WO2020134713A1 - 网络节点的选举方法及节点设备 - Google Patents

网络节点的选举方法及节点设备 Download PDF

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Publication number
WO2020134713A1
WO2020134713A1 PCT/CN2019/119350 CN2019119350W WO2020134713A1 WO 2020134713 A1 WO2020134713 A1 WO 2020134713A1 CN 2019119350 W CN2019119350 W CN 2019119350W WO 2020134713 A1 WO2020134713 A1 WO 2020134713A1
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node
network
nodes
election
network node
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PCT/CN2019/119350
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English (en)
French (fr)
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王峰
刘刚
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电信科学技术研究院有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/30—Decision processes by autonomous network management units using voting and bidding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/46—Cluster building
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/28—Connectivity information management, e.g. connectivity discovery or connectivity update for reactive routing

Definitions

  • the present disclosure relates to the technical field of communication applications, and in particular, to a network node election method and node equipment.
  • Hierarchical mobile ad hoc networks is an important way to solve the problem of scalability, and clustering is one of the important means to realize the hierarchization of mobile ad hoc networks.
  • the efficiency of clustering algorithm directly affects the performance of mobile ad hoc network application systems.
  • the cluster structure formed by clustering can also provide various services for network management.
  • the network In the flat structure, all nodes have equal status, so it can also be called a peer-to-peer structure; in the hierarchical structure, the network is divided into clusters, each cluster is composed of a cluster head and multiple cluster members, these cluster heads form a high
  • the first-level network can be clustered in the higher-level network.
  • Commonly used distributed clustering algorithms include maximum connectivity method, minimum serial number (identifier, ID) method, weighting method, and energy-based minimum ID algorithm.
  • the distributed clustering or connected dominant set algorithm in the related art uses the local information of the nodes to independently elect the cluster head or the dominant set.
  • the election generates more redundant nodes, and when the connectivity of each node in the network When it is very large, all nodes participating in the election of cluster heads or dominant sets will generate a lot of redundant system message overhead and cause a waste of network resources.
  • the purpose of the present disclosure is to provide a network node election method and node equipment to solve the problem that when using distributed clustering or connected dominating set algorithms in the related art for election, more redundant nodes and a large number of redundant nodes will be generated I system message overhead.
  • the present disclosure provides a network node election method, which is applied to network nodes, including:
  • the network node determines whether the network node is a node other than a preset node, where the preset node includes a DS node and a node that directly communicates with the DS node;
  • second election information is sent, and the second election information includes the DS node elected by the network node.
  • the method before determining whether the network node is a node other than a preset node according to the first election information, the method further includes:
  • third election information is sent, and the third election information includes the DS node elected by the network node.
  • the sending of the third election information includes:
  • the first set of nodes select the first node with the highest dominating factor.
  • the first set of nodes includes a network node and a node that directly communicates with the network node;
  • a first DS node is determined, and the first DS node is sent.
  • determining the DS node according to the first node includes:
  • the first node includes one node, determine the first node as a DS node;
  • the node with the largest node ID among the at least two nodes is selected as the DS node.
  • sending the second election information includes:
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • the second node with the highest dominating factor in the first node set includes:
  • a second node that does not belong to the second node set and has the highest dominating factor is selected, and the second node set includes nodes that communicate directly with the DS node.
  • determining the second DS node according to the second node includes:
  • the second node includes one node, determine the second node as a second DS node;
  • the node with the smallest node ID among the at least two nodes is selected as the second DS node.
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the second election information after sending the second election information, it also includes:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • updating the state information of the network node and the neighbor nodes of the network node includes:
  • the network node In the case where the network node is elected as the DS node, update the state information of the network node to the first state, and update the state information of the neighbor nodes directly communicating with the network node to the second state;
  • the state information of the network node is updated to the second state, and the The state information of the neighbor nodes elected as the DS nodes directly communicated by the network nodes is updated to the first state;
  • the state information of the network node is updated to a third state.
  • some embodiments of the present disclosure also provide a node device, including: a transceiver, a memory, a processor, and a program stored on the memory and executable on the processor, and the processor executes the The program implements the following steps:
  • first election information sent by a target neighbor node of a network node, where the first election information includes a dominating set DS node elected by the target neighbor node, and the probability factor of the target neighbor node is less than the network probability factor;
  • the network node determines whether the network node is a node other than a preset node, where the preset node includes a DS node and a node that directly communicates with the DS node;
  • the transceiver sends second election information when the network node is a node other than a preset node, and the second election information includes the DS node elected by the network node.
  • the processor also implements the following steps when executing the program:
  • third election information is sent, and the third election information includes the DS node elected by the network node.
  • the processor also implements the following steps when executing the program:
  • the first set of nodes select the first node with the highest dominating factor.
  • the first set of nodes includes a network node and a node that directly communicates with the network node;
  • a first DS node is determined, and the first DS node is sent.
  • the processor also implements the following steps when executing the program:
  • the first node includes one node, determine the first node as a DS node;
  • the node with the largest node ID among the at least two nodes is selected as the DS node.
  • the processor also implements the following steps when executing the program:
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • the processor also implements the following steps when executing the program:
  • a second node that does not belong to the second node set and has the highest dominating factor is selected, and the second node set includes nodes that communicate directly with the DS node.
  • the processor also implements the following steps when executing the program:
  • the second node includes one node, determine the second node as a second DS node;
  • the node with the smallest node ID among the at least two nodes is selected as the second DS node.
  • the processor also implements the following steps when executing the program:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the processor also implements the following steps when executing the program:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the processor also implements the following steps when executing the program:
  • the network node In the case where the network node is elected as the DS node, update the state information of the network node to the first state, and update the state information of the neighbor nodes directly communicating with the network node to the second state;
  • the state information of the network node is updated to the second state, and the The state information of the neighbor nodes elected as the DS nodes directly communicated by the network nodes is updated to the first state;
  • the state information of the network node is updated to a third state.
  • some embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the network node election method described above.
  • some embodiments of the present disclosure also provide a node device, including:
  • the receiving module is configured to receive first election information sent by a target neighbor node of a network node, the first election information includes a dominant set DS node elected by the target neighbor node, and the probability factor of the target neighbor node is less than the network probability factor;
  • a determining module configured to determine whether the network node is a node other than a preset node according to the first election information, the preset node includes a DS node and a node that directly communicates with the DS node;
  • the first sending module is configured to send second election information when the network node is a node other than the preset node, and the second election information includes the DS node elected by the network node.
  • the above node equipment also includes:
  • the acquisition module is used to acquire the probability factor of the network node itself
  • the second sending module is configured to send third election information when the probability factor of the network node itself is less than the network probability factor, and the third election information includes the DS node elected by the network node.
  • the second sending module includes:
  • the first selection submodule is used to select the first node with the highest dominating factor in the first node set.
  • the first node set includes a network node and a node that directly communicates with the network node;
  • the first sending submodule is configured to determine a first DS node according to the first node and send the first DS node.
  • the first sending submodule includes:
  • a first determining unit configured to determine the first node as a DS node when the first node includes one node
  • the first selecting unit is configured to select the node with the largest node ID among the at least two nodes as the DS node when the first node includes at least two nodes.
  • the first sending module includes:
  • the second selection submodule is used to select the second node with the highest dominating factor in the first node set
  • a second sending submodule configured to determine a second DS node according to the second node, and send the second DS node
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • the second selection sub-module is used to select a second node that does not belong to the second node set and has the highest dominating factor in the first node set.
  • the second node set includes nodes that communicate directly with the DS node .
  • the second sending sub-module includes:
  • a second determining unit configured to determine the second node as a second DS node when the second node includes one node
  • the second selecting unit is configured to select the node with the smallest node ID among the at least two nodes as the second DS node when the second node includes at least two nodes.
  • the above node devices also include:
  • the first update module is configured to update the state information of the network node and the neighbor nodes of the network node according to the first election information and the third election information.
  • the above node devices also include:
  • the second update module is configured to update the state information of the network node and the neighbor nodes of the network node according to the second election information.
  • the network node first receives the first election information sent by the target neighbor node whose self probability factor is less than the network probability factor, that is, in some embodiments of the present disclosure, the self probability factor is first less than the network probability factor
  • the node sends the first election information; when the network node determines that the network node is a node other than the preset node according to the first election information, it sends the second election information, that is, participates in the second stage of the election.
  • the election process is performed step by step, the information generated during the election process is fully utilized and transmitted, the number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election and the DS election are reduced System overhead incurred.
  • FIG. 1 is a schematic flowchart of a network node election method according to some embodiments of the present disclosure
  • Figure 2 is the first schematic diagram of the dominant set selected by the UCDS algorithm
  • Figure 3 is a second schematic diagram of the dominant set selected using the UCDS algorithm
  • FIG. 4 is a first schematic diagram of a dominant set selected using a P-DS algorithm in some embodiments of the present disclosure
  • FIG. 5 is a second schematic diagram of the dominant set selected by the P-DS algorithm in some embodiments of the present disclosure.
  • FIG. 6 is a structural block diagram of a node device according to some embodiments of the present disclosure.
  • FIG. 7 is a schematic block diagram of a node device according to some embodiments of the present disclosure.
  • Hierarchical mobile ad hoc networks is an important way to solve the problem of scalability, and clustering is one of the important means to realize the hierarchization of mobile ad hoc networks.
  • the efficiency of clustering algorithm directly affects the performance of mobile ad hoc network application systems.
  • the cluster structure formed by clustering can also provide various services for network management.
  • the clustering technology is usually researched with the help of graph theory to improve the clustering algorithm and optimize the hierarchical structure of the mobile ad hoc network.
  • some embodiments of the present disclosure adopt a widely used general model of wireless mobile ad hoc networks, abstracting nodes in the network as points in graph theory, and abstracting the communication links between nodes It is an edge in graph theory and uses the following definition.
  • D is a node subset of graph G. For any node v of G, either v belongs to D or is adjacent to a node in D, then D is called graph G A dominant set. If after removing any element in the D set, D is no longer the dominant set, then D is called the minimal dominant set.
  • the dominating set with the smallest number of nodes in all dominating sets of graph G is called the smallest dominating set, and the number of nodes in the smallest dominating set is called the dominating number of graph G.
  • Definition 4 The number of one-hop neighbors of a node is called the connectivity of the node.
  • a cluster in a wireless network is composed of a group of nodes C, where Generally, a group of nodes in the neighboring area form a cluster, and coordinate and control the behavior of the nodes in the cluster through a coordination node (center node). This coordination node (center node) is called the cluster head.
  • a node that implements data transmission between clusters in a clustered network is called a gateway.
  • the network domination set is defined as the set of all cluster heads and gateways in the network, and the virtual backbone network is a network domination set.
  • the network In the flat structure, all nodes have equal status, so it can also be called a peer-to-peer structure; in the hierarchical structure, the network is divided into clusters, each cluster is composed of a cluster head and multiple cluster members, these cluster heads form a high
  • the first-level network can be clustered in the higher-level network.
  • Commonly used distributed clustering algorithms include maximum connectivity method, minimum identifier (ID) method, weighting method, energy-based minimum ID algorithm, weighted clustering (Weighted Clustering Algorithm, WCA) algorithm, etc.
  • the algorithm is also called a clustering algorithm based on connectivity, where the connectivity of a node is determined by its distance from surrounding nodes. The number of neighbors of a node is called its connectivity. The node with the largest connectivity is selected as the cluster head, and its neighbors are used as general nodes in the cluster. They can only communicate with the cluster head.
  • This algorithm is also called a clustering algorithm based on recognizer. This is a simple clustering algorithm.
  • each node has a unique sequence number. The node with the smallest sequence number is selected as the cluster head, and the sequence number of the general node in each cluster must be greater than the sequence number of the cluster head. .
  • Nodes located in two or more clusters are called gateways. For two clusters that do not cover each other, two nodes located in different clusters can be defined as distributed gateways. Nodes and gateways jointly form a dominant set , That is, the entire backbone network.
  • distributed clustering algorithm distributed Clustering Algorithm, DCA
  • distributed mobile adaptive clustering algorithm distributed Mobility-Adaptive Clustering Algorithm, DMAC.
  • Each node is assigned a weight (a real number greater than zero) according to its suitability to become a cluster head, the node with the highest weight among the node and neighbor nodes is selected as the cluster head, and its neighboring nodes are used as general nodes in the cluster . If the weights are the same, the node with the smaller ID is selected as the cluster head.
  • each node is assigned a weight to indicate the appropriateness of the node as the cluster head.
  • the weight is related to the remaining energy of the node. When the weight is large, it can be selected as the cluster head. It can be regarded as an improved weighting algorithm after considering the life of the node.
  • the algorithm comprehensively considers factors such as the connectivity of nodes, the speed of movement and the distance to neighboring nodes, which makes the choice of cluster heads more reasonable. However, this algorithm only selects nodes whose neighbors are less than a certain fixed upper limit as the cluster head, which is not applicable to the dense network.
  • the connected dominating set is used as a Multi-Point Relay (MPR) node for broadcast data, which ensures the broadcast of the entire network while reducing the amount of broadcast data and reducing the network load; in routing, Construct a virtual backbone network for data routing through connected dominating sets, and adopt corresponding routing mechanisms to complete multi-hop transmission of unicast data; in network clustering, connected dominating sets are equivalent to 1-hop clustering (that is, all nodes in the cluster The distance from the cluster head is 1 hop) The set of cluster heads and gateways in the network, that is, the network dominating set.
  • MPR Multi-Point Relay
  • the original MPR is a source-dependent (broadcast-dependent) strategy, that is, in the broadcast process, the set of forwarding nodes is determined by the broadcast source node and the communication delay, and the node is given using the greedy algorithm (algorithm 1)
  • the forwarding node set of i is MPR(i).
  • a new, local, non-source-dependent algorithm is proposed based on the MPR algorithm, and rules one and two are defined.
  • an improved rule 1 is defined, and an improved source independent multipoint relay (Enhanced source-independent MPR, EMPR) algorithm (algorithm 2) is proposed, which reduces the redundant nodes in the CDS and improves the algorithm's effectiveness.
  • an improved rule 2 is proposed, and an extended and improved source-independent MPR (EEMPR) algorithm (Algorithm 3) is proposed.
  • EEMPR extended and improved source-independent MPR
  • Rule 1 The ID of a node is less than the ID of any neighbor node.
  • Rule 2 The node is elected as the forwarding node by the neighbor node with the smallest ID.
  • the improved rules are as follows:
  • the ID of a point is less than the ID of any neighbor node, and the node has disconnected neighbor nodes.
  • the node is directly elected as the forwarding node by the neighbor node with the smallest ID, or the node is indirectly elected as the forwarding node by the node whose ID is less than all its 1-hop neighbor nodes among its 2-hop neighbor nodes.
  • an energy-efficient distributed connected dominating set algorithm based on wireless sensor networks first constructs a maximum independent set (Maximum Independent Set, MIS) by using a dyeing algorithm through the initiating node, and then uses an approximate greedy algorithm from the non-maximum independent set
  • MIS Maximum Independent Set
  • the connected nodes are obtained from the nodes, and finally the connected nodes and the largest independent set constitute the minimum connected dominating set.
  • the distributed clustering or connected dominant set algorithm in the related art uses the local information of the nodes to independently elect the cluster head or the dominant set.
  • the election generates more redundant nodes, and when the connectivity of each node in the network When it is very large, all nodes participating in the election of cluster heads or dominant sets will generate a lot of redundant system message overhead and cause a waste of network resources.
  • This standard can be the characteristics of the node, such as ID number (minimum ID method), node residual energy (minimum ID method based on energy), moving speed (WCA algorithm); can be based on the status of the communication link between nodes, such as link bandwidth , Transmission distance, transmission delay, etc.; can be based on the characteristics of the network, such as the connectivity of the node (maximum connectivity method), the coverage of the node, the type of the node, etc.; can also consider various factors to adapt to different network states And demand, such as a weighting method that combines various factors. Therefore, the choice of the standard largely determines the environmental adaptability and upper performance limit of the clustering or dominating set algorithm. In some embodiments of the present disclosure, this standard is called the dominating factor.
  • How to select the dominant factor is a direction in the research of clustering and dominant set. Different environments and needs determine different optimal dominant factors; and some embodiments of the present disclosure are directed to how to use known dominant factors to be simple, fast and stable Heads or dominant sets of elections.
  • the election method of some embodiments of the present disclosure is suitable for all types of dominating factors.
  • some embodiments of the present disclosure provide a method for network node election, which is applied to network nodes and includes:
  • Step 101 Receive first election information sent by a target neighbor node of a network node, where the first election information includes a Dominating Set (DS) node elected by the target neighbor node, and a probability factor of the target neighbor node Less than the network probability factor.
  • DS Dominating Set
  • the target neighbor node refers to a neighbor node whose self probability factor is smaller than the network probability factor among the neighbor nodes of the network node. That is, in some embodiments of the present disclosure, only network nodes whose own probability factor is less than the network probability factor will actively send the first election information. That is to say, in some embodiments of the present disclosure, a node whose own probability factor is less than the network probability factor participates in the first stage of the election, and sends the first election information.
  • Step 102 According to the first election information, determine whether the network node is a node other than a preset node, where the preset node includes a DS node and a node that directly communicates with the DS node.
  • the node directly communicating with the DS node refers to the 1-hop neighbor node of the DS node.
  • the 2-hop neighbor node of a network node refers to a node that is one node away from the network node.
  • Step 103 When the network node is a node other than the preset node, send second election information, where the second election information includes the DS node elected by the network node.
  • the above network node When the above network node is not a DS node or a node that directly communicates with the DS node, it participates in the second stage of the election and sends second election information.
  • the network node first receives the first election information sent by the target neighbor node whose self probability factor is less than the network probability factor, that is, in some embodiments of the present disclosure, the self probability factor is firstly less than the network
  • the node with the probability factor sends the first election information; when the network node determines that the network node is a node other than the preset node according to the first election information, it sends the second election information, that is, participates in the second stage of the election.
  • the election process is performed step by step, the information generated during the election process is fully utilized and transmitted, the number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election and the DS election are reduced System overhead incurred.
  • the method further includes:
  • third election information is sent, and the third election information includes the DS node elected by the network node.
  • the network node obtains its own probability factor and compares its own probability factor with the network probability factor. When its own probability factor is less than the network probability factor, it actively sends third election information to participate in the first stage of DS election Otherwise, they will not participate in the first stage of DS elections.
  • the election method of some embodiments of the present disclosure may be implemented based on the DS algorithm of probability p, that is, the P-DS algorithm.
  • the core idea is to divide the DS node election process into two steps.
  • the first step some nodes in the network are based on Elect the DS node with the 2-hop neighbor information obtained by yourself, and update the status of the node and neighbor nodes according to the election results.
  • the second step the non-DS node and the DS neighbor node select the DS node according to the 2-hop neighbor information obtained by themselves, and according to The election results update the status of the nodes and neighbor nodes.
  • the second step uses the results of the first step to reduce the number of participating nodes in the entire process, and at the same time can reduce the number of DS nodes finally elected.
  • the step-by-step basis of the P-DS algorithm is the size of its own probability factor p i and the network probability factor p. If p i is less than p, node i participates in the first stage of DS election; if p i is greater than or equal to p, then does not participate in the first stage of DS election.
  • the network probability factor p is determined by the characteristics of the entire network and is a relatively fixed value. It can also be adjusted as needed during the entire network life cycle.
  • the probability factor p i of the node reflects the relative state of the node, and represents the size of the node's willingness not to participate in the first stage of DS election, following the principle of determining the probability factor as follows.
  • Probability factor determination principle the probability factor of a node is inversely proportional to the change of the node state, that is, the more stable the relative state of node i is, the smaller the probability factor p i is .
  • the change of the node state can be characterized by various indicators, such as node function, node type, moving speed, number of neighbors, link status, remaining battery energy, etc., and the node probability factor determined by a combination of various factors essentially changes the network
  • the nodes are divided into different types of nodes for processing.
  • the sending third election information includes:
  • the first set of nodes select the first node with the highest dominating factor.
  • the first set of nodes includes a network node and a node that directly communicates with the network node;
  • a first DS node is determined, and the first DS node is sent.
  • the first node when the first node includes one node, the first node is determined to be a DS node; when the first node includes at least two nodes, among the at least two nodes The node with the largest node ID is selected as the DS node.
  • the sending second election information includes:
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • selecting the second node with the highest dominating factor includes:
  • a second node that does not belong to the second node set and has the highest dominating factor is selected, and the second node set includes nodes that communicate directly with the DS node.
  • determining the second DS node according to the second node includes:
  • the second node includes one node, determine the second node as a second DS node;
  • the node with the smallest node ID among the at least two nodes is selected as the second DS node.
  • the P-DS algorithm is mainly determined by the P-DS rule, which reflects the core idea of the algorithm, Specifically divided into rule 1 and rule 2:
  • any node topology determination inequality i p i ⁇ p is satisfied. If it is true, then find the node j with the highest dominating factor d j in the set N (1) [i] of itself and 1-hop neighbor nodes, and designate the node as a DS member. If multiple nodes have the same highest dominating factor d n , the node with the largest node ID is elected as a DS member.
  • Node i which is neither a DS node nor a 1-hop neighbor node N (1) (DS) of the DS node, looks for a set of N (1) [i] that does not belong to N (1) in itself and the 1-hop neighbor node DS) and the node k with the highest dominating factor d k and designates this node as a DS member. If multiple nodes have the same highest dominating factor d n , the node with the smallest node ID number is elected as a DS member.
  • the method after receiving the first election information sent by the target neighbor node of the network node, the method further includes:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the second election information after sending the second election information, it also includes:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • updating the state information of the network node and the neighbor nodes of the network node includes:
  • the network node In the case where the network node is elected as the DS node, update the state information of the network node to the first state, and update the state information of the neighbor nodes directly communicating with the network node to the second state;
  • the state information of the network node is updated to the second state, and the The state information of the neighbor nodes elected as the DS nodes directly communicated by the network nodes is updated to the first state;
  • the state information of the network node is updated to a third state.
  • each network node may first initialize the node and mark its own state as W(White), if the node is selected as the DS node, then mark its own state as B(Black), which is the One state; if a node is not selected as a DS node, but its 1-hop neighbor node is selected as a DS node, its own state is marked as G (Gray), which is the above-mentioned second state.
  • Step 1 First, node i marks its own state as W, and obtains local neighbor information and dominant factor information by sending and receiving broadcast messages, and calculates the probability factor p i according to itself and the network state.
  • the local neighbor information may specifically be 2-hop neighbor information.
  • Step 2 Node i compares the probability factor p i with the network probability factor p:
  • node i participates in the DS node election process of this cycle, according to its own dominating factor and neighboring node’s dominating factor, the node with the largest dominating election factor is the DS node, if multiple nodes have the same dominating factor The node with the largest node ID is elected. At the same time receive the election information of neighbor nodes. At the end of this cycle, the node updates its own status, marked as DS node or ordinary node.
  • Step 3 The DS node and the 1-hop neighbor node of the DS node do not participate in the election, and other nodes participate in the election. However, the DS neighbor node does not elect the DS 1-hop neighbor node as the DS node. At the end of this week, all nodes update their status and neighbor node status, and the network completes the DS election.
  • nodes with relatively rapid state changes may be avoided from participating in the first stage of DS election as much as possible, which can improve the stability of the election information at this stage .
  • the information generated during the election process is fully utilized and transmitted, and the number of redundant DS nodes generated by the election is reduced.
  • the following describes the election method of network nodes in combination with application scenarios of dense networks.
  • the number of neighbors of a node is very large. If the traditional distributed election strategy of local information is used, all nodes participate in the election. On the one hand, the amount of information required for the election is relatively large, on the other hand, it will produce very Multiple redundant DS nodes. In some scenarios, redundant nodes will bring many disadvantages.
  • the starting point of P-DS is to reduce the number of nodes participating in the election, the system overhead of the election, and the redundant DS nodes generated. Step by step execution allows some nodes to participate in the election of DS nodes on the basis of obtaining more information. At the same time, the nodes whose status changes relatively fast try not to participate in the first stage of DS elections, which can improve the stability of the election information in this stage and provide more and more reliable information for the second stage of DS elections.
  • the dominant set selected by the Unifying Connected Dominating Set (UCDS) algorithm node 1, node 10, Node 11, Node 16, Node 17, Node 23, Node 30, and Node 4, Node 30, Node 17, Node 21, Node 15, Node 31, Node 22 in Figure 3
  • the P-DS algorithm selected Comparison of the dominant set (node 1, node 11, and node 16 in FIG. 4, and node 4, node 30, node 16, node 21, and node 24 in FIG. 5), in which FIG. 4 using the P-DS algorithm
  • node 1 is the DS node selected in the first stage.
  • Fig. 5 node 1 is the DS node selected in the first stage.
  • node 4, node 30, and node 21 are the DS nodes selected in the first stage. It can be seen that the number of DS nodes given by the P-DS algorithm is smaller and more dispersed, and it is more suitable as a cluster head in a cluster network.
  • the nodes in the network can be divided into three categories according to the way of participation:
  • the first type of nodes their own p i ⁇ p, they participate in the first stage of DS elections according to the election rules, actively send election messages, and determine their identity based on the election results.
  • the second type of node does not actively send election messages, but can receive the election messages sent by the first type of node, and judges that it is a DS node (the election message elects itself as a DS node) or a 1-hop neighbor node of the DS node according to the election message (The DS node elected by the election message is its own 1-hop neighbor).
  • This type of node does not participate in the second stage DS election process, and directly determines its identity based on the information in the first stage.
  • the third type of node participating in the second stage of the election, including nodes that did not participate in the first stage of the DS election and did not receive the first stage of the DS election message, and received the first stage of the DS election message, but determined to be a DS neighbor Neighbor node (the DS node elected by the election message is not its own 1-hop neighbor).
  • the second type of nodes in the P-DS algorithm only need to receive information and then determine their own identity, and do not need to actively participate in the election process; and the third type of node division makes full use of the first stage The results of the DS election and the neighbor relationship between nodes make the distributed algorithm better obtain and use local information.
  • Some embodiments of the present disclosure use a step-by-step execution method based on the local distributed DS algorithm in the related art to make full use of the additional information provided by the 1-hop neighbor and 2-hop neighbor during the election process for network DS
  • an improved DS algorithm based on probability p that is, P-DS algorithm.
  • the distributed DS election process is divided into two or more steps, which fully utilizes and transmits the information generated during the election process, reducing The number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election is reduced, and the system overhead generated by the DS election is reduced.
  • the above-mentioned distributed DS algorithm refers to an election algorithm for distributed cluster heads or dominant sets that use local information (a dominating factor of 2-hop or 3-hop neighbors).
  • the complexity, convergence speed, stability, and topology of the algorithm The advantages and disadvantages of changing response speed directly affect the performance of broadcast transmission, routing, and network clustering.
  • the network node first receives the first election information sent by the target neighbor node whose self probability factor is less than the network probability factor, that is, in some embodiments of the present disclosure, the self probability factor is firstly less than the network
  • the node with the probability factor sends the first election information; when the network node determines that the network node is a node other than the preset node according to the first election information, it sends the second election information, that is, participates in the second stage of the election.
  • the election process is performed step by step, the information generated during the election process is fully utilized and transmitted, the number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election and the DS election are reduced System overhead incurred.
  • an embodiment of the present disclosure also provides a node device, including: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes The following steps are realized when describing the computer program:
  • the network node determines whether the network node is a node other than a preset node, where the preset node includes a DS node and a node that directly communicates with the DS node;
  • second election information is sent, and the second election information includes the DS node elected by the network node.
  • the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by the processor 600 and various circuits of the memory represented by the memory 620 are linked together.
  • the bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be further described in this article.
  • the bus interface provides an interface.
  • the transceiver 610 may be a plurality of elements, including a transmitter and a transceiver, and provides a unit for communicating with various other devices on a transmission medium.
  • the user interface 630 may also be an interface that can be externally connected to the required device.
  • the connected devices include, but are not limited to, a keypad, a display, a speaker, a microphone, and a joystick.
  • the processor 600 is responsible for managing the bus architecture and general processing, and the memory 620 may store data used by the processor 600 when performing operations.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • third election information is sent, and the third election information includes the DS node elected by the network node.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the first set of nodes select the first node with the highest dominating factor.
  • the first set of nodes includes a network node and a node that directly communicates with the network node;
  • a first DS node is determined, and the first DS node is sent.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the first node includes one node, determine the first node as a DS node;
  • the node with the largest node ID among the at least two nodes is selected as the DS node.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • a second node that does not belong to the second node set and has the highest dominating factor is selected, and the second node set includes nodes that communicate directly with the DS node.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the second node includes one node, determine the second node as a second DS node;
  • the node with the smallest node ID among the at least two nodes is selected as the second DS node.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the state information of the network node and the neighbor nodes of the network node is updated.
  • the processor 600 is also used to read the program in the memory 620 and perform the following steps:
  • the network node In the case where the network node is elected as the DS node, update the state information of the network node to the first state, and update the state information of the neighbor nodes directly communicating with the network node to the second state;
  • the state information of the network node is updated to the second state, and the The state information of the neighbor nodes elected as the DS nodes directly communicated by the network nodes is updated to the first state;
  • the state information of the network node is updated to a third state.
  • the node device of some embodiments of the present disclosure first receives the first election information sent by the target neighbor node whose self probability factor is less than the network probability factor, that is, in some embodiments of the present disclosure, it is first sent by the node whose self probability factor is less than the network probability factor First election information; when the node device determines that the network node is a node other than the preset node according to the first election information, it sends second election information, that is, participates in the second stage of the election.
  • the election process is performed step by step, the information generated during the election process is fully utilized and transmitted, the number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election and the DS election are reduced System overhead incurred.
  • a computer-readable storage medium on which a computer program is stored, and when the program is executed by the processor, the following steps are realized:
  • the network node determines whether the network node is a node other than a preset node, where the preset node includes a DS node and a node that directly communicates with the DS node;
  • second election information is sent, and the second election information includes the DS node elected by the network node.
  • some embodiments of the present disclosure also provide a node device, including:
  • the receiving module 701 is configured to receive first election information sent by a target neighbor node of a network node, where the first election information includes a dominating set DS node elected by the target neighbor node, and the probability factor of the target neighbor node is less than the network Probability factor
  • the determining module 702 is configured to determine whether the network node is a node other than a preset node according to the first election information, where the preset node includes a DS node and a node that directly communicates with the DS node;
  • the first sending module 703 is configured to send second election information when the network node is a node other than a preset node, and the second election information includes the DS node elected by the network node.
  • the acquisition module is used to acquire the probability factor of the network node itself
  • the second sending module is configured to send third election information when the probability factor of the network node itself is less than the network probability factor, and the third election information includes the DS node elected by the network node.
  • the second sending module includes:
  • the first selection submodule is used to select the first node with the highest dominating factor in the first node set.
  • the first node set includes a network node and a node that directly communicates with the network node;
  • the first sending submodule is configured to determine a first DS node according to the first node and send the first DS node.
  • the first sending submodule includes:
  • a first determining unit configured to determine the first node as a DS node when the first node includes one node
  • the first selecting unit is configured to select the node with the largest node ID among the at least two nodes as the DS node when the first node includes at least two nodes.
  • the first sending module includes:
  • the second selection submodule is used to select the second node with the highest dominating factor in the first node set
  • a second sending submodule configured to determine a second DS node according to the second node, and send the second DS node
  • the first set of nodes includes network nodes and nodes that communicate directly with the network nodes.
  • the second selection submodule is used to select a second node that does not belong to the second node set and has the highest dominating factor in the first node set, the second node set Including nodes that communicate directly with DS nodes.
  • the second sending submodule includes:
  • a second determining unit configured to determine the second node as a second DS node when the second node includes one node
  • the second selecting unit is configured to select the node with the smallest node ID among the at least two nodes as the second DS node when the second node includes at least two nodes.
  • the first update module is configured to update the state information of the network node and the neighbor nodes of the network node according to the first election information and the third election information.
  • the second update module is configured to update the state information of the network node and the neighbor nodes of the network node according to the second election information.
  • the first update module or the second update module is used to update the state information of the network node to the first state when the network node is elected as the DS node A state, and update the state information of the neighbor node directly communicating with the network node to the second state;
  • the state information of the network node is updated to the second state, and the The state information of the neighbor nodes elected as the DS nodes directly communicated by the network nodes is updated to the first state;
  • the state information of the network node is updated to a third state.
  • the node device of some embodiments of the present disclosure first receives the first election information sent by the target neighbor node whose self probability factor is less than the network probability factor, that is, in some embodiments of the present disclosure, it is first sent by the node whose self probability factor is less than the network probability factor First election information; when the node device determines that the network node is a node other than the preset node according to the first election information, it sends second election information, that is, participates in the second stage of the election.
  • the election process is performed step by step, the information generated during the election process is fully utilized and transmitted, the number of redundant DS nodes generated by the election is reduced, and the number of nodes participating in the election and the DS election are reduced System overhead incurred.

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Abstract

本公开提供一种网络节点的选举方法及节点设备。本公开的方法包括：接收网络节点的目标邻居节点发送的第一选举信息，目标邻居节点的概率因子小于网络概率因子；根据第一选举信息，确定网络节点是否为除预设节点之外的节点，预设节点包括DS节点及与DS节点直接通信的节点；在网络节点为除预设节点之外的节点的情况下，发送第二选举信息，第二选举信息包括网络节点选举出的DS节点。

Description

网络节点的选举方法及节点设备
相关申请的交叉引用
本申请主张在2018年12月25日在中国提交的中国专利申请号No.201811592107.4的优先权，其全部内容通过引用包含于此。
技术领域
本公开涉及通信应用的技术领域，尤其涉及一种网络节点的选举方法及节点设备。
背景技术
移动自组网的层次化是解决可扩展问题的重要途径，而分簇是实现移动自组网层次化的重要手段之一，分簇算法的效率直接影响移动自组网应用系统的性能。此外，分簇形成的簇结构还能提供网络管理的多种服务。
平面结构中，所有节点的地位平等，所以又可称为对等结构；而层次结构中，网络被划分为簇，每个簇由一个簇首和多个簇成员组成，这些簇首形成了高一级的网络，在高一级的网络中，又可以分簇。常用的分布式分簇算法包括最大连通度法、最小序列号(identifier，ID)法、加权法以及基于能量的最小ID算法等。
但相关技术中的分布式分簇或者连通支配集算法利用节点的局域信息独立的进行簇首或者支配集的选举，选举产生了较多的冗余节点，且当网络中各个节点的连通度很大时，所有节点参与簇首或者支配集的选举，将产生大量的冗余的系统消息开销，造成网络资源的浪费。
发明内容
本公开的目的在于提供一种网络节点的选举方法及节点设备，用以解决采用相关技术中的分布式分簇或者连通支配集算法进行选举时，会产生较多的冗余节点以及大量的冗余系统消息开销的问题。
为了实现上述目的，本公开提供了网络节点的选举方法，应用于网络节 点，包括：
接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
其中，所述根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点之前，还包括：
获取网络节点自身的概率因子；
在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
其中，所述发送第三选举信息，包括：
在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
其中，根据所述第一节点，确定DS节点，包括：
在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
其中，所述发送第二选举信息，包括：
在第一节点集合中，选取具有最高支配因子的第二节点；
根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
其中，在第一节点集合中，选取具有最高支配因子的第二节点，包括：
在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
其中，根据所述第二节点，确定第二DS节点，包括：
在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
其中，接收网络节点的目标邻居节点发送的第一选举信息之后，还包括：
根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，发送第二选举信息之后，还包括：
根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，更新所述网络节点和所述网络节点的邻居节点的状态信息，包括：
在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
为了实现上述目的，本公开的一些实施例还提供了一种节点设备，包括：收发机、存储器、处理器及存储在存储器上并可在处理器上运行的程序，所述处理器执行所述程序时实现以下步骤：
通过收发机接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节 点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
通过收发机在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
其中，所述处理器执行所述程序时还实现以下步骤：
获取网络节点自身的概率因子；
在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
其中，所述处理器执行所述程序时还实现以下步骤：
在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
其中，所述处理器执行所述程序时还实现以下步骤：
在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
其中，所述处理器执行所述程序时还实现以下步骤：
在第一节点集合中，选取具有最高支配因子的第二节点；
根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
其中，所述处理器执行所述程序时还实现以下步骤：
在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
其中，所述处理器执行所述程序时还实现以下步骤：
在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
其中，所述处理器执行所述程序时还实现以下步骤：
根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，所述处理器执行所述程序时还实现以下步骤：
根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，所述处理器执行所述程序时还实现以下步骤：
在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
为了实现上述目的，本公开的一些实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上所述网络节点的选举方法的步骤。
为了实现上述目的，本公开的一些实施例还提供了一种节点设备，包括：
接收模块，用于接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
确定模块，用于根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
第一发送模块，用于在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
其中，上述节点设备还包括：
获取模块，用于获取网络节点自身的概率因子；
第二发送模块，用于在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
其中，所述第二发送模块包括：
第一选取子模块，用于在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
第一发送子模块，用于根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
其中，所述第一发送子模块包括：
第一确定单元，用于在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
第一选取单元，用于在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
其中，所述第一发送模块包括：
第二选取子模块，用于在第一节点集合中，选取具有最高支配因子的第二节点；
第二发送子模块，用于根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
其中，所述第二选取子模块用于在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
其中，所述第二发送子模块包括：
第二确定单元，用于在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
第二选取单元，用于在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
其中，上述节点设备，还包括：
第一更新模块，用于根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，上述节点设备，还包括：
第二更新模块，用于根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
本公开的一些实施例具有以下有益效果：
本公开的一些实施例的上述技术方案，网络节点先接收自身概率因子小于网络概率因子的目标邻居节点发送的第一选举信息，即本公开的一些实施例中先由自身概率因子小于网络概率因子的节点发送第一选举信息；网络节点根据第一选举信息确定出该网络节点为除预设节点之外的节点的情况下，发送第二选举信息，即参与第二阶段的选举。本公开的一些实施例中，将选举过程分步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量以及DS选举产生的系统开销。
附图说明
图1为本公开的一些实施例的网络节点的选举方法的流程示意图；
图2为采用UCDS算法选出的支配集的第一示意图；
图3为采用UCDS算法选出的支配集的第二示意图；
图4为本公开的一些实施例中采用P-DS算法选出的支配集的第一示意图；
图5为本公开的一些实施例中采用P-DS算法选出的支配集的第二示意图；
图6为本公开的一些实施例的节点设备的结构框图；
图7为本公开的一些实施例的节点设备的模块示意图。
具体实施方式
为使本公开要解决的技术问题、技术方案和优点更加清楚，下面将结合 具体实施例及附图进行详细描述。
为使本领域人技术人员能够更好地理解本公开的方案，先进行如下说明。
1.移动自组网的概念和定义。
移动自组网的层次化是解决可扩展问题的重要途径，而分簇是实现移动自组网层次化的重要手段之一，分簇算法的效率直接影响移动自组网应用系统的性能。此外，分簇形成的簇结构还能提供网络管理的多种服务。目前，通常借助图论的相关理论研究分簇技术，改进分簇算法，优化移动自组网的层次化结构。为了方便描述和分析，本公开的一些实施例采用一个广泛使用的无线移动自组网的通用模型，将网络中的节点抽象为图论中的点，将节点与节点之间的通信链路抽象为图论中的边，并使用如下定义。
定义1(无向图)：设图G＝(E,V)，V表示节点的集合，E表示边的集合。节点x和y之间存在边(x，y)，则x和y是可以互相通信的一跳邻居节点。如果G为无自环的连通无向图且G中任意两个节点之间至多有一条边，则G称为简单无向图。
定义2(支配集和支配数)：D是图G的一个节点子集，对于G的任一节点v，要么v属于D，要么与D中的一个节点相邻，则D称为图G的一个支配集。若在D集中去掉任何元素后，D不再是支配集，则称D是极小支配集。称图G的所有支配集中节点个数最少的支配集为最小支配集，最小支配集中的节点个数称为图G的支配数。
定义3(连通支配集)：D为图G的一个支配集，C为图中节点E的一个子集，如果CD＝D∪C构成的节点子集在图G中是相互连通的，则称子集CD为连通支配集(Connected Dominating Set，CDS)。
定义4(连通度)：一个节点的一跳邻居节点的数目称为该节点的连通度。
定义5(簇和簇首)：无线网络中的一个簇是由一组节点C组成，其中
Figure PCTCN2019119350-appb-000001
一般将临近区域的一组节点组成一个簇，并通过一个协调节点(中心节点)协调和控制簇内节点的行为，这个协调节点(中心节点)称为簇首。
定义6(网关)：分簇网络中实现簇间数据传输的节点称为网关。
定义7(网络支配集)：网络支配集定义为网络中所有簇首和网关组成的集合，虚拟骨干网即为一个网络支配集。
2.常用的分布式分簇算法。
平面结构中，所有节点的地位平等，所以又可称为对等结构；而层次结构中，网络被划分为簇，每个簇由一个簇首和多个簇成员组成，这些簇首形成了高一级的网络，在高一级的网络中，又可以分簇。常用的分布式分簇算法包括最大连通度法、最小序列号(identifier，ID)法、加权法、基于能量的最小ID算法、加权分簇(Weighted Clustering Algorithm，WCA)算法等。
(1)最大连通度法。
该算法也称为基于连通度的分簇算法，其中节点的连通度由它与周围节点的距离来确定。一个节点的邻居节点的个数称为它的连通度，具有最大连通度的节点被选择为簇首，其周围邻居节点作为该簇内的一般节点，它们只能与簇首进行通信。
(2)最小ID法。
该算法也称为基于识别器的分簇算法。这是一种简单的分簇算法，算法中每个节点都具有唯一的序列号，序列号最小的节点被选择为簇首，而每个簇内一般节点的序列号必定大于簇首的序列号。位于两个或者多个簇内的节点称为网关，对于不互相覆盖的两个簇，可以定义两个分别位于不同簇内的节点作为分布式网关，节点与网关之间联合构成了一个支配集，即整个骨干网。
(3)加权法。
即分布式分簇算法(Distributed Clustering Algorithm，DCA)以及分布式移动自适应分簇算法(Distributed Mobility-Adaptive Clustering Algorithm，DMAC)。其中每个节点根据其成为簇首的适合程度分配一个权值(大于零的实数)，该节点和邻居节点中权值最高的节点被选择作为簇首，而其相邻节点作为簇内一般节点。如果权值相同，则选择ID较小的节点作为簇首。
(4)基于能量的最小ID算法。
簇首的选举对于网络的性能至关重要，基于以上考虑，可以采用一种基于能量的最小ID分簇算法。在这种算法中，每个节点分配一个权值来表示该节点作为簇首的合适程度。该权值和节点剩余能量有关，当权值较大时就可以选其作为簇首。它可以看作一种考虑了节点寿命后的改进加权算法。
(5)WCA算法。
该算法综合考虑了节点的连通度、移动速度以及与周围邻居节点的距离等多方面的因素，使得簇首的选择更加合理。然而，该算法只选择邻居小于某一固定上限的节点作为簇首，这对于网络较密集的情况该算法将不适用。
3.移动自组织网络的连通支配集。
图论中连通支配集的概念和理论经常被用于研究移动自组织网络中的广播传输、路由选择、网络分簇等问题。在广播传输中，将连通支配集作为广播数据的多点中继(Multi-Point Relay，MPR)节点，在保证数据全网广播的同时减少广播数据的数量，减轻网络负载；在路由选择中，通过连通支配集构建数据路由的虚拟骨干网，采取相应的路由选择机制，完成单播数据的多跳传输；在网络分簇中，连通支配集等效于1跳分簇(即簇中所有节点与簇首之间的距离为1跳)网络中簇首和网关的集合，即网络支配集。
相关技术中已对移动自组织网络中MPR机制和算法的发展和研究进行了描述。最初的MPR是一种信源依赖(广播依赖)的策略，即在广播过程中，转发节点的集合是由广播的源节点和通信延时决定的，并使用贪婪算法(算法1)给出节点i的转发节点集合MPR(i)。在此基础上，基于MPR算法提出了一种新的、局部的、非信源依赖的算法，并定义了规则一和规则二。然后，在此基础上定义了改进规则一，提出了改进的源独立多点中继(Enhanced source-independent MPR，EMPR)算法(算法2)，减少了CDS中的冗余节点，提高了算法的效率。进而，提出了改进规则二，提出了扩展改进的源独立多点中继(Extended Enhanced source-independent MPR，EEMPR)算法(算法3)，通过使用节点的3跳邻域信息，选择能够覆盖节点2跳邻居集的转发节点，进一步减少了CDS中的冗余节点，提高了算法效率。
如果一个节点属于连通支配集，则其需要满足以下任意一条规则：
规则一：节点的ID小于其任意邻居节点的ID。
规则二：节点被其ID最小的邻居节点选举为转发节点。
在以上两条规则的基础上，改进规则如下：
改进规则一：点的ID小于其任意邻居节点的ID，同时节点具有不相连的邻居节点。
改进规则二：节点被其ID最小的邻居节点直接选举为转发节点，或者节点被其2跳邻居节点中ID小于其所有1跳邻居节点的节点间接选举为转发节点。
目前基于无线传感器网络的高能效的分布式连通支配集算法，首先通过发起节点使用一种染色算法构建一个最大独立集合(Maximum Independent Set，MIS)，然后使用一种近似贪婪算法从非最大独立集合的节点中获得连接节点，最后由连接节点和最大独立集合构成最小连通支配集。
但相关技术中的分布式分簇或者连通支配集算法利用节点的局域信息独立的进行簇首或者支配集的选举，选举产生了较多的冗余节点，且当网络中各个节点的连通度很大时，所有节点参与簇首或者支配集的选举，将产生大量的冗余的系统消息开销，造成网络资源的浪费。
由上述移动自组织网络的分簇或者连通支配集的描述可知，在选举簇首或者支配集的时候，节点需要一个比较标准进行比较，进而确定哪个节点更适合作为簇首或者支配集。这个标准可以是节点的特性，如ID号(最小ID法)、节点剩余能量(基于能量的最小ID法)、移动速度(WCA算法)；可以基于节点间通信链路的状态，如链路带宽、传输距离、传输时延等；可以基于网络的特性，如节点的连通度(最大连通度法)、节点的覆盖范围、节点的种类等；还可以综合考虑各种因素以适应不同的网络状态和需求，如综合各种因素的加权法。因此，该标准的选择很大程度上决定了分簇或者支配集算法的环境适应性和性能上限，本公开的一些实施例中称这个标准为支配因子。
如何选取支配因子是分簇和支配集研究中的一个方向，不同的环境和需求决定了不同的最优支配因子；而本公开的一些实施例针对如何利用已知的支配因子简单、快速、稳定的选举簇首或者支配集。本公开的一些实施例的选举方法适合所有类型的支配因子。
下面对结合表1对本公开的一些实施例中可能会用到的符号所代表的含义进行说明。
Figure PCTCN2019119350-appb-000002
Figure PCTCN2019119350-appb-000003
表1
如图1所示，本公开的一些实施例提供了一种网络节点的选举方法，应用于网络节点，包括：
步骤101：接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集(Dominating Set，DS)节点，所述目标邻居节点的概率因子小于网络概率因子。
这里，目标邻居节点是指所述网络节点的邻居节点中自身概率因子小于网络概率因子的邻居节点。即本公开的一些实施例中，只有自身概率因子小于网络概率因子的网络节点才会主动发送第一选举信息。也就是说，本公开的一些实施例中，先由自身概率因子小于网络概率因子的节点参与第一阶段的选举，发送第一选举信息。
步骤102：根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点。
这里，与DS节点直接通信的节点是指该DS节点的1跳邻居节点。网络节点的2跳邻居节点是指与网络节点间隔一个节点的节点。
步骤103：在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
在上述网络节点不是DS节点以及与DS节点直接通信的节点时，参与第二阶段的选举，发送第二选举信息。
本公开的一些实施例的网络节点的选举方法，网络节点先接收自身概率因子小于网络概率因子的目标邻居节点发送的第一选举信息，即本公开的一些实施例中先由自身概率因子小于网络概率因子的节点发送第一选举信息；网络节点根据第一选举信息确定出该网络节点为除预设节点之外的节点的情况下，发送第二选举信息，即参与第二阶段的选举。本公开的一些实施例中，将选举过程分步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量以及DS选举产生的系统开销。
进一步地，所述根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点之前，还包括：
获取网络节点自身的概率因子；
在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
这里，网络节点获取自身的概率因子，并将自身的概率因子与网络概率因子进行比较，在自身的概率因子小于网络概率因子的情况下，主动发送第三选举信息，参与第一阶段的DS选举，否则，不参与第一阶段的DS选举。
本公开的一些实施例的选举方法可以基于概率p的DS算法，即P-DS算法来实现，其核心思想是将DS节点的选举过程分成两个步，第一步，网络中的部分节点根据自己获取的2跳邻居信息选举DS节点，并根据选举结果更新节点和邻居节点的状态；第二步，非DS节点和DS邻居节点的节点根据自己获得的2跳邻居信息选举DS节点，并根据选举结果更新节点和邻居节点的状态。通过这样一个分步产生DS节点的过程，第二步利用了第一步的结果，减少了整个过程中参与选举的节点的数量，同时可以减少最终选举产生的DS节点的数量。
该P-DS算法分步的依据是自身概率因子p i和网络概率因子p的大小。如果p i小于p，则节点i参与第一阶段的DS选举；如果p i大于或者等于p，则不参与第一阶段的DS选举。网络概率因子p由整个网络的特点确定，是 一个相对固定的值，在整个网络生存周期内也可根据需要调整。节点的概率因子p i反映节点的相对状态，表征节点不参加第一阶段DS选举的意愿的大小，遵循如下概率因子确定原则。
概率因子确定原则：节点的概率因子与节点状态的变化成反比，即节点i的相对状态越稳定，概率因子p i越小。
这里节点状态的变化可以用多种指标来表征，例如节点功能、节点类型、移动速度、邻居数量、链路状态、电池剩余能量等，而综合各种因素确定的节点概率因子本质上将网络中的节点分为了不同的几类节点进行处理。
进一步地，所述发送第三选举信息，包括：
在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
具体的，在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
进一步地，所述发送第二选举信息，包括：
在第一节点集合中，选取具有最高支配因子的第二节点；
根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
更进一步地，在第一节点集合中，选取具有最高支配因子的第二节点，包括：
在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
具体的，根据所述第二节点，确定第二DS节点，包括：
在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
本公开的一些实施例的网络节点的选举方法，在确定网络概率因子和节 点概率因子的情况下，P-DS算法主要由P-DS规则确定，P-DS规则体现了该算法的核心思想，具体分为规则1和规则2：
DS规则1：
拓扑图G中任意节点i判断不等式p i＜p是否成立。如果成立，则在其自身和1跳邻居节点的集合N (1)[i]中查找具有最高支配因子d j的节点j，并指定该节点为DS成员。如果多个节点具备相同的最高支配因子d n，则选举节点ID最大的节点为DS成员。
DS规则2：
既不是DS节点也不是DS节点的1跳邻居节点N (1)(DS)的节点i，在其自身和1跳邻居节点的集合N (1)[i]中查找不属于N (1)(DS)且具有最高支配因子d k的节点k，并指定该节点为DS成员。如果多个节点具备相同的最高支配因子d n，则选举节点ID号最小的节点为DS成员。
进一步地，本公开的一些实施例中，接收网络节点的目标邻居节点发送的第一选举信息之后，还包括：
根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
进一步地，发送第二选举信息之后，还包括：
根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
其中，更新所述网络节点和所述网络节点的邻居节点的状态信息，包括：
在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第 三状态。
本公开的一些实施例中，每个网络节点可先进行节点初始化，将自身状态标记为W(White)，如果节点被选为DS节点，则将自身状态标记为B(Black)，即上述第一状态；如果节点未被选为DS节点，但是其1跳邻居节点被选为DS节点，则将其自身状态标记为G(Gray)，即上述第二状态。
下面结合具体实施例来对本公开的一些实施例进行说明。
步骤1：首先，节点i将自身状态标记为W，并通过发送和接收广播消息获得局域邻居信息、支配因子信息，并根据自身和网络状态计算概率因子p i。
该局域邻居信息可以具体是2跳邻居信息。
步骤2：节点i比较概率因子p i与网络概率因子p的大小：
2.1)，如果p i＜p，则节点i参与本周期的DS节点选举过程，根据自身支配因子和邻居节点的支配因子，选举支配因子最大的节点为DS节点，如果多个节点的支配因子相同则选举节点ID最大的节点。同时接收邻居节点的选举信息。在此周期末，节点更新自身状态，标记为DS节点或者普通节点。
2.2)，如果p i≥p，则不参与选举，但是接收邻居节点的选举信息，并在此周期末更新自身状态，标记为DS节点或者普通节点。同时，不管是参与选举的节点还是未参与选举的节点，在此周期的末期都需要根据已有的信息判断自身是否为DS节点、DS节点的1跳邻居节点或者DS的1跳邻居节点的邻居节点。
步骤3：DS节点、DS节点的1跳邻居节点不参与选举，其他节点参与选举，但是，DS邻居的邻居节点不选举DS的1跳邻居节点作为DS节点。本周期末期，所有节点更新自身状态和邻居节点状态，网络完成DS的选举。
本公开的一些实施例的网络节点的选举方法，通过适当的节点概率因子计算策略，可以使得状态相对变化较快的节点尽量不参与第一阶段的DS选举，能够提高此阶段选举信息的稳定性，为后续阶段的DS选举提供更多更可靠的信息，且通过分步执行选举，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量。
下面结合密集网络的应用场景来说明网络节点的选举方法。
假设在一个30km×30km的二维平面内随机选择32个移动自组网节点， 所有节点的能力相同且传输的有效覆盖范围为半径12km的圆，即两个节点之间的欧几里德距离小于12km时节点间存在双向通信链路。
在密集网络中，一个节点的邻居节点数量非常多，如果采用传统的局域信息的分布式选举策略，所有节点均参与选举，一方面选举所需要的信息量比较大，另一方面会产生非常多的冗余DS节点。在某些场景下，冗余节点会带来很多坏处。而P-DS的出发点就是减少参与选举的节点数量、选举的系统开销和产生的冗余DS节点，通过分步执行让一部分节点在获得更多信息的基础上参与DS节点的选举。同时，状态相对变化较快的节点尽量不参与第一阶段的DS选举，能够提高此阶段选举信息的稳定性，为第二阶段的DS选举提供更多更可靠的信息。
以两种不同节点分布情况的网络场景为例，比较相同网络拓扑情况下，使用统一连通支配集(Unifying Connected Dominating Set，UCDS)算法选出的支配集(图2中的节点1、节点10、节点11、节点16、节点17、节点23、节点30，以及图3中的节点4、节点30、节点17、节点21、节点15、节点31、节点22)，与P-DS算法选出的支配集(图4中的节点1、节点11和节点16，以及图5中的节点4、节点30、节点16、节点21以及节点24)的对比情况，其中，采用P-DS算法的图4中，节点1为第一阶段选出的DS节点，采用P-DS算法的图5中，节点4、节点30和节点21为第一阶段选出的DS节点。可见，P-DS算法给出的DS节点数量更少、更分散，更适合作为分群网络中的簇首。
在整个选举过程中，网络中的节点根据参与的方式可以分为三类：
第一类节点：自身的p i＜p，它们根据选举规则参与第一阶段的DS选举，主动发送选举消息，并根据选举结果确定自己的身份。
第二类节点：不主动发送选举消息，但是能够收到第一类节点发送的选举消息，并根据选举消息判断自己是DS节点(选举消息选举自己为DS节点)或者DS节点的1跳邻居节点(选举消息选举的DS节点是自己的1跳邻居)，这类节点不参与第二阶段的DS选举过程，直接根据第一阶段的信息确定自己的身份。
第三类节点：参与第二阶段的选举，包括未参与第一阶段的DS选举同时 也未收到第一阶段DS选举消息的节点，以及收到第一阶段DS选举消息，但是确定为DS邻居的邻居节点(选举消息选举的DS节点不是自己的1跳邻居)。
根据以上节点种类的划分可知，P-DS算法中第二类节点只需要接收信息，然后判断自己的身份即可，不需要主动参与选举过程；而第三类节点划分则充分利用了第一阶段DS选举的结果，以及节点与节点之间的邻居关系，使得分布式算法更好地获得和利用了局域信息。
本公开的一些实施例在相关技术中的局域分布式DS算法的基础上，采用一种分步执行的方式，充分利用1跳邻居和2跳邻居在选举过程中提供的额外信息进行网络DS节点的选举，提出一种改进的基于概率p的DS算法，即P-DS算法，将分布式DS选举过程分为两步或者多步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量，减少了DS选举产生的系统开销。
此外，上述分布式DS算法是指利用局域信息(2跳或者3跳邻居的支配因子)的分布式簇首或者支配集的选举算法，该算法的复杂度、收敛速度、稳定性、对拓扑变化的反应速度等方面的优劣直接影响广播传输、路由选择、网络分簇的性能。
本公开的一些实施例的网络节点的选举方法，网络节点先接收自身概率因子小于网络概率因子的目标邻居节点发送的第一选举信息，即本公开的一些实施例中先由自身概率因子小于网络概率因子的节点发送第一选举信息；网络节点根据第一选举信息确定出该网络节点为除预设节点之外的节点的情况下，发送第二选举信息，即参与第二阶段的选举。本公开的一些实施例中，将选举过程分步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量以及DS选举产生的系统开销。
如图6所示，本公开的实施例还提供了一种节点设备，包括：收发机、存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：
接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息 包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
其中，在图6中，总线架构可以包括任意数量的互联的总线和桥，具体由处理器600代表的一个或多个处理器和存储器620代表的存储器的各种电路链接在一起。总线架构还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路链接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口提供接口。收发机610可以是多个元件，即包括发送机和收发机，提供用于在传输介质上与各种其他装置通信的单元。针对不同的用户设备，用户接口630还可以是能够外接内接需要设备的接口，连接的设备包括但不限于小键盘、显示器、扬声器、麦克风、操纵杆等。
处理器600负责管理总线架构和通常的处理，存储器620可以存储处理器600在执行操作时所使用的数据。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
获取网络节点自身的概率因子；
在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在第一节点集合中，选取具有最高支配因子的第二节点；
根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
可选的，处理器600还用于读取存储器620中的程序，执行如下步骤：
在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
本公开的一些实施例的节点设备，先接收自身概率因子小于网络概率因子的目标邻居节点发送的第一选举信息，即本公开的一些实施例中先由自身概率因子小于网络概率因子的节点发送第一选举信息；节点设备根据第一选举信息确定出该网络节点为除预设节点之外的节点的情况下，发送第二选举信息，即参与第二阶段的选举。本公开的一些实施例中，将选举过程分步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量以及DS选举产生的系统开销。
在本公开的一些实施例中，还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现以下步骤：
接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
该程序被处理器执行时能实现上述方法实施例中的所有实现方式，为避免重复，此处不再赘述。
如图7所示，本公开的一些实施例还提供了一种节点设备，包括：
接收模块701，用于接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
确定模块702，用于根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
第一发送模块703，用于在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
本公开的一些实施例的节点设备，还包括：
获取模块，用于获取网络节点自身的概率因子；
第二发送模块，用于在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
本公开的一些实施例的节点设备，所述第二发送模块包括：
第一选取子模块，用于在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
第一发送子模块，用于根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
本公开的一些实施例的节点设备，所述第一发送子模块包括：
第一确定单元，用于在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
第一选取单元，用于在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
本公开的一些实施例的节点设备，所述第一发送模块包括：
第二选取子模块，用于在第一节点集合中，选取具有最高支配因子的第二节点；
第二发送子模块，用于根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
本公开的一些实施例的节点设备，所述第二选取子模块用于在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
本公开的一些实施例的节点设备，所述第二发送子模块包括：
第二确定单元，用于在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
第二选取单元，用于在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
本公开的一些实施例的节点设备，还包括：
第一更新模块，用于根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
本公开的一些实施例的节点设备，还包括：
第二更新模块，用于根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
本公开的一些实施例的节点设备，所述第一更新模块或所述第二更新模块用于在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
本公开的一些实施例的节点设备，先接收自身概率因子小于网络概率因子的目标邻居节点发送的第一选举信息，即本公开的一些实施例中先由自身概率因子小于网络概率因子的节点发送第一选举信息；节点设备根据第一选举信息确定出该网络节点为除预设节点之外的节点的情况下，发送第二选举信息，即参与第二阶段的选举。本公开的一些实施例中，将选举过程分步执行，充分利用和传递了选举过程中产生的信息，减少了选举产生的冗余DS节点的数量，且减少了参与选举的节点数量以及DS选举产生的系统开销。
在本公开的各种实施例中，应理解，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本公开的一些实施例的实施过程构成任何限定。
以上所述是本公开的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本公开所述原理的前提下，还可以作出若干改进和 润饰，这些改进和润饰也应视为本公开的保护范围。

Claims (25)

  1. 一种网络节点的选举方法，应用于网络节点，包括：
    接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
    根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
    在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
  2. 根据权利要求1所述的网络节点的选举方法，其中，所述根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点之前，还包括：
    获取网络节点自身的概率因子；
    在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
  3. 根据权利要求2所述的网络节点的选举方法，其中，所述发送第三选举信息，包括：
    在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
    根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
  4. 根据权利要求3所述的网络节点的选举方法，其中，根据所述第一节点，确定DS节点，包括：
    在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
    在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
  5. 根据权利要求1所述的网络节点的选举方法，其中，所述发送第二选举信息，包括：
    在第一节点集合中，选取具有最高支配因子的第二节点；
    根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
    所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
  6. 根据权利要求5所述的网络节点的选举方法，其中，在第一节点集合中，选取具有最高支配因子的第二节点，包括：
    在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
  7. 根据权利要求5所述的网络节点的选举方法，其中，根据所述第二节点，确定第二DS节点，包括：
    在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
    在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
  8. 根据权利要求2所述的网络节点的选举方法，其中，接收网络节点的目标邻居节点发送的第一选举信息之后，还包括：
    根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
  9. 根据权利要求2所述的网络节点的选举方法，其中，发送第二选举信息之后，还包括：
    根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
  10. 根据权利要求8或9所述的网络节点的选举方法，其中，更新所述网络节点和所述网络节点的邻居节点的状态信息，包括：
    在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
    在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
    在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
  11. 一种节点设备，包括：收发机、存储器、处理器及存储在存储器上并可在处理器上运行的程序，所述处理器执行所述程序时实现以下步骤：
    通过收发机接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
    根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
    通过收发机在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
  12. 根据权利要求11所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    获取网络节点自身的概率因子；
    在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
  13. 根据权利要求12所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
    根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
  14. 根据权利要求13所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    在所述第一节点包括一个节点的情况下，将所述第一节点确定为DS节点；
    在所述第一节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最大的节点作为DS节点。
  15. 根据权利要求11所述的节点设备，其中，所述处理器执行所述程序 时还实现以下步骤：
    在第一节点集合中，选取具有最高支配因子的第二节点；
    根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
    所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
  16. 根据权利要求15所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    在第一节点集合中，选取不属于第二节点集合，且具有最高支配因子的第二节点，所述第二节点集合包括与DS节点直接通信的节点。
  17. 根据权利要求15所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    在所述第二节点包括一个节点的情况下，将所述第二节点确定为第二DS节点；
    在所述第二节点包括至少两个节点的情况下，在所述至少两个节点中选取节点ID最小的节点作为第二DS节点。
  18. 根据权利要求12所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    根据所述第一选举信息和所述第三选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
  19. 根据权利要求12所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    根据所述第二选举信息，更新所述网络节点和所述网络节点的邻居节点的状态信息。
  20. 根据权利要求18或19所述的节点设备，其中，所述处理器执行所述程序时还实现以下步骤：
    在所述网络节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第一状态，并将与所述网络节点直接通信的邻居节点的状态信息更新为第二状态；
    在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点被选举为DS节点的情况下，将所述网络节点的状态信息更新为第二 状态，并将与所述网络节点直接通信的被选举为DS节点的邻居节点的状态信息更新为第一状态；
    在所述网络节点未被选举为DS节点，且与所述网络节点直接通信的邻居节点未被选举为DS节点的情况下，将所述网络节点的状态信息更新为第三状态。
  21. 一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如权利要求1至10中任一项所述网络节点的选举方法的步骤。
  22. 一种节点设备，包括：
    接收模块，用于接收网络节点的目标邻居节点发送的第一选举信息，所述第一选举信息包括所述目标邻居节点选举出的支配集DS节点，所述目标邻居节点的概率因子小于网络概率因子；
    确定模块，用于根据所述第一选举信息，确定所述网络节点是否为除预设节点之外的节点，所述预设节点包括DS节点以及与DS节点直接通信的节点；
    第一发送模块，用于在所述网络节点为除预设节点之外的节点的情况下，发送第二选举信息，所述第二选举信息包括所述网络节点选举出的DS节点。
  23. 根据权利要求22所述的节点设备，还包括：
    获取模块，用于获取网络节点自身的概率因子；
    第二发送模块，用于在所述网络节点自身的概率因子小于网络概率因子的情况下，发送第三选举信息，所述第三选举信息包括网络节点选举出的DS节点。
  24. 根据权利要求23所述的节点设备，其中，所述第二发送模块包括：
    第一选取子模块，用于在第一节点集合中，选取具有最高支配因子的第一节点，所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点；
    第一发送子模块，用于根据所述第一节点，确定第一DS节点，并发送所述第一DS节点。
  25. 根据权利要求22所述的节点设备，其中，所述第一发送模块包括：
    第二选取子模块，用于在第一节点集合中，选取具有最高支配因子的第二节点；
    第二发送子模块，用于根据所述第二节点，确定第二DS节点，并发送所述第二DS节点；
    所述第一节点集合包括网络节点以及与所述网络节点直接通信的节点。
PCT/CN2019/119350 2018-12-25 2019-11-19 网络节点的选举方法及节点设备 WO2020134713A1 (zh)

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