Joint optimization gateway election method for hierarchical ad hoc network

Abstract

The invention discloses a joint optimization gateway election method for a hierarchical ad hoc network. The method comprises the following steps: step 1, establishing a multi-objective optimization model for communication cost in a subnet and connectivity between gateways by introducing connectivity constraints; step 2, in each subnet, screening out a group of candidate gateway node sets by taking an index of average communication cost as an optimization target, and reducing a search space; and step 3, gaming the candidate gateway nodes through a game theory strategy, selecting a node with the maximum Shapley value from the candidate gateway node set of each subnet as a gateway node, and rapidly obtaining an upper layer gateway node set with the optimal overall performance of the whole network to be obtained in the step 1. The method has the characteristics that the convergence speed in the gateway election process is high, and the performance of the whole network after election is optimal. The method and the device are used for solving the problem of gateway optimization selection when multiple sub-networks in the hierarchical ad hoc network communicate with one another through gateway nodes.

Description

Technical Field

[0001] The invention belongs to the technical field of subnet gateway selection optimization, and in particular relates to a joint optimization gateway election method for a hierarchical self-organizing network. Background Art

[0002] In a hierarchical self-organizing network, each node in a subnet can communicate with multiple hops, and communication between subnets must be carried out through the gateway node of each subnet. Each subnet has a gateway node, and each gateway node forms an upper-layer network for inter-subnet communication. Traditional gateway election methods usually conduct independent elections based on local information within the subnet, and usually only rely on performance indicators within the subnet, such as electing nodes with the least hops, the highest energy, or the strongest signal strength as gateways. Although this local optimization election strategy can improve the communication performance within the subnet to a certain extent, it ignores the connectivity and comprehensive performance between gateways in the upper-layer network, resulting in performance bottlenecks when communicating between subnets. Therefore, how to ensure the communication performance within the subnet while optimizing the connectivity between gateways to improve the overall efficiency of the network has become a key issue that needs to be solved in the selection of gateways for hierarchical self-organizing networks.

[0003] In the paper "Research on Dynamic Clustering Management Mechanism of MANET Based on Intelligent Prediction [D], He Ke, Sichuan: University of Electronic Science and Technology, 2023", it is proposed that when electing a gateway, two adjacent clusters need to first select a certain number of candidate gateways, and then comprehensively consider the quality of the gateways on both sides between the two clusters to elect a reasonable gateway pair instead of independently selecting their own gateways. The defect is that the optimization goal tends to be local optimal, and lacks comprehensive consideration of the performance of the entire network. At the same time, whenever a subnet is connected to an adjacent subnet, it is necessary to elect additional gateway nodes in the subnet, which may result in a large number of gateway nodes in the same subnet. This not only increases the complexity of management and coordination, but may also cause redundant information exchange and unnecessary resource consumption, thereby affecting the overall performance and stability of the network.

[0004] Therefore, in most existing technologies, each subnet independently completes gateway election based on the information within the subnet, and only considers the specific situation within the subnet during the gateway election process, resulting in poor performance of the network composed of gateway nodes. Although the above literature proposes the idea of ​​comprehensively considering the quality of the gateways on both sides between the two clusters to elect a reasonable gateway pair instead of independently electing their own gateways, its optimization goal is mainly focused on the gateway election within a single subnet, lacking comprehensive consideration of the performance of the entire network. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a joint optimization gateway election method for a hierarchical self-organizing network, which has the characteristics of fast convergence speed of the gateway election process and optimal performance of the entire network after the election. It is used to solve the problem of gateway optimization selection when multiple subnets are interconnected and communicated through gateway nodes in a hierarchical self-organizing network.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A joint optimization gateway election method for a hierarchical self-organizing network comprises the following steps:

[0008] Step 1: Modeling the election of the best gateway set in the entire network

[0009] In this step, connectivity constraints are introduced to establish a multi-objective optimization model for the communication cost within the subnet and the connectivity between gateways;

[0010] Step 2: Subnet candidate gateway election

[0011] In this step, in each subnet, a set of candidate gateway nodes is selected with the average communication cost as the optimization target to narrow the search space;

[0012] Step 3: Inter-subnet gateway group election

[0013] In this step, the candidate gateway nodes are screened through game theory strategies, and the nodes with the largest Shapley value are selected as gateway nodes from the candidate gateway node set of each subnet, so as to quickly obtain the upper-level gateway node set with the best comprehensive performance of the whole network.

[0014] By screening the candidate gateway nodes in step 2, the search space is narrowed. Then, the game theory strategy in step 3 is used to play games on these candidate gateway nodes, and the set of upper-level gateway nodes with the best overall performance in the whole network to be obtained in step 1 is quickly obtained.

[0015] The step 1 is specifically as follows:

[0016] Step 1.1, define variables:

[0017] x ij : Binary variable, indicating whether node j in subnet i is selected as a gateway, x ij ∈{0,1};

[0018] x ij =1: Node j in subnet i is a gateway;

[0019] x ij =0: Node j in subnet i is not a gateway;

[0020] N: number of subnets;

[0021] M i : Set the number of nodes in subnet i;

[0022] Step 1.2, define performance indicators;

[0023] According to the performance of individual subnets, the average communication cost index Cost between node l in subnet k and other nodes in the subnet is defined as kl ;

[0024] For the upper layer network composed of gateway nodes between subnets, define the inter-subnet node connectivity index y mn，pq ;

[0025] Through the inter-subnet node connectivity index y mn，pq , get the connectivity matrix L of the upper gateway network G , the matrix L G The element L in G [m,n] indicates whether gateway node m and gateway node n are directly connected. A value of 1 indicates direct connection and a value of 0 indicates indirect connection:

[0026] L G [m,n]=y mn，pq ·x mn ·x pq ;

[0027] Step 1.3, define the objective function and constraints.

[0028] In step 1.2:

[0029] The average communication cost indicator Cost kl That is, the average number of hops from other nodes in subnet k to node l;

[0030]

[0031] Among them, HopCount vl Represents the number of hops from node v to node l in subnet k.

[0032] The inter-subnet node connectivity index y mn，pq Indicates whether node n in subnet m is directly connected to node q in subnet p;

[0033]

[0034] The step 1.3 is specifically as follows:

[0035] Objective function:

[0036]

[0037] Constraints:

[0038] (1) Select a gateway for each subnet:

[0039]

[0040] (2) Algebraic connectivity constraints: ensure that there is a path between any two gateway nodes in the upper network, to prevent the network from being split when the gateway nodes form the upper network;

[0041] λ 2 (L G )≥λ thresh

[0042] Among them, L G is the connectivity matrix of the upper gateway network, λ 2 (L G ) is the matrix L G The second smallest eigenvalue, λ thresh is the algebraic connectivity threshold (set according to the specific network situation).

[0043] Find a set of x ij The value of minimizes the objective function Z and satisfies the above constraints at the same time, that is, a set of gateways with the best overall performance of the entire network is found.

[0044] However, when traversal or genetic algorithm is used to find the optimal upper-level gateway set, the computational complexity is high, which will lead to increased network load and increased delay. Therefore, a fast convergence method of election combining the candidate gateway screening mechanism in step 2 and the game theory in step 3 is considered.

[0045] The step 2 specifically includes the following steps:

[0046] Step 2.1, define variables and indicators;

[0047] Node set K: K = {1, 2, ..., m}, which represents the set of nodes that can receive information from other subnets;

[0048] Candidate gateway set G: And |G|=n, indicating the n selected candidate gateways;

[0049] Decision variables:

[0050]

[0051] in,

[0052] The average communication cost between the candidate node and other nodes in the subnet (the number of hops from other nodes in the network to the gateway node) i :

[0053]

[0054] Step 2.2, define the objective function and constraints;

[0055] Objective function:

[0056] MaximizeF(G)=x i Cost i

[0057] Constraints:

[0058]

[0059] Step 2.3, solve the objective function to select the candidate gateway set G for each subnet.

[0060] The step 3 specifically includes the following steps:

[0061] Step 3.1, define the algebraic connectivity of the upper-level gateway network composed of gateway nodes selected from the candidate gateway set G of each subnet as the utility function of the upper-level gateway set C;

[0062] v(C)=λ 2 (L C )

[0063] Among them, L C is the direct connectivity matrix of alliance C, λ 2 (L C ) is the matrix L C The second smallest eigenvalue;

[0064] Step 3.2, establish the cooperative game model as follows:

[0065] Participants: All candidate gateway nodes in each subnet are considered as participants in the cooperative game;

[0066] Alliance: The upper-level gateway set C is a set of nodes selected by each subnet from its candidate gateway node set;

[0067] Alliance utility: The algebraic connectivity of the upper network is taken as the utility v(C) of the alliance;

[0068] The goal is to select a gateway node from the candidate gateway node set of each subnet to form an upper-level gateway node set, so that the algebraic connectivity of the upper-level gateway network is maximized.

[0069] Step 3.3, calculate the Shapley value of the node. Since each subnet is only allowed to select one node from its candidate gateway node set to join the upper-level alliance, the hierarchical marginal contribution method is used to calculate the Shapley value.

[0070] Step 3.4, calculate the Shapley value of the candidate node in each subnet, and select the candidate gateway node with the largest Shapley value in each subnet as the final gateway node of the subnet.

[0071] The step 3.3 is specifically as follows:

[0072] For subnet S i , which selects the candidate gateway node g ik The marginal contribution of the choice needs to measure the impact of the choice on the global utility. Suppose the gateway node of the current other subnet is selected as C -i =(g 1 ,…,g i-1 ,g i+1 ,…,g M )(Node g 1 ,…,g i-1 ,g i+1 ,…,g M are the gateway nodes selected by other subnets, where each subnet can only select one node), then node g ik The marginal contribution calculation formula is as follows:

[0073] Δ i (g ik ,C -i )=v(C -i ∪{g ik})-v(C -i ∪{g i0})

[0074] Among them, g i0 Indicates subnet S i Not participating in the alliance.

[0075] Subnet S i Select candidate gateway node g ik The average marginal contribution of node g in all possible combinations ik The Shapley value calculation formula is as follows:

[0076]

[0077] in, Select the space for the gateway of the other subnet, is the number of possible combinations.

[0078] The step 3.4 is specifically as follows:

[0079] For each subnet in the network, the gateway node finally elected by each subnet is obtained through the above method. Then the final elected upper-layer gateway node set U can be obtained;

[0080]

[0081] The joint optimization gateway election method of the hierarchical ad hoc network is used for Internet of Things (IoT) networks, communication networks, and farmland monitoring and irrigation.

[0082] Beneficial effects of the present invention:

[0083] First, reduce computational complexity. In the present invention, only candidate gateways participate in the election, reducing the amount of information exchange. In the traditional gateway election method, all nodes in the subnet participate in the election of the gateway, and each node directly needs to exchange information. In the present invention, a certain number of nodes are screened out as game participants in step 3 through step 2, and the search space for understanding is narrowed through pre-screening.

[0084] Second, the selected gateways have good comprehensive performance. When electing a gateway, the overall performance of the gateway network is considered at the same time to elect a reasonable gateway combination instead of independently electing each gateway, so that the gateway set can achieve the best comprehensive performance of the entire network. Traditional gateway election methods only consider performance indicators within the subnet, such as the number of hops, energy or signal strength, while ignoring the communication performance of the upper network composed of gateway nodes. This scheme uses the algebraic connectivity of the upper network composed of gateway nodes as the election indicator, and considers the indicators within the subnet and the algebraic connectivity indicators between subnets to establish the objective function and obtain the optimal solution, and selects a set of gateway nodes with the best comprehensive performance of intra-subnet communication performance and direct algebraic connectivity of gateways. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0086] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0087] like Figure 1 As shown, a joint optimization gateway election method for a hierarchical self-organizing network includes the following steps:

[0088] Step 1: Modeling the election of the best gateway set in the entire network:

[0089] A multi-objective optimization model is established for the communication cost within the subnet and the connectivity between gateways. By introducing connectivity constraints, it is ensured that the selected gateway combination can form an efficient connectivity graph, avoiding the possibility that gateway nodes may not be able to communicate directly, and improving the overall communication quality of the network.

[0090] Step 1.1, define variables.

[0091] x ij : Binary variable, indicating whether node j in subnet i is selected as a gateway, xij ∈{0,1}.

[0092] x ij =1: Node j in subnet i is a gateway.

[0093] x ij =0: Node j in subnet i is not a gateway.

[0094] N: number of subnets

[0095] M i : Set the number of nodes in subnet i

[0096] Step 1.2, define performance indicators

[0097] According to the performance of individual subnets, the average communication cost index Cost between node l in subnet k and other nodes in the subnet is defined as kl (The calculation formula is as follows, where HopCount vl obtained through the routing table entries in the network), that is, the average number of hops from other nodes in subnet k to node l.

[0098]

[0099] Among them, HopCount vl represents the number of hops from node v to node l in subnet k;

[0100] For the upper layer network composed of gateway nodes between subnets, define the inter-subnet node connectivity index y mn，pq , indicating whether node n in subnet m is connected to node q in subnet p; used to describe the connectivity performance of the upper-layer network composed of gateway nodes.

[0101]

[0102] Through the inter-subnet node connectivity index y mn，pq , we can get the connectivity matrix L of the upper gateway network G , the matrix L G The element L in G [m,n] indicates whether gateway node m and gateway node n are directly connected. A value of 1 indicates direct connection and a value of 0 indicates indirect connection. It is used for algebraic connectivity constraints:

[0103] L G [m,n]=y mn，pq ·x mn ·x pq

[0104] Step 1.3, define the objective function and constraints.

[0105] Objective function:

[0106]

[0107] Constraints:

[0108] (1) Select a gateway for each subnet:

[0109]

[0110] (2) Algebraic connectivity constraint: ensures that there is a path between any two gateway nodes in the upper-layer network to prevent the network from being split when the gateway nodes form the upper-layer network.

[0111] λ 2 (L G )≥λ thresh

[0112] Among them, L G is the connectivity matrix of the upper gateway network, λ 2 (L G ) is the matrix L G The second smallest eigenvalue. thresh is the algebraic connectivity threshold (set according to the specific network situation).

[0113] Find a set of x ij The value of minimizes the objective function Z and satisfies all constraints at the same time, that is, a set of gateways with the best overall performance of the entire network is found, so that the selected gateways not only have the best performance in the subnet, but also have the best connectivity in the upper network composed of gateway nodes. However, when using traversal or heuristic methods to find the optimal solution, the computational complexity is high, which will lead to increased network load and delay. Therefore, a fast solution method combining the candidate gateway mechanism and game theory is considered.

[0114] Step 2: Election of candidate gateways within the subnet:

[0115] In each subnet, a group of candidate gateway nodes are selected based on the above average communication cost index as the optimization target, and the search space for gateway election is narrowed by pre-screening the candidate gateways.

[0116] The specific steps include:

[0117] Step 2.1, define variables and indicators;

[0118] Node set K: K = {1, 2, ..., m}, which represents the set of nodes that can receive information from other subnets;

[0119] Candidate gateway set G: And |G|=n, indicating the n selected candidate gateways;

[0120] Decision variables:

[0121]

[0122] in,

[0123] The average communication cost between the candidate node and other nodes in the subnet (the number of hops from other nodes in the network to the gateway node) i :

[0124]

[0125] Step 2.2, define the objective function and constraints;

[0126] Objective function:

[0127] MaximizeF(G)=x i Cost i

[0128] Constraints:

[0129]

[0130] Step 2.3, solve the objective function to select the candidate gateway set G for each subnet.

[0131] Step 3, inter-subnet gateway group election:

[0132] A gateway is selected from each subnet set to form a gateway set, so that the overall benefit of the gateway network is maximized. Here, the selection process is modeled as a coalition formation problem using a cooperative game method, and the core concept of game theory, Shapley value, is used to distribute benefits and select gateways.

[0133] The node with the largest Shapley value is selected from the candidate gateway node set of each subnet as the gateway node. The gateway nodes elected by each subnet form the upper-level gateway node set for inter-subnet communication, maximizing the overall benefit of the network.

[0134] The specific steps include:

[0135] Step 3.1, define the algebraic connectivity of the upper-level gateway network composed of gateway nodes selected from the candidate gateway set G of each subnet as the utility function of the upper-level gateway set C;

[0136] v(C)=λ 2 (L C )

[0137] Among them, L C is the direct connectivity matrix of alliance C. 2 (L C) is the matrix L C The second smallest eigenvalue.

[0138] Step 3.2, establish a cooperative game model. All candidate gateways are participants in the game. A coalition is a set of candidate gateways. And there is exactly one gateway in each subnet in the coalition. In the cooperative game, participants (candidate gateways) can form coalitions and obtain utility through cooperation, which is recorded as the coalition utility function.

[0139] Step 3.3, calculate the Shapley value of each candidate gateway node. According to the calculated Shapley value, select the candidate gateway node with the largest Shapley value in each subnet as the final gateway node of the subnet. For each subnet in the network, the gateway node finally elected by each subnet is obtained through the above method, and the final set of upper-level gateway nodes elected can be obtained.

[0140] The method is applied to:

[0141] Internet of Things (IoT) Networks:

[0142] In scenarios such as smart cities, smart homes, and industrial IoT, a large number of IoT devices communicate through hierarchical self-organizing networks. By optimizing gateway election, the communication efficiency between subnets is improved, ensuring the real-time and reliability of data transmission.

[0143] Military Communications Networks:

[0144] In the field, soldiers, vehicles, drones, etc. communicate and collaborate on tasks through ad hoc networks. By optimizing gateway elections, communication connectivity can be maintained when network nodes are damaged or moved.

[0145] Emergency Communications Network:

[0146] In natural disasters (such as earthquakes, floods) or emergencies, emergency communication networks need to be deployed quickly and achieve efficient communication. The present invention can quickly select the optimal gateway combination and support the rapid construction and recovery of the network.

[0147] Smart Agriculture:

[0148] In scenarios such as farmland monitoring and irrigation control, a large number of sensors and controllers communicate through ad hoc networks. Optimizing gateway elections can reduce data transmission delays and improve agricultural monitoring and control efficiency.

[0149] Smart Factory Wireless Network Gateway Election:

[0150] A certain smart factory has deployed a hierarchical wireless sensor network for real-time monitoring of the production line status. A device in each product line serves as a network node, and each product line serves as a subnet. The present invention selects a device from each product line device as a gateway to form an upper-level gateway network for data communication between different product lines, which can ensure that the data of the entire factory is efficiently aggregated to the control center, while avoiding network splitting caused by uneven distribution of gateways, resulting in poor data communication status between product lines.

Claims

1. A joint optimization gateway election method for a hierarchical ad hoc network, characterized in that: The steps include: Step 1, introduce connectivity constraints to establish a multi-objective optimization model for the communication cost within the subnet and the connectivity between gateways; Step 2: In each subnet, take the average communication cost as the optimization target, select a set of candidate gateway nodes, and narrow the search space; Step 3: Screen the candidate gateway nodes through game theory strategies, select the node with the largest Shapley value from the candidate gateway node set of each subnet as the gateway node, and quickly obtain the upper-level gateway node set with the best comprehensive performance of the entire network.

2. The method for selecting a joint optimized gateway in a hierarchical ad hoc network according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1.1, define variables: x ij : Binary variable, indicating whether node j in subnet i is selected as a gateway, x ij ∈{0,1}; x ij =1: Node j in subnet i is a gateway; x ij =0: Node j in subnet i is not a gateway; N: number of subnets; M i : Set the number of nodes in subnet i; Step 1.2, define performance indicators; According to the performance of individual subnets, the average communication cost index Cost between node l in subnet k and other nodes in the subnet is defined as kl ; For the upper layer network composed of gateway nodes between subnets, define the inter-subnet node connectivity index y mn，pq ; Through the inter-subnet node connectivity index y mn，pq , get the connectivity matrix L of the upper gateway network G , the matrix L G The element L in G [m,n] indicates whether gateway node m and gateway node n are directly connected. A value of 1 indicates direct connection and a value of 0 indicates indirect connection: L G [m,n]=y mn，pq ·x mn ·x pq ; Step 1.3, define the objective function and constraints.

3. The method for selecting a joint optimized gateway in a hierarchical ad hoc network according to claim 2, characterized in that: In step 1.2: The average communication cost indicator Cost kl That is, the average number of hops from other nodes in subnet k to node l; Among them, HopCount vl Represents the number of hops from node v to node l in subnet k.

4. The method for selecting a joint optimized gateway in a hierarchical ad hoc network according to claim 2, characterized in that: In step 1.2: The inter-subnet node connectivity index y mn，pq Indicates whether node n in subnet m is directly connected to node q in subnet p; 5. The method for selecting a joint optimized gateway in a hierarchical ad hoc network according to claim 2, characterized in that: The step 1.3 is specifically as follows: Objective function: Constraints: (1) Select a gateway for each subnet: (2) Algebraic connectivity constraints: ensure that there is a path between any two gateway nodes in the upper network, to prevent the network from being split when the gateway nodes form the upper network; λ2(LG)≥λ thresh Among them, L G is the connectivity matrix of the upper gateway network, λ2(L G ) is the matrix L G The second smallest eigenvalue, λ thresh is the algebraic connectivity threshold; Find a set of x ij The value of minimizes the objective function Z and satisfies the above constraints at the same time, that is, finds a set of gateways with the best comprehensive performance of the entire network.

6. The method for selecting a joint optimized gateway in a hierarchical ad hoc network according to claim 1, characterized in that: The step 2 specifically includes the following steps: Step 2.1, define variables and indicators; Node set K: K = {1, 2, ..., m}, which represents the set of nodes that can receive information from other subnets; Candidate gateway set G: And |G|=n, indicating the n selected candidate gateways; Decision variables: in, The average communication cost between the candidate node and other nodes in the subnet i : Step 2.2, define the objective function and constraints; Objective function: MaximizeF(G)=x i ·Cost i Constraints: Step 2.3, solve the objective function to select the candidate gateway node set G for each subnet.

7. The method for selecting a joint optimization gateway in a hierarchical ad hoc network according to claim 1, characterized in that: The step 3 specifically includes the following steps: Step 3.1, define the algebraic connectivity of the upper-layer gateway network composed of gateway nodes selected from the candidate gateway node set G of each subnet as the utility function of the upper-layer gateway set C; v(C)=λ2(L C ) Among them, L C is the direct connectivity matrix of alliance C, λ2(L C ) is the matrix L C The second smallest eigenvalue; Step 3.2, establish the cooperative game model as follows: Participants: All candidate gateway nodes in each subnet are considered as participants in the cooperative game; Alliance: The upper-level gateway set C is a set of nodes selected by each subnet from its candidate gateway node set; Alliance utility: The algebraic connectivity of the upper network is taken as the utility v(C) of the alliance; The goal is to select a gateway node from the candidate gateway node set of each subnet to form an upper-level gateway node set, so that the algebraic connectivity of the upper-level gateway network is maximized; Step 3.3, using the hierarchical marginal contribution method to calculate the Shapley value of the node; Step 3.4, calculate the Shapley value of the candidate node in each subnet, and select the candidate gateway node with the largest Shapley value in each subnet as the final gateway node of the subnet.

8. The method for selecting a joint optimization gateway in a hierarchical ad hoc network according to claim 7, characterized in that: The step 3.3 is specifically as follows: For subnet S i , which selects the candidate gateway node g ik The marginal contribution of the choice needs to measure the impact of the choice on the global utility. Suppose the gateway node of the current other subnet is selected as C -i =(g1,…,g i-1 ,g i+1 ,…,g M ), then node g ik The marginal contribution calculation formula is as follows: Δ i (g ik ,C -i )=v(C -i ∪{g ik })-v(C -i ∪{g i0 }) Among them, g i0 Indicates subnet S i Not participating in alliances; Subnet S i Select candidate gateway node g ik The average marginal contribution of node g in all possible combinations ik The Shapley value calculation formula is as follows: in, Select the space for the gateway of the other subnet, is the number of possible combinations.

9. The method for selecting a joint optimization gateway in a hierarchical ad hoc network according to claim 7, characterized in that: The step 3.4 is specifically as follows: Elect a gateway node Get the final elected upper-layer gateway node set U; 10. A method for selecting a joint optimization gateway in a hierarchical ad hoc network according to any one of claims 1 to 9, characterized in that: The joint optimization gateway election method of the hierarchical self-organizing network is used for Internet of Things networks, communication networks, and farmland monitoring and irrigation.

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