Energy efficiency optimization method for wireless sensor network

Abstract

The invention is applicable to the technical field of wireless sensors, and particularly relates to a wireless sensor network energy efficiency optimization method, which comprises the following steps of: constructing a data model of a wireless sensor network; obtaining an original adjacent matrix and the residual energy of each sensor node, dividing the sensor nodes into different energy levels according to the difference of the residual energy, and deploying the sensor nodes in energy spaces of different dimensions; the sink node calculates a route from the node to the sink node in the lowest energy space, and calculates a route from the node to the sink node in the higher-dimensional space layer by layer until a complete path from all the nodes to the sink node is established; and performing spatial migration on the sensor nodes based on a preset spatial migration principle, and updating the connection relationship, the residual energy, the energy space and the hierarchical information of the nodes of the whole network. According to the method, residual energy is introduced for dynamic evaluation, a space communication mechanism is established, and a simulation result shows that energy consumption can be remarkably balanced, the network service life can be prolonged, and the energy efficiency can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless sensors, and in particular relates to a method for optimizing energy efficiency of a wireless sensor network. Background Art

[0002] A wireless sensor network (WSN) is a network system composed of a large number of low-power, miniaturized sensor nodes interconnected via wireless communication technology. These nodes can sense, collect, and process physical or environmental conditions within the network's coverage area, such as temperature, humidity, pressure, and sound, and transmit this information to users or other application systems for further analysis and decision-making. WSNs, with their strong self-organization, fault tolerance, and flexible deployment, are widely used in environmental monitoring, smart agriculture, health monitoring, industrial automation, smart cities, and other fields. With the development of the Internet of Things (IoT), wireless sensor networks, as a key component, are playing an increasingly critical role.

[0003] With the development of wireless communication and sensor technology, wireless sensor networks have attracted more and more attention. In wireless sensor networks, energy consumption, network capacity and network lifetime are extremely limited, and energy consumption has always been an important issue that has not been resolved. Summary of the Invention

[0004] The present invention aims to provide a method for optimizing energy efficiency in wireless sensor networks, aiming to solve the problem that wireless sensor networks are gaining increasing attention with the development of wireless communication and sensor technologies. In wireless sensor networks, energy consumption, network capacity, and network lifetime are all extremely limited, and the energy consumption problem has not yet been solved.

[0005] The present invention is implemented as follows: a method for optimizing energy efficiency of a wireless sensor network, the method comprising:

[0006] Constructing a data model of a wireless sensor network, wherein the data model includes a plurality of sensor nodes, the sensor nodes are divided into a plurality of energy levels, and the entire wireless sensor network is divided into a plurality of energy spaces with different energy levels;

[0007] The original adjacency matrix and the residual energy of each sensor node are obtained. According to the difference in residual energy, the sensor nodes are divided into different energy levels and deployed in energy spaces of different dimensions.

[0008] The sink node calculates the route from the node in the lowest energy space to itself, and calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established;

[0009] The sensor nodes are spatially migrated based on the preset spatial migration principle, and the connection relationship, remaining energy, energy space and hierarchical information of the entire network nodes are updated.

[0010] Preferably, the step of constructing a data model of a wireless sensor network specifically includes:

[0011] Definition of wireless sensor network: N is the number of randomly distributed sensor nodes in a square monitoring area with a side length of L. The initial energy of each sensor node is limited to ε0, the transmission range is R, the entire network is connected, and the sink node is deployed at a fixed position in the middle of the monitoring area. All sensor data flows from the sensor nodes to the sink node.

[0012] Preferably, the sink node calculates the route from the node in the lowest energy space to itself, and calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established, including:

[0013] For the node i and the space S K The subnetwork consists of all nodes in the network, and the ISLs of the nodes outside the subnetwork are marked as NAISLs, and the subnetwork adjacency matrix A is obtained. c , a ij is the subnetwork adjacency matrix A c For elements in , link availability follows the following rules:

[0014] when a ij =a ji =1, the link Make it available;

[0015] when a ij =a ji = 0, the link Set to Unavailable.

[0016] Preferably, if the shortest path from node i to the sink node is found and the next hop node NH is determined i , then move node i to NH i The links of node i are set as AISLs, and the rest of ISLs are set as NAISLs. If the path search fails, nodes with lower dimensional energy space are added to the sub-network and the process is repeated. When the energy space ES of node i is i When all nodes in the sub-network are added and there is still no valid path, the node is reduced to a lower energy space and A is updated. c Then repeat this process.

[0017] Preferably, after completing the all-node routing calculation, the aggregation node encapsulates the routing information into a data packet and distributes it to all nodes through the hop-by-hop transmission mechanism of SDWSNs.

[0018] Preferably, in the step of spatially migrating the sensor nodes based on the preset spatial migration principle and updating the connection relationship, residual energy, energy space and hierarchical information of the nodes in the entire network, the aggregation node periodically detects the node status: when the residual energy RE of node i is i Lowering the energy level NH i When descending, if the dimension of its current energy space is too high, it will be migrated to a lower-dimensional space. Then the aggregation node will update the connection relationship, remaining energy, energy space and hierarchical information of the nodes in the entire network, and recalculate the routing.

[0019] Preferably, in the inter-space links, according to the adjacency matrix A, there are multiple ISLs between two energy spaces, and these ISLs are divided into two categories: available inter-space links AISLs and unavailable inter-space links NAISLs. AISLs are used to transmit data packets between different energy spaces.

[0020] Preferably, the step of constructing a data model of a wireless sensor network further includes:

[0021] Define the adjacency matrix. After topology discovery, the sink node obtains the connection relationship between sensor nodes, that is, the network topology graph. The adjacency matrix A is defined by the network topology:

[0022] (1) A is an N+1-order square matrix;

[0023] (2) The elements in A satisfy:

[0024]

[0025] d ij represents the distance between nodes i and j, R max represents the maximum transmission distance, then A is a symmetric matrix.

[0026] Preferably, the step of constructing a data model of a wireless sensor network further includes:

[0027] Definition of energy level: The energy status of the sensor node is divided into K energy levels according to the residual energy, and the interval of each energy level is ε0 / K. If the residual energy of the sensor node is between [(n-1)ε0 / K,nε0 / K], the energy level of the node is called n.

[0028] Preferably, the step of constructing a data model of a wireless sensor network further includes:

[0029] Definition of energy level: The energy status of the sensor node is divided into K energy levels according to the residual energy, and the interval of each energy level is ε0 / K. If the residual energy of the sensor node is between [(n-1)ε0 / K,nε0 / K], the energy level of the node is called n.

[0030] The present invention provides a wireless sensor network energy efficiency optimization method, which introduces residual energy for dynamic evaluation and establishes a spatial communication mechanism. Simulation results show that the present invention can significantly balance energy consumption, extend network life, and improve energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A conceptual diagram of energy space and inter-space links in SDWSNs provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of node distribution changes from time t1 to time t2 provided in an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of energy distribution under different algorithms provided in an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of the change in the number of dead nodes under different algorithms provided in an embodiment of the present invention;

[0035] Figure 5 A schematic diagram of the energy consumption of the entire network under different algorithms provided in an embodiment of the present invention;

[0036] Figure 6 A schematic diagram of energy consumption of a single data packet under different algorithms provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] With the development of wireless communication and sensor technologies, wireless sensor networks (WSNs) are gaining increasing attention. Energy consumption, network capacity, and network lifetime are all extremely limited in WSNs, and energy consumption remains a significant and unresolved issue. To address this issue, numerous energy-efficiency optimization algorithms have been proposed to improve the energy efficiency of WSNs.

[0039] In SDWSNs, the controller is responsible for generating a flow table for each sensor node to reduce energy consumption during route discovery. Before the controller can take over the sensor nodes, topology discovery must be performed. The controller broadcasts beacon packets to collect sensor node information, and the sensor nodes reply to the controller with packets containing neighbor and energy information. Once the controller understands the connectivity and information of the entire network, it can quickly generate forwarding rules and implement algorithms such as traffic balancing and energy balancing. The multidimensional space-based energy efficiency optimization algorithm proposed in this paper is also used on the controller in SDWSNs. First, a multidimensional energy space is defined, and a new inter-space link communication mechanism is adopted to improve the energy utilization of wireless sensor networks. Simulation results show the superiority of this algorithm over traditional packet transmission schemes.

[0040] An embodiment of the present invention provides a method for optimizing energy efficiency of a wireless sensor network, the method comprising:

[0041] A data model of a wireless sensor network is constructed, wherein the data model includes multiple sensor nodes, the sensor nodes are divided into multiple energy levels, and the entire wireless sensor network is divided into multiple energy spaces with different energy levels.

[0042] In this step, the wireless sensor network in this invention is based on a new network structure called software-defined networking (SDWSNs). The feasibility and effectiveness of SDWSNs have been proven. This article focuses on the energy consumption of sensor nodes rather than the working method of SDWSNs. Therefore, this invention assumes that the sensor network is operating well, and that the sink node, acting as a controller, can control all sensor nodes directly or indirectly. Before the network begins operating, the controller has initiated route discovery to obtain the network topology. Once the sink node completes the topology discovery process, the physical connection relationship between sensor nodes is assumed to be fixed. These assumptions are reasonable in most cases, because almost all sensor nodes are deployed in a fixed manner and the wireless conditions rarely change.

[0043] Assume a wireless sensor network with the following properties: N is the number of randomly distributed sensor nodes within a square monitoring area with side length L. Each sensor node has a finite initial energy of ε0 and a transmission range of R. The entire network is connected, and the sink node is deployed at a fixed location in the middle of the monitoring area. All sensor data flows from the sensor nodes to the sink node. Some important definitions involved in this invention are as follows:

[0044] Adjacency Matrix: After topology discovery, the sink node obtains the connection relationship between sensor nodes - the network topology. The network topology can be used to define the adjacency matrix A with the following conditions:

[0045] (1) A is an N+1-order square matrix;

[0046] (2) The elements in A satisfy:

[0047]

[0048] d ij represents the distance between nodes i and j, R max represents the maximum transmission distance, then A is a symmetric matrix.

[0049] Energy Level: Sensor nodes are divided into several energy levels based on their remaining energy. This paper uses K energy levels, with each level separated by ε0 / K. If a sensor node's remaining energy is between [(n-1)ε0 / K,nε0 / K], the node is said to be at energy level n.

[0050] Energy space: Define K discrete energy spaces S n , n=1,2,...,K, and corresponds to K energy levels one by one. The sensor node with energy level n always sends energy to the energy space S n (or lower energy space) move.

[0051] Inter-space links: Wireless links between nodes in different energy spaces are called inter-space links. ISLs enable cross-space communication. According to the adjacency matrix A, there may be multiple ISLs between two energy spaces. These ISLs can be divided into two categories: active inter-space links (AISLs) and non-active inter-space links (NAISLs). AISLs can be used to transmit data packets between different energy spaces, while NAISLs cannot be used. The specific situation of ISLs is as follows: Figure 1 shown.

[0052] like Figure 1 As shown in Figure 1, the entire network is divided into K energy spaces with different energy levels. Sensor nodes 2 and 4 have to move to a lower space S due to their own low energy levels. K-1 In the figure, node 2 has 3 adjacent nodes and 3 ISLs, but since node 2 is in space S K-1 , and 2 of the 3 links are unavailable, it cannot assist nodes in higher energy space to transmit data packets.

[0053] The original adjacency matrix and the residual energy of each sensor node are obtained. According to the difference in residual energy, the sensor nodes are divided into different energy levels and deployed in energy spaces of different dimensions.

[0054] In this step, it is assumed that the sink node has already broadcast a beacon data packet. After receiving the beacon packet, the sensor node forwards it and returns a response packet to the sink node, which contains the node's neighbor information and remaining energy information. After a short period of time, the sink node will obtain the complete network topology shown in Figure A. Then, based on the wireless sensor network energy efficiency optimization method provided by the present invention, it will optimize the energy allocation of network nodes.

[0055] First, independent logical energy spaces are divided according to energy levels. Sensor nodes are initially deployed into the appropriate energy space dimension based on their energy levels. Nodes in higher-dimensional energy spaces have higher packet forwarding priority. Nodes in lower-dimensional energy spaces, even if physically closer to the sink node, do not forward packets from the higher-dimensional space. Instead, they generate packets and upload them to the higher-dimensional space via AISLs. This mechanism ensures that packets are transmitted along the longest paths formed by nodes with higher energy levels, distributing the communication load to nodes with more residual energy, achieving energy balance and extending the network lifecycle. When a node's residual energy level drops to a lower level, it automatically migrates to a lower-dimensional energy space. When all nodes migrate to the lowest-dimensional space, the network degenerates into a traditional wireless sensor network until critical nodes are depleted of energy.

[0056] Step 1: Initial distribution of sensor nodes

[0057] In the initial stage, the sink node obtains the original adjacency matrix and the residual energy of each sensor node. According to the residual energy difference, the nodes are divided into different energy levels and deployed in energy spaces of different dimensions, such as Figure 1 shown.

[0058] The sink node calculates the route from the node in the lowest energy space to itself, and calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established.

[0059] In this step, Step 2: Generate packet forwarding rules

[0060] The sink node first calculates the route from the node in the lowest energy space to itself, and then calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established. The specific process is as follows:

[0061] For the node i and the space S K The subnetwork consists of all nodes in the network, and the ISLs of the nodes outside the subnetwork are marked as NAISLs, and the subnetwork adjacency matrix A is obtained. c . a ij is the subnetwork adjacency matrix A c For elements in , link availability follows the following rules:

[0062] when a ij =a ji =1, the link Make it available.

[0063] when a ij =a ji = 0, the link Set to Unavailable.

[0064] If the shortest path from node i to the sink node is found and the next hop node NH is determined i , then move node i to NH i The links of are set as AISLs, and the rest of the ISLs are set as NAISLs. If the path search fails, add nodes of lower dimensional energy space to the sub-network and repeat the above process. i When all nodes in the sub-network are added and there is still no valid path, the node is reduced to a lower energy space and A is updated. c Then repeat step 2.

[0065] After completing the full-node routing calculation, the sink node encapsulates the routing information into data packets and distributes them to all nodes through the hop-by-hop transmission mechanism of SDWSNs. The core process of step 2 is shown in Table 1.

[0066]

[0067]

[0068] The sensor nodes are spatially migrated based on the preset spatial migration principle, and the connection relationship, remaining energy, energy space and hierarchical information of the entire network nodes are updated.

[0069] In this step, the spatial migration principles are:

[0070] like Figure 2 As shown in Figure 2, the distribution of sensor nodes will change dynamically. The sink node periodically detects the node status: when the residual energy RE of node i i Lowering the energy level NH i When descending, if the dimension of its current energy space is too high, it will be migrated to a lower-dimensional space. The aggregation node then updates the connection relationship, remaining energy, energy space and layer information of all nodes in the network, and re-executes the routing calculation in step 2.

[0071] There are two typical situations that require special attention:

[0072] First, Figure 2At time t2, node 2 undergoes dimensionality reduction synchronously with node 1. At time t1, the ISLs between node 2 and node 1 are AISLs; while at time t2, the ISLs between node 2 and node 8 are set to AISLs.

[0073] Second, Figure 2 At time t2, node 5 is reduced in dimensionality due to the change in energy level, causing node 6 (actual energy level is K) to be forced to follow the dimensionality reduction because it loses the path through nodes 4 and 5.

[0074] This algorithm handles the above scenarios through the following priority rules: first, nodes in the high-dimensional energy space give priority to forwarding data packets; second, in the same-dimensional space, nodes closer to the aggregation node give priority to forwarding data packets. This mechanism can achieve energy balance more effectively.

[0075] In order to verify the effect of the wireless sensor network energy efficiency optimization method provided by the present invention, simulation and analysis are carried out:

[0076] Energy consumption model:

[0077] This paper analyzes in detail the performance of high energy efficiency algorithms based on multi-dimensional energy space. Assuming that the energy consumption of the transceiver circuit is E elec , the energy consumption of the transmitting amplifier under the free space model is ε fs , the energy consumption of the transmitting amplifier under the multipath model is ε mp , and assume that due to channel fading there are d 2 and d 4 The energy consumption of transmitting an m-bit data packet between nodes with a distance of d is:

[0078]

[0079] The energy consumption of receiving this data packet is:

[0080] E rr (m) = m × E elec (3)

[0081] The proposed algorithm was simulated and validated using MATLAB. Its performance was evaluated by comparing it with traditional shortest path algorithms and energy-aware algorithms. The simulation setup is as follows: N sensor nodes are randomly deployed within a square monitoring area with a side length of L. The sink node is located at the center of the area at coordinates (L / 2, L / 2). Each node generates monitoring data at a uniform rate of p packets per second. Initially, random time slot allocation is used to avoid conflicts, and all packets must ultimately be transmitted to the sink node. While traditional algorithms select the path with the minimum number of hops to the sink node, the energy-aware algorithm selects a path to the sink node based on the number of hops and the remaining energy of the sensor nodes.

[0082] This paper primarily analyzes energy distribution, the number of dead nodes, and energy consumption. By analyzing the remaining energy of nodes at specific locations, we can visually visualize the energy distribution and reflect the network's energy balance. By analyzing the number of energy-depleted nodes (i.e., the number of dead nodes), we can directly reflect the network's lifecycle. And by analyzing the energy consumption of sensor nodes, including the total network energy consumption (the cumulative energy consumption of all nodes in the network) and the energy consumption of transmitting a single data packet, we can reflect energy efficiency. The specific simulation parameters are shown in Table 2.

[0083]

[0084] Energy consumption balance analysis:

[0085] Figure 3 (a)(b)(c) are the residual energy distribution results of the network when different routing algorithms are used under the same simulation time conditions. Through comparative analysis, it can be seen that the algorithm proposed in this paper exhibits the best energy consumption balance characteristics. Figure 3 As shown in (a), the energy consumption rate of the sensor nodes around the sink node remains highly consistent, and they consume energy almost at the same time. The reason is that when the node energy drops to the threshold, the system automatically migrates it to a low-dimensional energy space, thereby effectively reducing its packet forwarding load. This dynamic adjustment mechanism slows down the node energy consumption rate until the surrounding nodes complete the energy space dimensionality reduction. Compared with the traditional algorithm ( Figure 3 (c)), because the shortest path priority strategy causes key nodes to continuously forward data packets and quickly exhaust their energy, while the premature failure of key routing nodes shortens the overall life cycle and reduces network efficiency. Compared with the energy-aware algorithm ( Figure 3 (b)) A dynamic routing adjustment strategy based on residual energy is adopted to achieve energy consumption balance at the basic level.

[0086] Network Lifecycle Analysis:

[0087] pass Figure 4 The curve showing the number of dead nodes over time shows that, if the network lifetime is measured using the time of first node death as the indicator, the traditional routing algorithm that does not consider energy efficiency has the shortest network lifetime. The algorithm proposed in this paper performs better in extending network lifetime. As K increases, the network lifetime increases; when K = 10, the sensor nodes run out of energy almost simultaneously. However, when K is sufficiently large, the performance improvement is not significant. It is important to note that when K = 2, the proposed algorithm has similar performance to the energy-aware algorithm.

[0088] Energy consumption analysis:

[0089] The energy consumption of the entire network is Figure 5 As shown in the figure, the energy consumption of each data packet when using different algorithms is as follows Figure 6 As shown. Figure 5It can be seen that before all sensor nodes become isolated nodes, the energy consumption of the traditional algorithm is the lowest. The reason is that the traditional algorithm always insists on the shortest path transmission, while this algorithm takes energy efficiency into consideration. The number of packet forwarding times may increase because the path to the sink node may not be the shortest path, so the packet forwarding time may increase. Figure 6 It can be seen that during the node isolation period, the traditional algorithm has the highest energy consumption per data packet, and the energy consumption per packet of the present invention is the lowest. However, as K increases, this performance index gradually decreases.

[0090] In summary, the wireless sensor network energy efficiency optimization method proposed in this paper performs better in terms of energy balance, network lifespan, and energy consumption. As the value of K increases, network performance continues to improve, but the algorithm complexity also increases. Therefore, an appropriate value of K can achieve a balance between algorithm complexity and performance improvement.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing energy efficiency of a wireless sensor network, characterized in that: The method comprises: Constructing a data model of a wireless sensor network, wherein the data model includes a plurality of sensor nodes, the sensor nodes are divided into a plurality of energy levels, and the entire wireless sensor network is divided into a plurality of energy spaces with different energy levels; The original adjacency matrix and the residual energy of each sensor node are obtained. According to the difference in residual energy, the sensor nodes are divided into different energy levels and deployed in energy spaces of different dimensions. The sink node calculates the route from the node in the lowest energy space to itself, and calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established; The sensor nodes are spatially migrated based on the preset spatial migration principle, and the connection relationship, remaining energy, energy space and hierarchical information of the entire network nodes are updated.

2. The wireless sensor network energy efficiency optimization method according to claim 1, characterized in that: The step of constructing the data model of the wireless sensor network specifically includes: Definition of wireless sensor network: N is the number of randomly distributed sensor nodes in a square monitoring area with a side length of L. The initial energy of each sensor node is limited to ε0, the transmission range is R, the entire network is connected, and the sink node is deployed at a fixed position in the middle of the monitoring area. All sensor data flows from the sensor nodes to the sink node.

3. The wireless sensor network energy efficiency optimization method according to claim 1, characterized in that: The sink node calculates the route from the node in the lowest energy space to itself, and calculates the route from the node in the higher dimensional space to itself layer by layer until a complete path from all nodes to the sink node is established, including: For the node i and the space S K The subnetwork consists of all nodes in the network, and the ISLs of the nodes outside the subnetwork are marked as NAISLs, and the subnetwork adjacency matrix A is obtained. c , a ij is the subnetwork adjacency matrix A c For elements in , link availability follows the following rules: when a ij =a ji =1, the link Make it available; when a ij =a ji = 0, the link Set to unavailable; a ij is the subnetwork adjacency matrix A c Elements in .

4. The wireless sensor network energy efficiency optimization method according to claim 3, characterized in that: If the shortest path from node i to the sink node is found and the next hop node NH is determined i , then move node i to NH i The links of node i are set as AISLs, and the rest of ISLs are set as NAISLs. If the path search fails, nodes with lower dimensional energy space are added to the sub-network and the process is repeated. When the energy space ES of node i is i When all nodes in the sub-network are added and there is still no valid path, the node is reduced to a lower energy space and A is updated. c Then repeat this process.

5. The wireless sensor network energy efficiency optimization method according to claim 4, characterized in that: After completing the routing calculation for all nodes, the aggregation node encapsulates the routing information into data packets and distributes them to all nodes through the hop-by-hop transmission mechanism of SDWSNs.

6. The wireless sensor network energy efficiency optimization method according to claim 1, characterized in that: In the step of spatially migrating sensor nodes based on the preset spatial migration principle and updating the connection relationship, residual energy, energy space and hierarchical information of the entire network nodes, the aggregation node periodically detects the node status: when the residual energy RE of node i is i Lowering the energy level NH i When descending, if the dimension of its current energy space is too high, it will be migrated to a lower-dimensional space. Then the aggregation node will update the connection relationship, remaining energy, energy space and hierarchical information of the nodes in the entire network, and recalculate the routing.

7. The wireless sensor network energy efficiency optimization method according to claim 2, characterized in that: In the inter-space links, according to the adjacency matrix A, there are multiple ISLs between two energy spaces. These ISLs are divided into two categories: available inter-space links AISLs and unavailable inter-space links NAISLs. AISLs are used to transmit data packets between different energy spaces.

8. The wireless sensor network energy efficiency optimization method according to claim 2, characterized in that: The step of constructing the data model of the wireless sensor network also includes: Define the adjacency matrix. After topology discovery, the sink node obtains the connection relationship between sensor nodes, that is, the network topology graph. The adjacency matrix A is defined by the network topology: (1) A is an N+1-order square matrix; (2) The elements in A satisfy: d ij represents the distance between nodes i and j, R max represents the maximum transmission distance, then A is a symmetric matrix.

9. The wireless sensor network energy efficiency optimization method according to claim 3, characterized in that: The step of constructing the data model of the wireless sensor network also includes: Definition of energy level: The energy status of the sensor node is divided into K energy levels according to the residual energy, and the interval of each energy level is ε0 / K. If the residual energy of the sensor node is between [(n-1)ε0 / K,nε0 / K], the energy level of the node is called n.

10. The wireless sensor network energy efficiency optimization method according to claim 3, characterized in that: The step of constructing the data model of the wireless sensor network also includes: Define energy space: Define K discrete energy spaces S n , n=1,2,...,K, and corresponds one to one with K energy levels; Definition of inter-space link: A wireless link between nodes in different energy spaces is called an inter-space link.

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