Self-adaptive energy-saving routing method for wireless sensor network in node heterogeneous environment

Abstract

The invention relates to a self-adaptive energy-saving routing method for a wireless sensor network in a node heterogeneous environment, and belongs to the field of wireless network routing. Through environment model construction, nodes in a network are divided into a plurality of areas according to functions, energy consumption modes and communication capabilities, the areas comprise an event sensing area, a data forwarding area and a dormancy area, and characteristic analysis is performed on the nodes in each area. According to the dynamic weight distribution, initial weights are distributed for different types of nodes, and the weights are updated in real time according to energy consumption and data traffic, so that the nodes with high energy and good performance are ensured to participate in routing preferentially. According to the routing decision based on the game theory, nodes are regarded as game players, and the optimal next hop node is selected to maximize the overall energy efficiency of the network. Meanwhile, a plurality of candidate paths are generated by adopting a multi-path routing strategy, and energy consumption, delay and reliability are considered.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless network routing, and more particularly relates to an adaptive energy-saving routing method for wireless sensor networks in a node heterogeneous environment. BACKGROUND

[0002] In recent years, with the rapid development of Internet of Things (IoT) and smart environments, wireless sensor networks (WSN) as its core component, have been widely applied. These networks are usually composed of a large number of low-power sensor nodes, used to monitor the environment, collect data, and transmit data to the center node or cloud through wireless means. However, due to the limited energy reserves of these sensor nodes, how to effectively manage energy consumption and prolong the life cycle of the network has become an important research topic.

[0003] Existing routing protocols are often based on static node characteristics and network topology, and do not fully consider the heterogeneity and dynamic changes of nodes. For example, the energy state, processing capacity and communication range of nodes often change over time, which makes the traditional single routing selection strategy face challenges in practical applications. In addition, the functions and tasks of nodes in different areas also have obvious differences, such as event sensing, data forwarding and sleep state, which require routing strategies to be flexible and adaptable to different network environments and conditions.

[0004] Therefore, there is an urgent need for a new adaptive energy-saving routing method that can dynamically adjust routing decisions based on the energy consumption patterns, communication capabilities and working states of nodes. By introducing the concept of game theory, nodes can not only consider their own energy state when selecting the best next-hop node, but also optimize the overall energy efficiency of the network, thereby effectively improving the reliability and efficiency of data transmission and prolonging the service life of wireless sensor networks. SUMMARY

[0005] The present application aims to solve the problem of low energy consumption management and energy efficiency of traditional wireless sensor network routing strategies in a node heterogeneous environment. Existing methods fail to fully consider the dynamic characteristics and regional functional differences of nodes, resulting in deficiencies in energy consumption and data transmission efficiency. By constructing an environmental model and dynamically allocating weights, and introducing game theory for routing decisions, the present application provides an adaptive energy-saving routing method that not only monitors and analyzes the energy state and functional characteristics of nodes in real time, but also flexibly adjusts the routing strategy according to network conditions, thereby improving the overall energy efficiency of the network, prolonging the life cycle of the wireless sensor network, and ensuring the reliability and efficiency of data transmission.

[0006] To achieve the above purpose, the present application adopts the following technical solution: The environment model is constructed by dividing the wireless sensor network into several areas according to the functions, energy consumption patterns and communication capabilities of the nodes. The characteristics of the nodes in each area are analyzed, including the types of nodes and their energy states. The dynamic weight allocation assigns initial weights to each type of node, including the remaining energy, data processing capacity and signal strength. The weights are updated in real time according to the energy consumption and data traffic of the nodes in the network, ensuring that nodes with high energy and good performance participate in routing first. The routing decision based on game theory regards nodes as players, and the strategy of a node is to choose the optimal next-hop node. The goal is to maximize the overall energy efficiency of the network. The multi-path routing strategy generates multiple candidate paths based on the weight values of the current node and the game decision, considering the energy consumption, delay and reliability of the path. The energy-aware adaptive adjustment monitors the energy level of the nodes and the network state in real time, records the energy consumption of each node, and adjusts the routing strategy adaptively based on the monitoring results, redefines the weights and updates the game model.

[0007] In one scheme, the environment model is constructed by dividing the wireless sensor network into several areas according to the functions, energy consumption patterns and communication capabilities of the nodes. These areas are event-aware area, data forwarding area and sleep area. In the event-aware area, nodes are densely distributed and continuously monitor environmental events to meet high energy demand. In the data forwarding area, nodes are responsible for delivering sensing data to the central node or sink node to ensure the reliability and efficiency of data transmission. In the sleep area, nodes are in low-energy state to prolong the service life. In addition, this step also includes the analysis of the characteristics of the nodes in each area, identifying the types of nodes including ordinary sensors, energy harvesting nodes and signal enhancement nodes, and recording the remaining power and charging frequency of each node in detail. Energy state information, so as to provide the basis for dynamic weight allocation and subsequent routing decision, improve the energy efficiency and stability of the network.

[0008] In one scheme, the dynamic weight allocation includes assigning initial weights to each type of node, which considers the remaining energy, data processing capacity and signal strength of the node to quantify the priority of each node. Specifically, by setting the weight calculation formula, the initial weight of the node is adjusted according to different weight coefficients, so that each index reflects its importance in the maximum weight. A dynamic adjustment mechanism is designed to update the weight of the node in real time, ensuring that nodes with sufficient energy and good performance participate in routing first. The dynamic adjustment mechanism determines the dynamic change of the weight by periodically monitoring the energy consumption rate and data flow of the node, so as to reduce the priority of the node when the energy consumption is higher than the set threshold, and increase the weight of the node when the data flow increases.

[0009] In one scheme, the routing decision based on game theory includes: constructing a game model, regarding each node in the network as a player, and the strategy of the node is to select the optimal next-hop node to maximize the overall energy efficiency of the network; First, define the utility function of the node, evaluate the performance of the node according to the current weight and energy consumption of the node, and the node selects the path most beneficial to itself by maximizing the utility function, while promoting the optimization of overall network energy utilization; Solve the Nash equilibrium in the game in an iterative manner to determine the optimal routing strategy of each node, and the equilibrium state ensures that unilateral change of strategy cannot obtain higher utility under the strategy combination of each node; In each iteration, the node reselects the next-hop node according to the current weight and energy state, and in this way, the network gradually converges to a stable Nash equilibrium, ensuring the minimization of energy consumption in the data transmission process.

[0010] In one scheme, the multi-path routing selection strategy includes: generating multiple candidate paths according to the weight value of the current node and the game decision, and the path evaluation comprehensively considers the energy consumption, delay and reliability factors; Specifically, define a comprehensive evaluation function P to evaluate the pros and cons of each path j, the calculation formula includes the energy consumption, delay and reliability of the path, and set weight coefficients to reflect the importance proportion of each factor; By scoring multiple candidate paths, the path with the highest score is selected as the main channel for data transmission to achieve the best data transmission effect under different network conditions; A dynamic flow distribution mechanism is designed to distribute the flow among multiple paths based on the proportional load balancing strategy to ensure uniform distribution of total flow, reduce delay and energy consumption caused by overload of a single path, and prolong the service life of the node.

[0011] In one scheme, the energy-aware adaptive adjustment includes: monitoring the energy level and energy consumption of each node in the network in real time to master the performance changes of the node and the overall energy status of the network; define an energy monitoring function to represent the energy state of node i at time t, including the initial energy of the node and the energy consumption of the node in the data transmission process; According to the monitoring result, the routing strategy is adaptively adjusted, the node weight is redefined, and the game model is updated to maintain the high efficiency of the network; The re-computation of the weight follows the previous weight calculation formula, so as to reflect the change of the node priority; The utility function of the target node is dynamically updated to adapt to the change of the energy level.

[0012] In one scheme, the multi-path routing strategy evaluates and optimizes the pros and cons of the candidate paths by combining the comprehensive evaluation function of the path.

[0013] In one scheme, the energy-aware adaptive adjustment includes setting a threshold, when the node energy is lower than the value, the node can choose to stop routing or preferentially select the neighbor with the highest energy, thereby avoiding data loss caused by insufficient energy.

[0014] The present application has the following beneficial effects: The adaptive energy-saving routing method of the present application has the following beneficial effects. First, by monitoring the energy state and functional characteristics of the nodes in real time, the routing selection can be dynamically adjusted to minimize energy consumption, thereby effectively prolonging the overall life cycle of the wireless sensor network. Second, with the introduction of game theory, the nodes can intelligently collaborate in routing decision-making, optimizing data transmission paths and improving network reliability and stability. In addition, the present application can flexibly adapt to different environmental and load conditions, providing higher routing efficiency and data transmission quality in the case of strong node heterogeneity. This adaptive strategy not only improves the energy utilization rate of the network, but also reduces potential communication delay, meeting the demand for efficient and reliable wireless sensor networks in practical applications. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The method flowchart of the present application. DETAILED DESCRIPTION

[0016] For the sake of understanding the present application, a more complete description of the present application will be made with reference to the relevant drawings. The drawings show typical embodiments of the present application. However, the present application can be realized in many different forms and is not limited to the embodiments described in the present application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0017] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used in the present application are only for the purpose of describing the specific embodiments of the present application and are not intended to limit the present application. For the sake of understanding the present application, a more complete description of the present application will be made with reference to the relevant drawings. The drawings show typical embodiments of the present application. However, the present application can be realized in many different forms and is not limited to the embodiments described in the present application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0018] As Figure 1 shown, a self-adaptive energy-saving routing method for wireless sensor networks in a node-heterogeneous environment, the specific implementation steps are as follows: Step 1: Environment model construction Wireless sensor networks are divided into several areas (heterogeneous node groups) according to the function, energy consumption mode and communication ability of nodes, such as event sensing area, data forwarding area and sleep area.

[0019] First, the environment model of the wireless sensor network needs to be constructed, and the environment division is the key first step. In order to effectively manage and optimize network resources, the wireless sensor network is divided into several areas according to the function, energy consumption mode and communication ability of nodes, which are called heterogeneous node groups. In actual operation, several main areas can be identified: event sensing area, data forwarding area and sleep area. The event sensing area is where nodes are densely distributed, mainly used for monitoring specific environmental events such as temperature changes or human activities. These nodes usually need higher energy supply to maintain continuous data collection capability. The data forwarding area is where the nodes that act as relays are located, and these nodes are responsible for delivering the sensed data to the central node or sink node. Their selection is crucial for the reliability and efficiency of data transmission. The sleep area is where nodes in the network are in low-energy state, and these nodes enter sleep mode when they do not need to sense or forward data, in order to prolong their service life.

[0020] Characteristic analysis of nodes in each area, including node type (such as ordinary sensor, energy harvesting node, signal enhancement node) and energy state (power, charging frequency).

[0021] After the environment is divided, the next step is to analyze the characteristics of the nodes in each area. The focus of this analysis is to identify the characteristics of each node type, including ordinary sensors, energy harvesting nodes and signal enhancement nodes. Ordinary sensors are mainly responsible for collecting environmental information, while energy harvesting nodes have the ability to collect energy from the environment (such as solar energy), which helps to improve the self-power supply capability of the network. Signal enhancement nodes are responsible for improving the signal quality and coverage of the network, especially in areas with weak signals. In addition to node type, energy state is also an important part of node characteristic analysis. The remaining power and charging frequency information of each node need to be recorded in detail to obtain the energy consumption mode of the node. These data will provide important basis for subsequent dynamic weight allocation and routing decision, so that the entire network can adaptively adjust its working strategy under different environmental conditions, thereby improving energy efficiency and network stability. Through the construction of the environment model and the analysis of the characteristics of the nodes, a solid foundation can be laid for the realization of an efficient self-adaptive energy-saving routing method.

[0022] Step 2: Dynamic Weight Allocation Assign initial weights to each node type, including energy remaining, data processing capability, signal strength, to quantify the priority of each node.

[0023] A dynamic weight allocation mechanism will be implemented to ensure that each node in the wireless sensor network can adjust its priority in participating in routing flexibly according to its characteristics and network conditions. First, in the weight calculation process, we need to assign initial weights to each type of node. The calculation of this weight takes into account multiple factors, including energy remaining, data processing capability and signal strength. We can define the initial weight of each node in the following form:

[0024] where, represents the remaining energy of the node, represents the data processing capability of the node, is the signal strength of the node, and , , are the corresponding weight coefficients, which reflect the importance of each indicator to the final weight. In the implementation process, these weight coefficients can be adjusted according to the actual network demand and node characteristics, to ensure that the final weight can reasonably reflect the relative priority of each node.

[0025] Design a dynamic adjustment mechanism to update the weight in real time according to the energy consumption and data traffic of the nodes in the network, to ensure that nodes with high energy and good performance participate in routing first.

[0026] Specifically, a periodic monitoring mechanism can be set up to decide the dynamic adjustment of the weight by collecting the energy consumption rate and data traffic of the nodes. The weight of the node can be updated in real time using the following formula:

[0027] In this formula, is the updated weight, is a regulation factor to quantify the impact of energy consumption on the weight, is a tuning factor that quantifies the gain in weight for data traffic. In this way, nodes with higher energy consumption will automatically reduce their priority, while in high data traffic situations, the weight of the node will be increased. This dynamic adjustment mechanism ensures that nodes with higher energy and better performance can participate in routing preferentially, thereby improving the overall energy efficiency of the network and the reliability of data transmission. Through continuous monitoring and adjustment, the network can adaptively respond to different workloads and environmental changes to achieve efficient resource utilization and longer network life cycle.

[0028] Step 3: Routing decision based on game theory The game model is constructed, considering nodes as players, and the strategy of a node is to choose the optimal next-hop node, aiming to maximize the overall energy efficiency of the network.

[0029] A routing decision model is constructed based on game theory to optimize data transmission efficiency in wireless sensor networks. First, in the construction phase of the game model, each node in the network is considered as a player. The strategy of each node is to choose an optimal next-hop node to effectively transmit data to the target location. The goal of the game is to maximize the overall energy efficiency of the network, which means that when choosing a path, the energy consumed in the data transmission process should be minimized.

[0030] To achieve this goal, the utility function of the node needs to be defined. The utility function of node i can be represented by the following formula :

[0031] Here, represents the current weight of node i, and represents the energy consumption of node i when choosing the next-hop node. By maximizing the utility function, the node can choose the most beneficial next-hop path for itself, while also positively affecting the overall energy efficiency of the network.

[0032] The Nash equilibrium in the game is solved iteratively to determine the optimal routing strategy for each node in the network, so that the overall network energy consumption is minimized.

[0033] During the evolution of the game, nodes will influence each other and adjust their strategies. In this process, the solution of Nash equilibrium is particularly important. Nash equilibrium represents that under a certain combination of strategies, each node cannot obtain higher utility by changing its strategy unilaterally. This can be achieved by constructing an iterative algorithm. In each iteration step, all nodes will re-evaluate and select their optimal next-hop node based on the current weight and energy state. This process can be represented by the following formula:

[0034] In this formula, is the strategy selection of node i after the th iteration, represents the possible next hop node, indicates the selection of the node that can maximize the utility. By continuously iterating this process, a Nash equilibrium state can be converged, that is, the strategies of all nodes are in a stable state and cannot be improved by unilateral strategy changes.

[0035] Through this game theory-based method, each node in the network can make intelligent decisions based on its own state and network environment, maximizing the overall energy efficiency. This method not only improves the efficiency of data transmission, but also effectively prolongs the service life of the network, providing an effective theoretical basis and implementation approach for building adaptive energy-saving routing.

[0036] Step 4: Multi-path routing strategy According to the weight value of the current node and the game decision, generate multiple candidate paths, considering the energy consumption, delay and reliability of the path.

[0037] First, in the path generation phase, multiple candidate paths are generated based on the weight value of the current node and the game decision. The selection of these candidate paths needs to consider the energy consumption, delay and reliability factors of the path. To achieve this goal, a comprehensive evaluation function P can be defined to evaluate the pros and cons of each path j, the specific formula is as follows:

[0038] In this formula, Ej represents the energy consumption of path j, Dj represents the delay of the path, Rj is the reliability of the path, , , is the corresponding maximum value for normalization. The weight coefficient , , indicates the importance proportion of each factor to path selection. After scoring multiple candidate paths, the path with higher score can be selected as the main channel for data transmission, ensuring the best data transmission effect under different network conditions.

[0039] Design a mechanism to dynamically allocate traffic among multiple paths to achieve load balancing and prolong the service life of nodes. This mechanism aims to prevent a single path from being overloaded and effectively prolong the service life of nodes. A load balancing strategy based on proportional allocation is adopted to define the traffic allocation scheme. Let be the total traffic, and The traffic allocation for path j can be achieved using the following formula:

[0040] In this formula, It is the sum of the evaluation values ​​of all candidate paths. By adopting this traffic allocation method based on relative scoring, the load can be evenly distributed across multiple paths, thereby effectively reducing latency and energy consumption caused by overload of a single path, while extending the lifespan of nodes in the network. In addition, the real-time dynamic adjustment of traffic allocation can optimize traffic distribution in a timely manner according to changes in network conditions, ensuring that the network maintains efficient operation under different operating conditions.

[0041] This step enhances the network's fault tolerance and flexibility, as well as its overall energy efficiency, enabling wireless sensor networks to better adapt to complex and changing environmental conditions. This multi-path routing strategy provides a solid foundation for future network optimization and intelligent decision-making.

[0042] Step 5: Adaptive Adjustment of Energy Sensing Real-time monitoring of node energy levels and network status is performed, recording the energy consumption of each node. This process involves recording node energy consumption to promptly understand node performance changes and the overall network energy status. An energy monitoring function is defined. Let represent the energy state of node i at time t. This function can be expressed as:

[0043] In this formula, It is the initial energy of node i. This is the energy consumed by node i when it sends data to other node k at time t. Through this monitoring mechanism, the network can dynamically grasp the energy status of each node and provide data support for subsequent routing strategy adjustments.

[0044] Based on monitoring results, the routing strategy is adaptively adjusted, weights are redefined, and the game theory model is updated to maintain the network's high-efficiency operation. This includes redefining weights and updating the game theory model to address decreases in node energy and changes in network state. The recalculation of node weights can be adjusted based on monitored energy levels and consumption, using the weight calculation formulas discussed earlier. :

[0045] Here, with energy levels As the value of a node decreases, its weight will also decrease accordingly, reflecting a reduction in its priority during routing. Simultaneously, the game model is dynamically updated based on the node's current state. For example, this can be achieved by adjusting the utility function of the target node. :

[0046] In this formula, and These are weights and energy levels that change over time. By continuously updating the utility function, nodes can adjust their next hop selection in real time.

[0047] In addition, a threshold is set. When a node's energy level falls below this threshold, the node can choose to stop participating in routing or prioritize selecting the neighbor with the highest energy as its next-hop node, thus avoiding data loss due to insufficient energy. This process ensures that the network can maintain stable transmission performance and efficient resource utilization even when faced with fluctuations in node energy.

[0048] Through this energy-aware adaptive adjustment mechanism, the network can respond to the challenges posed by energy consumption in real time, optimize routing strategies, and improve the overall reliability and efficiency of the network. This step provides strong support for dynamic and intelligent wireless sensor networks, ensuring their efficient operation in complex environments.

[0049] Example: To better illustrate the adaptive energy-saving routing method of the present invention, the following is a specific embodiment demonstrating how the method can be applied in a wireless sensor network environment and providing corresponding data.

[0050] Suppose we deploy multiple wireless sensor nodes in a farmland irrigation monitoring system. These nodes are primarily responsible for monitoring soil moisture, temperature, and light intensity, and transmitting the data to a central node for data analysis and decision-making. The network has 20 nodes, each with different energy reserves, processing capabilities, and communication ranges.

[0051] Network Topology In this embodiment, the distribution of our sensor nodes is shown in Table 1 below: Table 1 Sensor Node Distribution

[0052] Data collection and monitoring In actual operation, each node periodically collects environmental data and determines energy consumption. Table 2 shows the environmental data collected at a specific moment: Table 2 Environmental Data Table

[0053] Routing decision process In this embodiment, the routing decision follows these steps: 1. Energy Assessment: Calculate the energy consumption of each node for transmitting data based on its remaining energy and the size of the data packets. Assume the energy consumption for transmitting one data packet is 0.1 mAh, and for receiving it is 0.05 mAh.

[0054] 2. Game Theory Modeling: Using a game theory model, the payoff and cost of each node are evaluated to determine the optimal next-hop node. It is assumed that each node allocates its payoff based on transmission delay, energy consumption, and data integrity.

[0055] 3. Dynamic route adjustment: Based on real-time monitoring data and energy assessment results, the best next-hop node is dynamically selected to form an effective data transmission path.

[0056] Table 3 below shows the changes in energy consumption and remaining energy of the nodes after a period of time (1 hour) following the application of the adaptive energy-saving routing method: Table 3. Changes in Energy Consumption and Remaining Energy

[0057] Through the above embodiments, we can see that the adaptive energy-saving routing method of the present invention can effectively extend the life cycle of wireless sensor networks and improve the reliability of data transmission and energy efficiency. This method is not only applicable to specific application scenarios such as farmland monitoring, but can also be extended to other types of wireless sensor networks for wide application.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM).

[0059] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or substitute some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment, characterized in that: The method includes: Environmental model construction: The wireless sensor network is divided into several regions according to the function of the nodes, energy consumption patterns and communication capabilities; the characteristics of the nodes in each region are analyzed, including node type and energy status; Dynamic weight allocation: Assign initial weights to each node type, including remaining energy, data processing capability, and signal strength; update weights in real time based on the energy consumption and data traffic of nodes in the network to ensure that nodes with high energy and good performance participate in routing first; Game theory-based routing decision-making: Treating nodes as players, each node's strategy is to choose the optimal next-hop node, with the goal of maximizing the overall energy efficiency of the network. Multi-path routing strategy: Based on the weight value of the current node and the game decision, multiple candidate paths are generated. Path evaluation takes into account the energy consumption, delay and reliability of the path. Adaptive adjustment based on energy perception: Real-time monitoring of node energy levels and network status, recording the energy consumption of each node; based on the monitoring results, adaptive adjustment of routing strategies, redefining weights, and updating the game model.

2. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: The environmental model construction includes: dividing the wireless sensor network into several regions based on the function, energy consumption pattern, and communication capability of the nodes, including an event sensing region, a data forwarding region, and a dormant region; in the event sensing region, nodes are densely distributed and continuously monitor environmental events; in the data forwarding region, nodes are responsible for transmitting sensing data to the central node or aggregation node; in the dormant region, nodes are in a low-energy state to extend their service life. The construction of the environmental model also includes the characteristic analysis of nodes in each region, identifying node types including ordinary sensors, energy harvesting nodes and signal enhancement nodes, and recording the remaining power and charging frequency energy state information of each node in detail, thereby providing a basis for dynamic weight allocation and subsequent routing decisions.

3. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: The dynamic weight allocation includes: assigning an initial weight to each node type, which comprehensively considers the node's remaining energy, data processing capability, and signal strength to quantify the priority of each node, including the following sub-steps: By setting a weight calculation formula, the initial weight of a node is adjusted according to different weight coefficients, so that each indicator reflects its importance in the final weight. Design a dynamic adjustment mechanism to update node weights in real time, ensuring that nodes with sufficient energy and good performance participate in routing first. The dynamic adjustment mechanism determines the dynamic change of weight by periodically monitoring the energy consumption rate and data traffic of nodes, so as to reduce the priority of nodes when energy consumption exceeds a set threshold and increase the weight of nodes when data traffic increases.

4. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: The game theory-based routing decision includes: constructing a game model, treating each node in the network as a player, and the node's strategy is to select the optimal next-hop node to maximize the overall energy efficiency of the network; First, the utility function of a node is defined. The performance of a node is evaluated based on its current weight and energy consumption. Nodes choose the most advantageous path for themselves by maximizing the utility function, thereby promoting the optimization of the overall network energy utilization. An iterative approach is used to solve the Nash equilibrium in the game, determining the optimal routing strategy for each node. The equilibrium state ensures that, under the combination of strategies of each node, unilaterally changing the strategy cannot obtain higher utility. In each iteration, the node reselects the next hop node based on its current weight and energy state. In this way, the network gradually converges to a stable Nash equilibrium, ensuring that energy consumption is minimized during data transmission.

5. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: Multiple candidate paths are generated based on the current node's weight and the game decision, including the following sub-steps: Define a comprehensive evaluation function P to evaluate the merits of each path j. The calculation formula includes the energy consumption, delay and reliability of the path, and set weight coefficients to reflect the importance ratio of each factor. By scoring multiple candidate paths, the path with the highest score is selected as the main channel for data transmission. Design a dynamic traffic distribution mechanism: Distribute traffic among multiple paths based on a proportional load balancing strategy.

6. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: The adaptive adjustment of energy sensing includes: real-time monitoring of the energy level and energy consumption of each node in the network to understand the performance changes of the nodes and the overall energy status of the network; defining an energy monitoring function to represent the energy state of node i at time t, including the node's initial energy and its energy consumption during data transmission; Based on the monitoring results, the routing strategy is adaptively adjusted, node weights are redefined, and the game model is updated. The weights are recalculated following the previous weight calculation formula to reflect changes in node priority. Dynamically update the utility function of the target node.

7. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 1, characterized in that: The multi-path routing strategy described above evaluates and optimizes candidate paths by combining a comprehensive evaluation function of the paths.

8. The adaptive energy-saving routing method for wireless sensor networks in a heterogeneous node environment according to claim 6, characterized in that: The adaptive adjustment of energy perception includes setting a threshold value, which allows a node to choose to stop routing or prioritize the neighbor with the highest energy when the node's energy is below that value.

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