Self-adaptive clustering routing method based on dynamic threshold value and energy efficiency factor

Abstract

The invention is applicable to the field of wireless sensor networks, and provides a self-adaptive clustering routing method based on a dynamic threshold and an energy efficiency factor, which comprises the following contents: a cluster establishment stage: the task of the stage is to elect a cluster head and complete cluster construction; an adaptive data transmission stage: after the cluster structure is established, the network enters a stable data transmission stage, and the stage comprises two layers; the method comprises the following steps that: in-cluster member nodes transmit acquired data to belonging cluster heads according to an in-cluster self-adaptive transmission strategy; and 2, each cluster head transmits the summarized and fused data to a base station hop by hop or directly through an inter-cluster adaptive routing strategy. According to the method, the overall life cycle of the network is remarkably prolonged, the energy utilization efficiency and load balance of the network are greatly improved, the stability of network operation and the reliability of data acquisition are enhanced, and the adaptive capacity of the network to dynamic change is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless sensor networks, and in particular to an adaptive clustering routing method based on dynamic threshold and energy efficiency factor. BACKGROUND

[0002] While wireless sensor networks have shown great potential, they also face a fundamental constraint: the problem of node energy supply. Most sensor nodes are small in size and rely on limited energy batteries for power supply. In their typical application scenarios, such as vast farmland, rugged mountainous areas, remote areas prone to disasters, or large-scale industrial facilities, once the nodes are deployed, subsequent battery replacement or energy replenishment is often extremely difficult, if not economically and practically feasible. This means that the "life" of each node begins to count down from the moment the battery is installed, and the effective working time of the entire network is also strictly limited.

[0003] When some nodes in the network stop working due to energy depletion, the monitoring area they are responsible for will no longer be able to provide data, and important environmental changes or event information may be interrupted. In a multi-hop self-organizing wireless sensor network, each node not only collects and transmits its own data, but also may act as a "relay" for other nodes to forward data. If some key nodes in certain locations stop working, it may affect the data transmission path of other nodes with sufficient energy, and thus affect the overall data collection capability and coverage of the network.

[0004] In order to effectively manage network resources, organize data flow and prolong the overall working time of the network as much as possible, researchers have adopted various network organization and data transmission strategies in the design of wireless sensor networks. Among them, the hierarchical or clustering routing mechanism is a widely used technical idea. This mechanism organizes the nodes in the network into several "clusters" for management, and each cluster usually has a "cluster head" node. The cluster head node is responsible for collecting data sensed by other member nodes in its cluster, and performing preliminary integration or fusion processing on the data, and then sending the processed information to a higher-level cluster head or directly to the base station (data aggregation center) of the network. This hierarchical network structure aims to reduce unnecessary long-distance direct communication in the network and reduce the amount of redundant information through intra-cluster data fusion, thereby managing the overall energy consumption of the network. However, how to efficiently construct these cluster structures, dynamically adapt to changes in network energy and topology, and select the optimal cluster head and routing path to maximize the energy-saving potential of the hierarchical or clustering strategy remains a core issue for continuous exploration in this field. SUMMARY

[0005] The application provides an adaptive clustering routing method based on a dynamic threshold and an energy efficiency factor, and solves the technical problems mentioned in the background art.

[0006] An adaptive clustering routing method based on a dynamic threshold and an energy efficiency factor, the method comprising:

[0007] A cluster establishment phase: the core task of this phase is to elect a suitable cluster head and complete the construction of a cluster;

[0008] An adaptive data transmission phase: after the cluster structure is established, the network enters a stable data transmission phase, which includes two levels:

[0009] First, the normal nodes transmit the collected data to their home cluster head according to the adaptive transmission strategy in the cluster;

[0010] Second, each cluster head transmits the data fused after the integration to the base station through the inter-cluster adaptive routing strategy hop by hop or directly.

[0011] Preferably, the cluster establishment phase comprises:

[0012] Under the premise of meeting the basic operating conditions, the nodes meeting the qualifications in the network become candidate cluster heads through a probabilistic candidate qualification determination mechanism;

[0013] Calculation of an unbalanced competition radius: the candidate cluster heads calculate a competition radius according to their residual energy and the distance from the base station ;

[0014] Determination of the final cluster head node through an energy-based competition process: before broadcasting the ADV message, the candidate cluster head calculates a waiting time inversely proportional to its residual energy , and the node that successfully broadcasts the ADV message and is not suppressed by other candidate cluster heads with higher priority becomes the final cluster head in this round;

[0015] ;

[0016] wherein, represents the maximum residual energy of all active nodes in the current network, represents the residual energy of the current candidate cluster head, is a preset maximum waiting time upper limit, is an exponential adjustment factor for controlling the sensitivity of the energy level to the waiting time, to randomly adjust the coefficients;

[0017] After the final cluster head is confirmed, it broadcasts its ADV message. Other suppressed temporary cluster heads and normal nodes that have not been assigned to a cluster within the dynamic competition radius of the final cluster head send a "Join-REQ" message to complete association with the final cluster head after receiving the ADV message. The cluster head records the member information accordingly. For the remaining normal nodes in the network that have not been assigned to a cluster at this stage, the protocol provides a supplementary association mechanism: the remaining normal nodes will evaluate the signal strength (RSSI) of all final cluster heads that they can receive, and select the final cluster head with the strongest signal to send a "Join-REQ" message to complete association. After receiving and processing all "Join-REQ" messages, each final cluster head completes the construction and maintenance of its cluster member list.

[0018] Preferably, the content of becoming a candidate cluster head through the candidate qualification determination mechanism includes:

[0019] Each qualified node generates a random number ranging from 0 to 1;

[0020] The protocol sets a fixed probability threshold T (this value is set to 0.35);

[0021] If the random number generated by the node is less than or equal to T, the node is selected as a candidate cluster head.

[0022] Preferably, the calculation principle of the unbalanced competition radius is:

[0023] positively correlated with positively correlated with negatively correlated with, and limited within the maximum competition radius . This enables candidate cluster heads with more energy or better locations to cover a larger range, forming a non-uniform cluster that is in scale with its capabilities.

[0024] Preferably, in the adaptive data transmission phase, the cluster member nodes send the data collected to their home cluster head within the data transmission time slot allocated to them, and implement the following intra-cluster adaptive transmission strategy when sending the data to their home cluster head

[0025] S1.1, preliminary judgment of transmission mode: First, calculate the straight-line distance between the node and , if the straight-line distance is less than or equal to the preset intra-cluster direct transmission threshold , then direct single-hop transmission is determined as the current optimal mode, and the data is prepared to be sent directly to ;

[0026] S1.2, Multi-hop relay feasibility assessment and optimal relay selection:

[0027] If the straight-line distance is greater than the direct transmission threshold , then an evaluation and selection mechanism of multi-hop relay transmission is initiated, for which, the state information (including position to calculate distance and residual energy) of other member nodes (i.e. potential relay nodes) within its communication range and belonging to the same cluster as is acquired, which can be completed through a neighbor discovery mechanism or information synchronization assisted by the cluster head in the network initialization or cluster establishment phase; subsequently, the intra-cluster relay scoring function of each potential relay node is calculated, which is defined as follows: The intra-cluster relay scoring function integrates the theoretical energy saving potential of the path (quantified by the relative energy saving factor ) and the energy state of the potential relay node itself through a weighted combination manner, and is defined as follows:

[0028] ;

[0029] ;

[0030] wherein, the higher the score, the better the comprehensive benefits of selecting node as a relay, therefore, node will select the neighbor that can make its value the largest as the optimal relay node for the next hop, is the relative energy saving factor via node as a relay, , and are proportional coefficients, , and are the distances from the current node to the cluster head, the current node to the potential relay node , and the potential relay node to the cluster head, respectively; the value of when greater than 0 indicates that the transmission through the relay is energy-saving, is the residual energy of the potential relay node , With is a weight coefficient;

[0031] S1.3, relay forwarding, data compression and iterative decision: sending data to the selected optimal relay node optimal relay node After successfully receiving the data, first, the data is compressed to reduce the data packet size, thereby reducing the energy consumption of subsequent transmission; after compression, the optimal relay node will be a new data holder, and the decision-making process of steps S1 and S2 will be repeated: that is, whether the distance between itself and the home cluster head satisfies the direct transmission condition (less than or equal to ), or whether it needs to select the next more optimal relay node for further forwarding by maximizing its value again; this iterative relay process continues until the data is finally successfully transmitted to ; if in any decision-making, the node currently holding the data (whether the original CM node or the intermediate relay node) fails to find any effective relay that can indicate the clear energy saving benefit of its value, the node will give up multi-hop relay and choose to transmit data directly to to ensure the reliable delivery of data;

[0032] The relative energy saving factor (E) within the cluster is fused , and the residual energy of the candidate relay node is fused, which comprehensively considers the relative energy saving potential and the residual energy of the relay node, the data is compressed during the relay process, and then the relay node repeats the decision-making process until the data reaches the cluster head.

[0033] Preferably, the inter-cluster adaptive routing strategy:

[0034] S2.1, establishment and interpretation of core decision parameters: selection of inter-cluster routing mode and determination of next-hop relay cluster head, the primary basis is the straight-line distance between the current cluster head and the base station BS; at the same time, the protocol introduces a key energy efficiency switching critical distance coefficient , which is scientifically determined in the present application by combining rigorous wireless communication energy consumption model theoretical analysis and multi-scenario simulation experiment verification, and the optimal value is 1.25; this coefficient is combined with the widely used characteristic switching distance in the wireless communication energy consumption model (i.e. the distance demarcation point of the free space propagation model and the multipath fading propagation model energy consumption calculation formula switching) to calculate a core inter-cluster transmission distance threshold , the calculation formula is ; constitutes the preliminary energy efficiency boundary to distinguish short-distance direct transmission from medium-distance multi-hop transmission; in addition, in order to further optimize the transmission efficiency of very long distance and avoid forming a too long multi-hop chain, the protocol also introduces a long-distance routing strategy starting threshold ;

[0035] S2.2, multi-mode adaptive routing strategy based on distance partitioning and execution flow: according to the current distance of the source cluster head and the base station BS relative to the threshold and , one of the following three routing modes is dynamically activated:

[0036] S2.2.1, short-distance routing mode, i.e. : if the distance of the source cluster head to the base station BS is not greater than the threshold , the protocol judges that direct single-hop transmission to the base station BS is the lowest energy consumption and most reliable way, and directly sends the data packet to the base station BS;

[0037] S2.2.2, medium-distance routing mode, i.e. : if the distance of the source cluster head to the base station BS is in this range, the protocol will preferentially try to perform energy-saving transmission through double-hop or limited multi-hop by selecting an optimal next-hop relay cluster head ; in order to achieve this purpose, an active broadcast query is sent to obtain the current state of other candidate cluster heads within its communication range; the selection of the optimal relay cluster head is based on the calculation of the inter-cluster relay score function of all potential candidates, and the one with the maximum score value is selected, and the inter-cluster relay score function is defined as follows:

[0038] ;

[0039] ;

[0040] wherein is the final "inter-cluster relay score" of the candidate relay cluster head . The higher this score, the more advantageous it is to select k as the relay. is the relative energy-saving factor via node relay, , and are the distances from the source cluster head to the base station , relay cluster head To the base station , source cluster head To the relay cluster head distance; When the value is greater than 0, it indicates that the Transmission is energy-efficient, Relay cluster head The remaining energy, is the energy of the remaining cluster heads, and is the weight coefficient; is the power amplifier coefficient for the free space propagation model, is the power amplifier coefficient for the energy consumption of the multipath fading propagation model;

[0041] S2.2.3, long distance routing mode, i.e. :like The distance to the BS reaches or exceeds , indicating that direct transmission or simple double-hop may still face too high energy consumption or too long single-hop distance; at this time, the protocol will activate a more complex iterative multi-hop routing mechanism and introduce the concept of "virtual base station" to optimize the hop-by-hop forwarding decision of long-distance paths; under this mechanism, the source cluster head When selecting its next-hop relay, its target reference point is no longer the distant base station BS, but is dynamically set to the location of a closer "virtual base station" VBS; the location of VBS is usually based on the source cluster head. The coordinates of the base station BS are dynamically calculated and set at The midpoint of the line connecting the two nodes makes VBS a temporary target that gradually "approaches" the base station BS; the selection basis of the next hop relay cluster head is still to maximize the VBS calculated dynamically based on this target. value, then the formula Refers to the distance from the current cluster head to the VBS; after the data packet is sent to the selected next-hop relay cluster head, the relay cluster head will become the new data source node; it will re-evaluate the distance between itself and the base station BS: if it still meets , it will repeat this VBS positioning (setting a new VBS between itself and the base station BS) and the next-hop selection process based on the new VBS; if its distance to the base station BS has entered the medium or short range, it will switch to the corresponding mode (i.e., the routing strategy of ii or i above) for subsequent transmission; this iterative selection and forwarding process continues until the data finally reaches the base station BS efficiently and energy-savingly through a series of carefully selected relays.

[0042] Beneficial effects achieved by the present invention:

[0043] The application (DEXTRA-CR protocol), that is, an adaptive clustering routing method based on dynamic threshold and energy efficiency factor, is applied to a wireless sensor network, and through a unique multi-stage dynamic adaptive mechanism, the following remarkable effects are brought in network energy management and data routing. Through simulation experiments in a plurality of typical network scenes, including 500 nodes deployed in a 500*500 meter area, 600 nodes deployed in a 500*500 meter area, and 600 nodes deployed in a 600*600 meter area, and the like, the effectiveness of the DEXTRA-CR protocol is fully verified, and the specific performance is as follows:

[0044] Significantly prolonging the overall network life cycle:

[0045] Through the innovative cluster head election mechanism (which includes probabilistic candidate preliminary determination and subsequent energy optimization final confirmation), the dynamic unbalanced clustering strategy, and the energy optimization adaptive hierarchical routing in the cluster and between clusters, the application can more effectively balance the energy consumption rate of each node in the network. This significantly alleviates the "short board effect" caused by the premature energy depletion of some key nodes (such as cluster heads near the base station or high-load relay nodes). The simulation experiment results of all the above test scenes consistently show the advantages of the DEXTRA-CR protocol: compared with other representative protocols, the DEXTRA-CR protocol can significantly delay the time of the first node death (FND) in the network, prolong the total length of time of the last node death (LND) or network failure, and thus improve the effective working life of the entire network.

[0046] Significantly improving network energy utilization efficiency and load balancing:

[0047] The application adjusts the size of the cluster and the selection of the cluster head dynamically, so that the structure of the cluster is more suitable for the real-time energy status and network topology position of the node, and ensures that the nodes with sufficient energy and good connectivity bear more appropriate responsibilities. At the same time, in the data transmission process, whether the cluster members converge data to the cluster head or the cluster head relays data to the base station, the path selection based on real-time energy efficiency evaluation (implemented by and and the like) is adopted, and the transmission path and mode with the lowest energy consumption are preferentially used. In all the tested scenes, the experimental data (such as network total energy consumption, node average energy consumption, and the like, the effect of which can be indirectly reflected from the persistence of the number of surviving nodes) show that the DEXTRA-CR protocol not only reduces the average energy consumption of single data transmission, but more importantly, promotes the balanced distribution of network load in space and time, effectively slowing down the formation and deterioration of energy "hot spot" areas.

[0048] Enhancing the stability of network operation and the reliability of data acquisition:

[0049] Due to the extension of network life cycle and the balance of energy consumption, more sensor nodes can remain active for a longer period of time, thereby ensuring the continuous coverage of the network monitoring area and the integrity of data collection. The connectivity of the network is also better maintained due to the extension of the survival time of key nodes, improving the probability of successful data transmission to the base station and the reliability of the overall network. In the experiments of various test scenarios, the longer node survival time under the DEXTRA-CR protocol brings more persistent data collection capability and more stable network topology, thereby ensuring the success rate of data transmission and the quality of network service.

[0050] Improve the adaptive ability of the network to dynamic changes:

[0051] The decision mechanisms in the present application, such as cluster head election, competition radius calculation and routing selection, have dynamic adaptability and can be adjusted according to changes in node energy, local density and potential changes in network topology. The DEXTRA-CR protocol shows stable performance advantages in various simulation scenarios with different numbers of nodes and different sizes of network areas, which fully proves its good adaptability. Regardless of the initial conditions of the network, DEXTRA-CR can effectively adjust its network structure and routing strategy to adapt to the current state, maintain high energy efficiency and operating performance. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A general flowchart of an adaptive clustering routing method based on dynamic threshold and energy efficiency factor is provided in the present application.

[0053] Figure 2 A flowchart of the cluster establishment stage is provided in the present application.

[0054] Figure 3 A flowchart of the adaptive data transmission stage is provided in the present application.

[0055] Figure 4 A comparison chart of the death time of key life cycle nodes of each protocol for 500 nodes in a 500x500 meter scenario in an embodiment of the present application.

[0056] Figure 5 A comparison chart of the number of surviving nodes of each protocol for a 500-node network in a 500x500 meter scenario in an embodiment of the present application.

[0057] Figure 6 A comparison chart of the total residual energy of a 500-node network in a 500x500 meter scenario in an embodiment of the present application.

[0058] Figure 7 A comparison chart of the number of data packets sent to the base station by each protocol for 500 nodes in a 500x500 meter scenario in an embodiment of the present application.

[0059] Figure 8 Figure 6 is a graph showing the death time of key life cycle nodes for each protocol in a 500x500 meter scenario with 600 nodes, according to an embodiment of the present application.

[0060] Figure 9 Figure 7 is a graph showing the number of surviving nodes for each protocol in a 500x500 meter scenario with 600 nodes, according to an embodiment of the present application.

[0061] Figure 10 Figure 8 is a graph showing the total residual energy for a 500x500 meter scenario with 600 nodes, according to an embodiment of the present application.

[0062] Figure 11 Figure 9 is a graph showing the number of data packets sent to the base station for each protocol in a 500x500 meter scenario with 600 nodes, according to an embodiment of the present application.

[0063] Figure 12 Figure 10 is a graph showing the death time of key life cycle nodes for each protocol in a 600x600 meter scenario with 600 nodes, according to an embodiment of the present application.

[0064] Figure 13 Figure 11 is a graph showing the number of surviving nodes for each protocol in a 600x600 meter scenario with 600 nodes, according to an embodiment of the present application.

[0065] Figure 14 Figure 12 is a graph showing the total residual energy for a 600x600 meter scenario with 600 nodes, according to an embodiment of the present application.

[0066] Figure 15 Figure 13 is a graph showing the number of data packets sent to the base station for each protocol in a 600x600 meter scenario with 600 nodes, according to an embodiment of the present application. DETAILED DESCRIPTION

[0067] Various illustrative embodiments of the present application are described in detail below. This detailed description is not intended to be a limitation on the application, but rather an exemplification of the present application and its preferred use. Other applications may be used and variations from the specific embodiments described herein may be possible.

[0068] It should be understood that the terms used herein are for the purpose of describing particular embodiments and are not intended to limit the application. Additionally, for numerical ranges that are expressed in a lower limit and an upper limit, every numerical value between the lower and upper limits is specifically contemplated. The scope of the range is also intended to include any smaller sub-ranges between the stated limits as solely limited by the literal recitation of the range bounds in the claims. As in this example, every range of numerical values between the lower and upper limits in this context is specifically contemplated. The use of "about" in connection with a recited numerical value means that the exact value is not required for the application to be effective. The use of "substantially" in connection with a recited numerical value means that the exact value is not required for the application to be effective.

[0069] Reference will now be made to Figure 1The embodiment of the present application provides a self-adaptive clustering routing method based on a dynamic threshold and an energy efficiency factor, and the method comprises the following steps:

[0070] A cluster establishment phase: the core task of the phase is to elect a suitable cluster head and complete cluster construction;

[0071] An adaptive data transmission phase: after the cluster structure is established, the network enters a stable data transmission phase, and the phase comprises two levels:

[0072] First, normal nodes transmit collected data to the home cluster head according to an adaptive transmission strategy in the cluster (for example, based on the distance from the cluster head and a cluster relay scoring function to evaluate and select the optimal relay);

[0073] Second, each cluster head transmits the data fused after being summarized to the base station through an inter-cluster adaptive routing strategy (for example, based on the distance from the base station, a key energy efficiency switching threshold coefficient and an inter-cluster relay scoring function to evaluate and select the optimal relay, which can comprise a virtual base station mechanism) hop by hop or directly.

[0074] Please refer to Figure 2 In the embodiment, the cluster establishment phase comprises the following steps:

[0075] Under the premise of meeting the basic operating conditions (for example, the remaining energy is greater than zero), nodes meeting the qualifications in the network become candidate cluster heads through a probabilistic candidate qualification determination mechanism;

[0076] Calculation of an uneven competition radius: candidate cluster heads calculate a competition radius according to the remaining energy and the distance from the base station ;

[0077] Determination of the final cluster head node through an energy-based competition process: before broadcasting the cluster head identity (ADV message), the candidate cluster head calculates a waiting time inversely proportional to the remaining energy, and the node that successfully broadcasts the ADV message and is not suppressed by other candidate cluster heads with higher priorities becomes the final cluster head in this round; the candidate cluster head with higher energy has a shorter waiting time, thereby preferentially broadcasting and effectively suppressing the candidate cluster heads with lower energy in the competition radius, so that the final cluster head is quickly confirmed;

[0078] ;

[0079] wherein, represents the maximum residual energy of all active nodes in the current network, is a preset maximum waiting time upper limit, is an exponential adjustment factor for controlling the sensitivity of the energy level to the waiting time, is a random adjustment coefficient;

[0080] After the final cluster head is confirmed, it broadcasts its ADV message. Other suppressed temporary cluster heads and ordinary nodes that have not been allocated clusters within the dynamic competition radius of the final cluster head complete association with the final cluster head by sending a "join request" (Join-REQ) message after receiving the ADV. The cluster head records the member information accordingly; for the remaining ordinary nodes in the network that have not been allocated clusters at this stage, the protocol provides a supplementary association mechanism: the remaining ordinary nodes will evaluate the signal strength (RSSI) of all the final cluster heads that they can receive, and select the final cluster head with the strongest signal to send a "join request" message to complete the association; after receiving and processing all the "join request" messages, each final cluster head completes the construction and maintenance of its cluster member list. The detailed process of this stage can be referred to in Figure 2 : Cluster Establishment Stage Flow Chart】.

[0081] In this embodiment, the content of the nodes that become candidate cluster heads through the candidate qualification determination mechanism includes:

[0082] Each qualified node generates a random number ranging from 0 to 1;

[0083] The protocol sets a fixed probability threshold T (this value is set to 0.35);

[0084] If the random number generated by the node is less than or equal to T, the node is selected as a candidate cluster head.

[0085] This candidate cluster head election method ensures that a part of the nodes in the network obtain candidate qualifications. In this specific step of generating candidate cluster heads, whether a node can become a candidate is not directly determined by its specific operating parameters. In the subsequent final cluster head confirmation stage, further screening will be performed on the basis of the relevant attributes of these nodes that have obtained candidate qualifications to determine the final cluster head.

[0086] In this embodiment, the calculation principle of the unbalanced competition radius is:

[0087] is positively correlated with and is negatively correlated with and is limited within the maximum competition radius This allows candidate cluster heads with more energy or better locations to cover a larger range and form non-uniform clusters with a size that is adapted to their capabilities.

[0088] Referring to Figure 3 , in the adaptive data transmission phase, the cluster member node , within its allocated data upload time slot, transmits the collected data to its home cluster head , the following intra-cluster adaptive transmission strategy is executed to achieve energy-efficient intra-cluster data aggregation:

[0089] S1.1, preliminary judgment of transmission mode: First, calculate the straight-line distance between it and , if the straight-line distance is less than or equal to the preset intra-cluster direct transmission threshold , then direct single-hop transmission is judged as the current optimal mode, and the data is prepared to be sent directly to ;

[0090] S1.2, multi-hop relay feasibility assessment and optimal relay selection: if the straight-line distance is greater than the direct transmission threshold , then start the evaluation and selection mechanism of multi-hop relay transmission, for this purpose, obtain the state information (including position to calculate distance and residual energy) of other member nodes (i.e. potential relay nodes) within its communication range and belonging to , this can be done through the neighbor discovery mechanism or information synchronization assisted by the cluster head in the network initialization or cluster establishment phase; then, calculate the intra-cluster relay scoring function for each potential relay node , the intra-cluster relay scoring function integrates the theoretical energy-saving potential of the path (quantified by the relative energy-saving factor ) and the energy state of the potential relay node itself through weighted combination, the intra-cluster relay scoring function is defined as follows:

[0091] ;

[0092] ;

[0093] wherein, the higher the score, the better the comprehensive benefit of choosing node as a relay, therefore, node will choose the neighbor with the maximum value as the optimal relay node for the next hop , for the data to be transmitted via node the relative energy saving factor of the relay, , and is a proportional coefficient, , and is the distance from the current node to the cluster head, the distance from the current node to the potential relay node , the distance from the potential relay node to the cluster head, respectively; the value of when greater than 0 indicates that transmission through the relay is energy saving, is the residual energy of the potential relay node , is the initial energy, is the initial energy, and are weight coefficients;

[0094] S1.3, relay forwarding, data compression and iterative decision making: send the data to the selected optimal relay node , the optimal relay node , after successfully receiving the data, first performs a compression operation on the data to reduce the data packet volume, thereby reducing the energy consumption of subsequent transmission; after completing the compression, the optimal relay node will serve as a new data holder, repeating the decision-making process of steps S1 and S2: that is, determining whether its own distance to the home cluster head satisfies the direct transmission condition (less than or equal to ), or whether it needs to select the next more optimal relay node for further forwarding by maximizing its own value again; this iterative relay process continues until the data is finally successfully transmitted to ; if in any one decision, the node currently holding the data (whether the original CM node or the intermediate relay node) fails to find any effective relay that can indicate clear energy saving benefits , the node will give up multi-hop relay and instead choose to transmit data directly to to ensure reliable delivery of data;

[0095] combining the intra-cluster relative energy saving factor ( ) and the residual energy of the candidate relay node, integrating the relative energy saving potential of transmission through the relay and the residual energy of the relay node, the data will be compressed during the relay process, and then the relay node will repeat this decision-making process until the data reaches the cluster head.

[0096] Please refer to Figure 3 , in this embodiment, the inter-cluster adaptive routing strategy:

[0097] S2.1. Establishment and interpretation of core decision parameters: The selection of inter-cluster routing mode and the determination of the next-hop relay cluster head are primarily based on the current cluster head Straight-line distance to the base station BS At the same time, the protocol introduces a key energy-efficient switching critical distance coefficient , which is scientifically determined in the present invention through a combination of rigorous theoretical analysis of wireless communication energy consumption models and multi-scenario simulation experimental verification, with an optimal value of 1.25; Coefficients and characteristic switching distances in widely used wireless communication energy consumption models (i.e., the distance dividing point where the energy consumption calculation formula of the free space propagation model and the multipath fading propagation model switches) is combined to calculate the inter-cluster transmission distance threshold of a core , the calculation formula is ; It constitutes the initial energy efficiency boundary that distinguishes short-distance direct transmission from medium-distance multi-hop transmission. In addition, in order to further optimize the transmission efficiency of extremely long distances and avoid the formation of overly long multi-hop chains, the protocol also introduces a long-distance routing strategy startup threshold. ;

[0098] S2.2, Multi-mode adaptive routing strategy and execution process based on distance partitioning: According to the source cluster head Current distance to the base station BS Relative to threshold and Dynamically activate one of the following three routing modes:

[0099] S2.2.1, short distance routing mode, i.e. :like The distance to the BS is not greater than the threshold , the protocol determines that direct single-hop transmission to the BS is the current method with the lowest energy consumption and the most reliable. The data packet will be sent directly to the BS;

[0100] S2.2.2, medium distance routing mode, i.e. :like If the distance to the BS is within this range, the protocol will try to select an optimal next-hop relay cluster head first. To carry out energy-saving transmission of double hop or limited multi-hop; to achieve this goal, Actively broadcast query messages to obtain the current status of other candidate cluster heads within its communication range; the optimal relay cluster head The selection is based on calculating the inter-cluster relay score function for all potential candidates , and select the one that maximizes the score, the inter-cluster relay score function The following definitions apply:

[0101]

[0102]

[0103] wherein, is the final "inter-cluster relay score" for candidate relay cluster head . The higher this score, the more beneficial it is to select k as a relay. is the relative energy saving factor via node , , and are the distances from source cluster head to base station , from relay cluster head to base station , from source cluster head to relay cluster head ; the value of when greater than 1 indicates that transmission through relay is energy saving, is the residual energy of relay cluster head , are the energies of the remaining cluster heads, are the weights; is the power amplifier coefficient of the free space propagation model, is the power amplifier coefficient of the multipath fading propagation model energy consumption;

[0104] S2.2.3, long distance routing mode, i.e. : if the distance of to BS reaches or exceeds , it indicates that direct transmission or simple double-hop can still face excessively high energy consumption or excessively long single-hop distance; at this time, the protocol will activate a more complex iterative multi-hop routing mechanism and introduce the concept of "virtual base station" to optimize the hop-by-hop forwarding decision of the long-distance path; under this mechanism, when source cluster head selects its next-hop relay, its target reference point is no longer the remote base station BS, but is dynamically set to the position of a closer "virtual base station" VBS; the position of VBS is usually dynamically calculated according to the coordinates of source cluster head and base station BS, and is set at the midpoint of the line connecting and base station BS, so that VBS serves as a kind of temporary target for gradually "approaching" base station BS; the selection of the next-hop relay cluster head is still based on maximizing the value of calculated based on this dynamically calculated VBS as the target, at this time the​​ denotes the distance from the current cluster head to the VBS; after the data packet is sent to the selected next-hop relay cluster head, the relay cluster head will become the new data source node; it will re-evaluate its own distance to the base station BS: if it still satisfies , it will repeat the VBS positioning (set a new VBS between itself and the base station BS) and the next-hop selection process based on the new VBS; if its distance to the base station BS has entered the medium distance or short distance range, it will switch to the corresponding mode (i.e. the above ii or i) of the routing strategy for subsequent transmission; this iterative selection and forwarding process continues until the data finally reaches the base station BS efficiently and energy-saving through a series of carefully selected relays.

[0105] To verify the performance of the adaptive clustering routing method based on dynamic threshold and energy efficiency factor (DEXTRA-CR protocol) proposed in the present application, we evaluated it through simulation experiments and compared it with several representative wireless sensor network routing protocols (LEACH, EBUC, LEACH IM, LEACH EDP) existing in the art. The following uniform parameter configuration was used in all simulation examples, unless otherwise specified in a specific example.

[0106] The general simulation parameter configuration is shown in Table 1:

[0107] Table 1: General simulation parameter configuration

[0108] Performance evaluation indicators: network lifetime, number of surviving nodes in the network over time, total amount of data sent to the base station per round, total residual energy of the network per round, etc.

[0109] Example 1: Network scenario simulation based on a 500x500m area and 500 nodes

[0110] 1) Specific scenario configuration

[0111] Network area: 500m x 500m.

[0112] Number of nodes: 500 sensor nodes.

[0113] Base station (BS) location: (250, 500)

[0114] 2) Effect:

[0115] Network lifetime and surviving nodes:

[0116] The simulation results show that the network lifetime indicators of each protocol are as follows (protocol order: LEACH, EBUC, LEACH IM, LEACH EDP, DEXTRA-CR):

[0117] Please refer to Figure 4 : Comparison chart of death time of each protocol key life cycle node;

[0118] The first node death (FND) time (round): LEACH is 41 rounds, EBUC is 478 rounds, LEACH IM is 44 rounds, LEACH EDP is 63 rounds, and the FND of the DEXTRA-CR protocol of the application is 710 rounds.

[0119] The tenth node death time (round): LEACH is 74 rounds, EBUC is 753 rounds, LEACH IM is 106 rounds, LEACH EDP is 147 rounds, and the DEXTRA-CR protocol is 771 rounds.

[0120] All node death (LND) time (round): LEACH is 1750 rounds, EBUC is 1017 rounds, LEACH IM is 1805 rounds, LEACH EDP is 1316 rounds, and the network overall survival time of the DEXTRA-CR protocol is the longest, reaching 1860 rounds.

[0121] Refer to Figure 5 : Comparison chart of the number of surviving nodes of each protocol in a 500-node network in a 500*500m scenario. The chart directly verifies the above life cycle data points:

[0122] In the chart, the initial number of surviving nodes of all protocols is 500.

[0123] The DEXTRA-CR protocol (green curve) of the application performs most outstandingly: its FND is the latest (about 710 rounds), the node survival curve remains high for a long time after the initial stage, then the decline slope is relatively flat, and until the longest LND (about 1860 rounds) is reached. This clearly shows that the DEXTRA-CR protocol can most effectively prolong the active time of nodes in the network and delay the time of network functional failure.

[0124] Energy consumption characteristics:

[0125] Refer to Figure 6: 500-node network total residual energy comparison chart under 500x500m scenario. It can be clearly seen from the figure that the initial total energy of the network is about 500 joules. Compared with LEACH EDP, LEACH IM, LEACH and EBUC, the DEXTRA-CR protocol of the application shows the optimal energy consumption management. The network total energy decline rate of the DEXTRA-CR protocol is different from other protocols in the early stage, and when the energy of other main comparison protocols has been significantly reduced or depleted (such as at 1000 rounds), the DEXTRA-CR protocol network still has a considerable part of the residual energy. The network total energy is completely depleted until about 1800 rounds for the DEXTRA-CR protocol.

[0126] This result specifically shows that the DEXTRA-CR protocol has excellent energy balance and energy saving effect in the 500-node scale network, thereby supporting longer network operation.

[0127] Referring to Figure 7 : 500-node network cumulative data packet number sent to the base station under the 500x500m scenario. The figure shows the cumulative situation of the total amount of data packets successfully sent to the base station (sink node) during the network operation of each protocol:

[0128] The LEACH protocol (black curve) has the lowest cumulative data packet sending amount, about 16455.

[0129] The LEACH IM protocol (cyan curve) has a cumulative data packet sending amount of about 20695.

[0130] The EBUC protocol (blue curve) has a cumulative data packet sending amount of about 22783. Its curve tends to be flat after about 1050 rounds (corresponding to its LND).

[0131] The LEACH EDP protocol (red curve) has a cumulative data packet sending amount of about 23836. Its curve tends to be flat after about 1000 rounds (corresponding to its LND).

[0132] The DEXTRA-CR protocol (green curve) of the application performs the best, and the total amount of data packets sent to the base station reaches 33002, which is significantly higher than all the comparison protocols. Although the instantaneous data packet sending rate of the DEXTRA-CR protocol may not be the highest in the initial stage (about the first 200 rounds), it can maintain effective data collection and transmission for a longer time to achieve the maximum data throughput due to its significantly prolonged network lifetime and sustained node activity.

[0133] This result fully proves that the DEXTRA-CR protocol not only prolongs the network lifetime, but also guarantees higher data collection total amount and transmission reliability within the lifetime.

[0134] Experimental Example 2: Network scenario simulation based on a 500x500 meter area and a 600-node network

[0135] 1) Specific scenario configuration

[0136] Network area: 500m x 500m.

[0137] Number of nodes: 600 sensor nodes.

[0138] Base station (BS) location: (250, 500)

[0139] 2) Effects:

[0140] In this simulation scenario, the DEXTRA-CR protocol exhibits significant performance advantages, as follows:

[0141] Network lifetime and surviving nodes:

[0142] The simulation results show that the network lifetime indicators of each protocol are as follows (protocol order: LEACH, EBUC, LEACH IM, LEACH EDP, DEXTRA-CR):

[0143] First node death (FND) time (rounds): LEACH IM is the earliest, at 42 rounds; LEACH is 41 rounds; LEACH EDP is 61 rounds; EBUC is 370 rounds; and the FND of the DEXTRA-CR protocol of the present application is 680 rounds, significantly later than all other comparative protocols, effectively maintaining the integrity of the network in the initial stage.

[0144] Tenth node death time (rounds): LEACH is 71 rounds; LEACH IM is 106 rounds; LEACH EDP is 153 rounds; EBUC is 711 rounds; and the DEXTRA-CR protocol is 832 rounds, further proving the outstanding ability of DEXTRA-CR in delaying early node failure.

[0145] All node death (LND) time (rounds): EBUC is 1204 rounds; LEACH EDP is 1437 rounds; LEACH is 1855 rounds; LEACH IM is 1841 rounds; and the overall network survival time of the DEXTRA-CR protocol is the longest, reaching 2000 rounds, far exceeding all other comparative protocols.

[0146] These data clearly show that the DEXTRA-CR protocol, through its unique energy management and routing mechanism, can most effectively balance network energy consumption and significantly prolong various lifetime indicators of the network.

[0147] Figure 8: 600 nodes key life cycle node death time comparison chart of each protocol in 500x500m scenario, should be able to intuitively show that DEXTRA-CR protocol (usually green curve) can maintain more surviving nodes in each stage compared with other protocols, and the curve drops more gently, the overall effective working time of the network is the longest, which is consistent with the above data points.

[0148] Referring to Figure 9 : 600 nodes network survival node number comparison chart of each protocol in 500x500m scenario. The initial survival node number of all protocols in the figure is 600. The node number of LEACH IM (cyan curve), LEACH (black curve) and LEACH EDP (red curve) drops very quickly, the first node death (FND) appears in the 43th, 40th and 62th round respectively, which enters the network recession period too early. The FND (about 470th round) of EBUC protocol (blue curve) is relatively late, which can maintain network stability for a period of time, but the node number decreases sharply after that, and the network dies completely after about 1000 rounds. The DEXTRA-CR protocol (green curve) of the present application performs best: the FND is significantly delayed to the 712th round, and then the node number starts to decrease smoothly until the longest all node death time LND (1860th round) is reached. This clearly shows that the DEXTRA-CR protocol can most effectively balance the network energy consumption, maximize the stable operation time and overall life cycle of the network.

[0149] Energy consumption characteristics:

[0150] Referring to Figure 10 : 600 nodes network total residual energy comparison chart in 500x500m scenario. From the figure, it can be clearly seen that: the initial total energy of the network is about 600 joules. Compared with LEACH EDP (red curve), LEACH IM (cyan curve), LEACH (black curve) and EBUC (blue curve), the DEXTRA-CR protocol (green curve) of the present application shows the best energy consumption management. The network total energy decrease rate of DEXTRA-CR protocol is different from other protocols in the early stage, and there is still significant residual energy in the network of DEXTRA-CR protocol when the energy of other main comparison protocols has been significantly decreased or basically exhausted (for example, around the 1000th round, at this time the energy of EBUC protocol is close to exhaustion), DEXTRA-CR protocol network. The total energy of the network is completely exhausted only after about 1900 rounds (consistent with the LND data 2000 rounds).

[0151] This result specifically shows that the DEXTRA-CR protocol has excellent energy balance and energy saving effect in the 600 node scale network, thereby supporting longer network operation.

[0152] Data transmission performance:

[0153] Referring to Figure 11 Figure 6: Comparison of the total number of data packets sent to the base station by each protocol in a 500x500 meter scenario with 600 nodes. This figure shows the cumulative total of data packets successfully sent to the base station by each protocol:

[0154] The LEACH protocol (black curve) has the lowest total number of data packets sent, with a total of 19478.

[0155] The LEACH IM protocol (cyan curve) shows a higher total number of data packets than LEACH, eventually reaching approximately 27142.

[0156] The EBUC protocol (blue curve) has a total of 28305 data packets sent, and its curve flattens out after approximately 1000 rounds (close to its LND).

[0157] The LEACH EDP protocol (red curve) has a total of 31779 data packets sent, and its curve flattens out after approximately 1200 rounds (close to its LND).

[0158] The DEXTRA-CR protocol of the present application (green curve) performs the best, with a total of 36509 data packets sent to the base station, significantly higher than all the comparison protocols. The data packet accumulation curve of the DEXTRA-CR protocol shows that although the initial accumulation rate is similar to or slightly slower than that of the EBUC protocol, it can ultimately achieve the maximum data throughput by virtue of a longer network lifetime and continuous effective data transmission, and the curve flattens out only when it approaches its LND (1500 rounds).

[0159] This result shows that the DEXTRA-CR protocol not only has an advantage in extending the network lifetime, but also performs well in ensuring the total amount of network data collection and transmission reliability.

[0160] Example 3: Network scenario simulation based on a 600x600 meter area with 600 nodes

[0161] 1) Specific scenario configuration

[0162] Network area: 600m x 600m.

[0163] Number of nodes: 600 sensor nodes.

[0164] Base station (BS) location: (300, 600)

[0165] 2) Effects:

[0166] Network lifetime and surviving nodes:

[0167] The simulation results show that the network life cycle indicators of each protocol are as follows (the protocol order: LEACH, EBUC, LEACH IM, LEACH EDP, DEXTRA-CR):

[0168] Referring to Figure 12 : Comparison diagram of death time of key life cycle nodes of each protocol under a 600*600 meter scene with 600 nodes;

[0169] The first node death (FND) time (round): LEACH IM is the earliest, which is 3 rounds; LEACH is 5 rounds; LEACH EDP is 21 rounds; the DEXTRA-CR protocol of the application is 398 rounds; and EBUC is 166 rounds. In this scene, the FND performance of DEXTRA-CR is better than that of most comparison protocols.

[0170] The tenth node death time (round): LEACH is 38 rounds; LEACH IM is 49 rounds; LEACH EDP is 74 rounds; EBUC is 370 rounds; and the DEXTRA-CR protocol is 573 rounds, which shows a significant advantage in maintaining the number of node survival in the early network.

[0171] The all node death (LND) time (round): EBUC is 907 rounds; LEACH EDP is 1312 rounds; LEACH IM is 1597 rounds; LEACH is 1594 rounds; and the network overall survival time of the DEXTRA-CR protocol is the longest, which reaches 1886 rounds, far exceeding all other comparison protocols.

[0172] Referring to Figure 13 : Comparison diagram of the number of survival nodes of each protocol under a 600*600 meter scene with 600 nodes. The diagram directly verifies the above life cycle data points:

[0173] In the diagram, the initial survival nodes of all protocols are 600.

[0174] The number of nodes of LEACH IM (cyan curve), LEACH EDP (red curve) and LEACH (black curve) decreases rapidly and the FND is very early.

[0175] The FND (about 166 rounds) of the EBUC protocol (blue curve) is relatively late, but its nodes are rapidly depleted after about 700 rounds.

[0176] The DEXTRA-CR protocol (green curve) of the application performs best: after the FND (about 398 rounds), the number of nodes begins to decrease rapidly, but after about 800 rounds, some nodes can still survive for a long time until the longest LND (1886 rounds) is reached. This clearly shows that even under the condition of increasing network size and relatively sparse nodes, the DEXTRA-CR protocol can still most effectively prolong the active time of nodes in the network and delay the time of network failure.

[0177] Energy consumption characteristics:

[0178] Referring to Figure 14 : Comparison diagram of total residual energy of 600-node network in a 600x600-meter scenario. The diagram shows the total energy consumption of each protocol during network operation (initial total energy 600 joules):

[0179] As can be seen from the diagram, there are significant differences in the energy consumption rates of each protocol. At the 1000th round, the residual energy of each protocol is as follows: LEACH EDP is less than 1 joule, LEACH IM is about 2 joules, LEACH is about 6 joules, EBUC has exhausted all energy, and the DEXTRA-CR protocol of the application has about 9 joules of residual energy.

[0180] The energy consumption strategy of the DEXTRA-CR protocol focuses more on long-term balance and maintenance of the overall network lifetime. The energy consumption curve of the DEXTRA-CR protocol is more flat in the later period, ensuring its longest network operation time.

[0181] Data transmission performance:

[0182] Referring to Figure 15 : Comparison diagram of the number of data packets sent to the base station by each protocol in a 600x600-meter scenario. The diagram shows the cumulative situation of the total number of data packets successfully sent to the base station by each protocol under this network size:

[0183] The LEACH protocol (black curve) has the lowest total number of cumulative data packets sent, which is 15148.

[0184] The LEACH IM protocol (cyan curve) has a total number of cumulative data packets sent of 20889.

[0185] The EBUC protocol (blue curve) has a total number of cumulative data packets sent of 20205.

[0186] The LEACH EDP protocol (red curve) has a total number of cumulative data packets sent of 24799.

[0187] The DEXTRA-CR protocol (green curve) of the present application still performs best in this large network scenario, and the total number of data packets sent to the base station reaches 33788, which is significantly higher than all the comparative protocols. As shown in the figure, the curve of the DEXTRA-CR protocol continues to grow steadily until the later round and then tends to be flat, indicating that it can still maintain efficient data collection and transmission capabilities under the condition of expanding network range and relatively sparse nodes, and fully utilize its extended network life cycle.

[0188] This further verifies the robustness of the DEXTRA-CR protocol under different network sizes and densities and its effectiveness in improving the overall network data throughput.

[0189] It should be noted that in this paper, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0190] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An adaptive cluster routing method based on dynamic threshold and energy efficiency factor, characterized in that: The method comprises: Cluster establishment phase: The task of this phase is to elect cluster heads and complete cluster construction; Adaptive data transmission phase: After the cluster structure is established, the network enters the stable data transmission phase, which includes two levels: First, the member nodes in the cluster transmit the collected data to the cluster head to which they belong according to the adaptive transmission strategy within the cluster; Second, each cluster head aggregates and fuses the data and transmits it to the base station hop by hop or directly through the inter-cluster adaptive routing strategy.

2. The adaptive cluster routing method based on dynamic threshold and energy efficiency factor according to claim 1, characterized in that: The cluster establishment phase includes: Under the premise of meeting the basic operating conditions, the qualified nodes in the network become candidate cluster heads through the candidate qualification determination mechanism; Calculation of imbalanced competition radius: Each candidate cluster head According to its remaining energy and its distance from the base station , dynamically calculate a competition radius ; The final cluster head node is determined through an energy-based competition process: before broadcasting its ADV message, the candidate cluster head calculates a waiting time that is inversely proportional to its remaining energy. ,The node that successfully broadcasts the ADV message and is not suppressed by other ,candidate cluster heads with higher priority becomes the final cluster head of this ,round; ； in, Represents the maximum remaining energy of all active nodes in the current network, represents the remaining energy of the current candidate cluster head, The preset maximum waiting time limit, is an exponential adjustment factor used to control the sensitivity of energy levels to waiting time, is the random adjustment coefficient; After the final cluster head confirms, it will broadcast its ADV message. After receiving the ADV, other suppressed temporary cluster heads and ordinary nodes that have not yet been assigned a cluster within its dynamic competition radius will complete the association with the final cluster head by sending a "Join Request" message, and the cluster head will record its member information accordingly. For the remaining ordinary nodes in the network that have not yet been assigned a cluster after this stage, the protocol stipulates a supplementary association mechanism: the remaining ordinary nodes will evaluate the signal strength of all the final cluster heads they can receive, and select the final cluster head with the strongest signal to send a "Join Request" message to complete the association. After receiving and processing all "Join Request" messages, each final cluster head completes the construction and maintenance of its cluster member list.

3. The adaptive cluster routing method based on dynamic threshold and energy efficiency factor according to claim 2, characterized in that: The content of becoming a candidate cluster head through the candidacy determination mechanism includes: Each eligible node generates a random number between 0 and 1; The protocol sets a fixed probability threshold T; If the random number generated by a node is less than or equal to T, the node is selected as a candidate cluster head.

4. The adaptive cluster routing method based on dynamic threshold and energy efficiency factor according to claim 2, characterized in that: The calculation principle of the imbalanced competition radius is: and Positively correlated with Negatively correlated and limited to the maximum competition radius Inside.

5. The adaptive cluster routing method based on dynamic threshold and energy efficiency factor according to claim 1, characterized in that: In the adaptive data transmission phase, cluster member nodes In the allocated data upload time slot, When sending collected data, the following intra-cluster adaptive transmission strategy is implemented: S1.

1. Preliminary judgment of transmission mode: First calculate its Straight-line distance , if the straight-line distance Less than or equal to the preset intra-cluster direct transmission threshold ,but Determine that direct single-hop transmission is the current optimal mode and prepare to send data directly to ; S1.2, Multi-hop relay feasibility assessment and optimal relay selection: If the straight-line distance Greater than the direct transmission threshold ,but Start the evaluation and selection mechanism of multi-hop relay transmission. To this end, Get the information within its communication range and belonging to the same Status information of other member nodes; Then, For each potential relay node Calculate its intra-cluster relay score function , the relay scoring function within the cluster The theoretical energy saving potential of the path and the potential relay nodes are integrated through weighted combination. Its own energy state, intra-cluster relay scoring function The definition is as follows: ； ； in, The higher the score, the better the selection of the node. The better the comprehensive benefits as a relay, Select the one that will make it The neighbor with the largest value is the optimal relay node for the next hop , For nodes Relative energy saving factor of relay, 、 and is the proportionality coefficient, 、 and They are respectively the current node to the cluster head and the current node to the potential relay node , potential relay nodes Distance to cluster head; When the value is greater than 0, it indicates that the Transmission is energy-efficient, Potential relay nodes The remaining energy, is the initial energy, and is the weight coefficient; S1.3, Relay forwarding, data compression and iterative decision-making: Send data to the selected optimal relay node , the optimal relay node After successfully receiving the data, the data is first compressed to reduce the size of the data packet; after the compression is completed, the optimal relay node As a new data holder, it repeats the decision-making process of steps S1 and S2: that is, it determines whether it is related to the cluster head. Does the distance satisfy the direct transmission condition, or does it need to maximize its own The value is used to select the next better relay node for further forwarding; this iterative relay process continues until the data is finally successfully transmitted to ; If at any decision point, the node currently holding the data fails to find any If the value indicates an effective relay with clear energy saving benefits, the node will abandon multi-hop relay and choose to directly transfer data; Integrates the relative energy saving factor within the cluster As well as the residual energy of the candidate relay node, the relative energy saving potential of transmission through the relay and the residual energy of the relay node are combined. The data will be compressed during the relay process, and then the relay node repeats this decision process until the data reaches the cluster head.

6. The adaptive cluster routing method based on dynamic threshold and energy efficiency factor according to claim 1, characterized in that: Inter-cluster adaptive routing strategy: S2.

1. Establishment and interpretation of core decision parameters: The selection of inter-cluster routing mode and the determination of the next-hop relay cluster head are primarily based on the current cluster head Straight-line distance to the base station BS At the same time, an energy efficiency switching critical distance coefficient is introduced ,this Coefficients and characteristic switching distance in wireless communication energy consumption model Combined to calculate the inter-cluster transmission distance threshold of a core , the calculation formula is ; The preliminary energy efficiency boundary for distinguishing short-distance direct transmission from medium-distance multi-hop transmission is constructed; in addition, a threshold for starting the long-distance routing strategy is introduced. ; S2.2, Multi-mode adaptive routing strategy and execution process based on distance partitioning: According to the source cluster head Current distance to the base station BS Relative to threshold and Dynamically activate one of the following three routing modes: S2.2.1, short distance routing mode, i.e. :like The distance to the BS is not greater than the threshold , the protocol determines that direct single-hop transmission to the BS is the current method with the lowest energy consumption and the most reliable. The data packet will be sent directly to the BS; S2.2.2, medium distance routing mode, i.e. :like If the distance to the BS is within this range, the protocol will try to select an optimal next-hop relay cluster head first. To carry out energy-saving transmission of double hop or limited multi-hop; To achieve this goal, the source cluster head Actively broadcast query messages to obtain the current status of other candidate cluster heads within its communication range; the optimal relay cluster head The selection is based on calculating the inter-cluster relay score function for all potential candidates , and select the one that maximizes the score, the inter-cluster relay score function The definition is as follows: ； ； in, To represent the candidate relay cluster head The final "inter-cluster relay score" is, the higher the score, the more favorable it is to choose k as the relay. For nodes Relative energy saving factor of relay, 、 and Source cluster heads To the base station , relay cluster head To the base station , source cluster head To the relay cluster head distance; When the value is greater than 1, it indicates that the relay cluster head Transmission is energy-efficient, Relay cluster head The remaining energy, is the energy of the remaining cluster heads, and is the weight coefficient; is the power amplifier coefficient for the free space propagation model, is the power amplifier coefficient for the energy consumption of the multipath fading propagation model; S2.2.3, long distance routing mode, i.e. :If the source cluster head The distance to the BS reaches or exceeds At this point, the protocol will activate an iterative multi-hop routing mechanism and introduce the concept of "virtual base station" to optimize the hop-by-hop forwarding decision of long-distance paths; under this mechanism, the source cluster head When selecting its next-hop relay, its target reference point is no longer the base station BS, but is dynamically set to the location of a closer "virtual base station" VBS; the location of VBS is based on the source cluster head The coordinates of the base station BS are dynamically calculated and set at The midpoint of the line connecting the two nodes makes VBS a temporary target that gradually "approaches" the base station BS; the selection basis of the next hop relay cluster head is still to maximize the VBS calculated dynamically based on this target. value, then the formula Refers to the distance from the current cluster head to the VBS; after the data packet is sent to the selected next-hop relay cluster head, the relay cluster head will become the new data source node; it will re-evaluate the distance between itself and the base station BS: if it still meets , the VBS positioning and next-hop selection process based on the new VBS will be repeated; if the distance between it and the base station BS has entered the medium or short distance range, it will switch to the corresponding mode routing strategy for subsequent transmission; this iterative selection and forwarding process continues until the data reaches the base station BS.

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