Air-sea cross-domain clustering network cluster head selection method

Abstract

The invention relates to the technical field of communication networking, and discloses an air-sea cross-domain clustering network cluster head selection method. Aiming at the problem that residual energy of some nodes is exhausted too early due to node energy isomerism in a three-dimensional air-sea cross-domain network, significant differences between overwater and underwater network environments and differences between different types of nodes such as an air unmanned aerial vehicle, a water surface buoy and an underwater sensor are considered; in addition to factors such as difference between radio channels and underwater acoustic channels among nodes, a basic clustering algorithm is improved, and a cluster head selection algorithm based on an improved hawk algorithm for an air-sea cross-domain network is designed. The proposed cluster head selection algorithm comprehensively considers the residual energy level of nodes in the network, the communication coverage range of the nodes and other factors, and node type differences and heterogeneous characteristics of underwater and overwater networks are integrated in a clustering strategy. The method aims at achieving effective balance of energy consumption, prolonging the overall life cycle of the network and showing wide application potential.

Description

Technical Field

[0001] The present invention relates to the technical field of communication networking, and in particular to a method for selecting cluster heads in an air-sea cross-domain clustered network. Background Art

[0002] To meet the growing demand for air-sea cross-domain communication, air-sea cross-domain communication network technology has attracted wide attention. At present, air-sea cross-domain communication network technology has become a forefront technology that has received much attention, and the demand for air-sea cross-domain heterogeneous networking based on the air-sea environment with aerial drones, surface buoys, and underwater sensors is increasing. This network has characteristics such as cross-domain communication, node clustering, heterogeneous node types, and heterogeneous network types. Therefore, it is necessary to design a cross-domain heterogeneous network topology suitable for the air-sea environment so that heterogeneous nodes in the air-sea environment can effectively complete cross-domain network communication. Among them, the clustering algorithm is an efficient topology routing algorithm in the network. However, at present, the research on the clustering algorithm at home and abroad mainly focuses on the network environment of a single medium, and there is less research on the topology design of a cross-domain heterogeneous network composed of large-scale drones, surface buoys, and underwater sensor nodes in the air-sea environment.

[0003] Therefore, it is necessary to provide a method for selecting cluster heads in an air-sea cross-domain clustered network to realize the topology of the air-sea cross-domain network under the conditions of heterogeneous nodes and heterogeneous links. Summary of the Invention

[0004] The object of the present invention is:

[0005] Aiming at the problem that some nodes in a three-dimensional air-sea cross-domain network have their remaining energy depleted prematurely due to heterogeneous node energies, based on the differences between the water-based network and the underwater network, as well as factors such as the differences in node types of aerial drones, surface buoys, and underwater sensors, and the differences between the wireless radio channels and underwater acoustic channels between nodes, the basic clustering algorithm is improved, and a method for selecting cluster heads in an air-sea cross-domain clustered network is designed. This method comprehensively considers the elected cluster heads from aspects such as the remaining energy of nodes in the network and the node communication distance; and factors such as node types and the heterogeneous characteristics of the underwater and water-based networks are considered during the clustering process, which can effectively balance energy consumption, improve the network life cycle, and has broad application prospects.

[0006] The technical solution of the present invention is:

[0007] The present invention provides a method for selecting cluster heads in an air-sea cross-domain clustered network, and the method includes the optimization of the Tianying algorithm and the application of the improved Tianying algorithm in the clustering algorithm.

[0008] The method for selecting cluster heads in the air-sea cross-domain clustered network specifically includes the following steps:

[0009] 1. Network Model

[0010] The research object of this invention is a three-dimensional air-sea cross-domain network, and the network model mainly includes unmanned aerial vehicles (UAVs), buoys, and underwater sensor nodes. The following assumptions are made for the network in this paper:

[0011] (1) It is assumed that the number of nodes in the fixed monitoring area is large enough, and the node spacing is always less than the communication radius;

[0012] (2) All underwater nodes use underwater acoustic communication to transmit messages, and all above-water nodes use radio communication to transmit messages;

[0013] (3) All ordinary nodes have communication processing capabilities and location awareness capabilities;

[0014] (4) Underwater nodes periodically collect data, send it to the surface buoy through a clustered network, and the surface buoy then sends the collected data to the UAV. After the UAV collects the data, one round of transmission is considered complete;

[0015] (5) The UAV is regarded as the base station, and the node energy is infinite.

[0016] 2. Algorithm Description

[0017] To prevent nodes with insufficient remaining energy or far from the base station from being elected as cluster head nodes, this paper divides the air-sea cross-domain network into several clusters of different sizes, and conducts cluster head election through an improved Tianying algorithm. Each cluster consists of a cluster head node and ordinary nodes. Nodes within the cluster communicate in a single-hop manner, and the cluster head fuses the data and then forwards it between clusters. The selection of cluster head nodes is considered from the remaining energy of the nodes in the network and the communication distance of the nodes.

[0018] First, consider the energy factor. Suppose there are N nodes in the network, K cluster head nodes in the current round, the energy of node i is E(n i ), the remaining energy of cluster head node k is E(CH k ), the number of nodes within the cluster in the current round is C k , and the cluster head node is CH k . The calculation formula of the fitness function is:

[0019]

[0020] Among them, the value ranges of ω1 and ω2 are [0, 1], and the sum of their values is 1.

[0021]

[0022] Suppose the network contains N nodes, K cluster head nodes in the current round, and the number of candidate cluster heads is M. Generally, M >> K, then the possible clustering methods are For a certain type, in order to determine the optimal clustering method, it can be regarded as an optimization problem, and this paper uses an improved Tianying optimization algorithm to solve this problem.

[0023] First, generate an initial population. In the initialization stage, the population randomly initializes its positions within the search range. Then, introduce Tent chaotic mapping to generate a good initial population, improve population diversity, prevent the possible problem of uneven population distribution during the optimization process, accelerate the convergence speed, and improve the convergence accuracy.

[0024] The optimization process of the improved Tianying algorithm is represented by four methods: selecting the search space through high-altitude flight with vertical dives, exploring within the divergent search space through contour flight with short glides for attacks, developing within the convergent search space through low-altitude flight with slow descents for attacks, and pouncing on and seizing the prey on foot. If t ≤ 2 / 3T, the Tianying algorithm can use different behaviors to transfer from the exploration step to the development step; otherwise, the development step will be executed.

[0025] ① Expand the search (X1)

[0026] In the first method (X1), the Tianying identifies the prey area and selects the best hunting area through high-flying with a vertical bend. At this time, AO allows the explorers from high altitude to fly around to determine the search space area where the prey is located. The mathematical model of this behavior is:

[0027]

[0028] where X1(t + 1) is the solution of the (t + 1)-th iteration generated by the first search method X1; X best (t) is the best solution obtained before the t-th iteration, which reflects the approximate position of the prey; is used to control the expanded search through the number of iterations; rand is a random value between 0 and 1, and t and T represent the current iteration and the maximum number of iterations respectively. Dim is the size of the dimension, and N is the number of candidate solutions (population size).

[0029] ② Narrow the search (X2)

[0030] In the second method (X2), when the prey area is discovered from high altitude, the Tianying hovers above the target prey, prepares to land, and then attacks. This method is called contour flight with short glides for attacks. Here, AO narrowly explores the selected area of the target prey to prepare for the attack. The mathematical model of this behavior is:

[0031]

[0032] where X2(t + 1) is the solution of the (t + 1)-th iteration generated by the second search method X2; X R(t) is a random solution obtained within the range of [1, N] at the t-th iteration. s = 0.01, μ and ν are Gaussian distributed random numbers following N(0, σ 2 ) and N(0, 1) respectively, and the calculation of σ is as follows:

[0033]

[0034] where β = 1.5. In the calculation formula of X2(t + 1), y and x are used to represent the spiral shape in the search, and the calculation is as follows:

[0035] y = r × cos(θ)

[0036] x = r × sin(θ)

[0037] where r = r1 + U × D1, θ = -ω × D1 + θ1, r1 takes values between 1 and 20, which is used to fix the number of search cycles; U is a value fixed at 0.00565; D1 is an integer from 1 to the search space dimension (Dim); ω is a value fixed at 0.005.

[0038] ③ Expanded exploitation (X3)

[0039] In the third method (X3), when the eagle accurately designates the prey area and is ready to land and attack, the eagle descends vertically and makes a preliminary attack to detect the prey's reaction. However, the traditional eagle algorithm is difficult to play a key role in enabling the entire population to escape local entrapment at this stage. Although the addition of new individuals can generate a certain degree of perturbation at this stage, learning from newly generated eagle individuals ignores the information carried by other individuals in the population, and the population assimilation degree gradually increases in the later stage of iteration, making it easy to fall into local optimality. Therefore, a social free foraging strategy is introduced, and the current individual X i t that executes the spiral foraging strategy not only follows the previous individual but also moves along the spiral path towards the optimal individual position, effectively traversing the search space and increasing the possibility of escaping from local optimal solutions. The improved X3 model is as follows:

[0040]

[0041] where: r2 and r3 take values in [0, 1]; X r t is a random eagle individual in the current iteration population; X b t is the optimal eagle individual in the current iteration population, μ is the spiral coefficient, and t and T represent the current iteration and the maximum number of iterations respectively.

[0042] ④ Reduced exploitation (X4)

[0043] In the fourth method (X4), when the eagle approaches the prey, it attacks the prey on land according to its random movement. This method is called "walk and catch the prey", and AO attacks the prey at the last position. The mathematical model of this behavior is as follows:

[0044]

[0045] where X4(t + 1) is the solution of the (t + 1)-th iteration generated by the third search method X4; X(t) is the current solution of the t-th iteration, and t and T represent the current iteration and the maximum number of iterations respectively. Based on the new balancing method, the algorithm performs local search in the middle of the iteration, making the algorithm more dependent on Levy flight, thus searching the solution space more thoroughly; in the later stage of the iteration, it is less affected by the random large-scale search of Levy flight. Therefore, the optimal solution X best (t) and the historical solution X(t) will have a greater impact on the search result of this step, improving the local search ability in the later stage of the iteration.

[0046] The method for selecting cluster heads in the air-sea cross-domain clustering network specifically includes the following steps:

[0047] First, deploy the positions of the unmanned aerial vehicles, buoys, and underwater sensor nodes to obtain a three-dimensional coordinate set. Then, use the improved eagle algorithm to conduct cluster head election for each round of data transmission. In the underwater network, after the cluster head election is completed, other ordinary nodes join the cluster head nearby to form a cluster group, and then send messages to the cluster head. After the cluster head performs data aggregation and fusion, it sends the message to the nearby surface buoy. The data received by the buoy is collected by the unmanned aerial vehicle, which is regarded as the completion of one round. Repeat this process in the next round until the network energy is exhausted.

[0048] Compared with the related technologies, the method for selecting cluster heads in the air-sea cross-domain clustering network provided by the present invention has the following beneficial effects:

[0049] The technical solution of the present invention comprehensively considers the elected cluster heads from aspects such as the remaining energy of the nodes in the network and the node communication distance, and considers factors such as node types and the heterogeneous characteristics of the underwater and water surface networks during the clustering process, which can effectively balance the network energy consumption and improve the network life cycle. Brief Description of the Drawings

[0050] Figure 1 It is a schematic diagram of the network model in the present invention;

[0051] Figure 2 It is a flowchart of the cluster head election algorithm in the present invention. Detailed Embodiments

[0052] The present invention will be further described below in conjunction with the drawings and embodiments.

[0053] The network model in the present invention is as follows Figure 1 As shown, the process of the cluster head election algorithm in the present invention is as follows Figure 2 shown.

[0054] The specific implementation of the present invention is:

[0055] At the beginning of each cluster formation phase, all nodes send information about their current energy status and location to the drone node. Based on this information, the drone calculates the average energy level of all nodes, builds a fitness function based on energy and distance factors, and then uses the improved Sky Eagle algorithm to elect cluster heads to obtain the optimal cluster head position and number of cluster heads for the round. After the drone determines the best set of cluster heads and their associated cluster members, it sends information containing the cluster head ID of each node back to all nodes in the network. The node that becomes the cluster head acts as a local control center to coordinate data transmission in its cluster.

[0056] In each cluster, the cluster head node broadcasts the information of the elected cluster head node to other nodes. If the candidate cluster head node receives the information of the elected cluster head node from other nodes in the same layer, it will join this sub-cluster; if it does not receive any broadcast information, it will be elected as the cluster head node of the sub-cluster and broadcast the information of the elected cluster head node. When the ordinary nodes in other clusters receive the election information of the cluster head of the same layer, they will join this sub-cluster; if they have not received the election information of the cluster head node of this layer, they will choose the sub-cluster closest to themselves to join.

[0057] After the underwater network cluster is completed, the ordinary nodes send data to the cluster head node, and the cluster head node aggregates it. Considering that the current direct cross-domain communication technology does not have the conditions for large-scale long-distance use, it is assumed that when the underwater node needs to communicate with the aerial node, it must be forwarded through the surface node. After the underwater sensor cluster head node collects the information, it transmits it to the surface buoy, and then the drone completes the collection of the surface buoy information, which is considered a successful round of data collection. Repeat this process until the network energy is exhausted.

[0058] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above specific implementation methods. Although the present invention has been disclosed as above in the preferred embodiments, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for selecting cluster heads in an air-sea cross-domain clustering network, characterized in that It includes the following steps: S1. Deploy the positions of the drones, buoys, and underwater sensor nodes to obtain a three-dimensional coordinate set and construct a three-dimensional air-sea cross-domain network model; S2. Based on energy and distance factors, construct a fitness function, and use the improved Tianying algorithm to conduct cluster head elections for each round of data transmission to determine the cluster head nodes; S3. Ordinary nodes join the nearest cluster head to form a cluster group, and then send messages to the cluster head. After the cluster head node collects and fuses the data, it sends the message to the nearby surface buoy. The data received by the buoy is collected by the drone to complete one round of data transmission; S4. Repeat the above process in the next round until the network energy is exhausted.

2. The method for selecting a cluster head in the Konghai cross-domain clustering network according to claim 1, characterized in that, The method further includes: dividing the air-sea cross-domain network into several clusters of different sizes to prevent nodes with insufficient remaining energy or far from the base station from being elected as cluster head nodes.

3. The method for selecting a cluster head in the Konghai cross-domain clustering network according to claim 1, characterized in that The network model assumptions include: a. The number of nodes in the fixed monitoring area is large enough, and the node spacing is always less than the communication radius; b. All underwater nodes use underwater acoustic communication, and all surface nodes use radio communication; c. All ordinary nodes have communication processing capabilities and position awareness capabilities; d. Underwater nodes periodically collect data and send it to the surface buoy through the clustered network, and the buoy then sends the data to the drone; e. The drone is regarded as the base station, and the node energy is infinite.

4. The method for selecting a cluster head in the Konghai cross-domain clustering network according to claim 1, characterized in that, The optimization process of the improved Tianying algorithm is represented by the following four methods: Select the search space through high-altitude flight with vertical dives; Explore within the divergent search space through contour flight with short glide attacks; Develop within the convergent search space through low-altitude flight with slow descent attacks; Pounce on and catch the prey on foot.

5. The method for selecting cluster heads in the Konghai cross-domain clustering network according to claim 1, characterized in that, The calculation formula of the fitness function is: Among them, N is the number of nodes in the network, K is the number of cluster head nodes in the current round, E(n i ) is the energy of node i, E(CH k ) is the remaining energy of cluster head node k, C k is the number of nodes within the cluster in the current round, CH k is the cluster head node, the value ranges of ω1 and ω2 are [0, 1], and the sum of their values is 1,

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