A layered self-organizing network joint optimization gateway election method

Abstract

The application discloses a layered self-organizing network joint optimization gateway election method, comprising the following steps: step 1, a multi-objective optimization model is established for the communication cost in the sub-network and the connectivity between the gateway by introducing the connectivity constraint; step 2, in each sub-network, a group of candidate gateway node sets is screened out according to the average communication cost index as the optimization target to reduce the search space; step 3, the game theory strategy is used to game the candidate gateway nodes, and the node with the maximum Shapley value is selected from the candidate gateway node set of each sub-network as the gateway node, so that the upper gateway node set with the optimal overall performance of the whole network in step 1 is quickly obtained. The application has the characteristics of fast convergence speed of the gateway election process and optimal overall performance of the whole network after the election. The application is used for solving the gateway optimization selection problem when the multi-subnetworks in the layered self-organizing network are interconnected and communicated through the gateway nodes.

Description

Technical Field

[0001] This invention belongs to the field of subnet gateway selection and optimization technology, specifically relating to a joint optimization gateway election method for hierarchical self-organizing networks. Background Technology

[0002] In hierarchical ad hoc networks, communication between nodes within a subnet is multi-hop reachable, while communication between subnets requires passing through the gateway node of each subnet. Each subnet has one gateway node, and these gateway nodes form the upper-layer network for inter-subnet communication. Traditional gateway election methods typically rely on local information within the subnet for independent selection, usually depending solely on performance metrics within the subnet, such as electing the node with the fewest hops, highest energy, or strongest signal strength as the gateway. While this locally optimized election strategy can improve communication performance within a subnet to some extent, it neglects the connectivity and overall performance between gateways in the upper-layer network, leading to performance bottlenecks in inter-subnet communication. Therefore, how to improve overall network efficiency by simultaneously ensuring communication performance within a subnet and optimizing connectivity between gateways has become a critical issue that urgently needs to be addressed in gateway selection for hierarchical ad hoc networks.

[0003] The paper "Research on Dynamic Clustering Management Mechanism of MANET Based on Intelligent Prediction [D], He Ke, Sichuan: University of Electronic Science and Technology of China, 2023" proposes that when electing a gateway, two adjacent clusters should first select a certain number of candidate gateways. Then, a reasonable gateway pair is elected by comprehensively considering the quality of the gateways on both sides between the two clusters, rather than independently selecting their respective gateways. Its drawback is that the optimization objective is biased towards local optima, lacking a comprehensive consideration of the overall network performance. Furthermore, whenever a subnet connects to an adjacent subnet, an additional gateway node needs to be elected within that subnet, potentially leading to a large number of gateway nodes within the same subnet. This not only increases the complexity of management and coordination but may also cause redundant information exchange and unnecessary resource consumption, thus affecting the overall performance and stability of the network.

[0004] Therefore, most existing technologies involve each subnet independently electing its gateway based on information within that subnet. This gateway election process only considers the specific circumstances within the subnet, leading to poor network performance for the gateway nodes. While the aforementioned literature proposes a more comprehensive approach, considering the quality of gateways on both sides between two clusters to elect a suitable gateway pair rather than independently electing each gateway, its optimization objective primarily focuses on gateway election within a single subnet, lacking a comprehensive consideration of overall network performance. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the present invention aims to provide a joint optimization gateway election method for hierarchical ad hoc networks. This method features fast convergence speed in the gateway election process and optimal overall network performance after the election. It is used to solve the gateway optimization selection problem when multiple subnets communicate through interconnection via gateway nodes in a hierarchical ad hoc network.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A joint optimization gateway election method for a hierarchical ad hoc network includes the following steps;

[0008] Step 1: Modeling the election of the optimal gateway set across the entire network

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

[0010] Step 2, candidate gateway election within the subnet

[0011] In this step, within each subnet, a set of candidate gateway nodes is selected based on the average communication cost as the optimization objective, thus narrowing the search space.

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

[0013] In this step, a game theory strategy is used to screen candidate gateway nodes. The node with the largest Shapley value is selected from the candidate gateway node set of each subnet as the gateway node, so as to quickly obtain the set of upper-layer gateway nodes with the best overall performance of the entire network.

[0014] By filtering candidate gateway nodes in step 2, the search space is narrowed down. Then, the game theory strategy in step 3 is used to play against these candidate gateway nodes, quickly obtaining the set of upper-layer gateway nodes with the best overall network performance as required in step 1.

[0015] Step 1 specifically involves:

[0016] Step 1.1, Define variables:

[0017] x ij : A binary variable representing whether node j in subnet i is selected as the 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 metrics;

[0023] For the performance of individual nodes in a subnet, the average communication cost metric Cost is defined between node l in subnet k and other nodes in that subnet. kl ;

[0024] For the upper-layer network consisting 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 The connectivity matrix L of the upper-layer gateway network is obtained. G Matrix L G element L G [m,n] indicates whether gateway node m and gateway node n are directly connected; a value of 1 indicates a direct connection, and a value of 0 indicates a non-direct 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 metric Cost kl That is, the average number of hops from other nodes in subnet k to node l;

[0030]

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

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

[0033]

[0034] Step 1.3 specifically includes:

[0035] Objective function:

[0036]

[0037] Constraints:

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

[0039]

[0040] (2) Algebraic connectivity constraints: ensure that there is a path between any two gateway nodes in the upper-level network, and prevent network splitting when gateway nodes form the upper-level network.

[0041] λ2(L G )≥λ thresh

[0042] Among them, L G Let λ2(L) be the connectivity matrix of the upper-layer gateway network. G ) is a matrix L G The second smallest eigenvalue, λ thresh The algebraic connectivity threshold (set according to the specific network conditions).

[0043] Find a set of x ij The value of is found to minimize the objective function Z while satisfying the above constraints, thus finding the set of gateways that achieves the optimal overall performance of the entire network.

[0044] However, when using traversal or genetic algorithms to find the optimal set of upper-layer gateways, the computational complexity is high, which will lead to increased network load and latency. Therefore, we consider using a fast convergence election method that combines the candidate gateway screening mechanism in step 2 with the game theory in step 3.

[0045] Step 2 specifically includes the following steps:

[0046] Step 2.1, define variables and indicators;

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

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

[0049] Decision variables:

[0050]

[0051] The average communication cost between a candidate node and other nodes in the subnet (the number of hops from other nodes in the subnet to the gateway node) Cost i :

[0052]

[0053] Step 2.2, define the objective function and constraints;

[0054] Objective function:

[0055] MinimizeF(G) = x i Cost i

[0056] Constraints:

[0057]

[0058] Step 2.3: Solve the objective function to select the candidate gateway set G for each subnet.

[0059] Step 3 specifically includes the following steps:

[0060] Step 3.1, define the algebraic connectivity of the upper-layer gateway network composed of gateway nodes elected from the candidate gateway set G of each subnet as the utility function of the upper-layer gateway set C;

[0061] v(C)=λ2(L C )

[0062] Among them, L C Let λ2(L) be the directly connected matrix of the union C. C ) is a matrix L C The second smallest eigenvalue;

[0063] Step 3.2, establish the cooperative game model as follows:

[0064] Participants: All candidate gateway nodes in each subnet are considered as participants in the cooperative game;

[0065] Alliance: The upper-layer gateway set C is the set of nodes selected by each subnet from its candidate gateway node set;

[0066] Coalition utility: The algebraic connectivity of the upper-layer network is taken as the utility v(C) of the coalition;

[0067] The goal is to select a candidate gateway node from the set of candidate gateway nodes in each subnet to form the upper-layer gateway node set, thereby maximizing the algebraic connectivity of the upper-layer gateway network.

[0068] Step 3.3: Calculate the Shapley value of the node. Since each subnet is only allowed to select one node from its set of candidate gateway nodes to join the upper-layer alliance, the hierarchical marginal contribution method is used to calculate the Shapley value.

[0069] Step 3.4: Calculate the Shapley value for each candidate node in each subnet, and select the candidate gateway node with the largest Shapley value in each subnet as the final gateway node for the subnet.

[0070] Step 3.3 specifically involves:

[0071] For subnet S i It selects candidate gateway node g ik The marginal contribution needs to be measured in terms of the impact of this choice on global utility. Let C be the current gateway node selection for other subnets. -i =(g1,…,g i-1 ,g i+1 ,…,g M (nodes g1,…,g) i-1 ,g i+1 ,…,g M For the gateway nodes selected for each of the other subnets (meaning only one node can be selected for each subnet), then node g... ik The formula for calculating the marginal contribution is as follows:

[0072] Δ i (g ik C -i )=v(C -i ∪{g ik})-v(C -i ∪{g i0})

[0073] Among them, g i0 Subnet S i Not participating in the alliance.

[0074] Subnet S i Select candidate gateway node g ik The average marginal contribution across all possible combinations, i.e., node g ik The formula for calculating the Shapley value is as follows:

[0075]

[0076] in, Select space for gateways in other subnets. Let be the number of possible combinations.

[0077] Step 3.4 specifically involves:

[0078] For each subnet in the network, the final elected gateway node for each subnet is obtained through the above method. Then the final set of elected upper-level gateway nodes U can be obtained;

[0079]

[0080] The joint optimization gateway election method of the hierarchical self-organizing network is used in Internet of Things (IoT) networks, communication networks, and farmland monitoring and irrigation.

[0081] The beneficial effects of this invention are:

[0082] First, it reduces computational complexity. This invention involves only candidate gateways participating in the election, reducing the amount of information exchange. In traditional gateway election methods, all nodes within a subnet participate in the election, and each node needs to exchange information directly. This invention, through step 2, selects a certain number of nodes as game participants in step 3, thus narrowing the search space through pre-screening.

[0083] Second, the selected gateways exhibit superior overall performance. The gateway election process considers the overall performance of the gateway network, selecting a reasonable combination of gateways rather than independently electing individual gateways, thus achieving optimal overall network performance. Traditional gateway election methods only consider performance metrics within subnets, such as hop count, energy, or signal strength, neglecting the communication performance of the upper-layer network comprised of gateway nodes. This scheme uses the algebraic connectivity of the upper-layer network as the election metric, simultaneously considering both intra-subnet and inter-subnet algebraic connectivity metrics to establish an objective function and obtain the optimal solution. This results in the selection of a set of gateway nodes with the best overall performance in terms of both intra-subnet communication performance and inter-gateway algebraic connectivity. Attached Figure Description

[0084] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0085] The present invention will now be described in further detail with reference to the accompanying drawings.

[0086] like Figure 1 As shown, a joint optimization gateway election method for a hierarchical ad hoc network includes the following steps;

[0087] Step 1, Modeling the Election of the Optimal Gateway Set for the Entire Network:

[0088] A multi-objective optimization model is established for communication costs within the subnet and 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, thus improving the overall communication quality of the network.

[0089] Step 1.1, define variables.

[0090] x ij : A binary variable representing whether node j in subnet i is selected as the gateway, x ij ∈{0,1}.

[0091] x ij =1: Node j in subnet i is a gateway.

[0092] x ij=0: Node j in subnet i is not a gateway.

[0093] N: Number of subnets

[0094] M i : Set the number of nodes in subnet i

[0095] Step 1.2, Define performance metrics

[0096] For the performance of individual nodes in a subnet, the average communication cost metric Cost is defined between node l in subnet k and other nodes in that subnet. kl (The calculation formula is as follows, where HopCount) vl (obtained through routing table entries in the network), that is, the average number of hops from other nodes in subnet k to node l.

[0097]

[0098] Among them, HopCount vl This represents the number of hops from node v to node l in subnet k;

[0099] For the upper-layer network consisting 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.

[0100]

[0101] Through the inter-subnet node connectivity index y mn，pq The connectivity matrix L of the upper-layer gateway network can be obtained. G Matrix L G element L 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. This is used for algebraic connectivity constraints.

[0102] L G [m,n]=y mn，pq ·x mn ·x pq

[0103] Step 1.3: Define the objective function and constraints.

[0104] Objective function:

[0105]

[0106] Constraints:

[0107] (1) Select one gateway for each subnet:

[0108]

[0109] (2) Algebraic connectivity constraint: ensures that there is a path between any two gateway nodes in the upper layer network, and prevents the network from splitting when gateway nodes form the upper layer network.

[0110] λ2(L G )≥λ thresh

[0111] Among them, L G Let λ2(L) be the connectivity matrix of the upper-layer gateway network. G ) is a matrix L G The second smallest eigenvalue. λ thresh The algebraic connectivity threshold (set according to the specific network conditions).

[0112] Find a set of x ij Finding a value that minimizes the objective function Z while satisfying all constraints means finding the set of gateways that achieves the optimal overall network performance. This ensures that the elected gateways not only have the best performance within their subnets but also that the upper-layer network composed of these gateway nodes has optimal connectivity. However, finding the optimal solution using traversal or heuristic methods results in high computational complexity, leading to increased network load and latency. Therefore, a fast solution method combining a candidate gateway mechanism and game theory is considered.

[0113] Step 2, candidate gateway election within the subnet:

[0114] In each subnet, a set of candidate gateway nodes is selected based on the above average communication cost index as the optimization objective, and the search space for gateway election is narrowed through candidate gateway pre-screening.

[0115] Specifically, the following steps are included:

[0116] Step 2.1, define variables and indicators;

[0117] The node set k: K = {1, 2, ..., m} represents the set of nodes that can receive information from other subnets;

[0118] Candidate gateway set G: And |G|=n, representing the n candidate gateways selected;

[0119] Decision variables:

[0120]

[0121] in,

[0122] The average communication cost between a candidate node and other nodes in the subnet (the number of hops from other nodes in the subnet to the gateway node) Cost i :

[0123]

[0124] Step 2.2, define the objective function and constraints;

[0125] Objective function:

[0126] MinimizeF(G) = x i Cost i

[0127] Constraints:

[0128]

[0129] Step 2.3: Solve the objective function to select the candidate gateway set G for each subnet.

[0130] Step 3, Inter-subnet gateway group election:

[0131] One gateway is selected from each subnet set to form a gateway set, maximizing the overall revenue of the gateway network. Here, a cooperative game theory approach is used to model the selection process as a coalition formation problem, utilizing the core concept of Shapley value in game theory to allocate revenue and select gateways.

[0132] The node with the highest 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, thereby maximizing the overall network benefit.

[0133] Specifically, the following steps are included:

[0134] Step 3.1, define the algebraic connectivity of the upper-layer gateway network composed of gateway nodes elected from the candidate gateway set G of each subnet as the utility function of the upper-layer gateway set C;

[0135] v(C)=λ2(L C )

[0136] Among them, L C Let λ2(L) be the directly connected matrix of the union C. C ) is a matrix L C The second smallest eigenvalue.

[0137] 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 each subnet has exactly one gateway in the coalition. In cooperative games, participants (candidate gateways) can form coalitions and gain utility through cooperation, denoted as the coalition utility function.

[0138] Step 3.3: Calculate the Shapley value for each candidate gateway node. Based on the calculated Shapley values, select the candidate gateway node with the largest Shapley value in each subnet as the final gateway node for that subnet. For each subnet in the network, the final elected gateway node is obtained through the above method, thus yielding the final set of elected upper-layer gateway nodes.

[0139] The method is applied to:

[0140] Internet of Things (IoT) Networks:

[0141] In smart cities, smart homes, and industrial IoT scenarios, a large number of IoT devices communicate through hierarchical self-organizing networks. By optimizing gateway election, the efficiency of communication between subnets can be improved, ensuring the real-time performance and reliability of data transmission.

[0142] Military communication networks:

[0143] In field environments, soldiers, vehicles, drones, and other equipment communicate and coordinate missions through ad hoc networks. By optimizing gateway election, communication connectivity is ensured to be maintained even when network nodes are damaged or moved.

[0144] Emergency communication network:

[0145] In the event of natural disasters (such as earthquakes and floods) or other emergencies, emergency communication networks need to be deployed rapidly and achieve efficient communication. This invention can quickly select the optimal combination of gateways, supporting rapid network construction and recovery.

[0146] Smart agriculture:

[0147] In scenarios such as farmland monitoring and irrigation control, numerous sensors and controllers communicate via ad-hoc networks. Optimizing gateway election reduces data transmission latency and improves the efficiency of agricultural monitoring and control.

[0148] Smart Factory Wireless Network Gateway Election:

[0149] A smart factory has deployed a hierarchical wireless sensor network for real-time monitoring of production line status. Each product line has one device acting as a network node, and each product line forms a subnet. This invention selects one device from each product line as a gateway, forming an upper-layer gateway network for data communication between different product lines. This ensures efficient data aggregation from across the factory to the control center while avoiding network fragmentation due to uneven gateway distribution, which could lead to poor data communication between product lines.

Claims

1. A method for joint optimization gateway election in hierarchical self-organizing network, characterized in that, Comprising the following steps: Step 1, introducing connectivity constraints to establish a multi-objective optimization model for communication cost in the subnetwork and connectivity between gateways; Step 2, in each subnetwork, taking the average communication cost as the optimization target, screening out a set of candidate gateway node sets to narrow the search space; Step 3, screening the candidate gateway nodes through game theory strategy, selecting the node with the maximum Shapley value from the candidate gateway node set of each subnetwork as the gateway node, and quickly obtaining the upper gateway node set with the optimal overall performance of the whole network.

2. The method of claim 1, wherein, The step 1 is specifically: Step 1.1, define variables: x ij : binary variable indicating whether node j in subnet i is chosen 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 subnetworks; M i : Set the number of nodes in subnet i; Step 1.2, define performance indicators; For the performance of the subnet individual, the average communication cost index Cost of node l in subnet k and other nodes in the subnet is defined kl ; For the upper layer network composed of gateway nodes between subnets, define the inter-subnet node connectivity index y mn，pq ; By inter-subnet node connectivity index y mn，pq , get the connectivity matrix L of the upper gateway network G , the element L G [m,n] in the matrix L G [m,n] represents whether the gateway node m and the gateway node n are directly connected, and the value is 1 if directly connected and 0 if not directly connected: L G [m,n] = y mn，pq · x mn · x pq ; Step 1.3, define objective function and constraint conditions.

3. The method of claim 2, wherein, In the step 1.2: the average communication cost indicator Cost kl i.e. the average number of hops from other nodes in the subnet k to node l; where HopCount vl denotes the number of hops from node v to node I in subnet k.

4. The method of claim 2, wherein, In the step 1.2: The inter-subnet node connectivity indicator y mn，pq represents whether node n in subnet m is directly connected to node q in subnet p; 5. The method of claim 2, wherein, The step 1.3 is specifically: Objective function: Constraint conditions: (1) Select one gateway for each subnetwork: (2) Algebraic connectivity constraint: ensure that there is a path between any two gateway nodes in the upper network to prevent the network from splitting when the gateway nodes form the upper network; λ2(L G )≥λ thresh where L G is the connectivity matrix of the upper gateway network, λ2(L G ) is the second smallest eigenvalue of the matrix L G , and λ thresh is an algebraic connectivity threshold value. Find a set of x ij values that minimize the objective function Z while satisfying the above constraints, i.e., find a set of gateway sets that optimizes the overall network performance.

6. The method of claim 1, wherein, The step 2 specifically includes the following steps: Step 2.1, define variables and indicators; Node set K: K = {1, 2,..., m}, representing the node set that can receive information from other subnetworks; A set of candidate gateways G: and |G| = n, indicating n selected candidate gateways; Decision variables: Cost of average communication of candidate node with other nodes in the subnet i : Step 2.2, define objective function and constraint conditions; Objective function: Minimize F(G) = x i • Cost i Constraint conditions: Step 2.3, solve the objective function to select the candidate gateway node set G of each subnetwork.

7. The method of claim 1, wherein, The step 3 specifically includes the following steps: Step 3.1, define the algebraic connectivity of the upper gateway network composed of the gateway nodes selected from the candidate gateway node set G of each subnetwork as the utility function of the upper gateway set C; v(C) = λ2(L C ) where L C is the direct communication matrix of coalition C, λ2(L C ) is the second smallest eigenvalue of matrix L C ; and n is the number of nodes in coalition C. Step 3.2, establish a cooperative game model as follows: Participants: all candidate gateway nodes in each subnetwork are considered as participants in the cooperative game; Alliance: the upper gateway set C is a set composed of nodes selected from the candidate gateway node set of each subnetwork; Alliance utility: take the algebraic connectivity of the upper network as the utility v(C) of the alliance; The goal is to select a gateway node from the candidate gateway node set of each subnetwork to form the upper gateway node set, so that the algebraic connectivity of the upper gateway network is maximized; Step 3.3, adopt hierarchical marginal contribution method to calculate the Shapley value of the node; Step 3.4, calculate the Shapley value of each candidate node in each subnetwork, and select the candidate gateway node with the maximum Shapley value in each subnetwork as the final gateway node of the subnetwork.

8. The method of claim 7, wherein, The step 3.3 is specifically: For a subnet S i , the marginal contribution of a candidate gateway node g ik needs to measure the impact of the selection on the global utility, let the current gateway node selection of other subnets be C -i = (g i-1 1 , …, g i+1 , …, g M ), then the marginal contribution of node g ik is calculated as follows: Δ i (g ik ,C -i )=v(C -i ∪{g ik})-v(C -i ∪{g i0}) wherein g i0 represents a subnet S i does not participate in the coalition; Subnet S i Selecting candidate gateway nodes g ik The average marginal contribution in all possible combinations, i.e. the Shapley value of a node g ik The Shapley value of a node g is calculated as follows: wherein, selecting spaces for gateways of other subnets, is the number of possible combinations.

9. The method of claim 7, wherein, The step 3.4 is specifically: Election of gateway nodes A final elected upper gateway node set U is obtained. 。 10. The method of claim 1-9, wherein, The joint optimization gateway election method of the hierarchical self-organizing network is used for Internet of Things network, communication network, and farmland monitoring and irrigation.

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