Energy-saving routing method and system of wireless sensor network

Abstract

The invention discloses an energy-saving routing method and system for a wireless sensor network, and belongs to the field of wireless networks, and the method comprises the steps: firstly constructing a total energy consumption model of the network, considering the periodic change of a network flow load, adopting different network topologies in different time periods, and reducing the energy consumption; and then, designing and implementing a solution algorithm to find out a routing path with minimum energy consumption. According to the algorithm, an improved greedy strategy is adopted, an evaluation function is introduced, information of current node energy consumption and a minimum energy consumption path is considered, dynamic adjustment is carried out, and a random selection and backtracking mechanism is added. And finally, optimizing the obtained routing path, and further reducing the energy consumption by repeatedly checking and updating nodes on the path. The method provides an effective solution for saving the energy of the wireless sensor network.

Description

Technical Field

[0001] The present invention belongs to the field of wireless networks, and more specifically relates to an energy-saving routing method and system for a wireless sensor network. Background Art

[0002] Wireless sensor networks are networks composed of a large number of tiny, cheap wireless sensor nodes that are self-organized by wireless means, and have become an important form of modern communication. Because of its convenient deployment, ability to dynamically process network structures, and no need for line connections, it is widely used in military reconnaissance, environmental monitoring, health care, smart homes and other fields. However, the nodes of wireless sensor networks are usually powered by batteries, and the energy of batteries is limited. In addition, the nodes are often in working state to collect information, transmit data, etc., so the energy problem has become an important bottleneck restricting the development of wireless sensor networks. When some nodes in the network fail due to energy exhaustion, the entire network may be paralyzed. Therefore, how to effectively manage and utilize the energy of nodes and improve the life of the entire network is an important direction of wireless sensor network research.

[0003] In addition, due to the particularity and complexity of wireless sensor networks, its routing problem is also a focus worth studying. Unlike other networks, wireless sensor networks cannot use traditional network routing algorithms, but need to design special routing algorithms based on their particularity. Especially after considering the energy constraints of nodes, how to choose a route with the least energy consumption becomes particularly complex and difficult.

[0004] In previous studies, many different solutions have been proposed for the routing problem of wireless sensor networks, such as the shortest path algorithm, the minimum energy consumption algorithm, the greedy algorithm, etc. However, most algorithms have some problems. For example, the shortest path algorithm does not consider the energy consumption of nodes, which may cause some nodes to run out of energy too quickly. Although the greedy algorithm takes energy consumption into consideration, it only considers the local optimal solution when selecting a path and cannot guarantee the global optimal solution.

[0005] Therefore, the present invention proposes an energy-saving routing method for a wireless sensor network, the main goal of which is to find a routing path with the minimum energy consumption and extend the life cycle of the wireless sensor network. Summary of the invention

[0006] The technical problem to be solved by the present invention is how to design an energy-saving routing method in a wireless sensor network, by constructing a total energy consumption model, designing a solution algorithm and optimizing it, to obtain a routing path with the minimum energy consumption, while ensuring the performance and stability of network transmission, effectively reducing network energy consumption and achieving energy saving.

[0007] In order to achieve the above object, the present invention is implemented by adopting the following technical scheme: the method comprises:

[0008] Construct a total energy consumption model for wireless sensor networks;

[0009] Design a solution algorithm to solve the total energy consumption model of the constructed wireless sensor network.

[0010] The algorithm is implemented to obtain the routing path with the minimum energy consumption;

[0011] Algorithm optimization, optimizes the routing path with the minimum total energy consumption, including repeated, excessive inspection and update of the nodes in the path, so as to further reduce energy consumption.

[0012] In one solution, the wireless network traffic load in the total energy consumption model presents a periodic change pattern, and a cycle is divided into several time intervals. Under the premise of ensuring the performance and reliability of network transmission, different network working topologies are adopted for each time interval, and switches or links that are not in the network working topology are dormant to reduce network energy consumption and achieve energy saving; the modeling is as follows:

[0013] Objective function:

[0014]

[0015] Constraints:

[0016]

[0017] Wherein, the integer N is greater than or equal to 1, which is the number of time intervals divided by a cycle; n is the nth time interval, |I n | is I n The length of; if G is the complete set of network working topology, T n is the network working topology in the nth time interval, then A n =GT n , that is, the switches or links that are dormant in the nth time interval, |A n |It is A n The network power saved by putting switches and links in sleep mode;

[0018] s and d are the starting and ending switches of a network flow, respectively, and i and j are the switches at both ends of a link, respectively. If V is the set of all switches, then any s / d / i / j belongs to V.

[0019] is the bandwidth consumed by a network flow with a starting point and a terminal point sd passing through link ij; f ij is the bandwidth consumed by all network flows passing through link ij; tsd is the bandwidth consumed by all network flows starting and ending at sd; c ij is the total bandwidth of link ij; a is the threshold of link utilization; P(t sd ) is the set of switches and links through which all network flows with sd as the starting point and end point flow.

[0020] In one solution, the solution algorithm adopts an improved greedy strategy, and the improvements include:

[0021] (1) Selection of Greedy Strategy: In each decision step, an evaluation function is designed. This function not only considers the energy consumption of the currently selected node, but also the information of the minimum energy consumption path from the node to the target node. The evaluation function combines the two to obtain an evaluation index, and then selects the node with the smallest evaluation index as the next hop node.

[0022] (2) Dynamic adjustment: Design a dynamic adjustment mechanism, that is, whenever a node consumes a certain amount of energy, adjust the greedy strategy;

[0023] (3) Random selection and backtracking: When determining the next hop node, in addition to selecting the node with the smallest evaluation function score, a probability threshold is set. If the probability threshold is exceeded, a node is randomly selected to increase the diversity of paths and reduce network congestion. At the same time, if a dead end is encountered during the search process, a backtracking strategy is used to return to the previous step and reselect a node.

[0024] In one scheme, the selection of the greedy strategy is to design an evaluation function E(i) for each node i, taking into account the current energy consumption C(i) of the node and the minimum energy consumption path P(i,o) from the node to the target node O; the evaluation function is defined as follows:

[0025] E(i)=w1*C(i)+w2*P(i,o)

[0026] Among them, w1 and w2 are weight factors used to balance the importance between the current energy consumption and the minimum energy consumption path; in each decision step, the node with the smallest evaluation function E(i) is selected as the next hop node.

[0027] In one solution, the dynamic adjustment is to dynamically adjust the evaluation function E(i) during the operation of the network; when the energy consumption of a node exceeds the energy consumption threshold, the weight of the node in the evaluation function is increased, thereby reducing the possibility of it being selected as the next hop node; specifically, E(i) is adjusted to:

[0028] E(i)=w1*C(i)*(1+α)+w2*P(i,o)

[0029] Among them, α is a coefficient. When the node energy consumption exceeds the threshold, α>0, otherwise α=0.

[0030] In one scheme, the probability threshold in the random selection and backtracking is set between 20% and 30%; at each step, there is a 20% to 30% probability of randomly selecting a node instead of selecting the node with the smallest evaluation function score; the complexity of path selection is increased and the degree of network congestion is reduced.

[0031] In one embodiment, the threshold is 50%.

[0032] In one embodiment, the algorithm is implemented in the following specific steps:

[0033] (1) Initialization: Set a current node initial_node, which is initially set to the starting node S; initialize a path list route = [], and add S to route.

[0034] (2) Traverse adjacent nodes: obtain the adjacent node set N of initial_node; for each node iinN, calculate the energy consumption of reaching the target node D through initial_node, which is achieved by accessing the constructed energy consumption model;

[0035] The calculation of energy consumption is expressed as E(i) = w1*C(i) + w2*P(i,D), where C(i) represents the current energy consumption of node i, P(i,D) represents the path energy consumption from node i to node D, and w1 and w2 are corresponding weight factors;

[0036] (3) Select the next hop node: Select the node next_node with the lowest energy consumption in N, add it to the path list route, and update the current node to next_node;

[0037] (4) End judgment: If the current node is the target node D, the algorithm ends and outputs the path list route. At this time, route is the routing path with the lowest energy consumption; if the current node is not the target node D, return to (2) and continue the traversal, selection and update steps.

[0038] In one embodiment, the algorithm is optimized by frequently updating node energy consumption and checking and updating routing paths, as follows:

[0039] Re-estimate the distance and energy of all nodes to min(P(i,D)+d(i,j)) and min(E(i)+C(i,j)), where i and j are arbitrary nodes and d(i,j) is the distance between nodes i and j;

[0040] When the evaluated energy consumption E(i) of a node i is greater than the preset threshold, it is considered as a failed node, and this node and its adjacent nodes are no longer candidate routing nodes;

[0041] After each update, compare whether the energy consumption of the latest path is less than that of the previous best path. If so, accept it as the new best path; otherwise, keep the original best path.

[0042] In another aspect, an energy-saving routing system for a wireless sensor network, the system is applicable to the method, energy consumption model module, solution algorithm module, algorithm execution module and algorithm optimization module;

[0043] Among them, the energy consumption model module is used to build the total energy consumption model of the network;

[0044] The solution algorithm module has a built-in solution algorithm based on the constructed total energy consumption model;

[0045] The algorithm execution module obtains the routing path with the minimum total energy consumption according to the designed solution algorithm;

[0046] The algorithm optimization module optimizes the obtained optimal routing path, including repeated and excessive inspection and updating of the nodes of the path to reduce energy consumption.

[0047] Beneficial effects of the present invention:

[0048] A total energy consumption model of wireless sensor networks was constructed, which comprehensively considered the network's traffic load and periodic changes, and can more accurately describe and predict the network's energy consumption.

[0049] A dynamic adjustment scheme is adopted to adjust the algorithm in real time according to the operating status of the network and the energy consumption of the nodes, which improves the adaptability and performance of the algorithm.

[0050] By optimizing the routing path, including repeated and excessive checking and updating of the nodes in the path, the network's energy consumption is further reduced and the network's transmission efficiency is improved.

[0051] The algorithm is simple to implement and has a small amount of calculation, which is suitable for resource-constrained environments such as wireless sensor networks. At the same time, this algorithm is also highly universal and can be widely used in other types of wireless networks.

[0052] The present invention also introduces heuristic information, so that the greedy algorithm can better adapt to the dynamic changes of the network in the process of finding the minimum energy consumption path, improve the routing efficiency, and enable the network to better adapt to different working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 This is a flow chart of the improved greedy strategy algorithm of the present invention;

[0055] Figure 3 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0056] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0057] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0058] like Figure 1 As shown, the energy-saving routing method of a wireless sensor network includes:

[0059] S1. Construct the total energy consumption model of wireless sensor networks.

[0060] In the total energy consumption model, the wireless network traffic load presents a periodic change pattern. A cycle is divided into several time intervals. Under the premise of ensuring the performance and reliability of network transmission, different network working topologies are adopted for each time interval, and switches or links that are not in the network working topology are dormant to reduce network energy consumption and achieve energy saving. The modeling is as follows:

[0061] Objective function:

[0062]

[0063] Constraints:

[0064]

[0065] Wherein, the integer N is greater than or equal to 1, which is the number of time intervals divided by a cycle; n is the nth time interval, |I n | is I n The length of; if G is the complete set of network working topology, T n is the network working topology in the nth time interval, then A n =GT n , that is, the switches or links that are dormant in the nth time interval, |A n |It is A nThe network power saved by putting switches and links in sleep mode;

[0066] s and d are the starting and ending switches of a network flow, respectively, and i and j are the switches at both ends of a link, respectively. If V is the set of all switches, then any s / d / i / j belongs to V.

[0067] is the bandwidth consumed by a network flow with a starting point and a terminal point sd passing through link ij; f ij is the bandwidth consumed by all network flows passing through link ij; t sd is the bandwidth consumed by all network flows starting and ending at sd; c ij is the total bandwidth of link ij; a is the threshold of link utilization; P(t sd ) is the set of switches and links through which all network flows with sd as the starting point and end point flow.

[0068] S2. Design a solution algorithm to solve the total energy consumption model of wireless sensor networks. Introduce heuristic information or criteria. In the energy-saving routing problem of wireless sensor networks, consider the energy consumption of sensor nodes in the network and design an improved greedy algorithm as follows:

[0069] The solution algorithm adopts an improved greedy strategy, and the improvements include:

[0070] (1) Selection of Greedy Strategy: In each decision step, an evaluation function is designed that not only considers the energy consumption of the currently selected node, but also the information of the minimum energy consumption path from the node to the target node. The evaluation function can combine the two to obtain an evaluation index, and then select the node with the smallest evaluation index as the next hop node.

[0071] The selection of the greedy strategy described above designs an evaluation function E(i) for each node i, taking into account the current energy consumption C(i) of the node and the minimum energy consumption path P(i,o) from the node to the target node O; the evaluation function is defined as follows:

[0072] E(i)=w1*C(i)+w2*P(i,o)

[0073] Among them, w1 and w2 are weight factors used to balance the importance between the current energy consumption and the minimum energy consumption path; in each decision step, the node with the smallest evaluation function E(i) is selected as the next hop node.

[0074] When designing the improved greedy strategy, the two weight factors w1 and w2 correspond to the energy consumption C(i) of the current node and the importance measurement of the minimum energy consumption path P(i,o) from node i to the target node, respectively.

[0075] The values ​​of these two weight factors are determined according to the specific application scenario of the wireless sensor network and the importance of the two factors, node energy consumption and the minimum energy consumption path to the target node, in the application scenario. The weight factors w1 and w2 are often set equal, that is, w1 = w2 = 0.5.

[0076] If the energy consumption of nodes in the network is fast, or the stability and durability of the network are more important, then we may assign a higher weight to the minimum energy consumption path to the target node, and w2 may be greater than w1. On the contrary, if the communication load between nodes is heavy, the impact of the current energy consumption of the node may be greater, and w1 may be greater than w2. The values ​​of these two weight factors must satisfy w1+w2=1 to ensure that the value of the evaluation function E(i) is between 0 and 1.

[0077] (2) Dynamic adjustment: During the operation of the network, the energy of the nodes will be consumed over time. Therefore, a dynamic adjustment mechanism is designed, that is, whenever the energy consumption of a node reaches a certain level, the greedy strategy is adjusted.

[0078] The dynamic adjustment is to dynamically adjust the evaluation function E(i) during the operation of the network; when the energy consumption of a node exceeds the energy consumption threshold, the weight of the node in the evaluation function is increased, thereby reducing the possibility of it being selected as the next hop node; specifically, E(i) is adjusted to:

[0079] E(i)=w1*C(i)*(1+α)+w2*P(i,o)

[0080] Among them, α is a coefficient. When the node energy consumption exceeds the threshold, α>0, otherwise α=0.

[0081] The threshold is set to a certain proportion of 50% of the initial energy of the wireless sensor node. When the node energy is lower than this threshold, the weight of the node in the evaluation function is increased to reduce the possibility of it being selected as the next hop node. This threshold can also be dynamically adjusted according to actual needs.

[0082] (3) Random selection and backtracking: When determining the next hop node, in addition to selecting the node with the smallest evaluation function score, a probability threshold can also be set. If the probability threshold is exceeded, a node is randomly selected. This can increase the diversity of paths and reduce network congestion. At the same time, if a dead end is encountered during the search process, a backtracking strategy is used to return to the previous step and reselect a node. We randomly select a non-optimal node as the next hop, that is, there is a probability of (1-θ) to select the node with the smallest E(i), and there is a probability of θ to randomly select a node.

[0083] In addition, if a dead end is encountered during the search process (i.e., the target node cannot be reached or the energy consumption of the subsequently selected nodes is too large), a backtracking strategy is adopted, i.e., returning to the previous step to reselect the node.

[0084] The probability threshold in the random selection and backtracking is set between 20% and 30%; at each step, there is a 20% to 30% probability of randomly selecting a node instead of selecting the node with the smallest evaluation function score; the complexity of path selection is increased and the degree of network congestion is reduced.

[0085] These designs are all aimed at making the greedy strategy more flexible and able to adapt to the actual environment where the energy of network nodes changes dynamically, avoiding the algorithm from falling into the local optimal solution due to strict greediness, and improving the performance of wireless sensor networks.

[0086] like Figure 2 As shown, S3, algorithm implementation, calculates the routing path with the minimum total energy consumption according to the solution algorithm.

[0087] The specific steps of implementing the algorithm are as follows:

[0088] S301, initialization: set a current node initial_node, initially set to the starting node S; initialize a path list route = [], and add S to route.

[0089] S302, traverse adjacent nodes: obtain the adjacent node set N of initial_node; for each node iinN, calculate the energy consumption of reaching the target node D through initial_node, using the objective function formula (1) of the constructed energy consumption model.

[0090] The calculation of energy consumption is expressed as E(i)=w1*C(i)+w2*P(i,D), where C(i) represents the current energy consumption of node i, P(i,D) represents the path energy consumption from node i to node D, and w1 and w2 are corresponding weight factors.

[0091] S303, select the next hop node: select the node next_node with the minimum energy consumption in N, add it to the path list route, and update the current node to next_node.

[0092] When selecting the next hop node, we must also satisfy the constraints formula (2)(3)(4).

[0093] S304, end judgment: If the current node is the target node D, the algorithm ends and outputs the path list route. At this time, route is the routing path with the minimum energy consumption; if the current node is not the target node D, return to S302 and continue the traversal, selection and update steps.

[0094] This process is repeated until the end node is found and the path with the lowest energy consumption in the entire network is obtained. This path is the optimal solution we require. This process is similar to the general greedy algorithm, but in this algorithm, we consider the energy consumption of wireless sensor nodes and improve the algorithm so that while obtaining the optimal solution, the energy consumption of wireless sensors is also reduced as much as possible.

[0095] Compared with the existing greedy algorithm, the present invention has the following advantages:

[0096] 1. Energy saving: The energy-saving routing method of the wireless sensor network of the present invention can effectively reduce the total energy consumption of the wireless sensor network by constructing a total energy consumption model and introducing weight factors, comprehensively considering the energy consumption of different nodes, thereby achieving more energy-saving data transmission.

[0097] 2. Dynamic adjustment: During network operation, the present invention can dynamically adjust the greedy strategy and evaluation function to adapt to changes in energy consumption of different nodes, which makes the algorithm more flexible and adaptable.

[0098] 3. Increase path diversity: By introducing random selection and backtracking strategies, the diversity of the search process can be increased, the congestion of the network can be reduced, and the transmission efficiency of the network can be improved.

[0099] 4. Considering the actual working conditions of the network: During the implementation of the present invention, the duplicate nodes in the decision-making process will be checked and removed, and the node energy will be checked and updated, so as to better consider the actual working conditions of the network and reduce the possibility of wrong decisions.

[0100] 5. Accurate solution and comprehensive optimization: The present invention optimizes the routing path with the minimum total energy consumption through an iterative optimization method, which not only optimizes the energy consumption in the routing process, but also takes into account the network stability and reliability, thereby achieving all-round optimization in path selection.

[0101] S4, algorithm optimization, optimizes the routing path with the minimum total energy consumption, including repeated, excessive inspection and updating of the nodes in the path, so as to further reduce energy consumption.

[0102] The algorithm optimization is performed by frequently updating node energy consumption and checking and updating routing paths, as follows:

[0103] Re-estimate the distance and energy of all nodes to min(P(i,D)+d(i,j)) and min(E(i)+C(i,j)), where i and j are arbitrary nodes and d(i,j) is the distance between nodes i and j;

[0104] When the evaluated energy consumption E(i) of a node i is greater than the preset threshold, it is considered as a failed node, and this node and its adjacent nodes are no longer candidate routing nodes;

[0105] After each update, compare whether the energy consumption of the latest path is less than that of the previous best path. If so, accept it as the new best path; otherwise, keep the original best path.

[0106] like Figure 3 As shown, the energy-saving routing system of the wireless sensor network includes an energy consumption model module, a solution algorithm module, an algorithm execution module and an algorithm optimization module.

[0107] The energy consumption model module is mainly responsible for building an accurate and practical network total energy consumption model based on the characteristics of the wireless sensor network and the actual working environment. This model needs to fully consider the working status and energy consumption of each node in the network as well as the external environmental factors acting on the network, such as the propagation characteristics of wireless signals and the availability of power supplies, so as to provide basic theoretical support and actual parameter values ​​for the design of subsequent solution algorithms.

[0108] The solution algorithm module is designed to solve the energy consumption model. The algorithm designed needs to be able to handle complex optimization problems, including finding the routing path with the minimum total energy consumption. The solution method used may be a conventional algorithm, such as dynamic programming, greedy algorithm, etc., or it may be a new optimization algorithm based on heuristic and bionic algorithms.

[0109] The algorithm execution module is the part that actually performs path selection according to the designed solution algorithm. This module needs to quickly and accurately find the routing path with the lowest total energy consumption. During the algorithm execution process, it may be necessary to dynamically collect and process relevant network data, such as the energy consumption of network nodes, the distribution of network data flows, etc., which can be used as input parameters of the solution algorithm.

[0110] The algorithm optimization module optimizes the best routing path obtained by the solution algorithm to improve network performance and reduce energy consumption. The optimization method may include repeated and excessive inspection and updating of the nodes of the path to discover and utilize more effective routing solutions, and may also include adjustments to other relevant parameters of the network, such as transmission power, spectrum utilization, etc., to achieve energy saving. This module needs to be able to flexibly handle various optimization tasks and dynamically adjust the optimization strategy according to the actual network environment and network status.

[0111] Embodiment 1

[0112] Assume that a wireless sensor network consists of 10 sensor nodes, the nodes are numbered 1-10, where node 1 is the source node and node 10 is the target node. The parameters of the network energy consumption model are set as follows: the node's transmit power and receive power are set to 0.5W and 0.1W respectively, the link utilization threshold α is set to 0.7, and other parameters are set according to the actual working conditions of the wireless sensor network.

[0113] Assume that integer N=4, that is, there are 4 time intervals in one cycle. The time intervals can be: I1=1h, I2=2h, I3=3h, I4=4h, and the working state of the node can be adjusted according to the actual network traffic. The network working topology G and the network working topology Tn of each time interval are as follows: G=1-2-5-7-10, T1=1-3-6-10, T2=1-4-7-10, T3=1-2-5-8-10, T4=1-4-7-10.

[0114] Use the improved greedy algorithm to search for the routing path with the lowest energy consumption. When expanding the current node, it is necessary to calculate the evaluation function score of each adjacent node. For example, if the current node is 1, its set of adjacent nodes is [2,3,4]. Then for each adjacent node i, use the evaluation function E(i) = w1*C(i) + w2*P(i,D) to calculate. Here C(i) represents the current energy consumption of node i, P(i,D) represents the estimated minimum energy consumption path from node i to target node D, w1 and w2 are the corresponding weight factors, and here we set w1 = w2 = 0.5.

[0115] Assume that the energy consumption of node 1 is 10, and the energy consumption of nodes 2, 3, and 4 are 20, 30, and 15 respectively. Then the C(i) values ​​of nodes 2, 3, and 4 are 20, 30, and 15. P(i,D) can be estimated by using a search strategy like the A algorithm. The next step is to estimate the energy consumption from node i to the target node D as a constant, for example, all nodes are estimated to be 50. Then:

[0116] E(2)=0.520+0.550=3, E(3)=0.530+0.550=40, E(4)=0.515+0.5*50=32.5.

[0117] According to the greedy strategy, the node with the smallest evaluation function E(i) is selected as the next hop node. In this example, node 4 has the smallest evaluation function score, so node 4 is selected as the next hop. Node 4 is added to the current path list route, that is, route = [1, 4], and the current node is updated to node 4.

[0118] Then repeat the above process, continue to traverse the adjacent nodes for the new current node (i.e. node 4), calculate the evaluation function score, select the next hop node, and update the current node until the current node is the target node D. After the process of updating the current node, the routing path with the lowest energy consumption is: 1-2-3-7-10.

[0119] The obtained routing path with the lowest energy consumption is optimized. After repeated, excessive inspection and updating of the nodes of the path, the optimized path is obtained: 1-2-5-9-10.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0121] It should be understood that the detailed description of the technical solutions of the present invention by means of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art may modify the technical solutions described in the embodiments, or replace some of the technical features by equivalents, based on reading the specification of the present invention; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy-saving routing method for a wireless sensor network, characterized in that: The method includes: Construct a total energy consumption model for wireless sensor networks; Design a solution algorithm to solve the total energy consumption model of wireless sensor networks; Algorithm implementation, calculating a routing path with minimum total energy consumption according to the solution algorithm; Algorithm optimization,optimizes the routing path with the minimum total energy consumption,,including repeated and excessive inspection and update of the nodes in the path,,to further reduce energy consumption.

2. The energy-saving routing method for a wireless sensor network according to claim 1, characterized in that: The total energy consumption model of the wireless sensor network is constructed, including: the wireless network traffic load in the total energy consumption model presents a periodic change law, a cycle is divided into a number of time intervals, a different network working topology is used for each time interval, and switches or links that are not in the network working topology are dormant, and the modeling is as follows: Objective function: Constraints: Wherein, the integer N is greater than or equal to 1, which is the number of time intervals divided by a cycle; n is the nth time interval, |I n | is I n The length of; if G is the complete set of network working topology, T n is the network working topology in the nth time interval, then A n =GT n , that is, the switches or links that are dormant in the nth time interval, |A n |It is A n The network power saved by putting switches and links in sleep mode; s and d are the starting and ending switches of a network flow, respectively, and i and j are the switches at both ends of a link, respectively. If V is the set of all switches, then any s / d / i / j belongs to V. is the bandwidth consumed by a network flow with a starting point and a terminal point sd passing through link ij; f ij is the bandwidth consumed by all network flows passing through link ij; t sd is the bandwidth consumed by all network flows starting and ending at sd; c ij is the total bandwidth of link ij; a is the threshold of link utilization; P(t sd ) is the set of switches and links through which all network flows with sd as the starting point and end point flow.

3. The energy-saving routing method for a wireless sensor network according to claim 1, characterized in that: The solution algorithm adopts an improved greedy strategy, including: (1) Selection of Greedy Strategy: In each decision step, an evaluation function is designed to consider the energy consumption of the currently selected node and the information of the minimum energy consumption path from the node to the target node. The evaluation function combines the two to obtain an evaluation index, and then selects the node with the smallest evaluation index as the next hop node. (2) Dynamic adjustment: Design a dynamic adjustment mechanism to adjust the greedy strategy whenever the energy consumption of a node reaches a certain level; (3) Random selection and backtracking: When determining the next hop node, a probability threshold is set. If the probability threshold is exceeded, a node is randomly selected to increase the diversity of paths and reduce network congestion. At the same time, if a dead end is encountered during the search process, a backtracking strategy is used to return to the previous step and reselect a node.

4. The energy-saving routing method for a wireless sensor network according to claim 3, characterized in that: The selection of the greedy strategy described above designs an evaluation function E(i) for each node i, taking into account the current energy consumption C(i) of the node and the minimum energy consumption path P(i,o) from the node to the target node O; the evaluation function is defined as follows: E(i)=w1*C(i)+w2*P(i,o) Among them, w1 and w2 are weight factors used to balance the importance between the current energy consumption and the minimum energy consumption path; in each decision step, the node with the smallest evaluation function E(i) is selected as the next hop node.

5. The energy-saving routing method of a wireless sensor network according to claim 3, characterized in that: The dynamic adjustment is to dynamically adjust the evaluation function E(i) during the operation of the network; When the energy consumption of a node exceeds the energy consumption threshold, the weight of the node in the evaluation function is increased, thereby reducing the possibility of it being selected as the next hop node; specifically, E(i) is adjusted to: E(i)=w1*C(i)*(1+α)+w2*P(i,o) Among them, α is a coefficient. When the node energy consumption exceeds the threshold, α>0, otherwise α=0.

6. The energy-saving routing method for a wireless sensor network according to claim 3, characterized in that: The probability threshold in the random selection and backtracking is set between 20% and 30%; At each step, there is a 20% to 30% probability of randomly selecting a node instead of selecting the node with the smallest evaluation function score; this increases the complexity of path selection and reduces network congestion.

7. The energy-saving routing method of a wireless sensor network according to claim 5, characterized in that: The energy consumption threshold of the certain node is 50%.

8. The energy-saving routing method of a wireless sensor network according to claim 1, characterized in that: The specific steps of implementing the algorithm are as follows: (1) Initialization: Set a current node initial_node, which is initially set as the starting node S; initialize a path list route = [], and add S to route; (2) Traverse adjacent nodes: obtain the adjacent node set N of initial_node; for each node iinN, calculate the energy consumption of reaching the target node D through initial_node, which is achieved by accessing the constructed energy consumption model; The calculation of energy consumption is expressed as E(i) = w1*C(i) + w2*P(i,D), where C(i) represents the current energy consumption of node i, P(i,D) represents the path energy consumption from node i to node D, and w1 and w2 are corresponding weight factors; (3) Select the next hop node: Select the node next_node with the lowest energy consumption in N, add it to the path list route, and update the current node to next_node; (4) End judgment: If the current node is the target node D, the algorithm ends and outputs the path list route. At this time, route is the routing path with the lowest energy consumption; if the current node is not the target node D, return to (2) and continue the traversal, selection and update steps.

9. The energy-saving routing method of a wireless sensor network according to claim 1, characterized in that: The algorithm optimization is performed by frequently updating node energy consumption and checking and updating routing paths, as follows: Re-estimate the distance and energy of all nodes to min(P(i,D)+d(i,j)) and min(E(i)+C(i,j)), where i and j are arbitrary nodes and d(i,j) is the distance between nodes i and j; When the evaluated energy consumption E(i) of a node i is greater than the preset threshold, it is considered as a failed node, and this node and its adjacent nodes are no longer candidate routing nodes; After each update, compare whether the energy consumption of the latest path is less than that of the previous best path. If so, accept it as the new best path; otherwise, keep the original best path.

10. An energy-saving routing system for a wireless sensor network, the system being applicable to the method according to any one of claims 1 to 9, characterized in that: The system comprises: an energy consumption model module, a solution algorithm module, an algorithm execution module and an algorithm optimization module; Among them, the energy consumption model module is used to build the total energy consumption model of the network; The solution algorithm module has a built-in solution algorithm based on the constructed total energy consumption model; The algorithm execution module obtains the routing path with the minimum total energy consumption according to the designed solution algorithm; The algorithm optimization module optimizes the obtained optimal routing path, including repeated and excessive inspection and updating of the nodes of the path to reduce energy consumption.

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