Low-power-consumption broadband ad hoc network method

Abstract

The invention discloses a low-power-consumption broadband ad hoc network method, particularly relates to the technical field of wireless ad hoc networks, and is used for solving the problem that an existing broadband ad hoc network is low in energy utilization efficiency in an intermittent service and limited energy scene. The method comprises the following steps: generating an energy and communication state information set by dynamically collecting residual energy and historical data transmission records of each node in a network, carrying out clustering analysis on the state information set to identify an inherent energy consumption mode and a communication behavior mode of each node, and calculating a topological position value of each node in network connectivity by adopting a cooperative game theory. And dynamically allocating role types to each node based on the topological position value to form a role allocation scheme, and identifying whether a local node set which is dominated by a core relay node and has energy and communication self-sufficiency capacity exists in the network based on the role allocation scheme so as to generate an alliance self-governance signal or a centralized management signal, and a corresponding communication topology and management architecture are constructed in response to the signal to execute data forwarding, so that low-power-consumption communication is realized.

Description

Technical Field

[0001] This invention relates to the field of wireless ad hoc networking technology, and more particularly to a method for low-power broadband ad hoc networking. Background Technology

[0002] Broadband self-organizing network technology constructs a decentralized, multi-hop relay temporary communication network through distributed nodes, which has important application value in scenarios such as emergency communication and field monitoring where fixed infrastructure cannot be relied upon. In typical application scenarios such as animal monitoring and forest fire prevention, network nodes are usually deployed in the field environment, relying on limited energy sources such as solar power, and their business characteristics are intermittent data transmission triggered by events, rather than the continuous communication assumed in the traditional model.

[0003] Existing broadband self-organizing network technologies are primarily designed for continuous operation service models. Their network protocols and node power management strategies are based on the assumption that all nodes participate in communication equally and continuously. This makes it difficult to effectively coordinate the overall network communication needs with the individual energy status of nodes when dealing with intermittent services and energy-constrained scenarios common in practical applications. As a result, it is impossible to achieve efficient energy utilization while ensuring necessary real-time communication, which severely restricts the long-term deployment capability of equipment in environments without stable power supply. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for low-power broadband self-organizing networks.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for low-power broadband self-organizing networks includes: S1. Dynamically collect the remaining energy and historical data transmission records of each node in the network, and aggregate them according to a preset time window to generate a set of energy and communication status information for each node. S2. Perform cluster analysis on the state information set to identify the inherent energy consumption pattern and communication behavior pattern of each node; S3. Based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological location value of each node in network connectivity. S4. Dynamically assign role types to each node based on topological location value, forming a role allocation scheme that includes core relay nodes and ordinary nodes; S5. Based on the role allocation scheme, identify whether there is a set of local nodes in the network that is dominated by the core relay node and has the ability to be self-sufficient in energy and communication; if so, generate a federation autonomy signal; if not, generate a centralized management signal. S6. In response to alliance autonomy signals or centralized management signals, construct and run the corresponding communication topology and management architecture to perform data forwarding.

[0006] Furthermore, the remaining energy and historical data transmission records of each node within the network are dynamically collected and aggregated according to a preset time window to generate a set of energy and communication status information for each node, including: The remaining energy value and historical data transmission records of each node are obtained by combining periodic collection with event-triggered collection. The collected remaining energy values ​​and historical data transmission records are divided into fixed-length time windows; Within each time window, the remaining energy value is averaged to obtain the window average energy. Within each time window, the historical data transmission records are statistically analyzed to obtain the total amount of data transmitted in the window and the number of successful communication attempts. The average window energy, the total amount of data transmitted by the window, and the number of successful window communications are combined to form a set of energy and communication status information.

[0007] Furthermore, cluster analysis is performed on the state information set to identify the inherent energy consumption patterns and communication behavior patterns of each node, including: The average energy of the window, the total amount of data transmitted in the window, and the number of successful communication attempts in the window are normalized from the energy and communication status information set to form a standardized feature vector. Based on standardized feature vectors, a clustering algorithm is used to group all nodes, so that the energy and communication state information sets of nodes in the same group have high similarity. Based on the trend of average energy change of nodes within each group, the energy consumption pattern of the corresponding group is identified; Based on the distribution characteristics of the total amount of data transmitted in the window and the number of successful window communications for each node in each group, the communication behavior pattern of the corresponding group is identified.

[0008] Furthermore, based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological position value of each node in network connectivity, including: Based on energy consumption patterns and communication behavior patterns, the participation weight of each node in cooperative game is determined. Define a network connectivity utility function, which evaluates the connectivity level of any subset of nodes based on inter-node reachability and data transmission capability; Multiple random permutations of nodes are generated using a random sampling method; For each random permutation order, nodes are added to the virtual alliance in sequence, and the marginal contribution increment of each node to the virtual alliance connectivity utility value is calculated based on the network connectivity utility function. The topological position value of each node is obtained by taking the arithmetic mean of the marginal contribution increments of each node in all random permutations.

[0009] Furthermore, the network connectivity utility function evaluates the connectivity level of any subset of nodes based on inter-node reachability and data transmission capability by: calculating the ratio of the number of reachable node pairs in the subset of nodes to the total number of node pairs to obtain the network connectivity density; calculating the average signal-to-noise ratio of all links in the subset of nodes to obtain the data transmission reliability score; and multiplying the network connectivity density and the data transmission reliability score by weight to obtain the network connectivity utility value.

[0010] Furthermore, the marginal contribution increment of each node to the virtual alliance's connectivity utility value when it joins, based on the network connectivity utility function, includes: calculating the first network connectivity utility value of the virtual alliance before the node joins, calculating the second network connectivity utility value of the virtual alliance after the node joins, and subtracting the second network connectivity utility value from the first network connectivity utility value to obtain the marginal contribution increment.

[0011] Furthermore, based on the topological location value, role types are dynamically assigned to each node, forming a role allocation scheme that includes core relay nodes and ordinary nodes, including: Sort the nodes from highest to lowest based on their topological location value; Select nodes that represent a predetermined proportion before ranking by topological location value as the candidate set of core relay nodes; Nodes whose topological location value in the candidate set of core relay nodes is greater than a preset topological location value threshold are identified as core relay nodes. The remaining nodes are designated as ordinary nodes; The role allocation scheme is composed of all core relay nodes and ordinary nodes and their corresponding relationships.

[0012] Furthermore, based on the role allocation scheme, it identifies whether there exists a set of local nodes in the network that is dominated by a core relay node and possesses energy and communication self-sufficiency; if so, it generates a federation autonomy signal; if not, it generates a centralized management signal, including: Centered on each core relay node, determine all ordinary nodes within its single-hop communication range to form a candidate local node set; Calculate the average remaining energy of all nodes in the candidate local node set, and calculate the proportion of communication data within the candidate local node set to the total communication data. When the average remaining energy of the candidate local node set is greater than the energy threshold and the proportion of internal communication data is greater than the communication threshold, it is determined that there is a local node set with energy and communication self-sufficiency capabilities. A coalition autonomy signal is generated when there is at least one set of local nodes in the network that has the ability to be self-sufficient in energy and communication. A centralized management signal is generated when there is no set of local nodes in the network that are self-sufficient in energy and communication.

[0013] Furthermore, in response to alliance autonomy signals or centralized management signals, a corresponding communication topology and management architecture are constructed and run to perform data forwarding, including: When responding to the autonomous alliance signal, the core relay node is used as the management node, and the local node set centered on it is constructed into an autonomous alliance. A star communication topology is established within the autonomous alliance, and the nodes within the autonomous alliance are scheduled to adopt the corresponding working mode. When responding to a centralized management signal, a globally unified communication topology is constructed based on the role allocation scheme. The globally unified communication topology uses the core relay node as the backbone node and establishes a network-wide routing table to uniformly schedule the working mode of all nodes.

[0014] Furthermore, the operating modes include a minimum power consumption operating mode and a standby low power consumption operating mode.

[0015] The beneficial effects of this invention are: 1. By dynamically collecting node energy and communication status information and performing cluster analysis, the energy consumption and communication behavior characteristics of different nodes in the network can be accurately identified. This enables the establishment of a node role allocation mechanism that conforms to the actual operating state. Based on the method of calculating the topological position value based on cooperative game theory, the contribution of each node to network connectivity is effectively quantified. This allows the role allocation scheme to objectively reflect the actual importance of nodes in the network, significantly improving the rationality and adaptability of network resource allocation. It ensures that the selection of core relay nodes takes into account both energy status and communication capabilities, laying a solid foundation for building an efficient and energy-saving network topology.

[0016] 2. By introducing a dynamic management architecture selection mechanism based on the identification of local self-sufficiency capabilities, adaptive matching between network management mode and current operating state is achieved. When a set of local nodes with self-sufficiency capabilities is detected, the alliance autonomy mode can fully leverage the autonomous management capabilities of local areas and reduce global control overhead. When self-sufficiency is not possible, switching to centralized management mode can ensure unified optimization and allocation of network resources. This flexible management architecture, combined with corresponding working mode scheduling, enables the network to dynamically adjust its operating strategy according to actual business needs and energy status while ensuring necessary communication quality. This achieves the best balance between real-time communication and energy utilization efficiency in complex and ever-changing real-world deployment environments. Attached Figure Description

[0017] Figure 1 This is a flowchart of a low-power broadband self-organizing network method according to the present invention; Figure 2 This is a flowchart illustrating how the present invention identifies a set of self-sufficient local nodes and generates management signals. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: Figure 1 This invention provides a method for low-power broadband self-organizing networks, comprising: S1. Dynamically collect the remaining energy and historical data transmission records of each node in the network, and aggregate them according to a preset time window to generate a set of energy and communication status information for each node. S2. Perform cluster analysis on the state information set to identify the inherent energy consumption pattern and communication behavior pattern of each node; S3. Based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological location value of each node in network connectivity. S4. Dynamically assign role types to each node based on topological location value, forming a role allocation scheme that includes core relay nodes and ordinary nodes; S5. Based on the role allocation scheme, identify whether there is a set of local nodes in the network that is dominated by the core relay node and has the ability to be self-sufficient in energy and communication; if so, generate a federation autonomy signal; if not, generate a centralized management signal. S6. In response to alliance autonomy signals or centralized management signals, construct and run the corresponding communication topology and management architecture to perform data forwarding.

[0020] S1. Dynamically collect the remaining energy and historical data transmission records of each node in the network, and aggregate them according to a preset time window to generate a set of energy and communication status information for each node. The specific implementation is as follows: In the process of dynamically collecting the remaining energy and historical data transmission records of each node in the network, the remaining energy value and historical data transmission records of each node are first obtained through a combination of periodic collection and event-triggered collection. Periodic collection refers to automatically performing data collection operations at fixed time intervals, such as collecting the remaining energy value of a node every 5 minutes and recording the battery percentage at that moment, while also collecting information related to data transmission events that occur within that time interval. Event-triggered collection refers to immediately performing data collection operations when a specific event occurs, such as when a node successfully sends or receives a data packet, triggering the collection of the data volume and transmission result status of that transmission. The remaining energy value is calculated in real time by the node's built-in power management unit, monitoring the battery voltage and current, and is expressed in milliampere-hours (mAh). Historical data transmission records include the timestamp of each transmission, data packet size, transmission direction, receiving node identifier, and a flag indicating success or failure. This combination of collection methods ensures comprehensive coverage of the node's energy state changes and communication activity trajectory, providing a complete data foundation for subsequent analysis.

[0021] The collected remaining energy values ​​and historical data transmission records are divided into fixed-length time windows. A fixed-length time window refers to a pre-defined continuous period of time, such as dividing the timeline into multiple windows with one-hour intervals, each window starting from the hour and ending at the next hour. During the division process, remaining energy values ​​with all collected timestamps falling within the same time window are grouped together, and similarly, historical data transmission records with all transmitted timestamps falling within the same time window are also grouped together. The length of the time window is set according to network service characteristics and energy change frequency. For example, a shorter window, such as 30 minutes, is used in service-intensive scenarios, while a longer window, such as 2 hours, is used in service-sparse scenarios. The window length can be adjusted within the range of 30 minutes to 2 hours, dynamically determined based on network load monitoring data and node energy consumption rates. After the division is completed, each time window contains a sequence of remaining energy values ​​and a set of historical data transmission records, providing structured data units for subsequent aggregation processing.

[0022] Within each time window, the remaining energy values ​​are averaged to obtain the window average energy. Averaging involves calculating the arithmetic mean of all remaining energy values ​​collected within the time window. Specifically, the remaining energy values ​​collected each time within the window are summed, and then divided by the number of collections to obtain the window average energy. For example, within a 1-hour time window, 12 remaining energy values ​​are obtained through periodic collection: 80%, 79%, 78%, 77%, 76%, 75%, 74%, 73%, 72%, 71%, 70%, and 69%. The window average energy is the sum of these 12 remaining energy values ​​divided by 12, which is 74.5%. If no remaining energy values ​​are collected within a time window, the window average energy is set to the value of the previous time window or a preset default value, such as 50%. The window average energy reflects the average energy level of the node within that time window, eliminating the influence of instantaneous fluctuations and facilitating subsequent analysis of energy consumption trends. During the calculation process, it is ensured that all remaining energy values ​​use the same unit, such as uniformly converting to percentages or milliampere-hours, to avoid inconsistencies in units.

[0023] Within each time window, historical data transmission records are statistically analyzed to obtain the total data volume transmitted within the window and the number of successful communication transactions. The statistical process involves summing the packet sizes of all historical data transmission records within the window to obtain the total data volume transmitted, typically in bytes or kilobytes. For example, if a node transmits data 50 times within a time window, with packet sizes of 100 bytes, 150 bytes, 200 bytes, etc., the total data volume transmitted is the sum of all packet sizes. Simultaneously, the number of successful transmissions within the window is counted, i.e., the number of times the success flag is true in the historical data transmission records. For example, if 45 out of 50 transmissions are successful, the window communication success count is 45. If there are no transmission records within the window, both the total data volume transmitted and the window communication success count are set to zero. The window communication success count reflects the node's communication reliability within that time window. During the statistical analysis, it is essential to ensure that the success flag is determined based on a network layer acknowledgment mechanism or application layer feedback, such as by confirming successful transmission through a received ACK signal or response message.

[0024] The average energy of a window, the total data volume transmitted within a window, and the number of successful communication attempts within a window are combined to form an energy and communication status information set. The process involves using these three metrics as elements of the information set and storing or transmitting them according to a predefined format. For example, the energy and communication status information set can be a data structure containing three fields: the average energy field stores a value such as 74.5%, the total data volume transmitted within a window stores a value such as 5000 bytes, and the number of successful communication attempts within a window stores a value such as 45. The generation of the information set ensures that each time window corresponds to an independent instance, representing the overall state of the node within that window. During the composition process, the average energy of a window, the total data volume transmitted within a window, and the number of successful communication attempts all come from the calculation results of the same time window, ensuring spatiotemporal consistency of the data. The energy and communication status information set serves as input for subsequent cluster analysis, providing a standardized data source for identifying node behavior patterns.

[0025] S2. Perform cluster analysis on the state information set to identify the inherent energy consumption pattern and communication behavior pattern of each node. Specifically, this is implemented as follows: In the process of clustering analysis on the state information set to identify the inherent energy consumption pattern and communication behavior pattern of each node, the average energy of the window, the total data transmission volume of the window, and the number of successful communication transactions in the energy and communication state information set are first normalized to form a standardized feature vector. The purpose of normalization is to eliminate the dimensional differences between different features and make all features on the same scale to facilitate subsequent clustering analysis. Specifically, a min-maximum normalization method is used to calculate the minimum and maximum values ​​of each feature in the entire dataset. The minimum and maximum values ​​are dynamically obtained from the energy and communication state information set of all nodes, for example, by traversing all data records to find the minimum and maximum observed values ​​of each feature. Then, each feature value is subtracted from the minimum value and divided by the difference between the maximum and minimum values, thereby mapping the feature value to the range of zero to one. For example, the minimum value of the average energy of the window may be 0%, and the maximum value may be 100%. The average energy value of a window is 74.5%, which becomes 0.745 after normalization. The minimum total data transmission volume of a window may be 0 bytes, and the maximum may be 10,000 bytes, so the total data transmission volume of a window is 5,000 bytes, which becomes 0.5 after normalization. The minimum number of successful communication attempts of a window may be 0, and the maximum may be 100, so the number of successful communication attempts of a window is 45, which becomes 0.45 after normalization. During the normalization process, if the maximum and minimum values ​​of a certain feature are equal, all feature values ​​are set to 0.5 to avoid division by zero errors. The three normalized feature values ​​are combined sequentially into a three-dimensional vector called the standardized feature vector. Each node corresponds to a standardized feature vector, which is used to characterize its normalized energy and communication status. The entire normalization process ensures data consistency and provides standardized input for cluster analysis. The normalization parameters are obtained based on real-time data statistics to ensure adaptation to changes in data distribution.

[0026] Based on standardized feature vectors, a clustering algorithm is used to group all nodes, ensuring high similarity between the energy and communication state information sets of nodes within the same group. The clustering algorithm employs the K-means algorithm, which iteratively optimizes the allocation of nodes into clusters, minimizing the Euclidean distance between the standardized feature vectors of nodes within the same cluster. In practice, the number of clusters K needs to be determined first. The value of K is selected based on the elbow rule and business requirements. For example, by calculating the sum of squared errors within clusters corresponding to different K values, the K value where the rate of decrease in the sum of squared errors slows down is selected as the optimal number of clusters. The value of K typically ranges from 2 to 10 and is dynamically adjusted according to network size and data distribution. For instance, a larger K value is chosen when the number of network nodes is large to capture finer-grained patterns. During the initialization phase, the standardized feature vectors of K nodes are randomly selected as the initial cluster centers. Then, an iterative process is performed, in each... In each iteration, the Euclidean distance from each node to the cluster center is calculated, and the node is assigned to the nearest cluster. The Euclidean distance is calculated by taking the square root of the sum of the squares of the differences in each dimension of the two vectors. After the assignment, the cluster center of each cluster is recalculated, i.e., the mean of the standardized feature vectors of all nodes in the cluster is taken as the new cluster center. The iteration process continues until the change in the cluster center is less than a preset threshold or the maximum number of iterations is reached. For example, the cluster center change threshold is set to 0.001, and the maximum number of iterations is set to 100. Finally, the grouping results are output, and each node is assigned to a group. The standardized feature vectors of nodes in the same group are similar, i.e., the energy and communication state information sets have high similarity after normalization. The clustering and grouping process ensures that nodes are reasonably classified according to energy and communication characteristics. The selection of the number of clusters K is based on data feature analysis, such as using visualization methods to help determine the inflection point.

[0027] High similarity means that the standardized feature vectors of nodes within the same group are close in the feature space. Clustering algorithms achieve grouping by minimizing intra-group distance or maximizing inter-group differences, thereby ensuring that nodes within the group exhibit high consistency in energy consumption and communication behavior.

[0028] Based on the window average energy change trend of nodes within each group, the energy consumption mode of the corresponding group is identified. The window average energy change trend is obtained by analyzing the window average energy sequence of each node in a continuous time window. For each group, the average of the window average energy of all nodes within the group in the same time window is calculated to form a group-level window average energy time series. Then, trend analysis is performed on this time series, and a trend line is fitted using linear regression. The slope of the trend line represents the rate of energy change. For example, if the slope is negative and the absolute value is large, the energy consumption mode is identified as a high-consumption mode, indicating a rapid decrease in energy; if the slope is close to zero, it is identified as a stable mode, indicating a gradual change in energy; if the slope is positive, it is identified as a charging mode, indicating an increase in energy. The identification of the energy consumption mode is based on a slope threshold. Rate thresholds are set by analyzing historical energy data distribution, for example, by statistically determining the percentiles of energy change rates in typical scenarios, such as using the 20th and 80th percentiles of the slope distribution in historical data as threshold boundaries; a slope less than -0.1 is identified as a high-consumption mode, a slope between -0.1 and 0.1 is identified as a stable mode, and a slope greater than 0.1 is identified as a charging mode; threshold settings also consider network application scenarios, such as using stricter thresholds in energy-sensitive environments; in addition, the volatility of the average energy within a window can be considered, for example, by calculating the standard deviation of the time series, and if the standard deviation is large, it is further identified as a volatile mode to ensure the comprehensiveness of mode identification; the energy consumption mode identification process ensures that each group is assigned a clear energy behavior characteristic; the slope threshold is obtained based on long-term monitoring data to ensure adaptability to different network conditions.

[0029] Based on the distribution characteristics of the total window data transmission volume and the number of successful window communications for each node within a group, the communication behavior pattern of the corresponding group is identified. The distribution characteristics are characterized by statistically analyzing the mean and variance of the total window data transmission volume and the number of successful window communications for all nodes within each group. Specifically, for each group, the average value of the total window data transmission volume and the average value of the number of successful window communications are calculated, and their variances are also calculated to assess the degree of dispersion. Then, the communication behavior pattern is identified based on the combination of the mean and variance. For example, if the average value of the total window data transmission volume is higher than a preset data volume reference value and the average value of the number of successful window communications is higher than a preset success rate reference value, it is identified as an efficient communication pattern; if the average value of the total window data transmission volume is lower than the data volume reference value and the average value of the number of successful window communications is lower than the success rate reference value, it is identified as an inefficient communication pattern; if the average value of the total window data transmission volume is higher than the data volume reference value but the average value of the number of successful window communications is lower than the success rate reference value... If the variance is less than the preset fluctuation range, it is identified as a high-load, low-reliability mode; variance is used to supplement the identification of stability. For example, if the variance is less than the preset fluctuation range, it is identified as a stable mode; if the variance is greater than or equal to the fluctuation range, it is identified as a fluctuating mode. The setting of data volume reference value and success rate reference value is based on network performance requirements. For example, it is determined by analyzing the typical data volume distribution and success rate distribution in historical operation and maintenance data. The data volume reference value may be set as a proportion of the historical average data volume, and the success rate reference value may be set as the network reliability target value. The data volume reference value and success rate reference value are adjusted through network simulation or business priority. For example, in real-time communication scenarios, the success rate reference value is increased according to latency requirements. The identification of communication behavior patterns ensures that each packet is assigned a unique pattern label for subsequent decision support. The entire identification process is based on data distribution characteristics to ensure the accuracy and practicality of pattern description. The acquisition of reference values ​​also considers network load conditions. For example, the data volume reference value is adjusted during peak periods to reflect the actual traffic pattern.

[0030] S3. Based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological location value of each node in network connectivity. The specific implementation is as follows: In calculating the topological position value of each node in network connectivity using cooperative game theory based on energy consumption patterns and communication behavior patterns, the participation weight of each node in the cooperative game is first determined based on these patterns. The setting of participation weights requires comprehensive consideration of the energy consumption and communication behavior patterns identified by the nodes in previous steps, achieved by establishing a mapping relationship between pattern combinations and weight values. Specifically, a weight coefficient is assigned to each combination of energy consumption and communication behavior patterns, determined based on the importance of that combination in maintaining network connectivity. For example, a node with a stable energy consumption pattern and an efficient communication behavior pattern might be assigned a higher weight of 0.9, while a node with a high energy consumption pattern and an inefficient communication behavior pattern might be assigned a lower weight of 0.3. The weight coefficient ranges from 0 to 1, with the specific value determined according to network operational requirements and calibrated by analyzing the impact of different pattern combinations on connectivity in historical network performance data. The establishment of a grid-like weight mapping table allows each node to obtain a corresponding participation weight value based on its specific pattern combination.

[0031] A network connectivity utility function is defined to quantify the connectivity level of any subset of nodes. The function is constructed based on two dimensions: inter-node reachability and data transmission capability. Inter-node reachability is characterized by network connectivity density, which is the ratio of the number of reachable node pairs in a subset to the total number of node pairs. A reachable node pair is a node pair with a valid communication path within the current subset, including direct communication and indirect communication via other nodes. Data transmission capability is characterized by a data transmission reliability score, which is the average signal-to-noise ratio (SNR) of all communication links within the subset. SNR data is obtained from physical layer measurements of the nodes and reflects the communication quality of the links. Network connectivity density and data transmission reliability score are combined into a network connectivity utility value through a weighted multiplication, where the sum of the weighting coefficients for network connectivity density and data transmission reliability score is 1. For example, in a scenario prioritizing network connectivity, the weighting coefficient for network connectivity density can be set to 0.7, and the weighting coefficient for data transmission reliability score can be set to 0.3. The weighting coefficients are adjusted according to the characteristics of the network application scenario. For example, in scenarios requiring high-reliability transmission, the weighting coefficients for data transmission reliability scores are appropriately increased.

[0032] Multiple random permutations of nodes are generated using a random sampling method. Random sampling is implemented using a pseudo-random number generator, employing a linear congruential generation algorithm to produce a uniformly distributed sequence of random numbers. Each random permutation is a full permutation of node indices, ensuring that each node appears exactly once in the permutation. The number of random permutations generated is determined by the network size; for example, 1000 random permutations are generated for a network with 50 nodes, and 2000 random permutations are generated for a network with 100 nodes. A random number seed is set during the generation process to ensure the reproducibility of the results; for example, system time is used as the random number seed. By generating a sufficient number of random permutations, all possible orders in which nodes join the virtual alliance can be comprehensively covered, providing sufficient statistical samples for subsequent marginal contribution calculations.

[0033] For each random permutation order, nodes are added to the virtual alliance sequentially, and the marginal contribution increment generated by each node upon addition is calculated. The virtual alliance is constructed starting from an empty set, adding nodes in the current permutation order. Before adding each node, the network connectivity utility value of the current virtual alliance is calculated as the first network connectivity utility value. After adding the node, the network connectivity utility value of the virtual alliance is recalculated as the second network connectivity utility value. The marginal contribution increment is the difference between the second and first network connectivity utility values. During the calculation, it is necessary to ensure that the calculation parameters of the network connectivity utility function remain consistent; for example, the calculation method for network connectivity density and the weighting coefficients for data transmission reliability scoring remain unchanged. For each node in each permutation order, a marginal contribution increment value is calculated. This value may be positive, zero, or negative, reflecting the degree to which the node improves or decreases the connectivity level of the virtual alliance under a specific addition order.

[0034] The topological position value of each node is obtained by taking the arithmetic mean of the marginal contribution increments for each node across all random permutations. This arithmetic mean is calculated by summing the marginal contribution increments of each node across all random permutations and then dividing by the total number of random permutations. For example, if a node has 1000 marginal contribution increments across 1000 random permutations, summing these increments and dividing by 1000 gives the node's topological position value. The calculation process handles possible numerical anomalies, such as when a virtual alliance is empty in a permutation, in which case the first network connectivity utility value is set to 0. Ultimately, each node receives a topological position value that reflects its average contribution to overall network connectivity, providing a quantitative basis for subsequent role allocation. The calculation of topological position values ​​ensures an objective assessment of node importance, laying the foundation for optimal allocation of network resources.

[0035] S4. Dynamically assign role types to each node based on topological location value, forming a role allocation scheme that includes core relay nodes and ordinary nodes. The specific implementation is as follows: In the process of dynamically assigning role types to each node based on its topological location value to form a role allocation scheme that includes core relay nodes and ordinary nodes, the nodes are first sorted from highest to lowest topological location value. The sorting process uses standard sorting algorithms, such as quicksort or mergesort, to arrange all nodes in descending order of their topological location value. The topological location value is a numerical value representing the importance of a node in network connectivity, calculated in the previous step using cooperative game theory. This value is a real number, typically ranging from zero to one, although the specific range may vary depending on the network configuration. During the sorting process, the correct correspondence between each node and its topological location value is ensured by maintaining a mapping table between node identifiers and topological location values ​​to achieve data consistency. The sorting result generates an ordered list of nodes, where the first node in the list corresponds to the node with the highest topological location value, and the last node corresponds to the node with the lowest topological location value. The choice of sorting algorithm considers network scale factors; for example, when the number of nodes is large, a sorting algorithm with lower time complexity is chosen to improve processing efficiency. Quicksort has a time complexity of O(n log n), making it suitable for most network scenarios. The sorting process also includes handling boundary cases, such as when multiple nodes have the same topological location value, performing a secondary sort according to the lexicographical order of the node identifiers to ensure order uniqueness.

[0036] Nodes with a predetermined percentage of topological location value are selected as the core relay node candidate set. This predetermined percentage is a crucial configuration parameter, determined based on network scale and application requirements, through analysis of network topology characteristics and service load patterns. Methods for determining the predetermined percentage include using historical operational data to statistically determine the optimal range for core nodes, such as retrospectively analyzing the changing trends of network performance indicators like throughput and latency under different percentages. The specific value of the predetermined percentage can be dynamically adjusted according to the network scenario; for example, a higher predetermined percentage (e.g., 30%) can be set in dense networks, while a lower predetermined percentage (e.g., 10%) can be set in sparse networks. The adjustment of the predetermined percentage also considers the total number of network nodes; for example, a lower predetermined percentage (e.g., 15%) is used when the number of nodes exceeds 100, and a higher predetermined percentage (e.g., 25%) is used when the number of nodes is less than 50. The selection process involves selecting a specified number of nodes starting from the beginning of the sorted node list. The selected number is equal to the total number of nodes multiplied by the predetermined percentage, rounded up or to the nearest integer. For example, when the total number of nodes is 95 and the predetermined percentage is 20%, the selected number is calculated as 19 nodes. The construction of the core relay node candidate set ensures coverage of the node group with the highest topological location value, providing a foundation for subsequent fine-tuning.

[0037] Nodes in the core relay node candidate set whose topological location value exceeds a preset topological location value threshold are identified as core relay nodes. This preset topological location value threshold is a key criterion used to ensure that core relay nodes possess sufficient importance. The preset topological location value threshold is set based on the statistical distribution characteristics of topological location values. For example, it can be set by calculating the average and standard deviation of the topological location values ​​of all nodes, and then setting the threshold as the average plus one standard deviation. Another method for setting the preset topological location value threshold uses percentiles; for example, setting the threshold to the 75th percentile of the topological location value distribution ensures that nodes with higher importance are selected. The setting of the preset topological location value threshold also needs to consider network performance requirements. For example, a higher preset topological location value threshold is used in networks with high reliability requirements. The threshold size is determined by analyzing network connectivity objectives such as minimum connectivity. The decision process iterates through each node in the core relay node candidate set, comparing its topological location value with the preset topological location value threshold, and retaining only nodes whose topological location value exceeds the preset threshold as core relay nodes. The comparison process addresses numerical precision issues, for example, by using floating-point comparisons with a tolerance of 0.0001 to avoid rounding errors. If no node in the core relay node candidate set meets the preset topology location value threshold, the preset topology location value threshold is adjusted or the preset ratio is re-evaluated, for example, by temporarily lowering the preset topology location value threshold to the average value or using a historical threshold as a backup value.

[0038] The remaining nodes are designated as ordinary nodes. These include nodes not selected for the core relay node candidate set, and nodes in the candidate set whose topological location value does not reach a preset threshold. Ordinary nodes are determined through an elimination process: core relay nodes are excluded from the total node set, leaving ordinary nodes. This elimination process uses set operations, such as subtracting the core relay node set from the total node set to obtain the ordinary node set. Ordinary nodes perform basic data acquisition and transmission functions in the network, but are not responsible for network relay forwarding tasks. Their role allocation is based on the relatively low position of their topological location value. The determination process ensures that each node is explicitly classified as either a core relay node or an ordinary node, and that the classification results do not overlap. A unique role identifier is used to label the type of each node. Role allocation also considers the physical attributes of the nodes; for example, even nodes with high topological location value may be classified as ordinary nodes in energy-constrained environments to extend network lifetime.

[0039] The role allocation scheme is composed of all core relay nodes and ordinary nodes, along with their corresponding relationships. The role allocation scheme is a structured dataset containing the identifier of each node and its assigned role type. The mapping is established by creating a mapping table between node identifiers and role types, such as using a hash table or key-value data structure to store the association information between each node and its role. The construction of the mapping table ensures fast querying and updating; for example, role information is indexed by the hash value of the node identifier. The construction process of the role allocation scheme includes collecting the identifiers of all core relay nodes and all ordinary nodes, and organizing this information according to a predefined format, such as structured data formats like JSON or XML. The role allocation scheme also includes metadata information, such as the scheme generation timestamp, network identifier, and version number, to ensure the integrity and traceability of the scheme. After the scheme is generated, verification checks are performed, such as confirming that the number of core relay nodes is not zero and that ordinary nodes cover the remaining nodes, to prevent allocation errors. The final role allocation scheme will serve as the basis for subsequent network topology construction and management architecture operation; for example, it can be referenced in autonomous associations or centralized management architectures to configure node roles. The dynamic update mechanism of the role allocation scheme allows the allocation process to be re-executed based on changes in network state, such as when a node's topological location value changes significantly, triggering a reassignment.

[0040] Figure 2 The flowchart of the present invention for identifying a set of self-sufficient local nodes and generating a management signal is given. S5: Based on the role allocation scheme, identify whether there is a set of local nodes in the network that is dominated by a core relay node and has energy and communication self-sufficiency; if so, generate a federation autonomy signal; if not, generate a centralized management signal. The specific implementation is as follows: In identifying the existence of a set of local nodes in a network dominated by a core relay node and possessing energy and communication self-sufficiency based on a role-assignment scheme, the first step is to determine all ordinary nodes within the single-hop communication range of each core relay node, forming a candidate set of local nodes. The definitions of core relay nodes and ordinary nodes originate from the role-assignment scheme formed in the previous steps, where core relay nodes are nodes with higher topological location value, and ordinary nodes are nodes with relatively lower topological location value. The single-hop communication range refers to the geographical area reachable from the core relay node via direct wireless communication. This range is determined based on the transmission characteristics of wireless signals, such as using the received signal strength indicator (RSI). When the RSI between an ordinary node and the core relay node exceeds a preset signal strength threshold, the ordinary node is considered to be within the single-hop communication range of the core relay node. The signal strength threshold is set based on the technical parameters of the wireless communication module, such as the minimum receiving sensitivity of the modem, with a typical value of -85 dBm. The process of constructing the candidate local node set involves traversing all core relay nodes, scanning the ordinary nodes within the communication range of each core relay node, and recording the identifiers of the ordinary nodes into the corresponding candidate local node set. During the construction process, the time-varying characteristics of the wireless channel need to be considered. For example, a stable communication range can be determined by averaging multiple measurements. The number of measurements can be set to 5 to 10 times depending on the environmental stability.

[0041] The average remaining energy of all nodes in the candidate local node set is calculated, and the proportion of internal communication data within the candidate local node set to the total communication data is statistically analyzed. The average remaining energy is calculated by taking the arithmetic mean of the remaining energy values ​​of each node in the candidate local node set. These remaining energy values ​​are derived from dynamically collected network node energy data and are typically expressed as a percentage or milliampere-hours (mAh). During the calculation, it is crucial to ensure the consistency of energy units; if energy values ​​exist in different units, unit conversion is necessary. The statistical analysis of the proportion of internal communication data to the total communication data requires analyzing the communication records between all nodes in the candidate local node set. Internal communication data refers to the total amount of data transmitted between any two nodes in the candidate local node set, while the total communication data refers to the total amount of data communicated between all nodes in the candidate local node set and any other node in the network. The proportion is calculated by dividing the internal communication data by the total communication data, and the result is expressed as a percentage. Data statistics are based on historical communication records within a preset time window, such as communication data from the most recent 24 hours. The time window length can be adjusted according to network service characteristics, ranging from 1 hour to 24 hours, to ensure that the current network status is reflected. When the number of nodes in the candidate local node set is zero, the average remaining energy and the proportion of internal communication data are both recorded as invalid values.

[0042] When the average remaining energy of a candidate local node set is greater than the energy threshold and the proportion of internal communication data is greater than the communication threshold, a local node set with energy and communication self-sufficiency is determined to exist. The energy threshold is set based on energy demand analysis for network operation, such as by studying the minimum energy level required for network nodes to maintain basic functions. The energy threshold can be set as a percentage of the node's initial energy, such as 60%, ensuring that the node has sufficient energy reserves to support autonomous operation. Adjustments to the energy threshold also need to consider node type and application scenario; for example, the threshold can be appropriately lowered in an energy harvesting environment. The communication threshold is set based on network communication pattern analysis, such as by statistically analyzing the typical proportion of local communication in historical data. The communication threshold can be set to 70%, indicating that most communication occurs within a local area. The communication threshold can also be determined using a sliding window statistical method, dynamically adjusted according to communication patterns over a recent period. The determination process requires evaluating each candidate local node set separately. When both the average remaining energy and the proportion of internal communication data are satisfied simultaneously, the candidate local node set is determined to be a local node set with energy and communication self-sufficiency. The determination process also needs to consider the size of the set. For example, if the number of nodes in the candidate local node set is less than 3, it will not be considered as having self-sufficiency even if the condition is met, so as to ensure that the set has a sufficient size to achieve effective autonomy.

[0043] A federated autonomy signal is generated when at least one set of local nodes in the network possesses self-sufficiency in energy and communication. The federated autonomy signal is a specific control signal indicating that the network can operate in a distributed management manner. Signal generation is based on a logical OR operation on the judgment results of all candidate sets of local nodes; a federated autonomy signal is generated as soon as at least one set is determined to be self-sufficient. The logical OR operation iterates through all judgment results, returning a true value and generating a signal immediately upon detecting the first true value. The generation of the federated autonomy signal also includes encapsulation of additional information, such as identifying the number and distribution of self-sufficient sets of local nodes. This additional information is used for subsequent topology construction decisions. The signal format uses a predefined data structure, including fields such as signal type, generation timestamp, and a list of valid sets of local nodes. The signal generation process needs to ensure real-time performance, for example, generating the signal immediately after the judgment is completed to reduce delays in network management decisions. After signal generation, format verification is also required to ensure that the signal content conforms to predefined specifications.

[0044] A centralized management signal is generated when no set of local nodes in the network possesses self-sufficiency in energy and communication. This centralized management signal is another specific control signal used to indicate that the network needs to operate under centralized management. The signal is generated based on the inverse result of a logical AND operation on all candidate sets of local nodes. A centralized management signal is generated when none of the sets meet the self-sufficiency condition. The logical AND operation iterates through all decision results, generating a signal only when all results are false. The generation of the centralized management signal also includes encapsulating relevant information, such as recording the specific reasons for not meeting the conditions—whether it's a failure to meet energy, communication, or both. The signal content also includes suggested management parameters, such as the centralized scheduling cycle and route update frequency. After signal generation, reliable transmission to the network management unit must be ensured, for example, by forwarding through multiple paths to prevent signal loss. The generation of the centralized management signal and the federation autonomy signal are mutually exclusive, ensuring that the network uses only one management architecture at a time. A timeout mechanism is also set during signal generation; for example, if the decision process exceeds a preset time limit, the centralized management signal is forcibly generated to ensure system reliability.

[0045] S6. In response to alliance autonomy signals or centralized management signals, construct and run the corresponding communication topology and management architecture to perform data forwarding, specifically as follows: In the process of constructing and operating the corresponding communication topology and management architecture to perform data forwarding in response to the alliance autonomy signal or centralized management signal, when responding to the alliance autonomy signal, the core relay node acts as the management node, and the set of local nodes centered on it is constructed into an autonomous alliance. The role of the core relay node is derived from the role allocation scheme formed in the previous steps, where the core relay node is the node with high topological location value, and the set of local nodes is the group of nodes led by the core relay node determined during the identification of self-sufficiency capabilities. The construction process of the autonomous alliance includes the core relay node sending an alliance establishment request to ordinary nodes within its single-hop communication range, and ordinary nodes joining the alliance after responding to the request, forming an autonomous alliance with the core relay node as the management node. The governing unit; the management node is responsible for coordinating communication scheduling and resource allocation within the alliance, such as maintaining the alliance member list by periodically broadcasting control messages; the frequency of alliance establishment requests is adjusted based on network dynamics, for example, increasing the sending frequency (e.g., once per second) when the network topology changes rapidly, and decreasing the frequency (e.g., once every 10 seconds) in a stable network; when ordinary nodes respond to requests, they need to confirm their own status, such as checking whether the remaining energy is higher than a certain reference value (e.g., 50%), to ensure that they can participate in alliance activities; the construction of autonomous alliances ensures that each alliance has independent management capabilities and can autonomously handle internal data forwarding tasks; the alliance size is controlled by a preset upper limit on the number of members, for example, each alliance can contain a maximum of 10 ordinary nodes, to avoid excessive management load.

[0046] Within the autonomous alliance, a star communication topology is established. A star communication topology refers to a network structure where a core relay node is the central node, and all ordinary nodes act as leaf nodes, communicating directly with the central node. The topology establishment process includes the core relay node allocating communication time slots and channel resources, for example, using time division multiple access (TDMA) to avoid collisions. Time slot lengths are dynamically adjusted based on service load, and these adjustments are based on historical data transmission statistics, such as determining the time slot allocation strategy by analyzing the average number of data packets within the most recent time window. Direct wireless links are established between ordinary nodes and the core relay node, and link quality is evaluated using signal-to-noise ratio (SNR) and received signal strength index (RSI). For example, link reconfiguration is triggered when the signal-to-noise ratio falls below a specific threshold, such as 10 dB; the maintenance of the star communication topology is achieved through periodic beacon frames, with the core relay node sending beacon frames periodically and ordinary nodes synchronizing communication timing according to the beacon frames; the beacon frame sending interval is set according to network stability, for example, a shorter interval, such as 100 milliseconds, is set in mobile scenarios, and a longer interval, such as 1 second, is set in static scenarios; after the topology is established, the data forwarding path within the alliance is fixed as a single-hop transmission from ordinary nodes to the core relay node, and the core relay node is responsible for data aggregation and forwarding decisions; the data aggregation strategy is based on the service type, for example, using compression algorithms to reduce the transmission volume of sensor data.

[0047] The scheduling system employs corresponding operating modes for nodes within the autonomous alliance, including a minimum power consumption mode and a standby low-power consumption mode. Scheduling decisions are based on real-time node status parameters, such as remaining energy levels, data traffic load, and network service priorities. The minimum power consumption mode involves nodes entering deep sleep, retaining only basic listening functions, and periodically waking up to detect network activity; for example, setting the wake-up interval to a certain number of seconds, such as five seconds, reduces energy consumption. The standby low-power consumption mode involves nodes maintaining continuous listening but reducing transmit power and processor frequency; for example, adjusting the transmit power to a certain percentage of the standard value, such as 50%, reduces energy consumption. The scheduling process is centrally controlled by the core relay node, which dynamically adjusts the operating mode by analyzing node status reports; for example, when a node's remaining energy falls below a certain reference value, it is forcibly switched to the minimum power consumption mode. The reference value is set based on node type and historical energy consumption data; for example, the reference value is determined by statistically analyzing the average energy consumption of similar nodes. The scheduling strategy also considers service requirements; for example, during peak data collection periods, nodes are temporarily switched to the standby low-power consumption mode to ensure communication reliability. The transmission of scheduling commands uses reliable protocols; for example, an acknowledgment mechanism ensures that nodes correctly receive mode switching commands.

[0048] When responding to a centralized management signal, a globally unified communication topology is constructed based on a role allocation scheme. This scheme originates from the node role classification determined in previous steps, including the identifiers and associations of core relay nodes and ordinary nodes. The globally unified communication topology uses core relay nodes as backbone nodes, with backbone nodes connected via multi-hop links to form a network backbone. The topology construction process includes backbone node discovery and link establishment, such as using distributed algorithms to elect backbone nodes and calculate the optimal connection path. Backbone node discovery is based on node capability assessment, such as comprehensively considering topology location value and remaining energy when selecting backbone nodes. After the backbone network is constructed, ordinary nodes... Points are connected to the nearest backbone node to form a hierarchical network structure; the access decision for ordinary nodes is based on signal quality measurement, such as selecting the backbone node with the highest received signal strength indicator value; topology construction ensures network connectivity, such as optimizing backbone links through the minimum spanning tree algorithm to reduce redundant connections; the minimum spanning tree algorithm uses weights based on link quality, such as using the reciprocal of the signal-to-noise ratio as the weight value to prioritize high-quality links; the maintenance of the global topology is achieved through a centralized management unit, which periodically collects node status information and updates the topology structure to cope with node movement or failure; the update cycle is set according to the network change rate, such as a shorter cycle of 30 seconds in dynamic networks.

[0049] A globally unified communication topology uses core relay nodes as the backbone and establishes a network-wide routing table. This table contains optimal forwarding path information between all nodes. Route calculation is based on the topology and uses routing protocols such as link-state routing or distance-vector routing. The routing table establishment process includes collecting network link-state information, calculating the shortest path, and distributing routing entries to all nodes. For example, Dijkstra's algorithm is used to calculate the shortest path from each node to other nodes and stores the path information in the routing table. The implementation of Dijkstra's algorithm iteratively selects the node with the smallest distance among unvisited nodes and updates the distance values ​​of its neighboring nodes until all nodes are visited. The routing table update mechanism responds to network changes, such as triggering recalculation and broadcast updates when a link is interrupted. Update triggering conditions are based on link-state monitoring; for example, a link is marked as failed when three consecutive probe packets are lost. The network-wide routing table ensures that data packets can reach their destination through multi-hop forwarding, supporting end-to-end communication. The routing table size is controlled by aggregating routing entries, for example, by using classless inter-domain routing techniques to reduce the number of entries.

[0050] The system unifies the scheduling of all nodes' operating modes. Operating modes include a minimum power consumption mode and a standby low-power consumption mode. Scheduling is determined by a centralized management unit based on the global network status. The unified scheduling process collects the energy status, communication load, and service requirements of all nodes and uses optimization algorithms to calculate the optimal operating mode configuration. For example, a linear programming model is used to minimize total energy consumption while satisfying communication delay constraints, allocating nodes to the corresponding operating modes. The constraints of the linear programming model include node energy limits and service delay requirements, with the objective function being the minimization of total power consumption. Scheduling instructions are broadcast to all nodes via a control channel, and nodes switch operating modes according to the instructions. The control channel uses a dedicated frequency or time slot to avoid data channel interference. Unified scheduling ensures balanced network energy consumption; for example, high-energy nodes are prioritized for standby low-power operation to handle more relay tasks, while low-energy nodes are set to minimum power consumption mode to extend network lifetime. The scheduling strategy also considers time factors, such as automatically activating more nodes in minimum power consumption mode during off-peak hours at night. The scheduling decision cycle is adjusted based on network load; for example, a shorter cycle (e.g., 1 minute) is set during peak periods, and a longer cycle (e.g., 10 minutes) is set during low-load periods.

[0051] The operating modes include a minimum power consumption mode and a standby low power consumption mode. The minimum power consumption mode involves shutting down unnecessary hardware modules such as the RF transmitter and high-speed processor, maintaining power only for basic timers and sensors, and reducing the operating current to the microamp level (e.g., 10 microamps). In this mode, nodes periodically wake up to enter an active state to detect network events; for example, the wake-up period can be set to several seconds (e.g., five seconds), and the wake-up duration can be in the millisecond range (e.g., 100 milliseconds). The standby low power consumption mode involves reducing the processor's operating frequency and RF transmission power to maintain continuous monitoring capability while limiting the data transmission rate, maintaining the operating current at the milliamp level (e.g., 5 milliamps). The operating mode is based on predefined conditions, such as automatically entering the lowest power consumption mode when the node's remaining energy is lower than a certain reference value or the network traffic volume is lower than a certain threshold. The reference value and threshold are determined by analyzing historical operating data, such as using statistical methods to calculate energy consumption trends and service modes. The management of the operating mode is implemented through the node's local state machine. The state transition rules are configured by the management node or centralized management unit to ensure that the mode transition meets the network energy efficiency target. The state machine design includes state definition and transition conditions, such as defining sleep state, listening state and active state, and transitioning according to events such as timer timeout or message reception.

[0052] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0053] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0054] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0055] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0057] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for low-power broadband self-organizing networks, characterized in that, include: S1. Dynamically collect the remaining energy and historical data transmission records of each node in the network, and aggregate them according to a preset time window to generate a set of energy and communication status information for each node. S2. Perform cluster analysis on the state information set to identify the inherent energy consumption pattern and communication behavior pattern of each node; S3. Based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological location value of each node in network connectivity. S4. Dynamically assign role types to each node based on topological location value, forming a role allocation scheme that includes core relay nodes and ordinary nodes; S5. Based on the role allocation scheme, identify whether there is a set of local nodes in the network that is dominated by the core relay node and has the ability to be self-sufficient in energy and communication; if so, generate a federation autonomy signal; if not, generate a centralized management signal. S6. In response to alliance autonomy signals or centralized management signals, construct and run the corresponding communication topology and management architecture to perform data forwarding.

2. The method for a low-power broadband self-organizing network according to claim 1, characterized in that, The system dynamically collects the remaining energy and historical data transmission records of each node within the network, and aggregates them according to a preset time window to generate a set of energy and communication status information for each node, including: The remaining energy value and historical data transmission records of each node are obtained by combining periodic collection with event-triggered collection. The collected remaining energy values ​​and historical data transmission records are divided into fixed-length time windows; Within each time window, the remaining energy value is averaged to obtain the window average energy. Within each time window, the historical data transmission records are statistically analyzed to obtain the total amount of data transmitted in the window and the number of successful communication attempts. The average window energy, the total amount of data transmitted by the window, and the number of successful window communications are combined to form a set of energy and communication status information.

3. The method for a low-power broadband self-organizing network according to claim 2, characterized in that, Cluster analysis of the state information set identifies the inherent energy consumption patterns and communication behavior patterns of each node, including: The average energy of the window, the total amount of data transmitted in the window, and the number of successful communication attempts in the window are normalized from the energy and communication status information set to form a standardized feature vector. Based on standardized feature vectors, a clustering algorithm is used to group all nodes; Based on the trend of average energy change of nodes within each group, the energy consumption pattern of the corresponding group is identified; Based on the distribution characteristics of the total amount of data transmitted in the window and the number of successful window communications for each node in each group, the communication behavior pattern of the corresponding group is identified.

4. The method for a low-power broadband self-organizing network according to claim 3, characterized in that, Based on energy consumption patterns and communication behavior patterns, cooperative game theory is used to calculate the topological location value of each node in network connectivity, including: Based on energy consumption patterns and communication behavior patterns, the participation weight of each node in cooperative game is determined. Define a network connectivity utility function, which evaluates the connectivity level of any subset of nodes based on inter-node reachability and data transmission capability; Multiple random permutations of nodes are generated using a random sampling method; For each random permutation order, nodes are added to the virtual alliance in sequence, and the marginal contribution increment of each node to the virtual alliance connectivity utility value is calculated based on the network connectivity utility function. The topological position value of each node is obtained by taking the arithmetic mean of the marginal contribution increments of each node in all random permutations.

5. The method for a low-power broadband self-organizing network according to claim 4, characterized in that, The network connectivity utility function evaluates the connectivity level of any subset of nodes based on inter-node reachability and data transmission capability. This includes: calculating the ratio of the number of reachable node pairs in the subset to the total number of node pairs to obtain the network connectivity density; calculating the average signal-to-noise ratio of all links in the subset to obtain the data transmission reliability score; and multiplying the network connectivity density and the data transmission reliability score by weight to obtain the network connectivity utility value.

6. The method for a low-power broadband self-organizing network according to claim 5, characterized in that, The marginal contribution increment of each node to the virtual alliance's connectivity utility value when it joins, based on the network connectivity utility function, includes: calculating the first network connectivity utility value of the virtual alliance before the node joins, calculating the second network connectivity utility value of the virtual alliance after the node joins, and subtracting the second network connectivity utility value from the first network connectivity utility value to obtain the marginal contribution increment.

7. The method for a low-power broadband self-organizing network according to claim 4, characterized in that, Based on topological location value, role types are dynamically assigned to each node, forming a role allocation scheme that includes core relay nodes and ordinary nodes, including: Sort the nodes from highest to lowest based on their topological location value; Select nodes that represent a predetermined proportion before ranking by topological location value as the candidate set of core relay nodes; Nodes whose topological location value in the candidate set of core relay nodes is greater than a preset topological location value threshold are identified as core relay nodes. The remaining nodes are designated as ordinary nodes; The role allocation scheme is composed of all core relay nodes and ordinary nodes and their corresponding relationships.

8. The method for a low-power broadband self-organizing network according to claim 7, characterized in that, Identify whether there exists a set of local nodes in the network that is dominated by a core relay node and has the ability to be self-sufficient in energy and communication based on the role allocation scheme; If it exists, generate a federation autonomy signal; If it does not exist, a centralized management signal is generated, including: Centered on each core relay node, determine all ordinary nodes within its single-hop communication range to form a candidate local node set; Calculate the average remaining energy of all nodes in the candidate local node set, and calculate the proportion of communication data within the candidate local node set to the total communication data. When the average remaining energy of the candidate local node set is greater than the energy threshold and the proportion of internal communication data is greater than the communication threshold, it is determined that there is a local node set with energy and communication self-sufficiency capabilities. A coalition autonomy signal is generated when there is at least one set of local nodes in the network that has the ability to be self-sufficient in energy and communication. A centralized management signal is generated when there is no set of local nodes in the network that are self-sufficient in energy and communication.

9. A method for a low-power broadband self-organizing network according to claim 8, characterized in that, In response to alliance autonomy signals or centralized management signals, construct and run the corresponding communication topology and management architecture to perform data forwarding, including: When responding to the autonomous alliance signal, the core relay node is used as the management node, and the local node set centered on it is constructed into an autonomous alliance. A star communication topology is established within the autonomous alliance, and the nodes within the autonomous alliance are scheduled to adopt the corresponding working mode. When responding to a centralized management signal, a globally unified communication topology is constructed based on the role allocation scheme. The globally unified communication topology uses the core relay node as the backbone node and establishes a network-wide routing table to uniformly schedule the working mode of all nodes.

10. A method for a low-power broadband self-organizing network according to claim 9, characterized in that, The operating modes include the lowest power operating mode and the standby low power operating mode.

Images