An environment fusion routing method for enhancing network invulnerability

Abstract

This invention proposes an environmental fusion routing method to enhance network resilience. The main function of wireless sensor networks (WSNs) is to collect monitoring data using sensor nodes and transmit the data to a aggregation node. In most cases, WSNs are deployed in unattended monitoring environments, where dynamic changes in the external environment can impact network performance and significantly affect network resilience. This invention first establishes an environmental impact model, mathematically modeling the phenomenon that sensor performance decreases due to changes in environmental factors to derive environmental factors. Secondly, in the data aggregation stage, environmental factors are incorporated into the probability calculation formula for cluster head selection, increasing the probability of nodes with favorable environments being selected as cluster heads. In the data routing stage, path planning is performed using an artificial potential field method, treating the harsh environment as a repulsive field and the distance to the aggregation point and the node's energy as an attractive field, constructing a combined potential field model.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor networks, and more particularly to the research of an environmental fusion routing method to enhance network resilience. Background Technology

[0002] Wireless sensor networks (WSNs) consist of a large number of self-organizing sensor nodes. Due to their large-scale deployment and self-organization capabilities, WSNs have found widespread application in numerous fields. The primary function of a WSN is to collect monitoring data using sensor nodes and transmit the data to a aggregation node. In most cases, WSNs are deployed in unattended monitoring environments, where dynamic changes in the external environment can impact network performance. Firstly, sensor nodes have limited resources and are easily affected by the external environment. Secondly, in complex monitoring environments, replenishing nodes' power by replacing batteries is impractical. Therefore, how to efficiently complete data collection, processing, and transmission under resource constraints is a significant challenge in the research of WSN resilience. When developing WSN routing protocols, the network environment should be fully considered to ensure stable operation even in harsh conditions. Traditional data aggregation and routing algorithms are based on network structure and node energy, neglecting the external environment. This results in their inability to respond correctly to dynamic environmental changes and hinders the network's survival in harsh environments.

[0003] This invention proposes an environmental fusion routing method to enhance network resilience. By establishing an environmental impact model and comprehensively considering environmental, distance, and energy factors during the data aggregation and routing stages, the routing method is improved, which can effectively enhance network resilience. Summary of the Invention

[0004] The purpose of this invention is to propose an environment fusion routing method to enhance network resilience, providing a theoretical basis for improving the resilience of wireless sensor networks, and which can be widely applied in the Internet of Things and related fields.

[0005] To achieve the above objectives, this invention proposes an environment-integrated routing method to enhance network resilience, which specifically includes three basic steps: establishing an environmental impact model, optimizing cluster head selection, and route optimization.

[0006] Step one, in one embodiment of the present invention, further includes establishing an environmental impact model: taking the most common temperature as an example, mathematical modeling is performed on the impact of the environment on the sensor node. The sensor node has a normal operating temperature range, and it cannot function properly at extremely high or low temperatures; when the ambient temperature deviates from this normal operating temperature range, the performance of the sensor node will decrease accordingly. Therefore, an environmental impact factor U(i) is defined as follows:

[0007]

[0008] Where A, B, C, a, b, and c are as shown in the following formula:

[0009]

[0010] Where U(i) is the environmental impact factor of sensor node i, [T L ,T H [T] represents the normal operating range of the node. min ,T max ] represents the survival range of a node; if the temperature T(i) of the environment in which node i is located is within [T L ,T H Within a certain range, the performance of node i can be considered unaffected by the environment, and the environmental factor U(i) is set to 1; conversely, the performance of node i is affected by the environment and decreases accordingly; if the temperature T(i) of the environment where node i is located is within [T... min ,T max Outside the range, it can be considered that the harsh environment causes node i to completely fail, and the environmental factor U(i) is set to 0;

[0011] Many environmental factors affect the normal operation of nodes, such as temperature, humidity, wind, and electromagnetic interference. Sensor nodes can easily acquire specific environmental information about their surroundings not only through their own sensing modules but also through information exchange with neighboring nodes to obtain various environmental information from adjacent areas. Therefore, it is necessary to establish a comprehensive environmental factor system to accurately characterize the actual monitoring environment. Assume that the monitoring environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q; For a certain node i, its comprehensive environmental factor is as follows:

[0012]

[0013] If multiple environmental factors of node i are low, its overall environmental factor is even lower, indicating that the area is affected by multiple adverse environmental factors and is a dangerous area; if a certain environmental factor is 0, its overall environmental factor is 0, the area is considered a restricted area, and the node cannot be used as the next hop; conversely, if multiple environmental factors are high, its overall environmental factor is high, indicating that the environment in the area is good and is a normal area.

[0014] Step two, in one embodiment of the present invention, the optimized selection of cluster heads further includes: during the data aggregation stage, selecting cluster heads in a probabilistic manner based on the ratio of the remaining energy of each node, environmental factors, and the average energy of the network; nodes with higher initial energy and remaining energy are more likely to be selected as cluster heads than nodes with lower energy, and nodes with higher environmental factors are more likely to be selected as cluster heads than nodes with lower environmental factors.

[0015] In a heterogeneous network, it is assumed that the monitored environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q; For a certain node i, its comprehensive environmental factors are as shown in the above formula; Introducing environmental factors into the probability calculation formula for selecting cluster heads makes nodes in favorable environments more likely to be selected as cluster heads; The probability calculation method for a node being selected as a cluster head in the network is shown in the following formula:

[0016]

[0017] In the formula, E i (r) represents the remaining energy of node i in the r-th round. The average energy of the network in the r-th round can be obtained from the following formula:

[0018]

[0019] Nodes with more energy have a greater chance of becoming cluster heads; therefore, the network's energy consumption is balanced during operation. If node i's comprehensive environmental impact factor U... m When (i) equals 0, it indicates that the node is in a harsh environment, which prevents the node from working properly. Therefore, the probability P of this node being elected as the cluster head is... i =0; if U m (i) When it equals 1, it indicates that the node is in a good environment and environmental factors will not affect the node; if U m When (i)∈(0,1), P i with U m (i) is a positive correlation, U m The larger (i) is, the more P i The larger, conversely, the larger U m (i) The smaller P is, the better i The smaller the value; because the initial energy and environment of each node in a heterogeneous network are different, the probability of each node becoming a cluster head is different, and the probability P of a node becoming a cluster head is... i The relationship between the threshold Y(i) and the threshold is shown in the following formula:

[0020]

[0021] In the formula, G represents the set of nodes in the network that have not yet been elected as cluster heads, and i is the node number, i∈[1,N]. When node i∈G, in each round r, when node i finds that it is qualified to become a cluster head, it will choose a random number between 0 and 1. If the number is less than the threshold Y(i), then the node becomes the cluster head of this round. In this way, the network energy can be balanced and the network's survivability in harsh environments can be improved.

[0022] Step 3, in one embodiment of the present invention, the routing optimization further includes: during the data routing stage, characterizing the potential field based on the fusion of distance from the sensor node to the convergence point, residual energy, and environmental information parameters, and generating a comprehensive potential field for data transmission by superimposing the distance field, residual energy field, and environmental field;

[0023] First, environmental factors are constructed for node awareness. Since harsh environments can negatively impact nodes, the environmental field is set as a repulsive force field. To ensure that dangerous areas are avoided during data transmission, the repulsive force generated by the environmental field increases as the environmental factors decrease. The environmental repulsive force field is shown in the following equation:

[0024]

[0025] In the formula, U H (i) represents the repulsive force field of node i, and U(i) represents the environmental influence factor of node i. The repulsive force is the derivative of the repulsive force field with respect to T(i), where T(i) is the environmental factor value of the region where node i is located. Since it is a repulsive force, it takes a negative value, as shown in the following formula:

[0026]

[0027]

[0028] Therefore, the harsher the environment, the greater the repulsion force on data packets. Data transmission should avoid passing through dangerous areas to ensure the safety of the transmission path.

[0029] Assume the monitored environment is affected by q environmental factors T. α (i), α=1,2…,q influence, that is, there are q environmental factors U α (i), α=1,2…,q, For a certain node i, its comprehensive environmental field is as follows:

[0030]

[0031] Clearly, for a given node i, its overall environmental repulsive force is the derivative of the environmental field with respect to the environmental factor value, as shown in the following equation:

[0032]

[0033] In the formula, U α (i) represents the influence factor of the αth environmental influence on node i, T α (i) is the value of the influence factor on node i under the αth environmental influence, F Hm (i) represents the overall environmental repulsion force at node i;

[0034] Secondly, an attraction field is established. Wireless sensor networks are centered on data transmission, and all data monitored by all nodes must be transmitted to the aggregation node. Therefore, the aggregation node attracts the monitored data, guiding the data to be transmitted to it, and the data is eventually transmitted to the aggregation node. In order to make the network energy consumption uniform and extend the network lifetime, nodes with high energy attract data, guiding the data to be transmitted to them. In summary, the distance field and the residual energy field are regarded as attraction fields.

[0035] To ensure that monitoring data from nodes far from the aggregation node can be successfully transmitted to the aggregation node, the attractive force generated by the distance field increases with increasing distance. The distance attractive force field is constructed as shown in the following equation:

[0036]

[0037] In the formula, U D (i) is the distance attraction field of node i, k is the distance scaling factor, and D is Let represent the distance between node i and the sink node; the distance attraction is the derivative of the distance attraction field with respect to distance, as shown in the following equation:

[0038]

[0039] Nodes farther from the sink node experience a greater attraction, thus all data monitored by the nodes is ultimately transmitted to the sink node. When network energy is consumed evenly, energy utilization is improved, significantly extending the network's lifespan. To ensure balanced network energy consumption, the gravitational force generated by the energy field is positively correlated with the node's energy; nodes with higher energy experience a greater gravitational force and undertake more forwarding tasks. Therefore, the constructed energy gravitational field is shown in the following equation:

[0040]

[0041] In the formula, U E (i) is the energy gravitational field, l is the energy scale factor, and E(i) represents the residual energy of node i; energy gravity is the derivative of the energy gravitational field with respect to energy, as shown in the following equation:

[0042]

[0043] Nodes with more remaining energy experience greater gravitational pull. Therefore, during data transmission, nodes with high remaining energy are selected as the next hop to balance network energy consumption and enhance network resilience. The potential field is characterized by the fusion of the distance from the sensor node to the sink node, remaining energy, and environmental factors. Finally, a comprehensive potential field for data transmission is generated by superimposing the distance field, remaining energy field, and environmental field. The comprehensive potential field is the superposition of the repulsive and gravitational fields, as shown in the following equation:

[0044] U S (i)=U Hm (i)+U D (i)+U E (i)

[0045] The combined force is the superposition of repulsive and attractive forces, as shown in the following formula:

[0046] F S (i)=F Hm (i)+F D (i)+F E (i)

[0047] With the guidance of the combined forces, data can be safely transmitted to the aggregation node, bypassing dangerous areas, thus enhancing the network's resilience in harsh environments.

[0048] The resilience of a network can be measured by comparing it with the DEEC algorithm and analyzing the number of dead nodes and the amount of data transmitted, as shown in the appendix to the manual. Figures 2-3 As shown.

[0049] The node death rate of the EFRP algorithm proposed in this invention is much lower than that of the DEEC algorithm. (See attached specification.) Figure 2 As shown, the EFRP algorithm failed completely by round 4200, while the DEEC algorithm failed completely by round 3000. This means the EFRP algorithm's lifespan was extended by approximately 40% compared to the DEEC algorithm. Due to the influence of harsh environments, nodes in dangerous areas could not function properly. The DEEC algorithm, which did not consider environmental factors, caused approximately 30% of its nodes to fail prematurely. However, the EFRP algorithm took environmental factors into account, so only about 30% of its nodes failed by round 2300. The EFRP algorithm extended the network's efficient operating time by 1800 rounds and its overall lifespan by 1200 rounds. The EFRP algorithm fully considered the impact of environmental factors in data aggregation and routing decisions, balancing energy consumption and avoiding dangerous areas, thus significantly extending the network's lifespan and improving its resilience in harsh environments.

[0050] Data transmission volume is a crucial indicator of network performance; larger data volumes facilitate better subsequent early warning and control efforts. A comparison of data transmission volumes between the EFRP and DEEC algorithms is provided in the appendix of the manual. Figure 3 As shown, calculations show that the EFRP algorithm, after 5000 iterations, transmits a total of 184928 bits of data. Compared to the 68095 bits transmitted by the DEEC algorithm, this represents 116833 more bits, or 1.72 times more data. Therefore, the method proposed in this invention can effectively improve the resilience of sensor networks. Attached Figure Description

[0051] Figure 1 This is a flowchart of an environment converged routing method for enhancing network resilience according to an embodiment of the present invention;

[0052] Figure 2 This is a comparison chart of the number of dead nodes in the network according to an embodiment of the present invention;

[0053] Figure 3 This is a comparison chart of network data transmission volume in an embodiment of the present invention. Detailed Implementation

[0054] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar meanings throughout. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0055] This invention addresses the security of wireless sensor networks by proposing an environmental fusion routing method to enhance network resilience.

[0056] To provide a clearer understanding of the invention, a brief description is provided below. The invention comprises three basic steps: Step 1, establishing an environmental impact model; Step 2, optimizing the selection of cluster heads; and Step 3, route optimization.

[0057] Specifically, Figure 1 The diagram shows a flowchart of a method for optimizing the deployment of heterogeneous nodes to enhance network resilience according to an embodiment of the present invention, including the following steps:

[0058] Step S101: Establish an environmental impact model.

[0059] In one embodiment of the present invention, taking the most common temperature as an example, a mathematical model is performed on the influence of the environment on the sensor node. The sensor node has a normal operating temperature range, and it cannot work normally at extremely high or low temperatures. When the ambient temperature leaves this normal operating temperature range, the working performance of the sensor node will decrease accordingly. Therefore, the environmental influence factor U(i) is defined as shown in formula (1):

[0060]

[0061] Where A, B, C, a, b, c are as shown in formula (2):

[0062]

[0063] Where U(i) is the environmental impact factor of sensor node i, [T L ,T H [T] represents the normal operating range of the node. min ,T max ] represents the survival range of a node; if the temperature T(i) of the environment in which node i is located is within [T L ,T H Within a certain range, the performance of node i can be considered unaffected by the environment, and the environmental factor U(i) is set to 1; conversely, the performance of node i is affected by the environment and decreases accordingly; if the temperature T(i) of the environment where node i is located is within [T... min ,T max Outside the range, it can be considered that the harsh environment causes node i to completely fail, and the environmental factor U(i) is set to 0;

[0064] Many environmental factors affect the normal operation of nodes, such as temperature, humidity, wind, and electromagnetic interference. Sensor nodes can easily acquire specific environmental information about their surroundings not only through their own sensing modules but also through information exchange with neighboring nodes to obtain various environmental information from adjacent areas. Therefore, it is necessary to establish a comprehensive environmental factor system to accurately characterize the actual monitoring environment. Assume that the monitoring environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q;For a certain node i, its comprehensive environmental factors are as shown in formula (3):

[0065]

[0066] If multiple environmental factors of node i are low, its overall environmental factor is even lower, indicating that the area is affected by multiple adverse environmental factors and is a dangerous area; if a certain environmental factor is 0, its overall environmental factor is 0, the area is considered a restricted area, and the node cannot be used as the next hop; conversely, if multiple environmental factors are high, its overall environmental factor is high, indicating that the environment in the area is good and is a normal area.

[0067] Step S102, optimized selection of cluster heads.

[0068] During the data aggregation phase, cluster heads are selected probabilistically based on the ratio of each node's remaining energy, environmental factors, and average network energy. Nodes with higher initial and remaining energy are more likely to be selected as cluster heads than nodes with lower energy, and nodes with higher environmental factors are more likely to be selected as cluster heads than nodes with lower environmental factors.

[0069] In a heterogeneous network, it is assumed that the monitored environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q;For a certain node i, its comprehensive environmental factors are as shown in formula (3); By introducing environmental factors into the probability calculation formula for selecting cluster heads, the probability of nodes in a superior environment being selected as cluster heads is higher; The probability calculation method for the probability of a node being selected as a cluster head in the network is as shown in formula (4):

[0070]

[0071] In the formula, E i (r) represents the remaining energy of node i in the r-th round. Let represent the average energy of the network in the r-th round, which can be obtained from formula (5):

[0072]

[0073] Nodes with more energy have a greater chance of becoming cluster heads; therefore, the network's energy consumption is balanced during operation. If node i's comprehensive environmental impact factor U... m When (i) equals 0, it indicates that the node is in a harsh environment, which prevents the node from working properly. Therefore, the probability P of this node being elected as the cluster head is... i =0; if U m (i) When it equals 1, it indicates that the node is in a good environment and environmental factors will not affect the node; if U m When (i)∈(0,1), P i with U m (i) is a positive correlation, U m The larger (i) is, the more P i The larger, conversely, the larger U m (i) The smaller P is, the better i The smaller the value; because the initial energy and environment of each node in a heterogeneous network are different, the probability of each node becoming a cluster head is different, and the probability P of a node becoming a cluster head is... i The relationship between the threshold Y(i) and the threshold is shown in equation (6):

[0074]

[0075] In the formula, G represents the set of nodes in the network that have not yet been elected as cluster heads, and i is the node number, i∈[1,N]. When node i∈G, in each round r, when node i finds that it is qualified to become a cluster head, it will choose a random number between 0 and 1. If the number is less than the threshold Y(i), then the node becomes the cluster head of this round. In this way, the network energy can be balanced and the network's survivability in harsh environments can be improved.

[0076] Step S103, route optimization.

[0077] During the data routing phase, the potential field is characterized by the fusion of parameters such as the distance from the sensor node to the convergence point, the remaining energy, and the environmental information. A comprehensive potential field for data transmission is generated by superimposing the distance field, the remaining energy field, and the environmental field.

[0078] First, construct the environmental factors for node perception, as shown in formula (1). Since harsh environments can have adverse effects on nodes, the environmental field is set as a repulsive force field. To ensure that dangerous areas are avoided during data transmission, the repulsive force generated by the environmental field increases as the environmental factors decrease. The environmental repulsive force field is shown in formula (7):

[0079]

[0080] In the formula, U H (i) is the environmental repulsive force field of node i, and U(i) is the environmental influence factor of node i; the repulsive force is the derivative of the repulsive force field with respect to T(i), and T(r) is the environmental factor value of the region where node i is located; since it is a repulsive force, it takes a negative value, as shown in formula (8):

[0081]

[0082]

[0083] In the formula, A, B, a, b are as shown in formula (2); therefore, the harsher the environment, the greater the repulsive force on the data packet, and the data transmission avoids passing through dangerous areas to ensure the safety of the transmission path;

[0084] Assume the monitored environment is affected by q environmental factors T. α (i), α=1,2…,q influence, that is, there are q environmental factors U α (i), α=1,2…,q, For a certain node i, its comprehensive environmental field is as shown in formula (10):

[0085]

[0086] Obviously, for a certain node i, its comprehensive environmental repulsion force is the derivative of the environmental field with respect to the environmental factor value, as shown in formula (11):

[0087]

[0088] In the formula, U α (i) represents the influence factor of the αth environmental influence on node i, T α (i) is the value of the influence factor on node i under the αth environmental influence, F Hm (i) represents the overall environmental repulsion force at node i;

[0089] Secondly, an attraction field is established. Wireless sensor networks are centered on data transmission, and all data monitored by all nodes must be transmitted to the aggregation node. Therefore, the aggregation node attracts the monitored data, guiding the data to be transmitted to it, and the data is eventually transmitted to the aggregation node. In order to make the network energy consumption uniform and extend the network lifetime, nodes with high energy attract data, guiding the data to be transmitted to them. In summary, the distance field and the residual energy field are regarded as attraction fields.

[0090] To ensure that monitoring data from nodes far from the aggregation node can be successfully transmitted to the aggregation node, the attractive force generated by the distance field increases with the distance. The distance attractive force field is constructed as shown in formula (12):

[0091]

[0092] In the formula, U D (i) is the distance attraction field of node i, k is the distance scaling factor, and D is Let represent the distance between node i and the sink node; the distance attraction is the derivative of the distance attraction field with respect to distance, as shown in formula (13):

[0093]

[0094] The farther a node is from the sink node, the greater the attraction it receives. Therefore, the data monitored by the nodes are eventually transmitted to the sink node. When the network energy is consumed evenly, the energy utilization rate can be improved, greatly extending the network's lifespan. To ensure balanced network energy consumption, the gravitational force generated by the energy field is positively correlated with the node energy. The higher the energy of the node, the greater the gravitational force it receives, and the more forwarding tasks it undertakes. Therefore, the constructed energy gravitational field is as shown in formula (14):

[0095]

[0096] In the formula, U E (i) is the energy gravitational field, l is the energy scale factor, and E(i) represents the residual energy of node i; the energy gravity is the derivative of the energy gravitational field with respect to energy, as shown in formula (15):

[0097]

[0098] The more energy a node has remaining, the greater the gravitational pull it experiences. Therefore, during data transmission, nodes with high remaining energy are selected as the next hop to balance network energy consumption and enhance network resilience. The potential field is characterized by the fusion of the distance between the sensor node and the sink node, remaining energy, and environmental factors. Finally, the comprehensive potential field for data transmission is generated by superimposing the distance field, the remaining energy field, and the environmental field. The comprehensive potential field is the superposition of the repulsive field and the gravitational field, as shown in formula (16).

[0099] U S (i)=U Hm (i)+U D (i)+U E (i) (16)

[0100] The combined force is the superposition of repulsive and attractive forces, as shown in formula (17):

[0101] F S (i)=F Hm (i)+F D (i)+F E (i) (17)

[0102] With the guidance of the combined forces, data can be safely transmitted to the aggregation node, bypassing dangerous areas, thus enhancing the network's resilience in harsh environments.

[0103] The resilience of a network can be measured by comparing it with the DEEC algorithm and analyzing the number of dead nodes and the amount of data transmitted, as shown in the appendix to the manual. Figures 2-3 As shown.

[0104] The node death rate of the EFRP algorithm proposed in this invention is much lower than that of the DEEC algorithm. (See attached specification.) Figure 2 As shown, all nodes in the EFRP algorithm failed by round 3600, while all nodes in the DEEC algorithm failed by round 3000. This means the EFRP algorithm's lifespan was extended by approximately 20% compared to the DEEC algorithm. Due to the influence of harsh environments, nodes in dangerous areas could not function properly. The DEEC algorithm, which did not consider environmental factors, resulted in approximately 30% of its nodes failing prematurely. However, the EFRP algorithm took environmental factors into account, so only about 30% of its nodes failed by round 2300. The EFRP algorithm extended the network's efficient operating time by 1800 rounds and extended the network's lifespan by 600 rounds. The EFRP algorithm fully considered the impact of environmental factors in data aggregation and routing decisions, balancing energy consumption and avoiding dangerous areas, thus significantly extending the network's lifespan and improving its resilience in harsh environments.

[0105] Data transmission volume is a crucial indicator of network performance; larger data volumes facilitate better subsequent early warning and control efforts. A comparison of data transmission volumes between the EFRP and DEEC algorithms is provided in the appendix of the manual. Figure 3 As shown, calculations show that the EFRP algorithm, after 5000 iterations, transmits a total of 184928 bits of data. Compared to the 68095 bits transmitted by the DEEC algorithm, this represents 116833 more bits, or 1.72 times more data. Therefore, the method proposed in this invention can effectively improve the resilience of sensor networks.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An environment-integrated routing method to enhance network resilience, characterized in that: Taking the most common temperature as an example, a mathematical model is performed to address the impact of the environment on sensor nodes. Sensor nodes have a normal operating temperature range, and they cannot function properly at extremely high or low temperatures. When the ambient temperature deviates from this normal operating temperature range, the performance of the sensor nodes will decrease accordingly. Therefore, an environmental impact factor U(i) is defined as shown in formula (1): Where A, B, C, a, b, c are as shown in formula (2): Where U(i) is the environmental impact factor of sensor node i, [T L ,T H [T] represents the normal operating range of the node. min ,T max ] represents the survival range of a node; if the temperature T(i) of the environment in which node i is located is within [T L ,T H Within a certain range, the performance of node i can be considered unaffected by the environment, and the environmental factor U(i) is set to 1; conversely, the performance of node i is affected by the environment and decreases accordingly; if the temperature T(i) of the environment where node i is located is within [T... min ,T max Outside the range, it can be considered that the harsh environment causes node i to completely fail, and the environmental factor U(i) is set to 0; Many environmental factors affect the normal operation of nodes, such as temperature, humidity, wind, and electromagnetic interference. Sensor nodes can easily acquire specific environmental information about their surroundings not only through their own sensing modules but also through information exchange with neighboring nodes to obtain various environmental information from adjacent areas. Therefore, it is necessary to establish a comprehensive environmental factor system to accurately characterize the actual monitoring environment. Assume that the monitoring environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q;For a certain node i, its comprehensive environmental factors are as shown in formula (3): If multiple environmental factors of node i are low, its overall environmental factor is even lower, indicating that the area is affected by multiple adverse environmental factors and is a dangerous area; if a certain environmental factor is 0, its overall environmental factor is 0, the area is considered a restricted area, and the node cannot be used as the next hop; conversely, if multiple environmental factors are high, its overall environmental factor is high, indicating that the environment in the area is good and is a normal area. During the data aggregation phase, cluster heads are selected probabilistically based on the ratio of each node's remaining energy, environmental factors, and average network energy. Nodes with higher initial and remaining energy are more likely to be selected as cluster heads than nodes with lower energy, and nodes with higher environmental factors are more likely to be selected as cluster heads than nodes with lower environmental factors. In a heterogeneous network, it is assumed that the monitored environment is affected by q environmental factors, i.e., there are q environmental factors U. α (i), α=1,2…,q;For a certain node i, its comprehensive environmental factors are as shown in formula (3); By introducing environmental factors into the probability calculation formula for selecting cluster heads, the probability of nodes in a superior environment being selected as cluster heads is higher; The probability calculation method for the probability of a node being selected as a cluster head in the network is as shown in formula (4): In the formula, E i (r) represents the remaining energy of node i in the r-th round. Let represent the average energy of the network in the r-th round, which can be obtained from formula (5): Nodes with more energy have a greater chance of becoming cluster heads; therefore, the network's energy consumption is balanced during operation. If node i's comprehensive environmental impact factor U... m When (i) equals 0, it indicates that the node is in a harsh environment, which prevents the node from working properly. Therefore, the probability P of this node being elected as the cluster head is... i =0; if U m (i) When it equals 1, it indicates that the node is in a good environment and environmental factors will not affect the node; if U m When (i)∈(0,1), P i with U m (i) is a positive correlation, U m The larger (i) is, the more P i The larger, conversely, the larger U m (i) The smaller P is, the better i The smaller the value; because the initial energy and environment of each node in a heterogeneous network are different, the probability of each node becoming a cluster head is different, and the probability P of a node becoming a cluster head is... i The relationship between the threshold Y(i) and the threshold is shown in equation (6): In the formula, G represents the set of nodes in the network that have not yet been elected as cluster heads, and i is the node number, i∈[1,N]. When node i∈G, in each round r, when node i finds that it is qualified to become a cluster head, it will choose a random number between 0 and 1. If the number is less than the threshold Y(i), then the node becomes the cluster head of this round. In this way, the network energy can be balanced and the network's survivability in harsh environments can be improved. During the data routing phase, the potential field is characterized by the fusion of parameters such as the distance from the sensor node to the convergence point, the remaining energy, and the environmental information. A comprehensive potential field for data transmission is generated by superimposing the distance field, the remaining energy field, and the environmental field. First, construct the environmental factors for node perception, as shown in formula (1). Since harsh environments can have adverse effects on nodes, the environmental field is set as a repulsive force field. To ensure that dangerous areas are avoided during data transmission, the repulsive force generated by the environmental field increases as the environmental factors decrease. The environmental repulsive force field is shown in formula (7): In the formula, U H (i) is the environmental repulsive force field of node i, and U(i) is the environmental influence factor of node i; the repulsive force is the derivative of the repulsive force field with respect to T(i), and T(i) is the environmental factor value of the region where node i is located; since it is a repulsive force, it takes a negative value, as shown in formula (8): In the formula, A, B, a, b are as shown in formula (2); therefore, the harsher the environment, the greater the repulsive force on the data packet, and the data transmission avoids passing through dangerous areas to ensure the safety of the transmission path; Assume the monitored environment is affected by q environmental factors T. α (i), α=1,2…,q influence, that is, there are q environmental factors U α (i), α=1,2…,q, For a certain node i, its comprehensive environmental field is as shown in formula (10): Obviously, for a certain node i, its comprehensive environmental repulsion force is the derivative of the environmental field with respect to the environmental factor value, as shown in formula (11): In the formula, U α (i) represents the influence factor of the αth environmental influence on node i, T α (i) is the value of the influence factor on node i under the αth environmental influence, F Hm (i) represents the overall environmental repulsion force at node i; Secondly, an attraction field is established. Wireless sensor networks are centered on data transmission, and all data monitored by all nodes must be transmitted to the aggregation node. Therefore, the aggregation node attracts the monitored data, guiding the data to be transmitted to it, and the data is eventually transmitted to the aggregation node. In order to make the network energy consumption uniform and extend the network lifetime, nodes with high energy attract data, guiding the data to be transmitted to them. In summary, the distance field and the residual energy field are regarded as attraction fields. To ensure that monitoring data from nodes far from the aggregation node can be successfully transmitted to the aggregation node, the attractive force generated by the distance field increases with the distance. The distance attractive force field is constructed as shown in formula (12): In the formula, U D (i) is the distance attraction field of node i, k is the distance scaling factor, and D is Let represent the distance between node i and the sink node; the distance attraction is the derivative of the distance attraction field with respect to distance, as shown in formula (13): The farther a node is from the sink node, the greater the attraction it receives. Therefore, the data monitored by the nodes are eventually transmitted to the sink node. When the network energy is consumed evenly, the energy utilization rate can be improved, greatly extending the network's lifespan. To ensure balanced network energy consumption, the gravitational force generated by the energy field is positively correlated with the node energy. The higher the energy of the node, the greater the gravitational force it receives, and the more forwarding tasks it undertakes. Therefore, the constructed energy gravitational field is as shown in formula (14): In the formula, U E (i) is the energy gravitational field, l is the energy scale factor, and E(i) represents the residual energy of node i; the energy gravity is the derivative of the energy gravitational field with respect to energy, as shown in formula (15): The more energy a node has remaining, the greater the gravitational pull it experiences. Therefore, during data transmission, nodes with high remaining energy are selected as the next hop to balance network energy consumption and enhance network resilience. The potential field is characterized by the fusion of the distance between the sensor node and the sink node, remaining energy, and environmental factors. Finally, the comprehensive potential field for data transmission is generated by superimposing the distance field, the remaining energy field, and the environmental field. The comprehensive potential field is the superposition of the repulsive field and the gravitational field, as shown in formula (16). IN S (i)=U Hm (i)+U D (i)+U E (and) (16) The combined force is the superposition of repulsive and attractive forces, as shown in formula (17): F S (i)=F Hm (i)+F D (i)+F E (i) (17) With the guidance of the combined forces, data can be safely transmitted to the aggregation node, bypassing dangerous areas, thus enhancing the network's resilience in harsh environments.

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