Adaptive multi-connectivity robust topology control method for mobile ad hoc networks

Abstract

The application discloses a kind of adaptive multi-connectivity robust topology control methods for mobile ad hoc network, mainly solve the problem of low network connectivity and large power consumption caused by the construction of topology using fixed K value in high dynamic scene by traditional topology control method.The implementation scheme is:1) the maximum power between nodes exchanges position and velocity information;2) node selects its K value according to the nature of target topology and the information exchanged, and constructs local topology according to the selected K value;3) nodes exchange the local topology generated by each other with maximum power, eliminate unidirectional link, and get its own logical neighbor list;4) node adjusts power according to its own logical neighbor list, and updates power regularly;Node re-executes 1) -4) every other topology update period.The application can maintain high connectivity, reduce power consumption and maintain link stability in high dynamic scene, and can be used in mobile ad hoc network to enhance the connectivity and robustness of network.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and further relates to an adaptive multi-connectivity topology control method, which can be used in mobile ad hoc networks to enhance network connectivity and robustness. Background Technology

[0002] For mobile ad hoc wireless networks, the connectivity of the network topology is the physical basis for normal communication between nodes. To ensure network connectivity, numerous topology control algorithms have been proposed. However, existing topology control algorithms for ad hoc wireless networks are primarily proposed without considering node mobility. Therefore, in scenarios involving high-speed node movement, existing topology control algorithms struggle to guarantee network connectivity. Furthermore, traditional K-connectivity topology control methods, in the face of node failures, ensure at least K unrelated paths between any two points to guarantee network robustness; a larger K value results in better robustness. However, a large K value increases node transmission power, reduces network lifetime, and increases internal network interference. Moreover, node movement in dynamic networks leads to uneven node distribution. Using the same K value in sparsely populated and densely populated areas can result in environments where nodes do not meet the conditions for generating a K-connectivity topology, introducing many unstable links and making the network topology highly susceptible to failure due to node movement. Increasing node power, on the other hand, causes severe network interference, reducing network capacity. Therefore, researching adaptive multi-connectivity robust topology control methods is of great significance for maintaining the connectivity and robustness of highly dynamic self-organizing networks.

[0003] Ning Li et al. proposed a distributed K-point connectivity topology control algorithm, FLSS (Fault-tolerant Local Spanning Subgraph), in their paper "Localized Fault-Tolerant Topology Control in Wireless Ad Hoc Networks". The main steps of this method are as follows: (1) Nodes send Hello packets at maximum power to obtain their own maximum power topology; (2) Nodes construct a local K-point connectivity generation subgraph based on the maximum power topology; (3) Nodes determine their logical neighbor list and adjust their transmission power based on the K-point connectivity generation subgraph. This method can guarantee the K-connectivity of the entire network by constructing a local K-point connectivity topology, that is, when any K-1 nodes in the network fail, other nodes in the network can still maintain connectivity. However, since this method does not consider the link failure problem caused by node mobility, and uses a uniform K value for the entire network, problems such as large node power and unstable links may occur when the node density is sparse.

[0004] Kenji Miyao et al. proposed a K-edge connectivity topology control algorithm based on local minimum spanning tree in their paper "LTRT: An Efficient and Reliable Topology Control Algorithm for Ad-Hoc Networks". The implementation steps are as follows: (1) The node sends Hello packets at maximum power to obtain its own maximum power topology; (2) The node applies the minimum spanning tree algorithm to obtain the edge set and fixes it, and counts once. When the count reaches K times, step (4) is executed; (3) The node deletes the topology generated in step (2) from the maximum power topology and re-executes step (3); (4) The node merges the topologies obtained in K times to obtain the final generated topology graph, and obtains its own logical neighbor list according to the topology graph, and adjusts its own transmission power according to the logical neighbor list. Although this method can achieve K-edge connectivity of the entire network, that is, the network can still maintain connectivity when any K-1 edges in the network fail, it also fails to consider the mobility of the nodes, so the generated topology will also fail due to the movement of the nodes. Moreover, since it does not consider the topology maintenance problem after the topology fails, the network connectivity is reduced.

[0005] In her master's thesis "Research on Distributed Wireless Network Topology Control Technology" (Xi'an University of Electronic Science and Technology, 2018.04), Chen Wen proposed a distributed two-channel connected and K-point connected topology control algorithm. The implementation steps of this method are: (1) Nodes exchange information twice with maximum power to obtain their own two-hop maximum power topology;

[0006] (2) The node generates a local k-point connectivity subgraph based on the two-hop maximum power topology and finds the logical conflicting nodes of the node; (3) The node determines its own logical neighbor nodes based on the generated subgraph in step (2) and determines the transmission power based on the logical neighbor nodes; (4) The node performs channel allocation based on the logical conflicting nodes to achieve two-channel connectivity and k-point connectivity of the entire network. This method considers the impact of node failure and channel failure on the network topology, and can ensure the connectivity of the network topology even if any channel in the network is interfered with and any K-1 nodes on other channels fail at the same time. However, since it is still designed for static networks, it cannot solve the problem of rapid failure of the topology structure in dynamic environments and is difficult to adapt to network scenarios where nodes move at high speed. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the prior art by proposing an adaptive multi-connectivity robust topology control method for mobile ad hoc networks, in order to maintain network connectivity in highly dynamic scenarios, reduce node transmission power, decrease the link failure rate of nodes in highly dynamic scenarios, and improve the adaptability of network topology to highly dynamic network scenarios.

[0008] The technical approach to achieving the objective of this invention is as follows: based on the nature of the node environment and the target topology, the K value of each node is adaptively adjusted to construct the required target topology and reduce the transmission power of the nodes; by periodically adjusting the transmission power of the nodes and periodically updating the topology, the impact of link failures caused by the high mobility of nodes in highly dynamic scenarios on the network topology is minimized, thereby improving the adaptability of the network topology to highly dynamic network scenarios.

[0009] Based on the above ideas, the implementation scheme of the present invention includes the following:

[0010] (1) All nodes in the network exchange Hello packets containing their own position and velocity information at maximum power to obtain their own maximum power topology map;

[0011] (2) Each node in the network adaptively selects its own K value based on the properties of the target topology and the surrounding network environment, and constructs a local topology based on the selected K value on the basis of the maximum power topology;

[0012] (3) Each node in the network interacts with its neighboring nodes to generate its own local topology in order to eliminate unidirectional links and obtain its own logical neighbor list;

[0013] (4) Each node in the network adjusts its own transmission power according to the logical neighbor list so that it can cover the logical neighbor node that is furthest away from it, and periodically adjusts its transmission power to ensure the connectivity of the link between itself and the logical neighbor node.

[0014] (5) Let the topology update period be T. topo Every time T topo Repeat steps (1)-(4) above to reconstruct the topology and ensure network connectivity.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] First, this invention adopts an adaptive method for determining the K value based on the properties of the target topology and the network environment around the nodes, and provides an adaptive K value calculation method to maintain high network connectivity in highly dynamic scenarios. This enables the construction of multi-connected topologies. Compared with the existing topology control method that uses a fixed K value, this invention has stronger adaptability to different network environments. The generated topology has higher connectivity, a lower link failure rate, and lower power loss, effectively improving network connectivity and dynamic adaptability, and extending network lifetime.

[0017] Secondly, by periodically adjusting the transmission power of nodes to maintain link connectivity and periodically updating the topology, this invention minimizes the impact of link failures caused by high node mobility on the network topology, thereby improving the network's adaptability to highly dynamic scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the overall implementation of the present invention;

[0019] Figure 2 This is a schematic diagram of the frame format of the Hello packet in this invention;

[0020] Figure 3 This is a schematic diagram of the frame format of the logical neighbor list in this invention;

[0021] Figure 4 This is a simulation result diagram of the relationship between the connectivity and node velocity of the topology generated using this invention;

[0022] Figure 5 This is a simulation result of the relationship between the average transmit power and node velocity of the topology generated using this invention;

[0023] Figure 6 This is a simulation result diagram showing the relationship between the link failure rate and node speed of the topology generated using this invention. Detailed Implementation

[0024] The embodiments and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0025] The working scenario of this example is a self-organizing network with N mobile nodes. The transmission power of the nodes is limited. Each node in the network can act as a source node, a destination node, or a relay node to provide relay functionality to other nodes.

[0026] Reference Figure 1 The implementation steps for this example are as follows.

[0027] Step 1: First interaction with information.

[0028] (1.1) Node u broadcasts a Hello packet containing its own position and motion information at maximum power. The frame format of the Hello packet is as follows: Figure 2 As shown, it includes source node ID, destination node ID, message type, source node location information, and source node speed information. The source node ID, destination node ID, and message type each occupy 4 bytes, while the source node location information and speed information are both three-dimensional vectors, each occupying 12 bytes.

[0029] (1.2) Node u receives Hello packets from other surrounding nodes and constructs a local maximum power subgraph of the node based on the obtained location information. in, Let u be the set of all nodes that can receive Hello packets. for An edge is an edge that connects two nodes. Here, a connection between two nodes means that the two nodes are within each other's maximum transmission radius R. max Inside.

[0030] Step 2: Adaptive selection of K value.

[0031] The K value of a node refers to the connectivity that the generated local topology subgraph must satisfy when the node is constructing a local topology. That is, when any K-1 nodes in the local topology subgraph fail, the remaining nodes can still remain connected.

[0032] Since the selection of the K value is related to the network environment in which the node is located and the target topology properties to be achieved, for example, in highly dynamic scenarios, the selection of the K value for a topology that maintains high network connectivity and a topology with strong resilience should follow different criteria.

[0033] This example uses the selection of a K value to maintain high network connectivity in highly dynamic scenarios as an example. The selection rules are given as follows:

[0034] (2.1) Node u calculates the local maximum power subgraph based on the position and velocity information of the nodes obtained from the interaction information. Link lifetime T of all links in the middle uv :

[0035]

[0036] Among them, L uv Let V be the distance vector between two points. uv Let R be the velocity vector between two points. max This represents the maximum transmission radius of the node.

[0037] (2.2) Set the range of K values ​​for each node as follows: in Set the minimum value for K. Set the maximum value for K;

[0038] (2.3) The density parameter of K is selected based on the density of the surrounding nodes. The range of values ​​is The calculation formula is:

[0039]

[0040] Where, N u It is the maximum power subgraph of node u The number of nodes in, N′ u yes The distance to the neutral node u is less than or equal to R. max / 2 number of nodes, This refers to rounding x down;

[0041] (2.4) Judgment Is it within the given range? Inside:

[0042] if Within a given range If it is inside, then no operation will be performed on it;

[0043] if Minimum value below the given range Then let

[0044] if Maximum value above a given range Then let

[0045] (2.5) Node u determines the link state parameter selection based on the link stability of surrounding nodes to determine the K value. The range of values ​​is The calculation formula is:

[0046]

[0047] in, This refers to the number of links between node u and its surrounding nodes whose link lifetime is greater than or equal to the topology update cycle. The calculation formula is based on a large number of simulation results. Through simulation, it was found that the K value and the average node degree in the network have the following approximate relationship:

[0048] degreee = 1.345K + 0.855, obtained through this approximation relationship. The calculation formula;

[0049] (2.6) Judgment Is it within the given range? Inside:

[0050] if Within a given range If it is inside, then no operation will be performed on it;

[0051] if Minimum value below the given range Then let

[0052] if Maximum value above a given range Then let

[0053] (2.7) The final K value of node u needs to consider both the density of nodes and the dynamic changes in the links. Therefore, the final K value of node u is taken as... and The smaller value:

[0054]

[0055] Step 3: Construct the local topology of node u.

[0056] (3.1) Node u follows the following rules for maximum power topology Calculate the weights of all edges:

[0057] To ensure the stability of the topology, links with a lifetime longer than the topology update cycle should be prioritized. For links with a lifetime shorter than the topology update cycle, their weight is mainly determined by their lifetime. For links with a lifetime longer than the topology update cycle, their weight is mainly determined by the average power of the link within an update cycle. That is, the lower the average power, the greater the weight of the link.

[0058] Based on the above rules, the link weight w is obtained. ij The specific calculation formula is as follows:

[0059]

[0060] Among them, t ij T represents the lifetime of the link. topo For the topology update cycle, x ij Let y be the relative position vector between node i and node j. ij Let be the relative velocity vector between node i and node j;

[0061] (3.2) Node u will be the maximum power topology All edges are sorted in descending order according to the weights calculated in step (3.1), and the topology-generating subgraph S is formed. u (V(S u ),E(S u The point set in ))

[0062] (3.3) Select an edge (i,j) from node u in sequence and calculate the topologically generated subgraph S. u The number of unrelated paths between any two points in the data.

[0063] if Then add edge (i,j) to edge set E(S) u In E(S) u )=E(S u )∪(i,j);

[0064] Otherwise, discard the edge and check the next edge, continuing until all edges have been traversed. All edges in;

[0065] (3.4) Node u generates a subgraph S based on the topology. u Get its own logical neighbor node set M u :

[0066] M u ={v i ,(u,v i )∈E(S u )}

[0067] Among them, v i This represents the logical neighbor nodes of node u, (u, v) i This represents the link between the current node u and the list of logical neighbors.

[0068] Step 4: Interact with the information a second time.

[0069] (4.1) Node u broadcasts its logical neighbor list according to the set frame format, as follows: Figure 3 As shown, it includes the source node ID, destination node ID, message type, and the ID of each logical neighbor in the logical neighbor list. The source node ID, destination node ID, and message type each occupy 4 bytes, and the ID of each logical neighbor occupies 4 bytes.

[0070] (4.2) Node u receives the logical neighbor list of surrounding nodes and updates its own logical neighbor list based on the received logical neighbor list:

[0071] After receiving the list of logical neighbors of node v, node u first determines whether node v is in its own list of logical neighbors:

[0072] If node v is not in the logical neighbor list of node u, no further processing is performed;

[0073] If node v is in node u's logical neighbor list, then determine whether node v's logical neighbor list contains node u:

[0074] If it is included, no further processing will be performed.

[0075] If not, node u will remove node v from its list of logical neighbors.

[0076] Step 5: Dynamically adjust the power.

[0077] (5.1) Node u infers the current position of its logical neighbors based on the position and velocity information of the logical neighbors in the most recently received Hello packet. That is, node u calculates the current position of node i. for:

[0078]

[0079] in Let be the position vector of node i obtained by node u through the Hello packet. Let t0 be the velocity vector of node i obtained by node u through the Hello packet, and t0 be the time when node u most recently received the Hello packet. now The current moment;

[0080] (5.2) Node u calculates the distance between node u and node i at the current time.

[0081]

[0082] Where x u Let u be the position vector of node u at the current moment;

[0083] (5.3) Node u calculates itself in a power update cycle T power The position vector after

[0084]

[0085] Where y u Let u be the velocity vector of node u at the current moment;

[0086] (5.4) Node u calculates node i in a power update cycle T power The position vector after

[0087]

[0088] (5.5) Node u calculates the relative distance between itself and node i after one power update cycle.

[0089]

[0090] (5.6) Based on the characteristic that the farthest distance between node u and node i within a power update cycle is always obtained at the two endpoints, calculate the maximum distance between node u and node i within a power update cycle.

[0091]

[0092] (5.7) Calculate the farthest distance between node u and all its logical neighbors within a power update cycle.

[0093]

[0094] Where M u This is the list of logical neighbors of node u. Let u be the maximum distance between node i and node i within a power update cycle;

[0095] (5.8) Node u will update the power p in the next power update cycle. u Set to:

[0096]

[0097] Among them, f u R is a power scaling factor introduced to address the potential inaccuracy in predicting the motion position of nodes in dynamic environments. max Here, α is the maximum transmission distance for the node, β is the path loss factor, and β is the receiver sensitivity.

[0098] (5.9) Node u every T power Steps (5.1)-(5.8) are re-executed at the same time to achieve dynamic adjustment of the node signal transmission power.

[0099] Step 6: Every T topo Repeat steps 1-5 periodically to update the topology and maintain high connectivity of the network topology.

[0100] The numbering of the steps above is only for the purpose of clearly describing the technical solution of this example, and the order of the numbers is not limited.

[0101] The following simulation experiments will further illustrate the effectiveness of this example:

[0102] 1. Simulation experimental conditions:

[0103] The simulation experiment was conducted on a platform consisting of a 64-core AMD Ryzen Threadripper 3990X 64-bit CPU with a clock speed of 3.7GHz and 128GB of memory.

[0104] The software platform for the simulation experiment is: Windows 10 operating system, MATLAB R2020a.

[0105] The network scenario in the simulation experiment is 100 network nodes randomly and evenly distributed in a 40km×40km space. Each node can communicate with nodes within its transmission range. The motion model of the nodes adopts a random waypoint model, and the movement of each node is independent.

[0106] The simulation parameter settings are shown in Table 1.

[0107] Table 1 Simulation Parameters

[0108] <![CDATA[K min ]]> 2 <![CDATA[K max ]]> 5 Number of nodes 100 Area size 40km*40km Topology update cycle 20s movement speed 100m / s~300m / s

[0109] 2. Simulation content and result analysis:

[0110] Simulation 1: In the aforementioned network scenario, the present invention, the traditional FLSSk algorithm, and the maximum power algorithm were used to construct topologies at different node movement speeds. The K value for the traditional FLSSk algorithm was set to 3. For each method, 1000 experiments were conducted at different movement speeds, and the average value was taken. The connectivity of the network topologies generated by the three methods was compared, i.e., the ratio of the number of nodes in the most connected subgraph of the network topology to the total number of nodes. The results are as follows: Figure 4 .

[0111] from Figure 4 It can be seen that the topology constructed using maximum power has the highest connectivity, which represents the upper bound of network connectivity performance under current conditions. When node speeds are low, the topologies generated by all three methods can maintain high connectivity. However, as node speeds increase, the network connectivity of the traditional FLSSk algorithm drops sharply. When the node speed is 300 km / s, its network connectivity is only 58.74%, while the network connectivity using this invention is 69.03%, an improvement of 17.51%. The main reason for this is that as node speeds increase, the links generated by the traditional FLSSk algorithm do not consider node movement, and therefore quickly fail, leading to a decrease in network connectivity. In contrast, this invention predicts link lifetimes, thus generating links that can effectively resist node movement, maintaining a high level of network connectivity performance. This indicates that this invention is better at maintaining network connectivity in highly dynamic scenarios than the FLSSk algorithm.

[0112] Simulation 2: In the aforementioned network scenario, topology construction was performed using the present invention, the traditional FLSSk algorithm, and the maximum power algorithm at different node movement speeds. The K value for the traditional FLSSk algorithm was set to 3. The node transmit power of the three methods was compared, and the results are shown below. Figure 5 .

[0113] from Figure 5 As can be seen, this invention effectively reduces node transmission power compared to the traditional FLSSk algorithm. When the node speed is 300 m / s, the average transmission power of the traditional FLSSk algorithm is 1.84 dBm, while the average transmission power of this invention is 1.32 dBm. This is because when the node density is sparse, the traditional algorithm uses excessive power to maintain a fixed K-point connectivity, while this invention can dynamically adjust the K value according to the network environment, reducing power loss. Therefore, this invention effectively reduces node transmission power and extends network lifetime compared to the FLSSk algorithm.

[0114] Simulation 3: In the aforementioned network scenario, topology construction was performed using the present invention, the traditional FLSSk algorithm, and the maximum power algorithm at different node movement speeds. The K value for the traditional FLSSk algorithm was set to 3. The network topology link failure rates of the three methods were compared. Results are shown below. Figure 6 .

[0115] from Figure 6 As can be seen, the failure rate of all three methods increases with node speed. When the node speed is 300 m / s, the failure rate using the traditional FLSSk algorithm reaches 42.09%, while the failure rate of the present invention is 24.29%, an improvement of 42.29%. This is because the present invention prioritizes stable links during link construction and adjusts the K value according to network conditions to avoid adding unstable links to the topology, thereby reducing the failure rate. Therefore, compared to the traditional FLSSk algorithm, the present invention can better reduce the failure rate of links in highly dynamic scenarios, generating a more stable topology with better dynamic adaptability.

[0116] In summary, this invention is an adaptive topology control method that can maintain high network connectivity, reduce node power loss, and effectively maintain link stability in highly dynamic scenarios.

Claims

1. An adaptive multi-connectivity robust topology control method for mobile ad hoc networks, characterized in that, Including the following: (1) All nodes in the network exchange Hello packets containing their own position and velocity information at maximum power to obtain their own maximum power topology map; (2) Each node in the network adaptively selects its own K value based on the properties of the target topology and the surrounding network environment, and constructs a local topology based on the maximum power topology according to the selected K value; the K value is determined according to the following steps: (2a1) Each node calculates the link lifetime of all links in the maximum power topology based on the node position and velocity information obtained in step (1). ； in, For link lifetime, Let be the distance vector between two points. Let be the velocity vector between the two points. This represents the maximum transmission radius of the node. (2b1) Set the range of K values ​​for each node as follows: ,in Minimize the setting of K value value, Set the maximum value for K; (2c1) The density selection parameter for the K value is determined by the density of the surrounding nodes. , ； in, It is the maximum power subgraph of the node The number of nodes in yes Neutralize itself at a distance less than or equal to The number of nodes, This refers to rounding x down; (2d1) judgment Is it within the given range? Inside: if Within a given range If it is inside, then no operation will be performed on it; if Minimum value below the given range Then let ; if Maximum value above a given range Then let ; (2e1) Each node determines the link state selection parameter K based on the stability of the links of surrounding nodes. : ； in, This refers to the number of links between a node and its surrounding nodes whose link lifetime is greater than or equal to the topology update cycle. The range of values ​​is ; (2f1) decision Is it within the given range? Inside: if Within a given range If it is inside, then no operation will be performed on it; if Minimum value below the given range Then let ; if Maximum value above a given range Then let ; (2g1) Each node according to Link status selection parameters Determine your own K value: ； The steps for constructing a local topology based on the maximum power topology according to the selected K value include the following: (2a2) Each node calculates the weight of its edges in the topology graph according to the following formula: ； in, Let be the weight value of the link between node i and node j. Let be the link lifetime of the link between node i and node j. Given a topology update period, Let i be the relative position vector between node i and node j. Let i be the relative velocity vector between node i and node j. This is the path loss factor. For receiver sensitivity; (2b2) ​​Each node sorts all edges in its maximum power topology in descending order according to the weights calculated in step (2b2), thus generating a subgraph from the topology. point set in edge set ;in, Let u be the set of all nodes that can receive Hello packets; (2c2) Select an edge (i,j) from each node in sequence to generate a subgraph. The number of unrelated paths between any two points in the data. By comparing the K value of each point with its own, the edge set of the topologically generating subgraph is determined: if Then add edge (i,j) to the edge set. In, that is ; Otherwise, discard the edge and check the next edge until all edges in the maximum power topology have been traversed, then execute step (2d2). (2d2) Each node generates a subgraph based on the topology. Get its own set of logical neighbor nodes : ； Where u represents the current node. This represents the logical neighbor nodes of node u. This represents the link between the current node u and the list of logical neighbors; (3) Each node in the network interacts with its neighboring nodes to generate its own local topology in order to eliminate unidirectional links and obtain its own logical neighbor list; (4) Each node in the network adjusts its own transmission power according to the logical neighbor list so that it can cover the logical neighbor node that is furthest away from it, and periodically adjusts its transmission power to ensure the connectivity of the link between itself and the logical neighbor node; Each node in the network adjusts its own transmission power according to the logical neighbor list, and the implementation steps include the following: (4a) Each node infers the positions of its logical neighbors at the current moment and in the future based on the position and velocity information of the logical neighbors in the most recently received Hello packet. Let the current node be node u, and a logical neighbor be node i. The current position of node i calculated by node u is: ； in, Let i be the position vector of node i at the current moment. Let be the position vector of node i obtained by node u through the Hello packet. Let u be the velocity vector of node i obtained from the Hello packet. This is the moment when node u most recently received the Hello packet. The current moment; (4b) Based on the current position of node i Calculate the distance between node u and node i at the current time. : ； in Let u be the position vector of node u at the current moment; (4c) Node u calculates the power after one power update cycle based on its own position and velocity information. The position vector of u of the subsequent node : ； in Let u be the velocity vector of node u at the current moment; (4d) Calculate node i after passing through The position vector after : ； (4e) Calculate the relative distance between node u and node i after one power update cycle. : ； Take the maximum distance between node u and node i within one power update cycle. : ； (4f) Calculate the farthest distance between node u and all its logical neighbors within a power update cycle. : ； in This is the list of logical neighbors of node u. Let u be the maximum distance between node i and node i within a power update cycle; (4g) Based on the farthest distance between node u and all its logical neighbors in a future power update cycle. Set the transmission power of node u in the next power update cycle. : ； in, It is a power scaling factor introduced to address the potential inaccuracy in predicting the movement position of nodes in dynamic environments. The maximum transmission distance for the nodes is set. This is the path loss factor. For receiver sensitivity; (4h) Every power update cycle Return to step (4a); (5) Let the topology update cycle be . Every time Repeat steps (1)-(4) above to reconstruct the topology and ensure network connectivity.

2. The method according to claim 1, characterized in that, In step (1), all nodes in the network interact with Hello packets containing their own location and speed information at maximum power. This is achieved by each node in the network first broadcasting a Hello packet at a set maximum transmission power, and then receiving Hello packets from its neighbors.

3. The method according to claim 2, characterized in that, The Hello packet has a frame format including: source node ID, destination node ID, message type, source node location information, and source node speed information. The source node ID, destination node ID, and message type each occupy 4 bytes, while the source node location information and source node speed information each occupy 12 bytes.

4. The method according to claim 1, characterized in that, In step (3), each node in the network interacts with its neighboring nodes to generate its own local topology. This is achieved by each node first broadcasting a data packet containing a list of neighbors at a set maximum transmission power, and then receiving data packets containing a list of neighbors from its neighboring nodes.

5. The method according to claim 4, characterized in that, The data packet containing the neighbor list has a frame format including: source node ID, destination node ID, message type, and the ID of each logical neighbor in the logical neighbor list. The source node ID, destination node ID, and message type each occupy 4 bytes, and the ID of each logical neighbor occupies 4 bytes.

Images