Wireless ad hoc network dynamic routing method and system based on multi-parameter optimization

Abstract

The invention discloses a wireless ad hoc network dynamic routing method and system based on multi-parameter optimization. The method comprises the following steps: dynamically monitoring and collecting state parameters of each node in a network; based on a weighted multi-objective optimization algorithm, taking node residual energy, network time delay and link stability as optimization objectives, constructing a multi-parameter optimization objective function, and calculating an optimal routing path; according to the change condition of the link stability, adaptively adjusting a route updating period; when link interruption is detected, local route repair is performed firstly, and if repair fails, global path re-calculation is performed; according to the method, a weighted multi-objective optimization function is constructed, key parameters such as node residual energy, network time delay and link stability are brought into a unified model, and dynamic acquisition and adaptive routing update period adjustment are combined, so that global optimization configuration of network resources is realized, and the data transmission efficiency, the energy consumption balance and the link reliability are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and in particular relates to a dynamic routing method and system for wireless ad hoc networks based on multi-parameter optimization. Background Technology

[0002] Ad Hoc Networks (AHN), as a type of distributed communication network that does not rely on fixed infrastructure, have shown broad application potential in fields such as emergency rescue, military communications, the Internet of Things (IoT), and intelligent transportation. Their self-organizing, rapid deployment, and robustness make them an ideal communication solution for complex environments. For example, in disaster scenarios such as earthquakes, traditional communication facilities may be paralyzed, while AHN can quickly restore communication; in the military field, the dynamic nature of the battlefield environment requires networks with high adaptability; furthermore, the rapid development of IoT and vehicular ad hoc networks has further driven the demand for AHN technology. With the increasing number of mobile nodes and network scale, higher demands are placed on efficient and stable routing protocols.

[0003] Currently, the routing protocols widely used in wireless ad hoc networks mainly include On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR). AODV dynamically establishes paths through Route Request (RREQ) broadcasts and Route Response (RREP) mechanisms, making it suitable for small-scale networks. Its on-demand nature reduces unnecessary routing overhead. DSR, on the other hand, uses source routing, with the packet header carrying complete path information, avoiding route calculations at intermediate nodes and thus reducing latency.

[0004] However, the aforementioned protocols perform poorly in highly dynamic or large-scale network environments. Their most significant drawback lies in their rigid path maintenance mechanisms, which cannot effectively adapt to rapid topology changes. For example, AODV relies on periodic route discovery and global rerouting, leading to a surge in control overhead; DSR's path maintenance costs increase significantly with network size, and route discovery must be restarted after a path break, resulting in low efficiency. This problem severely restricts the application effectiveness of wireless ad hoc networks in scenarios with high real-time requirements or frequent node movement. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a multi-parameter optimized dynamic routing method for wireless ad hoc networks that improves network performance through systematic modeling and adaptive strategies; on the other hand, it provides a multi-parameter optimized dynamic routing system for wireless ad hoc networks.

[0006] Technical solution: The dynamic routing method for wireless ad hoc networks described in this invention includes the following steps:

[0007] (1) Dynamically monitor and collect the status parameters of each node in the network, including the node's remaining energy, network latency, and link stability;

[0008] (2) Based on the weighted multi-objective optimization algorithm, the remaining energy of the node, network latency and link stability are taken as optimization objectives, a multi-parameter optimization objective function is constructed, and the optimal routing path is calculated;

[0009] (3) Adaptively adjust the route update cycle according to changes in link stability;

[0010] (4) When a link interruption is detected, local route repair is performed first. If the repair fails, global path recalculation is performed.

[0011] This invention achieves optimal path selection by dynamically monitoring node remaining energy, network latency, and link stability, combined with a weighted multi-objective optimization algorithm to construct an objective function. This results in energy balance, latency optimization, and link stability. By adaptively adjusting the route update cycle, it significantly reduces network overhead and improves response efficiency in dynamic environments. Simultaneously, by employing a local repair-first strategy, it quickly restores connectivity during link interruptions, reducing the frequency of global recalculation and effectively improving network reliability and resource utilization. This scheme extends network lifetime and enhances robustness against topology changes while ensuring data transmission efficiency.

[0012] Preferably, the method for collecting the remaining energy of the node in step 1 is as follows: each node measures the remaining battery power in real time through a built-in energy monitoring circuit. The energy monitoring circuit includes a voltage sensor and a current sensor. The real-time energy consumption is calculated by measuring the operating voltage and current values, and the remaining energy E of the node is obtained by subtracting the cumulative energy consumption value from the initial energy value.

[0013] By monitoring energy consumption data in real time using high-precision voltage and current sensors, and combining the dynamic calculation of initial energy and cumulative consumption, the real-time energy status of nodes can be accurately reflected. This hardware-level measurement method avoids the errors of software estimation, provides a reliable energy consumption basis for routing decisions, effectively supports energy balance optimization, extends the life cycle of low-power nodes, and enhances the overall energy efficiency of the network.

[0014] Preferably, the method for collecting network latency in step 1 is as follows: each node periodically sends a 64-byte probe packet to the target node, records the round-trip time (RTT), and calculates the average transmission latency based on the results of a preset number of probes. The network latency calculation formula is:

[0015]

[0016] Where n is the number of probes.

[0017] By periodically sending lightweight probe packets and employing a strategy of averaging multiple measurements, the impact of random errors and transient interference is effectively reduced while ensuring measurement accuracy. The dynamic monitoring mechanism based on round-trip time can accurately reflect changes in link quality, providing real-time and reliable latency data for route optimization. This avoids the additional overhead caused by excessive probes and ensures the accuracy of network status assessment, thereby supporting intelligent decision-making for dynamic routing.

[0018] Preferably, the link stability described in step 1 is comprehensively evaluated based on the Received Signal Strength Indication (RSSI) and the relative moving speed v of the node, and the calculation formula is as follows:

[0019]

[0020] Among them, RSSI max The maximum signal strength; v rel Estimated by Doppler frequency shift or positioning module, in m / s; α and β are adjustment coefficients that can be adjusted according to the environment; S ranges from [0,1], with a larger value indicating a more stable link.

[0021] By fusing dynamic parameters of Received Signal Strength Indicator (RSSI) and relative node speed, a comprehensive quantification of link quality is achieved. By introducing adjustable weighting coefficients, it can flexibly adapt to the needs of different network environments, enabling the evaluation results to reflect both signal attenuation trends and predict connection risks caused by node movement. This provides a forward-looking stability indicator for routing decisions, effectively improving the reliability and anti-disturbance capability of path selection.

[0022] Preferably, the dynamic acquisition of the state parameters in step 1 is achieved through periodic broadcasting of state packets, including:

[0023] Each node broadcasts a 128-byte status packet at an initial period of T0 = 1 second. The status packet contains at least the following fields: node ID, remaining energy, network latency, link stability, and timestamp.

[0024] After receiving the status packets from neighboring nodes, a node updates its local network status table, recording the real-time parameters and corresponding timestamps of each neighboring node.

[0025] If the state packet of a neighboring node has not been updated for more than 3 seconds, the node is marked as invalid and removed from the routing calculation.

[0026] By periodically broadcasting lightweight state packets, real-time synchronization of network topology and node status is achieved, ensuring that routing decisions are based on the latest node energy, latency, and link stability data. Through timestamp verification and failure detection mechanisms, abnormal nodes can be quickly identified and removed, which not only ensures the timeliness and accuracy of state information, but also avoids the impact of outdated topology information on routing performance, thereby significantly improving the network's adaptability and robustness.

[0027] Preferably, the objective function described in step 2 is:

[0028]

[0029] Among them, E min E max The minimum and maximum remaining energy of the nodes in the path are expressed in Joules (J); Dmin is the preset minimum delay threshold in milliseconds (ms); D is the actual path delay; S avg W1 represents the average link stability of the path; W1, W2, and W3 are adjustable weight coefficients with values ​​ranging from [0,1], and W1+W2+W3=1.

[0030] By intelligently integrating three key parameters—minimum / maximum remaining energy, latency deviation threshold, and average link stability—multi-dimensional collaborative optimization of network performance is achieved. The introduction of the energy range term effectively prevents premature depletion of low-power nodes, the latency threshold comparison mechanism ensures the timeliness of critical service transmission, and the stability factor enhances the persistent reliability of the path. Flexible configuration of adjustable weighting coefficients allows for adaptive balancing of the three core indicators—energy consumption equilibrium, transmission efficiency, and link quality—for different application scenarios, providing a scientific basis for optimal path selection in dynamic networking and significantly improving the overall network performance and adaptability.

[0031] Preferably, in the process of calculating the optimal route path in step 2, an energy threshold E is set. th and maximum hop count constraint H max Excluding cases where the remaining energy is below the energy threshold E. th The nodes, and limit the number of hops in the routing path to not exceed the maximum number of hops H. max .

[0032] By introducing dual constraints of energy threshold and maximum hop count, the quality of path selection is effectively optimized while ensuring network connectivity: energy threshold screening avoids the overuse of low-power nodes, significantly extending the overall network lifespan; while hop count limitation controls end-to-end transmission latency and path complexity, reducing the risk of accumulated errors in packet forwarding and lowering routing maintenance overhead, thus achieving a better balance between energy efficiency, timeliness, and reliability.

[0033] Preferably, the formula for calculating the routing update period in step 3 is:

[0034] T = T0·(1-S) avg )+T min

[0035] Where T0 is the reference period, S avg T represents the average stability of the current path. min This is the minimum period.

[0036] By dynamically feeding back path average stability into the periodic adjustment algorithm, an intelligent balance is achieved between network maintenance overhead and topology tracking sensitivity: the update period is automatically extended when link stability is high to reduce control overhead, while the period is shortened when stability decreases to quickly respond to topology changes. This nonlinear adjustment method avoids unnecessary resource consumption in stable environments, which is a problem with fixed-period mechanisms, while ensuring the timeliness of routing information in dynamic environments, significantly improving network resource utilization efficiency and adaptability to mobility.

[0037] Secondly, the wireless self-organizing network dynamic routing system of the present invention includes:

[0038] The status monitoring module is used to dynamically monitor and collect the status parameters of each node in the network, including node remaining energy, network latency, and link stability.

[0039] The path calculation module is used to construct a multi-parameter optimization objective function based on a weighted multi-objective optimization algorithm, taking node remaining energy, network latency, and link stability as optimization objectives, and to calculate the optimal routing path.

[0040] The route update module is used to adaptively adjust the route update cycle based on changes in link stability.

[0041] The route maintenance module is used to perform local route repair when a link interruption is detected. If the repair fails, a global path recalculation is performed.

[0042] Thirdly, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program capable of being loaded by the processor and executing the multi-parameter optimized dynamic routing method for wireless ad hoc networks.

[0043] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned multi-parameter optimized dynamic routing method for wireless ad hoc networks.

[0044] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: 1. By constructing a weighted multi-objective optimization function, key parameters such as node remaining energy, network latency, and link stability are incorporated into a unified model. Combined with dynamic data collection and adaptive route update cycle adjustment, global optimization of network resources is achieved, significantly improving data transmission efficiency, energy consumption balance, and link reliability. 2. Based on a comprehensive evaluation model of RSSI and node movement speed, link stability is quantified in real time. Through a two-layer fault-tolerant strategy of local repair and global recalculation, network topology changes are effectively addressed, the impact of link interruption on communication is reduced, and network robustness is enhanced. 3. Periodic broadcasting of small-capacity status packets and 64-byte probe packets is used to achieve real-time monitoring of network status with low overhead. Combined with timestamps and a rapid removal mechanism for failed nodes, the timeliness of route calculation is ensured while reducing network load. 4. By introducing constraints such as energy thresholds and maximum hop count, stable paths with high energy and low latency are prioritized. By flexibly adapting to different scenario requirements through weight coefficients, optimal route selection can be achieved in complex environments. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0046] Figure 2 This is a schematic diagram of the dynamic routing and optimal path calculation process of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, this invention provides a dynamic routing method for wireless ad hoc networks. By comprehensively evaluating node remaining energy (E), network transmission delay (D), and link stability (S), a multi-objective optimization model is constructed. Dynamic routing management is achieved by combining an improved Dijkstra algorithm and an adaptive update mechanism. Furthermore, an adaptive update mechanism based on link stability is proposed to improve routing efficiency and reliability. The specific technical solution includes the following five core steps:

[0049] Step 1: Network Status Parameter Collection

[0050] In a wireless ad hoc network, each node monitors its own and its neighboring nodes' status parameters in real time through a wireless communication module (such as an IEEE 802.11 or ZigBee module). These parameters include: Remaining node energy (E): The remaining battery power of the node is measured by built-in energy monitoring circuitry (such as voltage and current sensors), and the unit is joules (J). For example, if the node's initial energy is 10J, and the current voltage and current data indicate that 4J has been consumed, then E = 6J.

[0051] To improve accuracy, the sampling frequency is set to once per second.

[0052] Network transmission latency (D): Calculated by sending probe packets (Ping packets) and recording the round-trip time (RTT), the average transmission latency from the source node to the target node is expressed in milliseconds (ms). The probe packet size is set to 64 bytes, the sending interval is 0.5 seconds, and the latency calculation formula is as follows:

[0053]

[0054] Where n is the number of probes (e.g., 10 times).

[0055] Link stability (S): based on Received Signal Strength Indication (RSSI) and relative node speed (v). rel Based on comprehensive calculations, the formula for calculating link stability S is:

[0056]

[0057] Among them: RSSI max Maximum signal strength (e.g., -30dBm); v rel Estimated by Doppler frequency shift or positioning module (such as GPS), in m / s; α and β are adjustment coefficients, with initial values ​​of 0.6 and 0.5 respectively, which can be adjusted according to the environment; the value of S ranges from [0,1], and the larger the value, the more stable the link.

[0058] Each node exchanges the above parameters by periodically broadcasting "status packets". The format of the status packet is as follows:

[0059] Node ID Energy (E) Delay (D) Stability (S) Timestamp N1 6.5J 10ms 0.88 T1

[0060] Nodes periodically broadcast status packets (initial period T0 = 1 second, packet size 128 bytes). After receiving the status packet, the node updates its local network state table. An example of the table structure is shown below:

[0061] Neighbor Node ID Energy (E) Delay (D) Stability (S) Timestamp N2 5.8J 12ms 0.85 T2 N3 7.2J 8ms 0.90 T3

[0062] To ensure data consistency, nodes mark neighbor records that have not been updated for more than 3 seconds as "invalid" and remove them from the routing calculation.

[0063] Step 2: Construction of Multi-Parameter Optimization Model

[0064] To achieve comprehensive optimization of routing paths, this invention designs a multi-objective optimization function F to quantify the overall performance of the paths:

[0065]

[0066] Among them, E min and Emax Let be the minimum and maximum remaining energies of all nodes in the path, respectively, in J. D represents energy balance, with a value range of [0,1]; min The minimum latency threshold is set to 5ms in this invention, and D is the actual path latency. The value range is (0, 1). S avg The average stability of all links in the path is calculated using the following formula:

[0067]

[0068] Where k is the number of links in the path.

[0069] W1, W2, and W3 are adjustable weight coefficients, and W1 + W2 + W3 = 1. They can be dynamically adjusted according to the application scenario. For example, in an energy-sensitive network, W1 = 0.5, W2 = 0.3, and W3 = 0.2; in a latency-sensitive network, W1 = 0.3, W2 = 0.5, and W3 = 0.2.

[0070] The objective function F takes values ​​in the range [0,1], with larger values ​​indicating better overall path performance. To avoid selecting nodes with excessively low energy, an energy threshold Eth = 10%Einitial is set. Nodes below this threshold (e.g., with an initial energy of 10J, Eth = 1J) are excluded from route selection. Furthermore, to prevent overly circuitous paths, a maximum hop count constraint Hmax = 10 is set.

[0071] The mathematical derivation of the optimization model is as follows:

[0072] Energy Item: Paths with balanced energy distribution are encouraged to avoid premature depletion of some nodes. For example, if the energies of path ABC are [6J, 5J, 7J], then... If the path ADE is [6J,2J,8J], then:

[0073] Delay item: It tends to choose low-latency paths. For example, D min =5ms, D=15ms in path ABC, then:

[0074]

[0075] Stability term: For example, if S = [0.9, 0.85] for path ABC, then the average stability of all links in ABC is:

[0076]

[0077] Step 3: Dynamic route calculation

[0078] Based on the above optimization model, this invention uses an improved Dijkstra algorithm to calculate the optimal path from the source node to the target node. The specific process is as follows:

[0079] (3.1) Initialization: Set the F value of the source node to 0, and the F value of the remaining nodes to -1000. Set the path set P = {}, and the priority queue Q = {source node}. Path expansion: Select the node u with the largest F value from Q, and traverse its neighbor nodes v.

[0080] (3.2) Constraint check:

[0081] If E[v] < Eth (avoid selecting nodes with too low energy), skip node v; if the number of hops on the path exceeds Hmax (to prevent the path from being too circuitous), terminate the branch expansion.

[0082] (3.3) F-value calculation: For each valid neighbor v, calculate the F-value of the temporary path.

[0083]

[0084] (3.4) Path update: If the newly calculated F[v] is greater than the current value, then update F[v], add v to Q, and record the predecessor node.

[0085] (3.5) Termination condition: Repeat steps (3.2)-(3.4) until the target node is reached or Q is empty.

[0086] Step 4: Adaptive Route Update

[0087] To adapt to dynamic changes in network topology, this invention is based on the path average link stability S avg Adaptive adjustment of route update period T:

[0088] T = T0(1-S) avg )+T min

[0089] Where: T0 is the base period (e.g., 1 second); S avg T represents the average stability of the current path; min To ensure fast response times in highly dynamic scenarios, a minimum period (e.g., 0.1 seconds) is used. The dynamic routing calculation process is as follows: Figure 2 As shown.

[0090] The theoretical basis for the adjustment mechanism is: when S avg When the value is high (e.g., 0.9), the link is stable, and the update cycle T = 1·(1-0.9) + 0.1 = 0.2 seconds, reducing control overhead; when S avgAt low values ​​(e.g., 0.3), topology changes frequently, with a time interval of T = 1·(1-0.3) + 0.1 = 0.8 seconds. To avoid frequent switching, a hysteresis factor δ = 0.05 is introduced, applied only when... Adjust T as needed.

[0091] The update process includes:

[0092] Periodically broadcast status packets to refresh the network status table;

[0093] Recalculate the F value of the current path. If the value drops by more than a threshold (e.g., 0.1), trigger route reselection.

[0094] Step 5: Routing Maintenance and Fault Recovery

[0095] (5.1) Fault detection: The link is interrupted by monitoring the S value (if S < 0.3) or not receiving a neighbor status packet (timeout of 3 seconds).

[0096] (5.2) Local repair: Search for alternative paths from the breakpoint and select the local path with the largest F value. For example, if path ABC is broken at BC, A tries ADC.

[0097] (5.3) Global Recalculation: If local repair fails, the source node broadcasts an RREQ packet. The broadcast range of the RREQ is limited by H. max The target node responds with RREP, and the global path is rebuilt.

[0098] The following example will be used to better illustrate the implementation plan:

[0099] Scenario: 10 drone nodes, initial energy 10J, communication range 100m, speed 0-5m / s. Objective: Transmit data from node A to node J.

[0100] Parameter acquisition:

[0101] Node A: E = 10.0 J

[0102] Node B: E = 9.5 J, D A-B =10ms,S A-B =0.9

[0103] Node C: E = 8.0 J, D A-C =8ms,S A-C =0.9

[0104] Node J: E = 9.5J, D B-J =2ms,S B-J =0.7, D C-J =2ms,S C-J =0.8

[0105] Path calculation:

[0106] Let w1 = 0.4, w2 = 0.3, w3 = 0.3

[0107] The F-value for path ABJ is as follows:

[0108]

[0109] The F-value of path ACJ is as follows:

[0110]

[0111] Since 0.745 > 0.725, the path ABJ is selected.

[0112] Update cycle: T = 1·(1-0.80) + 0.1 = 0.3 seconds.

[0113] Simulation experiment: To verify the effectiveness of the method of the present invention, a simulation experiment was conducted and compared with the traditional AODV method.

[0114] Simulation setup and network size: 50 nodes are randomly distributed within a 500m × 500m area.

[0115] Node movement speed: 0-10m / s, using a random waypoint movement model.

[0116] Communication traffic: Constant bit rate (CBR) traffic, with a packet size of 512 bytes.

[0117] Performance metrics: average end-to-end latency, packet delivery rate, and network lifetime (time to first node's energy depletion).

[0118] Experimental results:

[0119] Performance indicators Method of the present invention AODV method Increase ratio Average end-to-end delay 10ms 15ms 33％ Group delivery rate 95％ 88％ 8％ Network lifecycle 200s 150s 33％

[0120] Results analysis:

[0121] Average end-to-end latency: The method of this invention significantly reduces data transmission latency by optimizing path selection. Packet delivery rate: Through link stability assessment and adaptive updates, the reliability of data transmission is improved. Network lifetime: Energy balancing optimization extends the overall network uptime.

[0122] Based on a similar inventive concept, this invention also provides a dynamic routing system for wireless ad hoc networks corresponding to the aforementioned dynamic routing method, comprising:

[0123] The status monitoring module is used to dynamically monitor and collect the status parameters of each node in the network, including node remaining energy, network latency, and link stability.

[0124] The path calculation module is used to construct a multi-parameter optimization objective function based on a weighted multi-objective optimization algorithm, taking node remaining energy, network latency, and link stability as optimization objectives, and to calculate the optimal routing path.

[0125] The route update module is used to adaptively adjust the route update cycle based on changes in link stability.

[0126] The route maintenance module is used to perform local route repair when a link interruption is detected. If the repair fails, a global path recalculation is performed.

[0127] The present invention also discloses an electronic device.

[0128] Specifically, the electronic device can be a desktop computer, laptop computer, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. The processor and memory can be connected via a bus or other means. The processor can be a Central Processing Unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, graphics processing units (GPUs), embedded neural network processing units (NPUs) or other dedicated deep learning coprocessors, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0129] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor executes various functional applications and data processing by running non-transitory software programs, instructions, and modules stored in memory. Memory may include a program storage area and a data storage area. The program storage area may store the control unit and the application program required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, memory may include high-speed random access memory and non-transitory memory. In some embodiments, memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0130] The present invention also discloses a computer-readable storage medium.

[0131] Specifically, the computer-readable storage medium is used to store a computer program, which, when executed by a processor, implements the methods described in the above method implementation.

[0132] Those skilled in the art will understand that all or part of the processes in the methods described above can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

Claims

1. A dynamic routing method for wireless ad hoc networks based on multi-parameter optimization, characterized in that, Includes the following steps: (1) Dynamically monitor and collect the status parameters of each node in the network, including the node's remaining energy, network latency, and link stability; (2) Based on the weighted multi-objective optimization algorithm, the remaining energy of the node, network latency and link stability are taken as optimization objectives, a multi-parameter optimization objective function is constructed, and the optimal routing path is calculated; (3) Adaptively adjust the route update cycle according to changes in link stability; (4) When a link interruption is detected, local route repair is performed first. If the repair fails, global path recalculation is performed.

2. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The method for collecting the remaining energy of the node in step 1 is as follows: Each node measures the remaining battery power in real time through a built-in energy monitoring circuit. The energy monitoring circuit includes a voltage sensor and a current sensor. The real-time energy consumption is calculated by measuring the operating voltage and current values, and the remaining energy E of the node is obtained by subtracting the cumulative energy consumption value from the initial energy value.

3. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The network latency collection method described in step 1 is as follows: Each node periodically sends a 64-byte probe packet to the target node, records the round-trip time (RTT), and calculates the average transmission latency based on the results of a preset number of probes. The network latency calculation formula is: Where n is the number of probes.

4. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The link stability described in step 1 is comprehensively evaluated based on the Received Signal Strength Indication (RSSI) and the relative moving speed v of the nodes. The calculation formula is as follows: Among them, RSSI max The maximum signal strength; v rel Estimated by Doppler frequency shift or positioning module, in m / s; α and β are adjustment coefficients that can be adjusted according to the environment; S ranges from [0,1], with a larger value indicating a more stable link.

5. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The dynamic acquisition of the state parameters described in step 1 is achieved through periodic broadcasting of state packets, including: Each node broadcasts a 128-byte status packet at an initial period of T0 = 1 second. The status packet contains at least the following fields: node ID, remaining energy, network latency, link stability, and timestamp. After receiving the status packets from neighboring nodes, a node updates its local network status table, recording the real-time parameters and corresponding timestamps of each neighboring node. If the state packet of a neighboring node has not been updated for more than 3 seconds, the node is marked as invalid and removed from the routing calculation.

6. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The objective function described in step 2 is: Among them, E min E max The minimum and maximum remaining energy of the nodes in the path are expressed in Joules (J); Dmin is the preset minimum delay threshold in milliseconds (ms); D is the actual path delay; S avg W1 represents the average link stability of the path; W1, W2, and W3 are adjustable weight coefficients with values ​​ranging from [0,1], and W1+W2+W3=1.

7. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, In step 2, during the calculation of the optimal route path, an energy threshold E is set. th and maximum hop count constraint H max Excluding cases where the remaining energy is below the energy threshold E. th The nodes, and limit the number of hops in the routing path to not exceed the maximum number of hops H. max .

8. The dynamic routing method for wireless ad hoc networks according to claim 1, characterized in that, The formula for calculating the route update period mentioned in step 3 is as follows: T=T0·(1-S avg )+T min Where T0 is the reference period, S avg T represents the average stability of the current path. min This is the minimum period.

9. A dynamic routing system for wireless ad hoc networks based on multi-parameter optimization, characterized in that, include: The status monitoring module is used to dynamically monitor and collect the status parameters of each node in the network, including node remaining energy, network latency, and link stability. The path calculation module is used to construct a multi-parameter optimization objective function based on a weighted multi-objective optimization algorithm, taking node remaining energy, network latency, and link stability as optimization objectives, and to calculate the optimal routing path. The route update module is used to adaptively adjust the route update cycle based on changes in link stability. The route maintenance module is used to perform local route repair when a link interruption is detected. If the repair fails, a global path recalculation is performed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic routing method for wireless ad hoc networks based on multi-parameter optimization as described in any one of claims 1 to 7.

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