Reliable routing method for wireless sensor network node failure

Abstract

The invention provides a reliable routing method for wireless sensor network node failure, which is used for evaluating the stability of a link between adjacent nodes in a wireless sensor network based on a link maintenance time measurement standard of link quality. The problem that a traditional routing protocol is difficult to accurately evaluate the link stability in a high-dynamic and high-density network is solved, and a quantitative index is provided for the stability of network topology; the invention discloses a reliable routing method for wireless sensor network node failure. The method comprises the following steps: step 1, a routing discovery stage: initializing a network, discovering neighbors and establishing a network topology; step 2, a route selection stage; and performing a dynamic search routing algorithm based on the link stability, and outputting an optimal route.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor networks, and in particular to a reliable routing method for wireless sensor network nodes under failure. Background Art

[0002] With the rapid development of information technology, Wireless Sensor Network (WSN) has emerged as an important Internet of Things technology. WSN consists of a large number of self-directed, miniature, low-power sensor nodes that are interconnected through wireless networks and can collect, process and transmit data in real time, providing key information for various application scenarios.

[0003] However, in complex environments such as substations, the deployment of wireless sensor nodes often faces many challenges. These nodes are usually deployed in uncontrolled harsh environments, such as high temperature, high humidity, electromagnetic interference, etc., which makes the nodes prone to crashes, packet loss, routing failures and other problems. In addition, since it is difficult for users to perform timely maintenance and processing on these scattered nodes, once an individual node fails, it will have a serious impact on the data transmission of the entire network, which will not only reduce the accuracy of the data, but also greatly consume the energy and network bandwidth of the nodes, thus affecting the stable operation of the entire system.

[0004] In response to this problem, the existing wireless sensor network routing protocols still have many deficiencies in design. Especially in highly dynamic and high-density network environments, routing protocols need to comprehensively consider multiple factors such as end-to-end delay, throughput, topology changes, etc. to ensure the stability and efficiency of the network. At the same time, since sensor nodes are usually powered by batteries, low power consumption is also one of the important considerations in routing protocol design. In addition, routing protocols must also ensure the security, reliability and integrity of data to prevent data loss or tampering due to network instability or attacks.

[0005] However, existing routing protocols often find it difficult to meet these requirements at the same time. In high-density networks, communication interference and topology changes between nodes are frequent, making it difficult for routing protocols to quickly adapt to network changes, thus affecting the stability and efficiency of data transmission. In addition, although some routing protocols can deal with node failures to a certain extent, they often increase network energy consumption and complexity, which is not conducive to long-term stable operation.

[0006] Therefore, in order to cope with the challenges brought by network density and topology changes, it is urgent to design an adaptive, intelligent and efficient routing protocol. This routing protocol needs to be able to provide stable communication performance in a dynamically changing environment and meet the needs of diverse tasks. At the same time, it also needs to have the characteristics of low energy consumption, high security, high reliability and high integrity to ensure that the stability and accuracy of data transmission can be maintained in the event of node failure.

[0007] Based on the above background, the present invention proposes a reliable routing technology for wireless sensor network data transmission. This technology aims to solve the data transmission problem under relay node failure through innovative routing strategies and optimization algorithms, improve the stability and efficiency of the network, and provide strong support for the application of wireless sensor networks in various complex environments. Summary of the invention

[0008] The present invention aims to provide a reliable routing method for wireless sensor network node failures in response to the technical defects of the prior art. The method is based on a link maintenance time measurement standard of link quality and is used to evaluate the stability of links between adjacent nodes in a wireless sensor network. It solves the problem that traditional routing protocols are difficult to accurately evaluate link stability in highly dynamic and high-density networks, and provides a quantitative indicator for the stability of network topology.

[0009] The present invention provides the following technical solution: a reliable routing method for wireless sensor network node failure, the method comprising the following steps:

[0010] Step 1: Routing discovery phase: network initialization, neighbor discovery and network topology establishment;

[0011] Step 1-1, broadcasting Hello message: each sensor node periodically sends a Hello message containing its identity, current location, status, remaining energy and other information to inform other sensor nodes that it can participate in the topology construction of the network as a neighbor;

[0012] Step 1-2, receiving Hello messages and updating neighbor tables; after each sensor node receives Hello messages from other neighbors, it records the sender's information and updates the neighbor table;

[0013] Step 1-3, construct the network topology: Based on the information in the neighbor table, construct the adjacency matrix C, where the elements describe the link maintainable time between node i and node j;

[0014] Step 2: Routing selection phase: Dynamically search for routing algorithms based on link stability and output the best route;

[0015] Step 2-1, the route with the maximum available time between two nodes ensures that the link maintenance time of the end-to-end delay is maximized to model P1.

[0016]

[0017] The link duration between adjacent nodes i and j is defined as the edge flow , the maximum link duration, i.e. the capacity of the edge, can be given by Calculated, is the maximum flow from source node S to destination node D, that is, the end-to-end routing available time, is a binary variable, Indicates that an edge is selected in the route ,in, , , , are decision variables, C1 and C2 are constraints on the flow to ensure that the flow is indivisible, C3 is the capacity limit of the arc, C5 is the constraint on the end-to-end delay, C6 is the power constraint, and C7 is used to prevent loops in the path;

[0018] Step 2-2, analysis and simplification, simplify P1 to P2,

[0019]

[0020] Step 2-3, solve P2 to obtain the available time that maximizes the end-to-end routing.

[0021] Further,

[0022] The fields of the neighbor table include neighbor ID, neighbor address, neighbor characteristics, and effective time. The neighbor characteristics include signal strength RSSI, remaining energy, speed, coordinates, and link maintenance time.

[0023] Further,

[0024] P2 in step 2-3 is solved using an improved CSRR algorithm.

[0025] Further,

[0026] The improved algorithm of CSRR includes the following steps:

[0027] Step S1, initialization,;Fig. , C is the capacity matrix, , can be calculated by the formula, X is the path selection matrix, Initialize to 0 and set the delay constraint and the maximum number of iterations max_iterations, set the threshold , which is used to control when to update the solution of the linear relaxation;

[0028] Step S2, linear relaxation: First, solve the linear relaxation problem of P2, and The linear relaxation is ;

[0029] Step S3, randomized rounding: Based on the preliminary relaxed solution, the traffic is allocated to specific paths using randomized rounding. For each product k, the probability of selecting path p is: , the probability of path p being selected and its flow Proportional to, according to the rounding results, select some paths and fix the product k on these paths;

[0030] Step S4, correction step: CSRR is corrected after each iteration by local adjustment or Lagrange multiplication

[0031] The sub-method corrects the path selection and flow distribution to ensure that the delay constraints are met and the flow is maximized, improving the convergence and accuracy of the results;

[0032] Step S5, iterative process; iterate the above steps multiple times, and each iteration is corrected and adjusted.

[0033] The flow distribution is improved until a predetermined termination condition, i.e., a maximum number of iterations or convergence is reached, is met.

[0034] Further,

[0035] The solution complexity of the linear relaxation in step S2 is ,in It is the complexity of linear relaxation solution, which is related to the number of nodes V, the number of edges E and the number of products K in the graph.

[0036] Further,

[0037] The time complexity of random rounding for each commodity k in step S3 is Each product can be selected at most path.

[0038] Further,

[0039] The correction step in step S4 is performed by local adjustment, and the complexity is , adjust the flow of each edge and verify the delay constraints.

[0040] Further,

[0041] The number of iterations in step S5, the algorithm executes iterations, and the time complexity of each iteration is .

[0042] The present invention discloses a reliable routing method for wireless sensor network node failure, and proposes for the first time a link maintenance time measurement standard based on link quality, which is used to evaluate the stability of links between adjacent nodes in wireless sensor networks. The standard models the link maintenance time as a time-varying function, and comprehensively considers factors such as the node's position, state (such as speed, signal strength RSSI) and residual energy. By introducing this standard, the actual availability of the link can be more accurately reflected, providing a scientific basis for subsequent routing selection. This innovation solves the problem that traditional routing protocols are difficult to accurately evaluate link stability in highly dynamic and high-density networks, and provides a quantitative indicator for the stability of network topology.

[0043] This method is mainly aimed at the scenario of node failure in wireless sensor networks. It aims to ensure the stability of the network and the effective transmission of data through a reliable routing method. The key lies in the careful design and optimization of the route discovery phase and the route selection phase.

[0044] In the route discovery phase, the sensor node broadcasts Hello messages to inform other sensor nodes of its existence and availability so that other nodes can consider the node when selecting neighbors and participating in the network topology construction. The sensor node receives Hello messages and updates the neighbor table, which is the basis for building the network topology. The neighbor table records the link information between each node and its neighbor nodes. The network topology is constructed. Based on the information in the neighbor table, the adjacency matrix C is constructed to quantify the stability of the links between nodes and provide a basis for subsequent route selection.

[0045] In the routing selection phase, a dynamic search routing algorithm is used to select the best route based on link stability. Specifically, through modeling and analysis, the link maintenance time of the end-to-end delay is maximized as the goal (i.e., the P1 problem), and it is simplified and solved (simplified to the P2 problem). This method uses a link maintenance time measurement standard based on link quality to evaluate the stability of links between adjacent nodes in wireless sensor networks. This measurement standard solves the problem that traditional routing protocols are difficult to accurately evaluate link stability in highly dynamic and high-density networks, and provides a quantitative indicator for the stability of network topology.

[0046] In summary, this method constructs the network topology in the route discovery phase by comprehensively considering factors such as link stability and node residual energy, and dynamically selects the best route based on the link maintenance time measurement standard of link quality in the route selection phase. This method helps to improve the routing reliability of wireless sensor networks in the event of node failure and ensure the stability and efficiency of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the algorithm flow chart of the present invention;

[0048] Figure 2 Show intentions for your neighbors. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0050] The innovations of the present invention are mainly reflected in the following three aspects:

[0051] 1. Innovatively proposed a measurement standard for link maintenance time

[0052] This patent proposes for the first time a link maintenance time measurement standard based on link quality, which is used to evaluate the stability of links between adjacent nodes in wireless sensor networks. This standard models the link maintenance time as a time-varying function, taking into account factors such as the node's location, state (such as speed, signal strength RSSI), and remaining energy. By introducing this standard, the actual availability of the link can be more accurately reflected, providing a scientific basis for subsequent routing selection. This innovation solves the problem that traditional routing protocols are difficult to accurately evaluate link stability in highly dynamic and high-density networks, and provides a quantitative indicator for the stability of network topology.

[0053] 2. Innovatively use the CSRR improved algorithm to solve routing problems

[0054] This patent proposes an improved algorithm based on the Constrained Sequential Randomized Rounding (CSRR) algorithm for solving mixed integer linear programming (MILP) problems. Specifically, the algorithm solves the problem that traditional MILP solving methods cannot guarantee delay constraints through linear relaxation, randomized rounding and correction steps. Compared with traditional methods, the CSRR improved algorithm ensures that traffic distribution meets delay constraints while maximizing the available time of end-to-end routing through local adjustments and Lagrange multiplier methods in each iteration. This innovation significantly improves the accuracy and efficiency of routing selection, especially for highly dynamic and resource-constrained network environments.

[0055] 3. Make innovative improvements on the original CSRR algorithm

[0056] This patent further optimizes the solution process and performance based on the original CSRR algorithm. The specific improvements include:

[0057] 3.1. Introduce a delay constraint correction mechanism: After randomization rounding, adjust the path selection and traffic distribution through the Lagrange multiplier method or other heuristic methods to ensure that the delay constraint is met.

[0058] 3.2. Dynamically update the linear relaxation solution: Set a threshold to control the update timing of the linear relaxation solution to avoid unnecessary computational overhead and improve the convergence speed of the algorithm.

[0059] 3.3. Iterative optimization process: Through multiple iterations and corrections, the traffic distribution results are gradually improved to ensure the robustness and stability of the algorithm in complex network environments.

[0060] These improvements enable the CSRR algorithm to better adapt to the high dynamics and resource-constrained characteristics of wireless sensor networks, and significantly improve the performance and reliability of routing selection.

[0061] In summary, this patent proposes a link maintenance time measurement standard, uses the CSRR improved algorithm to solve the routing problem, and optimizes the original CSRR algorithm to provide an efficient and reliable routing solution for wireless sensor networks, which has important theoretical value and practical application significance. The specific embodiments include the following:

[0062] 1. Reliable routing algorithm with QoS guarantee based on maximum maintenance time

[0063] In this section, we first define a metric called link quality, which is used to calculate the sustainable time of a link between two adjacent sensor nodes. The link quality is modeled as a time-varying function that is closely related to the state of the sensor nodes, the remaining energy, and the link quality. Subsequently, a dynamic search routing algorithm is proposed, the goal of which is to maximize the available time of end-to-end routing under the constraints of end-to-end delay and energy consumption. Usually, this time-dependent problem is transformed into a spatial dimension problem by creating a directed graph. By extending the network topology, graph theory is used to solve the routing problem. The algorithm consists of two stages: (1) route discovery stage: discover neighbors and establish network topology, (2) route selection stage: a dynamic search routing algorithm based on link stability. The algorithm flow chart is shown in the figure. Figure 1 shown.

[0064] Route discovery phase

[0065] In the route discovery phase, each sensor node in the wireless sensor network will send its location information and other status information (such as remaining energy, status, etc.) through broadcasting so that adjacent nodes can sense each other's existence. Each node will add the received neighbor node information to its own routing table, so that a preliminary network topology can be formed. The network initialization phase can be divided into the following steps:

[0066] Broadcast Hello message: Each sensor node periodically sends a Hello message containing its identity, current location, status, remaining energy and other information, in order to inform other sensor nodes that it can participate in the network topology construction as a neighbor.

[0067] Receive Hello messages and update neighbor table: After each sensor node receives Hello messages from other neighbors, it will record the sender's information and update the neighbor table. The neighbor table usually includes the following fields: Figure 2 shown.

[0068] Construct network topology: Based on the information in the neighbor table, construct the adjacency matrix C, where the elements

[0069] Describes the link maintainability time between node i and node j. Algorithm 2 shows the steps of the network initialization phase.

[0070]

[0071] Routing phase

[0072] This phase involves discovering routes between nodes using data on state, residual energy, and link quality. The goal of this invention is to design a routing scheme that maximizes the available time of end-to-end paths from a macro perspective, rather than relying solely on information from a single time slot. Finding the route with the maximum available time between two nodes is equivalent to solving problem (P2).

[0073] Algorithm 3 shows the steps of the second phase.

[0074]

[0075] Modeling of link maintenance time maximization problem with guaranteed end-to-end delay

[0076] In the set of nodes constituting a certain route, the link with the shortest available link time is called the bottleneck link. In the routing process, the effective time of the route is determined by the bottleneck link. The present invention maximizes the available time of the route under the constraints of end-to-end delay and energy consumption. The problem is finally abstracted as the problem of non-dividable traffic in the network. The present invention defines the sustainable time of the link as the flow of the arc, and the maximum sustainable time of the link as the network capacity. When performing route selection, the route with the longest available time is selected, which helps to reduce routing overhead and improve resource utilization.

[0077] The link duration between adjacent nodes i and j is defined as the edge flow , the maximum link duration, i.e. the capacity of the edge, can be given by Calculated, is the maximum flow from source node S to destination node D, that is, the end-to-end routing available time, is a binary variable, Indicates that an edge is selected in the route The goal of the present invention is to maximize the available time of end-to-end routing under the constraints of delay and energy consumption. The optimization problem can be modeled as P1:

[0078]

[0079] in, , , , is the decision variable, C1 and C2 are constraints that keep the flow unchanged to ensure that the flow is indivisible, C3 is the capacity limit of the arc, C5 is the constraint of end-to-end delay, C6 is the power constraint, and C7 is used to prevent loops in the path. Finally, considering the solvability of the optimization problem P1, P1 can be simplified to P2 through analysis and simplification:

[0080]

[0081] Reconstruction based on the indivisible maximum flow problem

[0082] According to the analysis, problem (P2) is essentially a mixed integer linear programming (MILP), which is an NP-hard problem. This is different from the traditional MILP solution method, which usually converts the nonlinear problem into a linear form, performs linear relaxation and obtains the solution, and then obtains the integer solution by rounding. However, this method cannot guarantee that the obtained solution satisfies the delay constraint C5. Therefore, the present invention solves this problem based on an improved algorithm based on the Constrained sequential randomized rounding algorithm (CSRR).

[0083] The specific solution steps of the improved algorithm based on CSRR are as follows, and the specific process is shown in Table 2-1:

[0084] 1. Initialization: Graph , C is the capacity matrix, , can be calculated by the formula, X is the path selection matrix, Initialize to 0 and set the delay constraint and the maximum number of iterations max_iterations, set the threshold , which controls when to update the solution of the linear relaxation.

[0085] 2. Linear relaxation: First solve the linear relaxation problem of P2, and The linear relaxation is .

[0086] 3. Randomized rounding: Based on the preliminary relaxed solution, randomized rounding is used to allocate traffic to specific paths. For each product k, the probability of selecting path p is: , which means that the probability of path p being selected is related to its flow Proportional to, according to the rounding results, some paths are selected and the commodity k is fixed on these paths.

[0087] 4. Correction step: Since randomized rounding may cause the delay constraint to not be fully satisfied, CSRR corrects the path selection and traffic allocation through local adjustment, Lagrange multiplier method or other heuristic methods after each iteration to ensure that the delay constraint is satisfied and the traffic is maximized, thereby improving the convergence and accuracy of the results.

[0088] 5. Iterative process: The above steps are iterated multiple times, and each iteration improves the traffic distribution through correction and adjustment until the predetermined termination condition is met (such as the maximum number of iterations or reaching a convergence state).

[0089] Improved algorithm based on CSRR

[0090]

[0091] CSRR algorithm complexity analysis:

[0092] 1. Linear relaxation solution: The complexity of solving the linear relaxation problem is ,in It is the complexity of solving the linear relaxation, which is usually related to the number of nodes V, the number of edges E, and the number of products K in the graph.

[0093] 2. Randomized rounding: The time complexity of randomizing rounding for each item k is , because each product can choose at most path.

[0094] 3. Correction step: The correction step may be performed through local adjustments, with a complexity of , because it is necessary to adjust the flow of each edge and verify the delay constraints.

[0095] 4. Number of iterations: Assume that the algorithm executes iterations, and the time complexity of each iteration is .

[0096] Therefore, the overall complexity is:

[0097] (P2) is essentially a mixed integer linear programming (MILP), which is an NP-hard problem. It is different from the traditional MILP solution method, which usually converts the nonlinear problem into a linear form, performs linear relaxation and obtains the solution, and then obtains the integer solution by rounding. However, this method cannot guarantee that the obtained solution satisfies the delay constraint. Therefore, the present invention solves this problem based on an improved algorithm of the restricted sequential random rounding algorithm.

[0098] The innovations of this aspect mainly include two parts. First, in order to measure the stability of the network topology, a metric is defined - link available time. Link available time is determined by link quality, and link quality is modeled as a time-varying function related to the location, state and remaining energy of the sensor node. Second, in order to maintain a stable network topology, the present invention proposes a QoS-guaranteed robust routing (QGRR) algorithm, which dynamically searches and optimizes routing based on link available time to achieve the purpose of improving network performance.

[0099] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the field without departing from the purpose of the present invention. These changes involve related technologies well known to those skilled in the art, which all fall within the scope of protection of the patent of the present invention.

[0100] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A reliable routing method for wireless sensor network node failure, characterized in that: The method comprises the following steps: Step 1: Routing discovery phase: network initialization, neighbor discovery and network topology establishment; Step 1-1, broadcasting Hello message: each sensor node periodically sends a Hello message containing its identity, current location, status, remaining energy and other information to inform other sensor nodes that it can participate in the topology construction of the network as a neighbor; Step 1-2, receiving Hello messages and updating neighbor tables; after each sensor node receives Hello messages from other neighbors, it records the sender's information and updates the neighbor table; Step 1-3, construct the network topology: Based on the information in the neighbor table, construct the adjacency matrix C, where the elements describe the link maintainable time between node i and node j; Step 2: Routing selection phase: dynamically search routing algorithms based on link stability and output the best route; Step 2-1, the route with the maximum available time between two nodes ensures that the link maintenance time of the end-to-end delay is maximized to model P1. ， The link duration between adjacent nodes i and j is defined as the edge flow , the maximum link duration, i.e. the capacity of the edge, can be given by Calculated, is the maximum flow from source node S to destination node D, that is, the end-to-end routing available time, is a binary variable, Indicates that an edge is selected in the route ,in, , , , are decision variables, C1 and C2 are constraints on the flow to ensure that the flow is indivisible, C3 is the capacity limit of the arc, C5 is the constraint on the end-to-end delay, C6 is the power constraint, and C7 is used to prevent loops in the path; Step 2-2, analysis and simplification, simplify P1 to P2, ； Step 2-3, solve P2 to obtain the available time that maximizes the end-to-end routing.

2. A method for detecting failure of wireless sensor network nodes according to claim 1 The routing method is characterized in that The fields of the neighbor table include neighbor ID, neighbor address, neighbor characteristics, and effective time. The neighbor characteristics include signal strength RSSI, remaining energy, speed, coordinates, and link maintenance time.

3. A reliable routing method for wireless sensor network node failure according to claim 2, characterized in that: P2 in step 2-3 is solved using an improved CSRR algorithm.

4. A reliable routing method for wireless sensor network node failure according to claim 3, characterized in that: The improved algorithm of CSRR includes the following steps: Step S1, initialization,;Fig. , C is the capacity matrix, , can be calculated by the formula, X is the path selection matrix, Initialize to 0 and set the delay constraint and the maximum number of iterations max_iterations, set the threshold , which is used to control when to update the solution of the linear relaxation; Step S2, linear relaxation: First, solve the linear relaxation problem of P2, and The linear relaxation is ; Step S3, randomized rounding: Based on the preliminary relaxed solution, the traffic is allocated to specific paths using randomized rounding. For each product k, the probability of selecting path p is: , the probability of path p being selected and its flow Proportional to, according to the rounding results, select some paths and fix the product k on these paths; Step S4, correction step; CSRR is corrected after each iteration by local adjustment or Lagrange multiplier method Correct path selection and flow distribution to ensure that delay constraints are met and flow is maximized, improving the convergence and accuracy of the results; Step S5, iterative process; Iterate the above steps multiple times, improving each time through corrections and adjustments Traffic is distributed until the predetermined termination condition is met, i.e., the maximum number of iterations or convergence is reached.

5. A reliable routing method for wireless sensor network node failure according to claim 4, characterized in that: The solution complexity of the linear relaxation in step S2 is ,in It is the complexity of linear relaxation solution, which is related to the number of nodes V, the number of edges E and the number of products K in the graph.

6. A reliable routing method for wireless sensor network node failure according to claim 5, characterized in that: The time complexity of random rounding for each commodity k in step S3 is Each product can be selected at most path.

7. A reliable routing method for wireless sensor network node failure according to claim 6, characterized in that: The correction step in step S4 is performed by local adjustment, and the complexity is , adjust the flow of each edge and verify the delay constraints.

8. A reliable routing method for wireless sensor network node failure according to claim 7, characterized in that: The number of iterations in step S5, the algorithm executes iterations, and the time complexity of each iteration is .

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