy comparison, if OFEi≥OFEavgThen LA takes an incentive action on this behavior:
if OFEi≤OFEavgThen LA makes a penalty action for this behavior:
wherein λ is an excitation parameter, and λ' is a penalty parameter.
In summary, the specific steps of adjusting the probability of the LA forwarding behavior in the community are shown in algorithm 2, and the specific flowchart is shown in fig. 2.
On the basis of the algorithm 2 for adjusting the probability of LA forwarding behaviors in the community, the method for adjusting the accessibility of the routing in the community is obtained, the detailed steps are shown as an algorithm 3, and a specific flow chart is shown as fig. 3. If the target node is within the communication range of the source nodeOtherwise, the message transmission will go through two stages from the source node to the community head node to the target node. In order to prevent the message transmission from being trapped in a dead loop or a message loss, the maximum HOP value of the two-stage forwarding is defined as HOPmaxIf the HOP value K exceeds HOPmaxThe source node or the head node will retransmit.
For the car networking community, LA deployed on a common node is used for finding a head node with higher connection probability, more residual load and smaller delay and a next hop of a path leading to the head node by adjusting the forwarding probability in the forwarding behavior probability vector; the LA deployed on the head node seeks to find a suitable path to the target node by adjusting the forwarding probability in the forwarding behavior probability vector. These LAs exchange data and compete with each other in the network. Specifically, as shown in fig. 4(a) and (b).
In fig. 4, (a) the CHSet representing the internet-of-vehicles community is composed of vehicle nodes, and (b) the CHSet representing the internet-of-vehicles community has not only the vehicle nodes but also the RSUs serving as head nodes. Since the communication capacity of the RSU node is much larger than that of a common vehicle-mounted communication device, through the head node screening in the algorithm 1, if the RSU node is contained in the vehicle networking community, the probability that the RSU node serves as a community head node or a gateway node is very high, and the content is verified through the experimental process of the invention.
Innovation point
The innovation points are as follows: based on a dynamic evolution method of the vehicle networking community, (the inventor of Chengjun et al, applied for dynamic evolution method of the vehicle networking community (applicant: college of Tongji, patent application number 2019101555842) in 2019, 3, 1), by utilizing a learning automaton technology, through information exchange and competition deployed among community nodes, corresponding excitation functions and penalty functions are set, forwarding probabilities of different routes are adjusted in a self-adaptive mode, a Nash equilibrium state is achieved, and communication and stability of the vehicle networking community are achieved.
The vehicle nodes in the Internet of vehicles move rapidly and the topology of the Internet of vehicles is changed highly dynamically, and the Internet of vehicles is easy to generate data aggregation, delay and other phenomena, so that great challenges are brought to the communication and stability of the Internet of vehicles to a great extent. Therefore, analyzing and understanding the connectivity and stability of the car networking community is an urgent problem to be solved in the current research on the accessibility of the car networking community. The invention applies the learning automata theory to the vehicle networking community, and adaptively adjusts the forwarding probability of different routes through information exchange and competition among the learning automata deployed in the community node, thereby achieving the purpose of optimizing network communication on the whole and improving the accessibility of the vehicle networking network.
Attached table of the specification
TABLE 1
TABLE 2
TABLE 3
Claims (1)
1. The accessibility method in the Internet of vehicles community specifically comprises the following steps:
step 1. defining step, comprising:
define 1 Community head node set (CHSet): community CiThe head node (CH) of (1) is a set of nodes with large community centripetal force in the community, if in the community CiThe existence node u satisfies the mathematical expression (1):
wherein η is head node selection factor, η is (0.75, 1)]One value of (1), community CiIn which a node u satisfying the above condition is added to CiCHSet of (1); the node in the CHSet is the current community CiThe communication in the community selects a CHSet node as a relay node;
define 2 Community common node (CM): except the head node in one community, the nodes can be called community common nodes;
the role of each node can change along with the change of the movement, the topology and the communication condition of the node, and the roles of the common nodes and the head nodes can be interchanged according to the requirement of network communication;
defining 3 Node Connectivity Probability (NCP) to mean the credibility of Node Connectivity in the Internet of vehicles;
if the node u is adjacent to the node v and within the respective wireless communication range, then their Direct Node Connectivity Probability (DNCP) is the mathematical expression (2):
where dist (u, v) represents the distance between nodes u and v, and TR represents the maximum communication radius of the node; when the distance between the nodes is larger than the maximum communication radius of the nodes, the communication probability between the nodes is 0; otherwise, the connection probability between the nodes is increased along with the reduction of the distance between the nodes;
step2, accessibility method in Internet of vehicles community
Step 2.1 initialization and update of node information table
In a network communication layer of the Internet of vehicles, each node is provided with a node information table, and fields contained in the table comprise the ID of the node, the current time, the speed, the acceleration, the longitude and latitude of the position, the community attribution, the role of the node, the ID of the head node of the community and the ID of a gateway node;
the node ID is the unique identification of the node in the Internet of vehicles;
the timestamp represents the current time;
the speed, the acceleration and the position longitude and latitude are obtained through sensors;
for node community attribution, the calculation process is as follows: at the initial moment, each node needs to broadcast NNDM (Neighbor node detection message) to nodes in a wireless signal propagation range, the nodes receiving the NNDM need to reply a confirmation message, whether edges exist between the nodes and Neighbor nodes is judged through the process, so that Neighbor information and a network topological structure are determined, and then the known dynamic evolution method of the Internet of vehicles community is adopted to determine the attribution of the node community;
the node roles comprise a common node, a head node and a gateway node;
in the NNDM message, not only the basic information of the Node itself, but also a Node Adjacency List (NAL) of the community where the Node is located, wherein the NAL is a two-dimensional array; if community CiThe number of nodes is m, and its NAL is:
wherein, neip,qWhen 0 denotes community CiNode v inpAnd vqThere is no edge in between, otherwise, v is representedpAnd vqIn the community merging process based on the similarity of the nodes and the evolution process based on the increment, the NAL is exchanged among the nodes, so that each node knows the node adjacent information of the community to which the node belongs;
after the Community structure at the current moment is determined, a Community Adjacency List (CAL) of each Community, that is, Adjacency Community information of the Community, can be obtained in a broadcast manner similar to the node Adjacency List;
step 2.2 Community head node screening
After the community structure at the current moment is determined, the community adjacency list of each community is obtained in a broadcast mode like the node adjacency list; screening of the community head nodes is realized by referring to the content of the definition 1 in the step 1;
step 2.3 common node to head node message forwarding
For the communication between nodes in a community with larger Internet of vehicles, if the target node is in the direct communication range of the source node, the communication is directly established according to the information in the adjacency list; if the target node is not in the direct communication range of the source node, the common node firstly selects to forward the message to a certain head node in the CHSet; in the selection process, each common node in the community has a group of forwarding behavior probability vector tables, namely a node viIs forwarded with a probability of action PFrIs for a forwarding event, is viV need to be co-communitypWhen sending information, selecting head node as vchsAnd the next hop node is vnextqThe probability of (d);
step 2.4 message forwarding from the head node to the common node after the common node forwards the message to the head node, the head node needs to select an optimal one from a plurality of routes maintained by the head node to the common node; in the selection process, a group of forwarding behavior vector tables are arranged on the community head nodes, and the head nodes vchrIs converted into probability PF in communityrDenotes vchsTo the same community node vpWhen sending information, the next hop node is vnextqThe probability of (d);
step 2.5, an intra-community accessibility routing algorithm:
the specific steps of adjusting the probability of the LA forwarding behavior of the nodes in the community are as follows:
algorithm 2 describes:
inputting: a network topology Ci (V, E, W) of a community Ci, a community head node set CHSet;
and (3) outputting: air conditioner
step1, if the node u which is not accessed exists in the community Ci, jumping to step2, otherwise, ending the algorithm;
step2, if u is a normal node and has a forwarding task, then go to step3, otherwise go to step 5;
step3, calculating the next hop to head node opportunity forwarding judgment factor for each forwarding line of the common node u;
step4, if the machine will forward judgment factor is larger than the average chance forward judgment factor, using the reward function; adjusting the i-row forwarding probability PF, otherwise, adjusting the i-row forwarding probability PF by using a penalty function;
step5, if u is the head node and there is a forwarding task, jump to step 6;
step6, calculating the next hop to head node opportunity forwarding judgment factor of each forwarding line of the head node u;
step7, if the machine will forward judgment factor is larger than the average chance forward judgment factor, using the reward function; adjusting the i-row forwarding probability PF, otherwise, adjusting the i-row forwarding probability PF by using a penalty function;
step8 returning to step 1;
the operation algorithm 3 is a method for obtaining the accessibility routing in the community on the basis of the adjustment algorithm 2 for the probability of the node LA forwarding behavior in the community, and comprises the following steps:
algorithm 3 describes:
inputting: source node Vori, target node Vdes, community head node set CHSet, and node adjacency list NAL of community
And (3) outputting: air conditioner
step1, judging whether Vori and Vdes are adjacent nodes, directly communicating, otherwise, turning to step 2;
step2, assigning an initial value of the node variable u as Vori and assigning an initial value of the hop number K as 0;
step3, if u is not the head node and the hop count K is less than the maximum hop count HOPmax, entering step4, otherwise, turning to step 5;
step4, selecting the PF forwarding message with the maximum forwarding probability, updating the forwarding table of u by using an algorithm 2, assigning the next hop Vnext to u, adding 1 to the K value, and turning to step 3;
step5, if the K value is larger than or equal to the maximum hop count HOPmax, then go to step6, otherwise go to step 7;
step6, assigning the value of u as Vori, and jumping to step 1;
step7, the value of the node variable v is u, and the value of K is 0;
step8, if v is not the target node and the hop count K is less than the maximum hop count HOPmax, then go to step9, otherwise go to step 10;
step9, selecting the PF forwarding message with the maximum forwarding probability, updating the forwarding table of u by using an algorithm 2, assigning the next hop Vnext to u, adding 1 to the K value, and turning to step 8;
step10, if the K value is larger than or equal to the maximum hop count HOPmax, then go to step11, otherwise go to step 12;
step11, the value of the node variable v is u, and the step8 is switched to;
step12 the algorithm ends.
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| CN102307373A (en) * | 2011-08-23 | 2012-01-04 | 哈尔滨工业大学 | VANET clustering method taking regard of vehicle traffic characteristic |
| CN109302696A (en) * | 2018-08-30 | 2019-02-01 | 同济大学 | The polygonal colour sorting Community Clustering method of car networking self-organizing network |
| CN109272175A (en) * | 2018-11-15 | 2019-01-25 | 山东管理学院 | A kind of data collection system and method based on Urban Migrant network |
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